Author: "Nicolas Baghdadi" / Topic: remote sensing - Searchworks@Jio Institute Digital Library Search Results

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1. Remote Sensing Data for Digital Soil Mapping in French Research—A Review

Author: Anne C. Richer-de-Forges, Qianqian Chen, Nicolas Baghdadi, Songchao Chen, Cécile Gomez, Stéphane Jacquemoud, Guillaume Martelet, Vera L. Mulder, Diego Urbina-Salazar, Emmanuelle Vaudour, Marie Weiss, Jean-Pierre Wigneron, and Dominique Arrouays
Subjects: remote sensing, soil digital soil mapping, scale, sampling density, resolution, sensors, Science
Abstract: Soils are at the crossroads of many existential issues that humanity is currently facing. Soils are a finite resource that is under threat, mainly due to human pressure. There is an urgent need to map and monitor them at field, regional, and global scales in order to improve their management and prevent their degradation. This remains a challenge due to the high and often complex spatial variability inherent to soils. Over the last four decades, major research efforts in the field of pedometrics have led to the development of methods allowing to capture the complex nature of soils. As a result, digital soil mapping (DSM) approaches have been developed for quantifying soils in space and time. DSM and monitoring have become operational thanks to the harmonization of soil databases, advances in spatial modeling and machine learning, and the increasing availability of spatiotemporal covariates, including the exponential increase in freely available remote sensing (RS) data. The latter boosted research in DSM, allowing the mapping of soils at high resolution and assessing the changes through time. We present a review of the main contributions and developments of French (inter)national research, which has a long history in both RS and DSM. Thanks to the French SPOT satellite constellation that started in the early 1980s, the French RS and soil research communities have pioneered DSM using remote sensing. This review describes the data, tools, and methods using RS imagery to support the spatial predictions of a wide range of soil properties and discusses their pros and cons. The review demonstrates that RS data are frequently used in soil mapping (i) by considering them as a substitute for analytical measurements, or (ii) by considering them as covariates related to the controlling factors of soil formation and evolution. It further highlights the great potential of RS imagery to improve DSM, and provides an overview of the main challenges and prospects related to digital soil mapping and future sensors. This opens up broad prospects for the use of RS for DSM and natural resource monitoring.
Published: 2023
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2. Assessment of Agricultural Practices From Sentinel 1 and 2 Images Applied on Rice Fields to Develop a Farm Typology in the Camargue Region

Author: Dominique Courault, Laure Hossard, Fabrice Flamain, Nicolas Baghdadi, and Kamran Irfan
Subjects: Agricultural practices, classification, farm typology, high resolution, remote sensing, rice, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract: In the global change context, an efficient management of the available resources has become one of the most important topics particularly for the sustainable crop development. Many questions concern the evolution of the rice farming systems in Camargue in Southeastern France, which play a crucial role in controlling the soil salinity. Their surface area significantly decreased from 20 000 ha in 2010 to 14 000 ha in 2014. The arrival of the new Sentinel satellites makes it possible to evaluate these crop evolutions. The objectives of this study were to propose operational methodologies to accurately assess the surface areas of the main crops, rice, wheat, and grassland, from classifications based on multispectral data; map agricultural practices (sowing and harvest residue burning); and elaborate a farm typology based on variables computed from remote sensing data to better understand the farming strategies. Dense time series of Sentinel images acquired at high spatial resolution (10 m) were analyzed for 2016 and 2017. A satisfactory accuracy was obtained for land use classification with 88% of correctly classified fields. The accuracy obtained for the estimation of the sowing date varied according to the studied year from 8 to 12 days, and burned areas were correctly identified (80%). The farm typology allowed to cluster farms at the territory level.
Published: 2020
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3. Detecting Irrigation Events over Semi-Arid and Temperate Climatic Areas Using Sentinel-1 Data: Case of Several Summer Crops

Author: Hassan Bazzi, Nicolas Baghdadi, Sami Najem, Hadi Jaafar, Michel Le Page, Mehrez Zribi, Ioannis Faraslis, and Marios Spiliotopoulos
Subjects: remote sensing, precision agriculture, sustainability, climate change, Agriculture
Abstract: Irrigation monitoring is of great importance in agricultural water management to guarantee better water use efficiency, especially under changing climatic conditions and water scarcity. This study presents a detailed assessment of the potential of the Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data to detect irrigation events at the plot scale. The potential of the S1 data to detect the irrigation events was carried out using the Irrigation Event Detection Model (IEDM) over semi-arid and temperate oceanic climates in five study sites in south Europe and the Middle East. The IEDM is a decision tree model initially developed to detect irrigation events using the change detection algorithm applied to the S1 time series data. For each study site and at each agricultural plot, all available S1 images during the period of irrigation were used to construct an S1 time series and apply the IEDM. Different types of major summer irrigated crops were analyzed in this study, including Maize, Soybean, Sorghum and Potato, mainly with the sprinkler irrigation technique. The irrigation detection accuracy was evaluated using S1 images and the IEDM against the climatic condition of the studied area, the vegetation development (by means of the normalized difference vegetation index, NDVI) and the revisit time of the S1 sensor. The main results showed generally good overall accuracy for irrigation detection using the S1 data, reaching 67% for all studied sites together. This accuracy varied according to the climatic conditions of the studied area, with the highest accuracy for semi-arid areas and lowest for temperate areas. The analysis of the irrigation detection as a function of the crop type showed that the accuracy of irrigation detection decreases as the vegetation becomes well developed. The main findings demonstrated that the density of the available S1 images in the S1 time series over a given area affects the irrigation detection accuracy, especially for temperate areas. In temperate areas the irrigation detection accuracy decreased from 70% when 15 to 20 S1 images were available per month to reach less than 56% when less than 10 S1 images per month were available over the study sites.
Published: 2022
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4. Retrieval of High-Resolution Vegetation Optical Depth from Sentinel-1 Data over a Grassland Region in the Heihe River Basin

Author: Zhilan Zhou, Lei Fan, Gabrielle De Lannoy, Xiangzhuo Liu, Jian Peng, Xiaojing Bai, Frédéric Frappart, Nicolas Baghdadi, Zanpin Xing, Xiaojun Li, Mingguo Ma, Xin Li, Tao Che, Liying Geng, and Jean-Pierre Wigneron
Subjects: Technology, Science & Technology, POLARIZATION, ASSIMILATION, vegetation optical depth (VOD), BACKSCATTER, Environmental Sciences & Ecology, Geology, AMSR-E, PARAMETERS, BIOMASS, Remote Sensing, SURFACE SOIL-MOISTURE, DEPENDENCE, Physical Sciences, Sentinel-1, General Earth and Planetary Sciences, Geosciences, Multidisciplinary, grassland, Imaging Science & Photographic Technology, C-band, Life Sciences & Biomedicine, Environmental Sciences, SMOS
Abstract: Vegetation optical depth (VOD), as a microwave-based estimate of vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. However, current global operational VOD products have a coarse spatial resolution (~25 km), which limits their use for agriculture management and vegetation dynamics monitoring at regional scales (1–5 km). This study aims to retrieve high-resolution VOD from the C-band Sentinel-1 backscatter data over a grassland of the Heihe River Basin in northwestern China. The proposed approach used an analytical solution of a simplified Water Cloud Model (WCM), constrained by given soil moisture estimates, to invert VOD over grassland with 1 km spatial resolution during the 2018–2020 period. Our results showed that the VOD estimates exhibited large spatial variability and strong seasonal variations. Furthermore, the dynamics of VOD estimates agreed well with optical vegetation indices, i.e., the mean temporal correlations with normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI) were 0.76, 0.75, and 0.75, respectively, suggesting that the VOD retrievals could precisely capture the dynamics of grassland.
Published: 2022
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5. A New Reflectivity Index for the Retrieval of Surface Soil Moisture From Radar Data

Author: Jérôme Demarty, Nicolas Baghdadi, Sekhar Muddu, Myriam Foucras, Mehrez Zribi, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Indian Institute of Science, European Space Agency, French National Centre for Space Studies (TAPAS TOSCA program), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Atmospheric Science, index of surface soil moisture, 010504 meteorology & atmospheric sciences, Geophysics. Cosmic physics, 0211 other engineering and technologies, 02 engineering and technology, Surface finish, 01 natural sciences, Signal, law.invention, index of surface soil moisture (ISSM), law, Computers in Earth Sciences, Radar, Water content, Image resolution, TC1501-1800, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Mathematics, Moisture, [SDE.IE]Environmental Sciences/Environmental Engineering, QC801-809, index of reflectivity (IR), Fresnel equations, surface soil moisture, surface soil moisture (SSM), Ocean engineering, [SDE]Environmental Sciences, Change detection, Sentinel-1, index of reflectivity, radar
Abstract: International audience; A new approach based on the change detection technique is proposed for the estimation of surface soil moisture (SSM) from a time series of radar measurements. A new index of reflectivity (IR) is defined that uses radar signals and Fresnel coefficients. This index is equal to 0 in the case of the smallest value of the Fresnel coefficient, corresponding to the driest conditions and the weakest radar signal, and is equal to 1 for the highest value of the Fresnel coefficient, corresponding to the wettest soil conditions and the strongest radar signal. The Integrated Equation Model (IEM) is used to simulate the behavior of radar signals as a function of soil moisture and roughness. This approach validates the greater usefulness of the IR compared with that of the commonly used index of SSM (ISSM), which assumes that the SSM varies linearly as a function of radar signal strength. The IR-based approach was tested using Sentinel-1 radar data recorded over three regions: Banizombou (Niger), Merguellil (Tunisia), and Occitania (France). The IR approach was found to perform better for the estimation of SSM than the ISSM approach based on comparisons with ground measurements over bare soils.
Published: 2021

6. Assessment of Agricultural Practices From Sentinel 1 and 2 Images Applied on Rice Fields to Develop a Farm Typology in the Camargue Region

Author: Laure Hossard, Kamran Irfan, Nicolas Baghdadi, Dominique Courault, Fabrice Flamain, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and This work was supported by the French National Center of Spatial Studies (CNES project,funded by the TOSC Acomity) and by INRAE.
Subjects: Atmospheric Science, Soil salinity, 010504 meteorology & atmospheric sciences, Geophysics. Cosmic physics, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, 0211 other engineering and technologies, Context (language use), 02 engineering and technology, Agricultural engineering, 01 natural sciences, Farm typology, Grassland, Crop, High resolution, Computers in Earth Sciences, TC1501-1800, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, 2. Zero hunger, geography.geographical_feature_category, Land use, QC801-809, business.industry, Sowing, Remote sensing, 15. Life on land, Classification, Ocean engineering, Agricultural practices, Agricultural practice, Geography, Agriculture, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Paddy field, Rice, business
Abstract: International audience; In the global change context, an efficient management of the available resources has become one of the most important topics particularly for the sustainable crop development. Many questions concern the evolution of the rice farming systems in Camargue in Southeastern France, which play a crucial role in controlling the soil salinity. Their surface area significantly decreased from 20 000 ha in 2010 to 14 000 ha in 2014. The arrival of the new Sentinel satellites makes it possible to evaluate these crop evolutions. The objectives of this study were to propose operational methodologies to accurately assess the surface areas of the main crops, rice, wheat, and grassland, from classifications based on multispectral data; map agricultural practices (sowing and harvest residue burning); and elaborate a farm typology based on variables computed from remote sensing data to better understand the farming strategies. Dense time series of Sentinel images acquired at high spatial resolution (10 m) were analyzed for 2016 and 2017. A satisfactory accuracy was obtained for land use classification with 88% of correctly classified fields. The accuracy obtained for the estimation of the sowing date varied according to the studied year from 8 to 12 days, and burned areas were correctly identified (80%). The farm typology allowed to cluster farms at the territory level.
Published: 2020

7. ASCAT IB: A radar-based vegetation optical depth retrieved from the ASCAT scatterometer satellite

Author: Nicolas Baghdadi, Lei Fan, Thomas Jagdhuber, Frédéric Frappart, Philippe Ciais, Jean-Pierre Wigneron, Xiaojun Li, Xiaojing Bai, Mengjia Wang, Mehrez Zribi, Xiangzhuo Liu, Christophe Moisy, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), ICOS-ATC (ICOS-ATC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Chongqing University [Chongqing], Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'études spatiales de la biosphère (CESBIO), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), German Aerospace Center (DLR), Beijing Normal University (BNU), Nanjing University (NJU), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 010504 meteorology & atmospheric sciences, NDVI, Soil Science, 01 natural sciences, Normalized Difference Vegetation Index, 03 medical and health sciences, forest, Computers in Earth Sciences, Leaf area index, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Water content, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0105 earth and related environmental sciences, Remote sensing, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, 0303 health sciences, biomass, VOD, Attenuation, Geology, Active microwave, Vegetation, Enhanced vegetation index, 15. Life on land, Scatterometer, Tree height, EVI, LAI, Lidar, ASCAT, 13. Climate action, [SDE]Environmental Sciences, Africa, Environmental science, Satellite
Abstract: International audience; Vegetation optical depth (VOD), as a microwave-based vegetation index for vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. Currently, VOD is mainly retrieved from passive microwave data and few studies focused on VOD retrievals from active microwave data. The Advanced SCATterometer (ASCAT) provides long-term C-band backscatter data at Vertical-Vertical (VV) polarization. In this study, a new ASCAT INRAE Bordeaux (IB) VOD (hereafter, IB VOD), was developed based on the Water Cloud Model (WCM) coupled with the Ulaby linear model for soil backscattering. The main features of IB VOD are that (i) the ERA5-Land soil moisture (SM) dataset was used as an auxiliary SM dataset in the retrievals, (ii) pixel-based soil model parameters were mapped using Random Forest (RF), and (iii) the vegetation model parameter was calibrated for each day. The IB VOD product was retrieved over Africa during 2015–2019, and its performances were evaluated in space and time by comparing with aboveground biomass (AGB), lidar tree height (TH), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and leaf area index (LAI). Results were inter-compared with three other VOD products at the same frequency. In terms of spatial correlation with AGB (R = 0.92) and TH (R = 0.89), IB VOD outperforms the other VOD products, suggesting IB VOD has a strong ability to capture spatial patterns of AGB and TH. By comparing all VOD products against NDVI, EVI and LAI, we found that the highest temporal correlation with NDVI (EVI, LAI) was obtained with IB VOD over 29.94% (36.65%, 30.19%) of the study region. Considering all three vegetations indices, highest temporal correlation values with IB VOD could be particularly noted for deciduous broadleaf forests, woody savannas and savannas.
Published: 2021

8. Sahara Subsurface Characterization Using Cygnss Gnss-R Data

Author: Nicolas Baghdadi, Nazzareno Pierdicca, Donato Stilla, Mehrez Zribi, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: Synthetic aperture radar, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, Satellite system, 02 engineering and technology, 01 natural sciences, Reflectivity, 13. Climate action, GNSS applications, [SDE]Environmental Sciences, Cyclone, Reflectometry, Image resolution, Geology, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Constellation
Abstract: The objective of this study is to analyze the potential of global navigation satellite system reflectometry (GNSS-R) data from the Cyclone Global Navigation Satellite System (CYGNSS) constellation to map the subsurface of a desert environment, investigate fossil river systems, and identify geological structures hidden beneath dry sand. This analysis is based on reflectivity estimates considering the main component of the coherent signal using an average of the signal over a period of 2.5 years on a grid with a resolution of 0.03°. Two sites are analyzed: Kufrah in the Libyan Desert and the Bir Safsaf region located in the southern Egyptian Desert. In both cases, we observe strong similarity with past observations derived from L-band synthetic aperture radar (SAR) data. Although the spatial resolution of CYGNSS data is lower than that of SAR data, different structures of the subsurface can be identified by the former.
Published: 2021

9. First Retrievals of ASCAT-IB VOD (Vegetation Optical Depth) at Global Scale

Author: Roberto Fernandez-Moran, Christophe Moisy, Nicolas Baghdadi, Mengjia Wang, Xiaojun Li, A. Al-Yaari, Mehrez Zribi, Zanpin Xing, Xiangzhuo Liu, Philippe Ciais, Bertrand Ygorra, Frédéric Frappart, Lei Fan, Thomas Jagdhuber, Hongliang Ma, Jean-Pierre Wigneron, Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'études spatiales de la biosphère (CESBIO), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Vegetation optical depth, 010504 meteorology & atmospheric sciences, vegetation mapping, 0211 other engineering and technologies, Scale (descriptive set theory), 02 engineering and technology, 01 natural sciences, Combinatorics, remote sensing, vegetation, optical sensor, C-band, ComputingMilieux_MISCELLANEOUS, attenuation, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Mathematics, prediction algorithm, biomass, Order (ring theory), 15. Life on land, Prediction algorithms, ASCAT, 13. Climate action, [SDE]Environmental Sciences, Vegetation optical Depth, Scatterometer, Biomedical optical imaging, Radar Measurement
Abstract: Global and long-term vegetation optical depth (VOD) dataset are very useful to monitor the dynamics of the vegetation features, climate and environmental changes. In this study, the radar-based global ASCAT (Advanced SCATterometer) IB (INRAE-BORDEAUX) VOD was retrieved using a model which was recently calibrated over Africa. In order to assess the performance of IB VOD, the Saatchi biomass and three other VOD datasets (ASCAT V16, AMSR2 LPRM V5 and VODCA LPRM V6) derived from C-band observations were used in the comparison. The preliminary results show that IB VOD has a promising ability to predict biomass $(\mathrm{R}=0.74,\ \text{RMSE} =44.82\ \text{Mg}\ \text{ha}^{-1})$ , which is better than V16 VOD $(\mathrm{R}=0.64,\ \text{RMSE} =51.27\ \text{Mg} \text{ha}^{-1})$ and VODCA VOD $(\mathrm{R}=0.72,\ \text{RMSE} =47.14\ \text{Mg}\ \text{ha}^{-1})$ . Some retrieval issues for IB VOD were found in boreal regions (e.g., Eastern America, Russia). In the future, we will focus on improving our algorithm in those regions, and produce a global and long-term dataset.
Published: 2021

10. Evaluation of the Performances of Radar and Lidar Altimetry Missions for Water Level Retrievals in Mountainous Environment: The Case of the Swiss Lakes

Author: Ibrahim Fayad, Jean-François Crétaux, Muriel Berge-Nguyen, Nicolas Baghdadi, Joël Schregenberger, Fabien Blarel, Song Shu, Frédéric Frappart, Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National d'Études Spatiales [Toulouse] (CNES), Appalachian State University, University of North Carolina System (UNC), University of Bern, CNES TOSCA grant Hydroweb, CNES/EUMETSAT TOSCA grant CTOH, CNES TOSCA grant CTOH, CNES/EUMETSAT TOSCA grant FOAM, Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)
Subjects: Synthetic aperture radar, radar and laser altimetry, lakes, validation, 010504 meteorology & atmospheric sciences, Science, 0208 environmental biotechnology, 02 engineering and technology, 01 natural sciences, law.invention, law, Altimeter, Radar, 0105 earth and related environmental sciences, Remote sensing, Elevation, Mode (statistics), Sampling (statistics), 020801 environmental engineering, Water level, Lidar, [SDE]Environmental Sciences, General Earth and Planetary Sciences, Environmental science
Abstract: International audience; Radar altimetry is now commonly used to provide long-term monitoring of inland water levels in complement to or for replacing disappearing in situ networks of gauge stations. Recent improvements in tracking and acquisition modes improved the quality the water retrievals. The newly implemented Open Loop mode is likely to increase the number of monitored water bodies owing to the use of an a priori elevation, especially in hilly and mountainous areas. The novelty of this study is to provide a comprehensive evaluation of the performances of the past and current radar altimetry missions according to their acquisition (Low Resolution Mode or Synthetic Aperture Radar) and tracking (close or open loop) modes, and acquisition frequency (Ku or Ka) in a mountainous area where tracking losses of the signal are likely to occur, as well as of the recently launched ICESat-2 and GEDI lidar missions. To do so, we evaluate the quality of water level retrievals from most radar altimetry missions launched after 1995 over eight lakes in Switzerland, using the recently developed ALtimetry Time Series software, to compare the performances of the new tracking and acquisition modes and also the impact of the frequency used. The combination of the Open Loop tracking mode with the Synthetic Aperture Radar acquisition mode on SENTINEL-3A and B missions outperforms the classical Low Resolution Mode of the other missions with a lake observability greater than 95%, an almost constant bias of (-0.17 +/- 0.04) m, a RMSE generally lower than 0.07 m and a R most of the times higher than 0.85 when compared to in situ gauge records. To increase the number of lakes that can be monitored and the temporal sampling of the water level retrievals, data acquired by lidar altimetry missions were also considered. Very accurate results were also obtained with ICESat-2 data with RMSE lower than 0.06 and R higher than 0.95 when compared to in situ water levels. An almost constant bias (0.42 +/- 0.03) m was also observed. More contrasted results were obtained using GEDI. As these data were available on a shorter time period, more analyses are necessary to determine their potential for retrieving water levels.
Published: 2021

11. Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data

Author: Marcel M. El Hajj, Ghaleb Faour, Mohamad Hamze, Bruno Cheviron, Hassan Bazzi, Nicolas Baghdadi, Mehrez Zribi, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), National Council for Scientific Research = Conseil national de la recherche scientifique du Liban [Lebanon] (CNRS-L), ITK [Clapiers], King Abdullah University of Science and Technology, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Gestion de l'Eau, Acteurs, Usages (UMR G-EAU), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), French Space Study Center (CNES, TOSCA 2021 project), National Research Institute for Agriculture, Food and the Environment (INRAE), French Ministry of Foreign Affairs (French Embassy in Beirut), Lebanese National Council of Scientific Research, King Abdullah University of Science and Technology (KAUST), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)
Subjects: Synthetic aperture radar, L band, 010504 meteorology & atmospheric sciences, Mean squared error, C band, Science, 0211 other engineering and technologies, Context (language use), 02 engineering and technology, 01 natural sciences, Surface roughness, C-band, Water content, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Mathematics, soil moisture, surface roughness, SAR, L-band, ALOS/PALSAR, Sentinel-1, artificial neural networks, [SDE]Environmental Sciences, General Earth and Planetary Sciences, Soil roughness
Abstract: International audience; Surface soil moisture (SSM) is a key variable for many environmental studies, including hydrology and agriculture. Synthetic aperture radar (SAR) data in the C-band are widely used nowadays to estimate SSM since the Sentinel-1 provides free-of-charge C-band SAR images at high spatial resolution with high revisit time, whereas the use of L-band is limited due to the low data availability. In this context, the main objective of this paper is to develop an operational approach for SSM estimation that mainly uses data in the C-band (Sentinel-1) with L-bands (ALOS/PALSAR) as additional data to improve SSM estimation accuracy. The approach is based on the use of the artificial neural networks (NNs) technique to firstly derive the soil roughness (Hrms) from the L-band (HH polarization) to then consider the L-band-derived Hrms and C-band SAR data (VV and VH polarizations) in the input vectors of NNs for SSM estimation. Thus, the Hrms estimated from the L-band at a given date is assumed to be constant for a given times series of C-band images. The NNs were trained and validated using synthetic and real databases. The results showed that the use of the L-band-derived Hrms in the input vector of NN in addition to C-band SAR data improved SSM estimation by decreasing the error (bias and RMSE), mainly for SSM values lower than 15 vol.% and regardless of Hrms values. Based on the synthetic database, the NNs that neglect the Hrms underestimate and overestimate the SSM (bias ranges between −8.0 and 4.0 vol.%) for Hrms values lower and higher than 1.5 cm, respectively. For Hrms 2.0 cm and SSM between 8 to 22 vol.%, the accuracy on the SSM estimation improved and the overestimation decreased by 2.2 vol.% (from 4.5 to 2.3 vol.%). From the real database, the use of Hrms estimated from the L-band brought a significant improvement of the SSM estimation accuracy. For in situ SSM less than 15 vol.%, the RMSE decreased by 1.5 and 2.2 vol.% and the bias by 1.2 and 2.6 vol.%, for Hrms values lower and higher than 1.5 cm, respectively.
Published: 2021

12. Cereal crops soil parameters retrieval using L-Band ALOS-2 and C-Band Sentinel-1 sensors

Author: Zeineb Kassouk, Emna Ayari, Safa Bousbih, Mehrez Zribi, Zohra Lili-Chabaane, Nicolas Baghdadi, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National Agronomique de Tunisie (INAT), Université de Carthage - University of Carthage, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Viana Project (ERA-Net ARIMNet), PHC Utique IPASS Project, Chaams Project (ERANET3-602 CHAAMS)Irrigation+ (ESA n°4000129870/20/I-NB)Project, and the TAPAS TOSCA/CNES project., European Project: 219262,EC:FP7:KBBE,FP7-ERANET-2007-RTD,ARIMNET(2008), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Synthetic aperture radar, Cereal Crops radar, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, Normalized Difference Vegetation Index, law.invention, law, vegetation, Radar, Leaf area index, lcsh:Science, C-band, Water content, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Moisture, water cloud model, Vegetation, 15. Life on land, ALOS-2, L-band, Soil water, [SDE]Environmental Sciences, soil roughness, General Earth and Planetary Sciences, Environmental science, Sentinel-1, lcsh:Q, soil moisture, radar
Abstract: This paper discusses the potential of L-band Advanced Land Observing Satellite-2 (ALOS-2) and C-band Sentinel-1 radar data for retrieving soil parameters over cereal fields. For this purpose, multi-incidence, multi-polarization and dual-frequency satellite data were acquired simultaneously with in situ measurements collected over a semiarid area, the Merguellil Plain (central Tunisia). The L- and C-band signal sensitivity to soil roughness, moisture and vegetation was investigated. High correlation coefficients were observed between the radar signals and soil roughness values for all processed multi-configurations of ALOS-2 and Sentinel-1 data. The sensitivity of SAR (Synthetic Aperture Radar) data to soil moisture was investigated for three classes of the normalized difference vegetation index (NDVI) (low vegetation cover, medium cover and dense cover), illustrating a decreasing sensitivity with increasing NDVI values. The highest sensitivity to soil moisture under the dense cover class is observed in L-band data. For various vegetation properties (leaf area index (LAI), height of vegetation cover (H) and vegetation water content (VWC)), a strong correlation is observed with the ALOS-2 radar signals (in HH(Horizontal-Horizontal) and HV(Horizontal-Vertical) polarizations). Different empirical models that link radar signals (in the L- and C-bands) to soil moisture and roughness parameters, as well as the semi-empirical Dubois modified model (Dubois-B) and the modified integral equation model (IEM-B), over bare soils are proposed for all polarizations. The results reveal that IEM-B performed a better accuracy comparing to Dubois-B. This analysis is also proposed for covered surfaces using different options provided by the water cloud model (WCM) (with and without the soil–vegetation interaction scattering term) coupled with the best accuracy bare soil backscattering models: IEM-B for co-polarization and empirical models for the entire dataset. Based on the validated backscattering models, different options of coupled models are tested for soil moisture inversion. The integration of a soil–vegetation interaction component in the WCM illustrates a considerable contribution to soil moisture precision in the HV polarization mode in the L-band frequency and a neglected effect on C-band data inversion.
Published: 2021

13. Automatic detection of inland water bodies along altimetry tracks fusing radar backscattering

Author: F. Seyler, Pierre Zeiger, J. Betbeder, Fabien Blarel, Frédéric Frappart, Valéry Gond, Luc Bourrel, Nicolas Baghdadi, José Darrozes, and R. Bellot
Subjects: geography, Radar tracker, geography.geographical_feature_category, law, Wetland, Vegetation, Altimeter, Radar, Geology, Radar altimetry, Remote sensing, law.invention
Abstract: Radar altimetry is commonly used to derive water levels over inland water bodies. If lakes and rivers are increasingly covered with radar altimetry virtual stations, wetlands and floodplains are still poorly monitored using this technique. In this study, an unsupervised classification of Ku-band radar altimetry backscattering coefficients from ENVISAT and Jason-2 is performed in the Congo Cuvette Centrale. Comparisons performed against a classification map of the study area shows a good agreement between the water and vegetation classes of the two datasets. Based on these results, radar altimetry-derived water levels are automatically derived over the water classes. Comparisons against radar altimetry-based water stages from Hydroweb database exhibits also a good agreement.
Published: 2021

14. STICS crop model and Sentinel-2 images for monitoring rice growth and yield in the Camargue region

Author: Dominique Courault, Françoise Ruget, Laure Hossard, Valérie Demarez, Nicolas Baghdadi, Hélène Dechatre, Fabrice Flamain, Kamran Irfan, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), CNES Centre national d'études spatiales (projet TOSCA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Production mapping, Environmental Engineering, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, 01 natural sciences, Yield (wine), Leaf area index, Paddy, 0105 earth and related environmental sciences, Remote sensing, 2. Zero hunger, Farm scale, business.industry, 04 agricultural and veterinary sciences, 15. Life on land, LAI, Remote sensing (archaeology), Agriculture, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Paddy field, Spatial variability, Scale (map), business, Agronomy and Crop Science, Cropping
Abstract: International audience; The assessment of rice yield at territory level is important for strategic economic decisions. Assessing spatial and temporal yield variability at regional scale is difficult because of the numerous factors involved, including agricultural practices, phenological calendars, and environmental contexts. New remote sensing data acquired at decametric resolution (Sentinel missions) can provide information on this spatial variability. The study objective was thus to evaluate the potential of Sentinel-2 images for monitoring rice cropping systems and yield from farm to region scales. The approach considered both observations and modeling. In-depth farmers surveys were carried out in the Camargue region, Southeastern France. The novelty was to use operational tools (BVNET and PHENOTB) to compute leaf area index, to daily interpolate this biophysical variable from 44 images acquired in 2016 and 2017 for each rice field, and to derive key phenological parameters from the analysis of the temporal profiles. The STICS crop model was spatially used, considering the biophysical variables derived from remote sensing. We tested four simulation strategies, differing in the integration intensity of remote sensing information into the model. Results have shown that (1) Sentinel-2 data allowed distinguishing early and late rice varieties. (2) The phenological stages mapped at the regional level allowed to better understand the agricultural practices of farmers. (3) The assimilation of remote sensing data to the STICS crop model significantly improved yield estimation and provided useful information on the spatial variability observed at regional scale. It was the first time that Sentinel-2 data are used with STICS crop model to assess rice yield at both farm and regional scale in the Camargue area. The proposed method is based on free open data and free access model, easily reproducible in other environmental contexts.
Published: 2021

15. New ASCAT Vegetation Optical Depth (IB-VOD) Retrievals over Africa

Author: Frédéric Frappart, Nicolas Baghdadi, Mehrez Zribi, Thomas Jagdhuber, Jean-Pierre Wigneron, Xiaojun Li, Lei Fan, Xiangzhuo Liu, Mengjia Wang, Christophe Moisy, Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Centre National de la Recherche Scientifique (CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), DLR Microwaves and Radar Institute, German Aerospace Center (DLR), Beijing Normal University (BNU), Nanjing University of Information Science and Technology (NUIST), Department of Earth System Science [Irvine] (ESS), University of California [Irvine] (UCI), University of California-University of California, TOSCA (Terre Ocean Surfaces Continentales et Atmosphere), CNES, France (Centre National d'Etudes Spatiales), and China Scholarship Council
Subjects: Vegetation optical depth, 010504 meteorology & atmospheric sciences, Backscatter, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, vegetation, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, attenuation, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, vegetation water content, [SDE.IE]Environmental Sciences/Environmental Engineering, VOD, Vegetation, 15. Life on land, Scatterometer, ASCAT, 13. Climate action, Africa, [SDE]Environmental Sciences, Environmental science, scatterometer, empirical, Water Cloud Model
Abstract: International audience; Vegetation Optical Depth (VOD) plays an important role in monitoring the earth ecosystems. There are many VOD products released based on different satellites and frequencies. But most of the VOD products are derived from passive microwave data, and very few active VOD products have been released to date. This study investigated retrievals of the active microwave VOD product from C-band ASCAT (Advanced SCATterometer) observations using the water cloud model in large areas. To achieve this, the ASCAT backscatter data and ECMWF soil moisture data were used as inputs to retrieve ASCAT VOD over the whole Africa. The correlation between the retrieved VOD product and proxies of vegetation density (Saatchi biomass) were used to evaluate the model performance.
Published: 2020

16. Study Flood Regime Using High Temporal Resolution Sentinel-1 Images

Author: Rumsaïs Blatrix, D. Ho Tong Minh, Doyle McKey, Yen-Nhi Ngo, Nicolas Baghdadi, I. El Moussawi, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UPVM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: Synthetic aperture radar, geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Flood myth, 0211 other engineering and technologies, DEM, Wetland, 02 engineering and technology, 15. Life on land, Structural basin, 01 natural sciences, InSAR, 13. Climate action, Remote sensing (archaeology), Radar imaging, [SDE]Environmental Sciences, High temporal resolution, Sentinel-1, Image resolution, Geology, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, SAR
Abstract: International audience; The objective of this paper is to evaluate the potential of radar images to study wetland areas on the mapping flood regime and generating digital elvelation model. The analysis is carried out on Sentinel-1 data acquired over the Congo Basin.
Published: 2020

17. Irrigation mapping using Sentinel-1 time series

Author: Dino Ienco, Nicolas Baghdadi, Mehrez Zribi, Hassan Bazzi, Hatem Belhouchette, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), The authors would like to thank the National Center for Space Studies (CNES, TOSCA project) and the National Institute for Agronomic and Environmental Research (INRAE) for financing this project. They would like to thank Maria Jose Escorihuela from the isardSAT, Catalonia, for providing the SIGPAC data., and IEEE
Subjects: Synthetic aperture radar, Random Forest, 010504 meteorology & atmospheric sciences, Series (mathematics), Scale (ratio), 0211 other engineering and technologies, Convolutional Neural Network, 02 engineering and technology, 15. Life on land, 01 natural sciences, Plot (graphics), Random forest, Wavelet, Spain, Principal component analysis, [SDE]Environmental Sciences, Sentinel-1, Time series, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Mathematics
Abstract: International audience; The objective of this paper is to present an approach for mapping irrigated areas at plot scale using the Sentinel-1 radar time series. Over a study site located in Catalonia region of north Spain, a dense temporal series of Si backscattering coefficients were first obtained at plot scale and grid scale (10km x 10km). The S1 time series at plot and grid scales were conjointly used to remove the ambiguity between rainfall events and irrigation events. The principal component analysis (PCA) and the wavelet transformation were applied to the SAR temporal series. Then, to classify irrigated/non-irrigated plots the random forest (RF) classifier was employed using the obtained principal components (PC) and the wavelet coefficients (WT). A convolutional neural network was also tested using the prepared Si temporal series. The result of the classification reaches 90.7% and 89.1% using the PC and the WT in a random forest classifier respectively. The accuracy of the classification reaches 94.1% using the CNN.
Published: 2020

18. Evaluation of the potential of Sentinel-1 and Sentinel-1 data for clay content mapping

Author: Safa Bousbih, Mehrez Zribi, Nadhira Ben Aissa, Nicolas Baghdadi, Azza Gorrab, Zohra Lili-Chabaane, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Carthage - University of Carthage, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 2. Zero hunger, Soil texture, [SDE.IE]Environmental Sciences/Environmental Engineering, SVM, 15. Life on land, Texture (geology), law.invention, classification, 13. Climate action, law, Temporal resolution, Soil water, Clay content, Environmental science, Sentinel-1, RF, Soil moisture, Radar, Sentinel-2, Scale (map), Porosity, Water content, Remote sensing
Abstract: Soil texture is a key parameter in agricultural processes and an important measure for agricultural prediction, water cycle, filtering of pollutants and carbon storage. Besides, its estimation is essential for agronomists, hydrologists, geologists and environmentalists and for modeling in these application areas. Several studies have been based on understanding and modeling the biological, physical and chemical processes in the soil. Regarding the texture of the soil, few researches propose soil texture spatialization, and are generally based on ground measurements. Among other things, field observations or laboratory analyzes are very expensive and are not very representative. Indeed, the soil texture presents a strong heterogeneity even at the scale of a field. It is then necessary to use precise and spatialized information on soils.These methods are generally based on remote sensing data and particularly optical data to restore soil component. However, these techniques are strongly affected by atmospheric conditions. This constraint is not valid for Radar sensors (Radio Detection And Ranging). Radar data are mainly sensitive to soil moisture and soil roughness, and has also been evaluated for its ability to perform texture measurements.The aim of this study is evaluate the potential of these techniques based on optical and radar data for soil texture estimation. By its composition, its structure, its texture and its porosity, soil moisture is strongly influenced by the soil nature. With the arrival of Sentinel-1 (S-1) and Sentinel-2 (S-2) ESA spatial missions, data are acquired with high spatial and temporal resolution between July and early December 2017, on a semi-arid area in central Tunisia. This study is therefore conducted using S-2 SWIR (Short-Wave Infrared) bands (B11 and B12, most sensitive to clay) and soil moisture products derived from radar data. And algorithms based on the support vector machine (SVM) and random forest (RF) methods are proposed for the classification and mapping of clay content.In order to evaluate the approach and determine the adequate data (between optical and radar data) allowing to precisely characterize the clay content, a cross-validation was used. The SWIR bands lead to less satisfactory outcomes compared to soil moisture. With an overall accuracy of approximately 65%, soil moisture achieved the best performance for estimating soil texture. The results also showed that RF and SVM are robust classifiers for texture estimation despite the small number of training data. However, RF displays greater accuracy and speed of simulation compared to SVM.
Published: 2020

19. Near real-time irrigation detection at plot scale using Sentinel-1 data

Author: Mehrez Zribi, Nicolas Baghdadi, Hassan Bazzi, Hatem Belhouchette, Ibrahim Fayad, Valérie Demarez, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: Synthetic aperture radar, IMAGING TECHNIQUES, Irrigation, near real-time, 010504 meteorology & atmospheric sciences, WATER MANAGEMENT, 0211 other engineering and technologies, FRANCE, Context (language use), 02 engineering and technology, SOUTH WEST, METHODE D'IRRIGATION, GESTION DES EAUX, 01 natural sciences, Normalized Difference Vegetation Index, REMOTE SENSING, IRRIGATION, plot scale, AIDE A LA DECISION, lcsh:Science, TELEDETECTION, Water content, FACTEUR TEMPS, ESPAGNE, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Hydrology, SPAIN, OCCITANIE, RESSOURCE EN EAU, Vegetation, DECISION SUPPORT, [SDE.ES]Environmental Sciences/Environmental and Society, 6. Clean water, IRRIGATION METHODS, SUD OUEST, CATALUNA, WATER RESOURCES, [SDE]Environmental Sciences, TECHNIQUE D'IMAGERIE, Sentinel-1, General Earth and Planetary Sciences, Environmental science, lcsh:Q, MONTPELLIER, Scale (map), irrigation, Change detection, TIME FACTOR
Abstract: International audience; In the context of monitoring and assessment of water consumption in the agricultural sector, the objective of this study is to build an operational approach capable of detecting irrigation events at plot scale in a near real-time scenario using Sentinel-1 (S1) data. The proposed approach is a decision tree-based method relying on the change detection in the S1 backscattering coefficients at plot scale. First, the behavior of the S1 backscattering coefficients following irrigation events has been analyzed at plot scale over three study sites located in Montpellier (southeast France), Tarbes (southwest France), and Catalonia (northeast Spain). To eliminate the uncertainty between rainfall and irrigation, the S1 synthetic aperture radar (SAR) signal and the soil moisture estimations at grid scale (10 km × 10 km) have been used. Then, a tree-like approach has been constructed to detect irrigation events at each S1 date considering additional filters to reduce ambiguities due to vegetation development linked to the growth cycle of different crops types as well as the soil surface roughness. To enhance the detection of irrigation events, a filter using the normalized differential vegetation index (NDVI) obtained from Sentinel-2 optical images has been proposed. Over the three study sites, the proposed method was applied on all possible S1 acquisitions in ascending and descending modes. The results show that 84.8% of the irrigation events occurring over agricultural plots in Montpellier have been correctly detected using the proposed method. Over the Catalonian site, the use of the ascending and descending SAR acquisition modes shows that 90.2% of the non-irrigated plots encountered no detected irrigation events whereas 72.4% of the irrigated plots had one and more detected irrigation events. Results over Catalonia also show that the proposed method allows the discrimination between irrigated and non-irrigated plots with an overall accuracy of 85.9%. In Tarbes, the analysis shows that irrigation events could still be detected even in the presence of abundant rainfall events during the summer season where two and more irrigation events have been detected for 90% of the irrigated plots. The novelty of the proposed method resides in building an effective unsupervised tool for near real-time detection of irrigation events at plot scale independent of the studied geographical context.
Published: 2020

20. Soil moisture retrieval using GNSS-R data

Author: Mireille Huc, Nicolas Baghdadi, Thierry Pellarin, Mehrez Zribi, Nazzareno Pierdicca, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut des Géosciences de l’Environnement (IGE), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA)
Subjects: Moisture, GNSS-R, Inversion (meteorology), Shuttle Radar Topography Mission, 15. Life on land, [SDE.ES]Environmental Sciences/Environmental and Society, 6. Clean water, GNSS applications, CYGNSS, [SDE]Environmental Sciences, Environmental science, soil moisture, Digital elevation model, change detection, Water content, Image resolution, Change detection, Remote sensing
Abstract: The aim of this study is to propose an inversion algorithm for CYGNSS constellation data, which can be used for the monitoring of soil moisture. A change detection approach based on a 21-month CYGNSS data time series is proposed, and is processed to retrieve daily average reflectivity over a grid with a spatial resolution equal to $0.5 ^{\circ} \mathrm{x}0.5 ^{\circ}$. Auxiliary data, including PROBA-V optical measurements and SRTM Digital Terrain Model products are used to account for the influence of vegetation and relief. The proposed estimates are validated at three different African sites (Merguellil, Tunisia; Basso Dallo, Niger; and Oueme, Benin), using field moisture measurements acquired at 5 cm soil depth.
Published: 2020

21. Sentinel-1/Sentinel-2-Derived Soil Moisture Product At Plot Scale (S2 MP)

Author: Loic Lozac'h, Mohammad El Hajj, Nicolas Baghdadi, Hassan Bazzi, Mehrez Zribi, and Rémi Cresson
Subjects: Artificial neural network, law, Pipeline (computing), Product (mathematics), Environmental science, Radar, Scale (map), Water content, Plot (graphics), Normalized Difference Vegetation Index, Remote sensing, law.invention
Abstract: The objective of this paper is to present an operational approach for mapping soil moisture at high spatial resolution over agricultural areas with vegetation cover. The developed approach uses the neural network (NN) technique based on coupling Sentinel-1 radar data and Sentinel-2 optical data. The neural networks were developed and validated using synthetic and real databases. To operationally map the soil moisture, the developed NN uses the C-band SAR signal in VV polarization, SAR incidence angle, and the Normalized Differential Vegetation Index “NDVI” as the inputs. To optimize the automatic production of soil moisture maps a pipeline using the Orfeo Toolbox is implemented.
Published: 2020

22. Desert Roughness Retrieval Using CYGNSS GNSS-R Data

Author: Mehrez Zribi, Nazzareno Pierdicca, Nicolas Baghdadi, Donato Stilla, Mireille Huc, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA)
Subjects: Synthetic aperture radar, 010504 meteorology & atmospheric sciences, Science, 0211 other engineering and technologies, Satellite system, 02 engineering and technology, Surface finish, 01 natural sciences, Stability (probability), Surface roughness, Reflectometry, desert, aerodynamic roughness, ALOS-2, CYGNSS, GNSS-R, roughness, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, 15. Life on land, 13. Climate action, GNSS applications, [SDE]Environmental Sciences, General Earth and Planetary Sciences, Cyclone, Geology
Abstract: [Notes_IRSTEA]743 [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOS; International audience; The aim of this paper is to assess the potential use of data recorded by the Global Navigation Satellite System Reflectometry (GNSS-R) Cyclone Global Navigation Satellite System (CYGNSS) constellation to characterize desert surface roughness. The study is applied over the Sahara, the largest non-polar desert in the world. This is based on a spatio-temporal analysis of variations in Cyclone Global Navigation Satellite System (CYGNSS) data, expressed as changes in reflectivity. In general, the reflectivity of each type of land surface (reliefs, dunes, etc.) encountered at the studied site is found to have a high temporal stability. A grid of CYGNSS measurements has been developed, at the relatively fine resolution of 0.03° x 0.03°, and the resulting map of average reflectivity, computed over a 2.5-year period, illustrates the potential of CYGNSS data for the characterization of the main types of desert land surface (dunes, reliefs, etc.). A discussion of the relationship between aerodynamic or geometric roughness and CYGNSS reflectivity is proposed. A high correlation is observed between these roughness parameters and reflectivity. The behaviors of the GNSS-R reflectivity and the Advanced Land Observing Satellite-2 (ALOS-2) Synthetic Aperture Radar (SAR) backscattering coefficient are compared and found to be strongly correlated. An aerodynamic roughness (Z0) map of the Sahara is proposed, using four distinct classes of terrain roughness.
Published: 2020
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23. Wetland restoration prioritization using artificial neural networks

Author: Mohamad EL-Hajj, Saeid Soltani Koupaei, Farid Sheikholeslam, Saeideh Maleki, Alireza Soffianian, Nicolas Baghdadi, Saeid Pourmanafi, University of Zabol, Isfahan University of Technology, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: 0106 biological sciences, Prioritization, Multilayer perceptron neural networks, 010504 meteorology & atmospheric sciences, Damaged ecosystems, Computer science, Wetland, Machine learning, computer.software_genre, Complex ecosystem, 01 natural sciences, Environmental Chemistry, 0105 earth and related environmental sciences, General Environmental Science, geography, geography.geographical_feature_category, Ecology, Artificial neural network, business.industry, 010604 marine biology & hydrobiology, Uncertainty, 15. Life on land, Remote sensing, Wetland restoration, Multilayer perceptron, [SDE]Environmental Sciences, Hidden layer, Artificial intelligence, Landscape ecology, business, computer
Abstract: International audience; Wetland destruction is currently one of the greatest environmental problems in the world. Despite the functions of wetlands, these valuable ecosystems have steadily decreased because of human activities and climate change. To protect these valuable ecosystems, wetland restoration and rehabilitation are important operations that have been conducted worldwide. Since a wetland is a complex ecosystem with a variety of phenomena, increasing the number of variables considered during a restoration project will further boost the success rate of a restoration project. However, the inclusion of more variables will increase the complexity of the analysis. Thus, a method that can analyze complex models using many input variables is valuable. In most scientific studies, artificial intelligence algorithms have been widely applied to complex projects. However, the main question is whether these algorithms can learn the ecological patterns of a restoration project. For this reason, a multilayer perceptron (MLP) neural network was applied in this paper to investigate the ability to use these algorithms for wetland restoration. An artificial neural network (ANN) with one hidden layer and 15 neurons was used to determine the best areas for wetland restoration. The neural network was trained using the Levenberg-Marquardt algorithm; then, the trained ANN was used to determine the best areas for wetland restoration. The root mean square error (RMSE) of the model that was trained to prioritize wetland restoration was 0.04 ha. Because of water limitations in the study area, it is not possible to restore entire wetlands. Therefore, areas for restoration are prioritized based on ecological objectives. The results of the ANN demonstrate its ability to learn the ecological patterns and illustrates the performance of using this method for wetland restoration. Neural networks can calculate the final weights mathematically, and these algorithms are able to analyze complex models using many input variables; thus, ANNs are practical for wetland restoration.
Published: 2020

24. Analysis of multi-frequency and multi-polarization SAR data for wetland mapping in Hamoun-e-Hirmand wetland

Author: Alireza Soffianian, Nicolas Baghdadi, Saeideh Maleki, Vahid Rahdari, Mohammad El Hajj, University of Zabol, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and University of Isfahan
Subjects: geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, HAMOUN-E-HIRMAND, Wetland, 02 engineering and technology, 15. Life on land, IRAN, Polarization (waves), 01 natural sciences, Vegetation types, WETLAND MAPPING, [SDE]Environmental Sciences, General Earth and Planetary Sciences, Environmental science, SAR DATA, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract: International audience; The complex, dynamic and narrow boundaries between vegetation types make wetland mapping challenging. Here after the case study of the Hamoun-e-Hirmand wetland is considered by analysing eight Synthetic Aperture Radar (SAR)Images acquired in dry and wet periods with three wavelengths (X-band~3cm,C-band~6cm,and L-band~ 25cm), three polarizations (HH,VV and VH),and four incidence angles (22°, 30°, 34° and 53°). Then, the Support Vector Machine (SVM) classiﬁcation method was applied to classify TerraSAR-X, Sentinel-1, and ALOS-PALSAR images. The ﬁnal wetland land cover map was created by combining the classiﬁcation results obtained from each sensor. In the case in question ,results show thatT erraSAR-X (X-band, HH-53°) and Sentinel-1 data (C-band, VV-34°) were useful for determining the ﬂooded vegetation area in the wet period. This is crucial for the conservation of water bird habitats since ﬂooded vegetation is an ideal environment for the nesting and feeding of water birds. PALSAR data (L-band in both HH and VH polarizations, 30°) were capable of separating the classes of vegetation density in the wetland. In the dry period, Sentinel-1 (VV and VH,34°) and TerraSAR-X (HH, 22°and53°) had higher potential in land cover mapping than PALSAR (HH and VH, 30°). Based on these results, Sentinel-1 in VV and VH provides the high stability to discriminate between dry and green plants. TerraSAR-X is better for separating meadow and bare land. The results obtained in this paper can reduce the ambiguity in selecting satellite data for wetland studies. The results can also be used to produce more accurate data from satellite images and to facilitate wetland investigation, conservation and restoration.
Published: 2020

25. Potential value of combining ALOS PALSAR and Landsat-derived tree cover data for forest biomass retrieval in Madagascar

Author: Dinh Ho Tong Minh, Ghislain Vieilledent, Emile Ndikumana, Nicolas Baghdadi, Doyle McKey, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), EUROPEAN COMMISSION JOINT RESEARCH CENTRE ISPRA ITA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), and Centre National de la Recherche Scientifique (CNRS)
Subjects: MEAN SQUARE ERROR, ABOVE GROUND BIOMASS, 010504 meteorology & atmospheric sciences, REDD, LANDSAT TREE COVER, ALOS PALSAR, 0211 other engineering and technologies, Soil Science, Climate change, Biomass, 02 engineering and technology, 01 natural sciences, SPACE-BASED RADAR, law.invention, law, Deforestation, K01 - Foresterie - Considérations générales, Computers in Earth Sciences, Radar, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Geology, 15. Life on land, MADAGASCAR, 13. Climate action, Greenhouse gas, [SDE]Environmental Sciences, Environmental science, Satellite, Spatial variability, Tree cover, Physical geography, U30 - Méthodes de recherche
Abstract: International audience; Reducing carbon emissions from deforestation and degradation (REDD) requires detailed insight into how the forest biomass is measured and distributed. Studies so far have estimated forest biomass stocks using rough assumptions and unreliable data. High-resolution data and robust methods are required to capture the spatial variability of forest biomass with sufficient precision. Here we aim to improve on previous approaches by using radar satellite ALOS PALSAR (25-m resolution) and optical Landsat-derived tree cover (30-m resolution) observations to estimate forest biomass stocks in Madagascar, for the years 2007'2010. The radar signal and in situ biomass were highly correlated (R2 = 0.71) and the root mean square error was 30% (for biomass ranging from 0 to 500 t/ha). Using our map at 25-m resolution for the entire island of Madagascar, we estimated the total above-ground forest carbon for the four years 2007, 2008, 2009 and 2010 to be 1.1173 ± 0.0304, 1.1029 ± 0.0303, 1.0916 ± 0.0301 and 1.0773 ± 0.0298 PgC, respectively. Carbon stocks were found to have decreased constantly over this period due to anthropogenic deforestation and likely also to climate change. The results are expected to serve as a more accurate benchmark for monitoring progress on REDD and to provide strong supports for current and future spaceborne missions such as ALOS-2, SAOCOM and BIOMASS.
Published: 2018

26. Bare soil hydrological balance model 'MHYSAN': Calibration and validation using SAR moisture products and continuous thetaprobe network measurements over bare agricultural soils (Tunisia)

Author: Vincent Simonneaux, Azza Gorrab, Nicolas Baghdadi, Mehrez Zribi, Zohra Lili Chabaane, Sameh Saadi, Pascal Fanise, Université de Carthage - University of Carthage, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: Synthetic aperture radar, 010504 meteorology & atmospheric sciences, Soil texture, 0208 environmental biotechnology, Extrapolation, Soil science, 02 engineering and technology, [SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study, 01 natural sciences, Continuous soil moisture measurements, modelling, Satellite soil moisture products, remote sensing, Semi-arid area, SURVEILLANCE, Calibration, Range (statistics), 14. Life underwater, TELEDETECTION, very high spatial resolution, Water content, Ecology, Evolution, Behavior and Systematics, 0105 earth and related environmental sciences, Earth-Surface Processes, Remote sensing, RADAR A SYNTHESE D'OUVERTURE, Ecology, Moisture, [SDE.IE]Environmental Sciences/Environmental Engineering, HUMIDITE DU SOL, Bare soil hydrological model, methodology, soil water content, soil texture, 15. Life on land, MODELISATION, 020801 environmental engineering, monitoring, Soil water, TRES HAUTE RESOLUTION SPATIALE, Environmental science, TEXTURE DU SOL, METHODOLOGIE, synthetic aperture radar
Abstract: [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOS; International audience; The present study highlights the potential of multi-temporal X-band Synthetic Aperture Radar (SAR) moisture products to be used for the calibration of a model reproducing soil moisture (SM) variations. We propose the MHYSAN model (Modèle de bilan HYdrique des Sols Agricoles Nus) for simulating soil water balance of bare soils. This model was used to simulate surface evaporation fluxes and SM content at daily time scale over a semi-arid, bare agricultural site in Tunisia (North Africa). Two main approaches are considered in this study. Firstly, the MHYSAN model was successfully calibrated for seven sites using continuous thetaprobe measurements at two depths. Then the possibility to extrapolate local SM simulations at distant sites, based on soil texture similarity only, was tested. This extrapolation was assessed using SAR estimates and manual thetaprobe measurements of SM recorded at these distant sites. The results reveal a bias of approximately 0.63% and 3.04%, and an RMSE equal to 6.11% and 4.5%, for the SAR volumetric SM and manual thetaprobe measurements, respectively. In a second approach, the MHYSAN model was calibrated using seven very high resolution SAR (TerraSAR-X) SM outputs ranging over only two months. The simulated SM were validated using continuous thetaprobe measurements during 15 months. Although the SM was measured on only seven different dates for the purposes of calibration, satisfactory results 30 were obtained as a result of the wide range of SM values recorded in these seven images. This led to good overall calibration of the soil parameters, thus demonstrating the considerable potential of Sentinel-1 images for daily soil moisture monitoring using simple models.
Published: 2017

27. Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

Author: Mehrez Zribi, Nicolas Baghdadi, Hatem Belhouchette, Dino Ienco, Hassan Bazzi, Maria Jose Escorihuela, Valérie Demarez, Mohammad El Hajj, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), isardSAT, This research received funding from the French Space Study Center (CNES, TOSCA 2019 project), and the National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA)., Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), and Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)
Subjects: 010504 meteorology & atmospheric sciences, INDEX DE VEGETATION, VEGETATION INDEX, 0211 other engineering and technologies, convolutional neural network, 02 engineering and technology, 01 natural sciences, irrigation, law.invention, GESTION DES RESSOURCES, Wavelet, law, Radar, Sentinel-1, principal components, wavelet transformation, random forest, deep learning, Spain, ESPAGNE, Mathematics, CARTOGRAPHY, 6. Clean water, Random forest, Agricultural sciences, Principal component analysis, [SDE]Environmental Sciences, METHODS, AGRICULTURE DE PRECISION, Synthetic aperture radar, Science, METHODE, Normalized Difference Vegetation Index, RESOURCE MANAGEMENT, TIME SERIES ANALYSIS, REMOTE SENSING, PRECISION AGRICULTURE, TELEDETECTION, CARTOGRAPHIE, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, RESSOURCE EN EAU, 15. Life on land, Transformation (function), CATALUNA, WATER RESOURCES, General Earth and Planetary Sciences, ANALYSE DE SERIES CHRONOLOGIQUES, Scale (map), Sciences agricoles
Abstract: International audience; Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km x 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNNapproach using S1 data.
Published: 2019
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28. Analysis of L band radar data over tropical agricultural areas

Author: Soumya Bandyopadhyay, Ahmad Al Bitar, Safa Bousbih, Sat Kumar Tomer, Sekhar Muddu, Mehrez Zribi, Nicolas Baghdadi, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Indian Institute of Science, Institut National Agronomique de Tunisie (INAT), SATYUKT ANALYTICS PVT LTD BANGALORE IND, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), and Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)
Subjects: L band, 010504 meteorology & atmospheric sciences, 0208 environmental biotechnology, 02 engineering and technology, 01 natural sciences, Vegetation cover, law.invention, soil, law, vegetation, moisture, backscattering model, Radar, Water content, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Remote sensing, Moisture, business.industry, [SDE.IE]Environmental Sciences/Environmental Engineering, Vegetation, 15. Life on land, ALOS-2, 020801 environmental engineering, L-band, Agriculture, Soil water, Environmental science, business, radar
Abstract: International audience; The abstract should appear at the top of the left-. The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture in agricultural tropical areas. Simultaneously to several radar acquisitions made between June and October 2018, using ALOS2-PALSAR sensor over the Berambadi site (south of India), ground measurements of soil roughness, soil water content, LAI were recorded. The sensitivity of the ALOS-2 measurements to variations in soil moisture, which has been reported in several scientific publications, is confirmed in this study, even for dense crops. The radar signals are simulated using different types of backscattering models (physical and semi-empirical) over bare soil and vegetation cover for different types of crops (tumeric, etc). WCM model parameterized with LAI for vegetation contribution allows a good estimation of soil moisture for tumeric.
Published: 2019

29. SOIL MOISTURE ESTIMATION USING CYGNSS CONSTELLATION

Author: Mehrez Zribi, Nazzareno Pierdicca, Nicolas Baghdadi, Michel Le Page, Mireille Huc, Sebastian Antokoletz, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)
Subjects: GNSS-R, [SDE.IE]Environmental Sciences/Environmental Engineering, Field data, 0208 environmental biotechnology, Inversion (meteorology), North africa, 02 engineering and technology, 020801 environmental engineering, [SDE]Environmental Sciences, Environmental science, Water content, Change detection, ComputingMilieux_MISCELLANEOUS, Constellation, Remote sensing
Abstract: International audience; The main objective of this study is to propose an inversion algorithm for the estimation of soil moisture from CYGNSS data. The algorithm based on the change detection technique is applied to CYGNSS data after several corrections taking in account the incidence effects as well as different noises. The algorithm is validated on a one study site in North Africa. Comparisons with field data and ASCAT products illustrate a strong potential for CYGNSS products.
Published: 2019

30. Analysis of Sentinel-1 derived soil moisture maps over Occitanie, South France

Author: Mohammad El Hajj, Hassan Bazzi, Nicolas Baghdadi, Mehrez Zribi, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Synthetic aperture radar, Irrigation, 010504 meteorology & atmospheric sciences, C band, business.industry, 0211 other engineering and technologies, Neural Network, Inversion (meteorology), 02 engineering and technology, 01 natural sciences, Plot (graphics), Agriculture, [SDE]Environmental Sciences, Environmental science, Sentinel-1, Soil moisture, Scale (map), business, C-band, Water content, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, SAR
Abstract: International audience; Monitoring the surface soil moisture (SSM) in agricultural areas at plot scale helps in many applications such as irrigation planning and crop management. Over the last decade, SAR (Synthetic Aperture Radar) data have shown great potential in estimating SSM over agriculture areas. Today, Sentinel-1 (S1) and Sentinel-2 (S2) satellites present a good opportunity for operational SSM estimates in agricultural areas because they provide free and open access data at high spatial resolution (10 m x 10 m) and high revisit time (6 days over Europe). The aim of this paper is to present an operational approach for mapping soil moisture at high spatial resolution (plot scale) in agriculture areas by coupling S1 and S2 images. The proposed approach is based on the inversion of the Water Cloud Model (WCM) using the neural network technique.
Published: 2019

31. An operational high resolution soil moisture retrieval algorithm using sentinel-1 images

Author: Nicolas Baghdadi, Mohammad El Hajj, Mehrez Zribi, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)
Subjects: Synthetic aperture radar, Irrigation, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, Cloud computing, 02 engineering and technology, NEURAL NETWORKS, SURFACE-ROUGHNESS, 01 natural sciences, C-BAND, PARAMETERS, Plot (graphics), INTEGRAL-EQUATION MODEL, Water content, TERRASAR-X DATA, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, SENTINEL-1&2, CALIBRATION, business.industry, Inversion (meteorology), 15. Life on land, CATCHMENTS, 13. Climate action, Agriculture, [SDE]Environmental Sciences, SOIL MOISTURE, SAR DATA, Environmental science, SENSITIVITY, Scale (map), business, SAR
Abstract: International audience; Monitoring the surface soil moisture (SSM) in agricultural areas at plot scale helps in many applications such as irrigation planning and crop management. Over the last decade, SAR (Synthetic Aperture Radar) data have shown great potential in the estimation SSM in agriculture areas. Today, Sentinel-1 (S1) and Sentinel-2 (S2) satellites present a good opportunity for operational SSM estimates in agricultural areas because they provide free and open access data at high spatial resolution (10 m x 10 m) and high revisit time (6 days over Europe). An operational approach for mapping soil moisture at high spatial resolution (plot scale) in agriculture areas was developed by coupling S1 and S2 images. The proposed approach is based on the inversion of the Water Cloud Model (WCM) combined with the modified IEM (Integral Equation Model). It use the neural networks technique. Comparison between estimated soil moisture and in situ measurements showed that the precision of the estimated soil moisture in agricultural areas is approximately 5 vol.%. The developed algorithm is currently used in operational mode on many study sites. For example, S1 soil moisture maps for the Occitanie region (South France) at high spatial resolution (up to plot scale) are available since September 2016 as open access data via the Theia French Land Data Center (http://www.theia-land.fr/en/thematic-products). This research was supported by IRSTEA (National Research Institute of Science and Technology for Environment and Agriculture) and the French Space Study Center (CNES, DAR 2018 TOSCA). The authors wish to thank the European Commission and the European Space Agency for providing the S1 images. We used Copernicus level 2A S2 data processed by Theia.
Published: 2019

32. Complementarity between Textural and Radiometric Indices From Airborne and Spaceborne Multi VHSR Data: Disentangling the Complexity of Heterogeneous Landscape Matrix

Author: Samuel Alleaume, Nicolas Baghdadi, Marc Lang, Sandra Luque, Jean-Baptiste Féret, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0106 biological sciences, HABITAT MONITORING, 010504 meteorology & atmospheric sciences, texture indices, Science, Biodiversity, 010603 evolutionary biology, 01 natural sciences, Normalized Difference Vegetation Index, LANDSCAPE STRUCTURE, Image resolution, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Remote sensing, Landscape structure, [SDE.IE]Environmental Sciences/Environmental Engineering, ESSENTIAL BIODIVERSITY VARIABLES, 15. Life on land, Biodiversity hotspot, Panchromatic film, Habitat destruction, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], General Earth and Planetary Sciences, Environmental science, Radiometric dating, heterogeneity, very high resolution optical images, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, FOTO
Abstract: The quantitative characterization of landscape structure is critical to assess conservation, and monitor and manage biodiversity. The Mediterranean Basin is a biodiversity hotspot that illustrates the strong relationship between biodiversity and the complexity of the landscape mosaic. Our objective was to test the relevance of two textural indices and one radiometric index (the normalized difference vegetation index (NDVI)) to characterize vegetation structure. These indices could be used as indicators of vegetation composition and organization of four vertical strata when derived from airborne and Pl&eacute, iades space-borne VHSR imagery. More specifically, we analyzed the influence of the spatial resolution and the radiometric information on the characterization of the landscape structure. Our results indicated that NDVI information at 0.5 m spatial resolution was necessary to be able to incorporate the heterogeneity of vegetation structure. Indices derived from lower resolution NDVI images or different radiometric information than airborne images also proved to be sensitive to vegetation fragmentation and composition. NDVI images brought out details on ligneous/herbs patterns while panchromatic image brought out more details on herbs/bare soil patterns. Combined textural and NDVI indices show strong potential for vegetation structure understanding, allowing detailed mapping. NDVI information shows good potential for applications related to landscape closure dynamics, related habitat degradation indicators caused by shrub encroachment. Panchromatic derived information, on the other hand, provides information relevant in applications focusing grazing management.
Published: 2019

33. Performance des données GNSS-R FLORI pour l'estimation de la biomasse sur la foret des Landes

Author: Mehrez Zribi, Sylvia Dayau, Erwan Motte, Nazzareno Pierdicca, Jean-Pierre Wigneron, Nicolas Baghdadi, Dominique Guyon, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Écologie fonctionnelle et physique de l'environnement (EPHYSE), Institut National de la Recherche Agronomique (INRA), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)
Subjects: ABOVE GROUND BIOMASS, 010504 meteorology & atmospheric sciences, LANDES FOREST, 0211 other engineering and technologies, Coherent integration, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, 02 engineering and technology, MARITIME PINE, Management, Monitoring, Policy and Law, Gps satellites, 01 natural sciences, Cross-validation, forest, High elevation, Computers in Earth Sciences, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Earth-Surface Processes, Remote sensing, Global and Planetary Change, Ground truth, LANDES, GNSS-R, [SDE.IE]Environmental Sciences/Environmental Engineering, Diameter at breast height, 15. Life on land, Reflectivity, GNSS applications, [SDE]Environmental Sciences, Environmental science, above ground biomass, landes forest, maritime pine
Abstract: The Above-Ground Biomass (AGB) is a key parameter used for the modeling of the carbon cycle. The aim of this study is to make an experimental assessment of the sensitivity of Global Navigation Satellite System (GNSS) reflected signals to forest AGB. This is based on the analysis of the data recorded during several GLORI airborne campaigns in June and July 2015, over the Landes Forest (France). Ground truth measurements of tree height, density and diameter at breast height (DBH), as well as AGB, were carried out for 100 maritime pine forest plots of various ages. The GNSS-R data were used to obtain the right-left (ΓRL) and right-right (ΓRR) reflectivity observables, which are geo-referenced in accordance with the known positions of relevant GPS satellites and the airborne receiver. The correlations between forest AGB and the GNSS-R observables yield the highest sensitivity at high elevation angles (70°-90°). In this case, for (ΓRL) and the reflectivity polarization ratio (PR = ΓRL/ΓRR) estimated with a coherent integration time Tc = 20 ms, the coefficients of determination R2 are equal to 0.67 and 0.51, with a sensitivity of −0.051 dB/[106g (Mg) ha−1], and −0.053 dB/[Mg ha−1], respectively. The relationships between AGB and the observables are confirmed through the use of a 5-fold cross validation approach, with several different coherent integration times.
Published: 2019

34. Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks

Author: Nicolas Baghdadi, Mohammad El Hajj, Mahmod Reza Sahebi, Hamid Reza Mirsoleimani, UNIVERSITY OF TECHNOLOGY TEHERAN IRN, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 010504 meteorology & atmospheric sciences, Mean squared error, BARE SOILS, Computer Science::Neural and Evolutionary Computation, 0211 other engineering and technologies, 02 engineering and technology, Soil surface, NEURAL NETWORKS, lcsh:Chemical technology, 01 natural sciences, Biochemistry, MODIFIED DUBOIS MODEL, Article, Physics::Geophysics, Analytical Chemistry, law.invention, law, lcsh:TP1-1185, Electrical and Electronic Engineering, Radar, Instrumentation, Water content, Physics::Atmospheric and Oceanic Physics, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Mathematics, Remote sensing, CALIBRATED IEM, Artificial neural network, Moisture, IRAN, Polarization (waves), Atomic and Molecular Physics, and Optics, SENTINEL-1, Soil water, [SDE]Environmental Sciences, SOIL MOISTURE
Abstract: The main purpose of this study is to investigate the performance of two radar backscattering models, the calibrated integral equation model (CIEM) and the modified Dubois model (MDB) over an agricultural area in Karaj, Iran. In the first part, the performance of the models is evaluated based on the field measurement and the mentioned backscattering models, CIEM and MDB performed with root mean square error (RMSE) of 0.78 dB and 1.45 dB, respectively. In the second step, based on the neural networks (NNS), soil surface moisture is estimated using the two backscattering models, based on neural networks (NNs), from single polarization Sentinel-1 images over bare soils. The inversion results show the efficiency of the single polarized data for retrieving soil surface moisture, especially for VV polarization.
Published: 2019

35. L-Band UAVSAR tomographic imaging in dense forests: Gabon forests

Author: Ibrahim El Moussawi, Olivier Strauss, Chadi Abdallah, Jalal Jomaah, Marco Lavalle, Nicolas Baghdadi, Dinh Ho Tong Minh, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), National Center For Remote Sensing [CNRS-L], National Council for Scientific Research = Conseil national de la recherche scientifique du Liban [Lebanon] (CNRS-L), Doctoral School of Sciences and Technology [Lebanese University], Lebanese University [Beirut] (LU), Image & Interaction (ICAR), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Jet Propulsion Laboratory (JPL), and NASA-California Institute of Technology (CALTECH)
Subjects: Synthetic aperture radar, Canopy, L band, 010504 meteorology & atmospheric sciences, tomography SAR (TomoSAR), above-ground biomass (AGB), tropical forests, AfriSAR, UAVSAR, phase calibration, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, TOMOGRAPHY SAR (TOMOSAR), Digital elevation model, lcsh:Science, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, AFRISAR, Tomographic reconstruction, PHASE CALIBRATION, Vegetation, 15. Life on land, ABOVE-GROUND BIOMASS (AGB), GABON, Tropical forest, Lidar, [SDE]Environmental Sciences, General Earth and Planetary Sciences, lcsh:Q, Geology
Abstract: International audience; Developing and enhancing strategies to characterize actual forests structure is a timely challenge, particularly for tropical forests. P-band synthetic aperture radar (SAR) tomography (TomoSAR) has previously been demonstrated as a powerful tool for characterizing the 3-D vertical structure of tropical forests, and its capability and potential to retrieve tropical forest structure has been discussed and assessed. On the other hand, the abilities of L-band TomoSAR are still in the early stages of development. Here, we aim to provide a better understanding of L-band TomoSAR capabilities for retrieving the 3-D structure of tropical forests and estimating the top height in dense forests. We carried out tomographic analysis using L-band UAVSAR data from the AfriSAR campaign conducted over Gabon Lopé Park in February 2016. First, it was found that L-band TomoSAR was able to penetrate into and through the canopy down to the ground, and thus the canopy and ground layers were detected correctly. The resulting TomoSAR vertical profiles were validated with a digital terrain model and canopy height model extracted from small-footprint Lidar (SFL) data. Second, there was a strong correlation between the L-band Capon beam forming profile in HH and HV polarizations with Land Vegetation Ice Sensor (LVIS) Level 1B waveform Lidar over different kinds of forest in Gabon Lopé National Park. Finally, forest top height from the L-band data was estimated and validated with SFL data, resulting in a root mean square error of 3 m and coefficient of determination of 0.92. The results demonstrate that L-band TomoSAR is capable of characterizing 3-D structure of tropical forests.
Published: 2019

36. Mapping of aboveground biomass in Gabon

Author: Mohammad El Hajj, Jean-Stéphane Bailly, Nicolas Labrière, Nicolas Baghdadi, Ludovic Villard, El Hajj, Mohammad, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Evolution et Diversité Biologique (EDB), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), AgroParisTech, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Centre d'études spatiales de la biosphère (CESBIO), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), IRSTEA (National Research Institute of Science and Technology for Environment and Agriculture), French Space Study Center (CNES, DAR 2018 TOSCA), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut de Recherche pour le Développement (IRD [ Madagascar])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 010504 meteorology & atmospheric sciences, Mean squared error, [SDE.MCG]Environmental Sciences/Global Changes, 010502 geochemistry & geophysics, Residual, 01 natural sciences, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Altimeter, Digital elevation model, Variogram, 0105 earth and related environmental sciences, Remote sensing, [STAT.AP]Statistics [stat]/Applications [stat.AP], Global and Planetary Change, Ice cloud, Elevation, GABON, 15. Life on land, Géologie appliquée, GLAS/ICESAT, Aboveground biomass, Gabon, MODIS, ALOS/PALSAR, [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, General Earth and Planetary Sciences, Environmental science, Satellite, ABOVEGROUND BIOMASS, Applied geology
Abstract: [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOS [ADD1_IRSTEA]Dynamiques spatiales d'anthropisation [ADD2_IRSTEA]Dynamique et fonctionnement des écosystèmes; International audience; The aim of this paper is to map the aboveground biomass (AGB) in Gabon. First, a random forest (RF) model that relates reference AGB values to remote sensing (RS)-derived variables (mainly radar and optical images) was built, and the significant predictive variables were determined. Second, the built RF model was applied to the significant RS-derived variables to predict AGB across Gabon. The results showed that the overall RMSE (Root Mean Square Error) on the RS-derived AGB map with a spatial resolution of 50 m was 63.3 t/ha (R 2 ¼ 0.53). To improve the accuracy of the RS-derived AGB map, the integration of LiDAR data provided by the Geoscience Laser Altimeter System (GLAS) onboard the Ice Cloud and Land Elevation Satellite (ICESat) was investigated. First, an RF model that relates reference AGB values to GLAS-derived metrics and a DEM (Digital Elevation Model) was built. Second, the calibrated RF model was applied to obtain a spatially distributed estimation of AGB (GLAS footprints geolocation) covering forested areas in Gabon, with a density of 0.13 footprints/ km 2. Third, the semivariogram of residuals (RS-derived AGB map e GLAS-derived AGB "surrogate AGB") was computed. Later, a regression kriging interpolation was performed by taking into account the spatial structure of residuals to provide a continuous residual map. Finally, the RS-derived AGB map and the residual map were summed, and a final AGB map was obtained. The results showed that the integration of GLAS surrogate AGB data slightly improves the accuracy of the RS-derived AGB map only for AGB values lower than 100 t/ha (bias and RMSE reduced by 13.9 and 10 t/ha, respectively).
Published: 2019

37. Soil texture estimation using radar and optical data from Sentinel-1 and Sentinel-2

Author: Safa Bousbih, Bernard Mougenot, Azza Gorrab, Nadhira Ben Aissa, Mehrez Zribi, Charlotte Pelletier, Nicolas Baghdadi, Zohra Lili-Chabaane, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Monash University [Melbourne], UNIVERSITE DE CARTHAGE INAT LR GREEN TEAM TUNIS TUN, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), CHAAMS project, ERANET-MED 03-62, CHAAMS TOSCA/CNES project, Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 010504 meteorology & atmospheric sciences, Soil texture, TEXTURE MEASUREMENT, Soil Moisture, SVM, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, law.invention, RANDOM FORESTS, SPACE-BASED RADAR, law, SHORT WAVE INFRARED BANDS, AGRICULTURAL FIELDS, Texture, Radar, lcsh:Science, Water content, TUNISIE, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, DECISION TREES, Random Forest, Moisture, RANDOM FOREST, THREE FOLD CROSS VALIDATION, SATELLITE ACQUISITION, Random forest, INFRARED RADIATION, Support vector machine, SENTINEL-1, SUPPORT VECTOR MACHINES, SENTINEL-2, Soil water, [SDE]Environmental Sciences, General Earth and Planetary Sciences, Environmental science, Sentinel-1, Clay, lcsh:Q, Satellite, Sentinel-2
Abstract: [Notes_IRSTEA]1520 [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [ADD1_IRSTEA]Dynamiques spatiales d'anthropisation; International audience; This paper discusses the combined use of remotely sensed optical and radar data for the estimation and mapping of soil texture. The study is based on Sentinel-1 (S-1) and Sentinel-2 (S-2) data acquired between July and early December 2017, on a semi-arid area about 3000 km2 in central Tunisia. In addition to satellite acquisitions, texture measurement samples were taken in several agricultural fields, characterized by a large range of clay contents (between 13% and 60%). For the period between July and August, various optical indicators of clay content Short-Wave Infrared (SWIR) bands and soil indices) were tested over bare soils. Satellite moisture products, derived from combined S-1 and S-2 data, were also tested as an indicator of soil texture. Algorithms based on the support vector machine (SVM) and random forest (RF) methods are proposed for the classification and mapping of clay content and a three-fold cross-validation is used to evaluate both approaches. The classifications with the best performance are achieved using the soil moisture indicator derived from combined S-1 and S-2 data, with overall accuracy (OA) of 63% and 65% for the SVM and RF classifications, respectively.
Published: 2019

38. Radar Backscattering Coefficient Over Bare Soils at Ka-Band Close to Nadir Angle

Author: Mohammad El Hajj, Frédéric Frappart, Eric Mougin, Mehrez Zribi, Nicolas Baghdadi, Christophe Fatras, Pierre Borderies, ONERA - The French Aerospace Lab [Toulouse], ONERA, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Géosciences Environnement Toulouse (GET), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)
Subjects: Materials science, 010504 meteorology & atmospheric sciences, Backscatter, KA BAND, BARE SOILS, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, Physics::Geophysics, law.invention, law, Surface roughness, Nadir, Bare soils, Ka band, Electrical and Electronic Engineering, Radar, Physics::Atmospheric and Oceanic Physics, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, SURFACE ROUGHNESS, Geotechnical Engineering and Engineering Geology, RADAR, Ka-band, [SPI.ELEC]Engineering Sciences [physics]/Electromagnetism, Ocean surface topography, surface roughness, SOIL MOISTURE, Millimeter, soil moisture, Microwave, radar
Abstract: [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOS; International audience; The behavior of the Ka-band backscattering coefficient at nadir and close-to-nadir angles for land applications is poorly documented. The measurements made during a ground-based campaign at Ka-band were performed at nadir and close-to-nadir angles over bare soils for different surface roughness and soil moisture conditions. The resulting backscattering levels exhibited a dynamic range of approximately 23 dB at nadir for soil moisture contents between 5% and 50% m3/m3 over both smooth and rough surfaces. These results were then compared to the geometric optics (GO) and millimeter microwave (MMW) models. Generally, GO finely fits the backscattering coefficients close to nadir, and MMW appeared to fit for larger incidence angles or rough surfaces. The results obtained in this letter can address prelaunch science and engineering considerations for the interferometry-altimetry Surface Water and Ocean Topography mission operating at Ka-band.
Published: 2016

39. Landscape structure estimation using Fourier-based ordination of high resolution airborne optical image

Author: Marc Lang, Nicolas Baghdadi, Sandra Luque, Samuel Alleaume, Jean-Baptiste Féret, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)
Subjects: TEXTURE INDICES, Coefficient of determination, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, Fragmentation (computing), Biodiversity, 02 engineering and technology, Vegetation, 15. Life on land, 01 natural sciences, Texture (geology), Regression, [SDE]Environmental Sciences, Environmental science, MEDITERRANEAN ECOSYSTEMS, Ordination, HIGH RESOLUTION AIRBORNE OPTICAL IMAGE, LANDSCAPE STRUCTURE, Scale (map), FOTO, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract: International audience; Landscape heterogeneity is a key factor for understanding ecosystems function and is often associated to high biodiversity. Yet, its characterization for biodiversity monitoring still remains challenging, especially for very heterogeneous landscapes mosaics. In this work, we study the potential of Fourier-based Textural Ordination (FOTO) applied on high resolution airborne optical images to provide synthetic information about landscape fragmentation of Mediterranean heterogeneous landscapes. The ability of texture indices resulting from FOTO combined with vegetation indices to predict landscape metrics was also tested using non linear SVM regression. Our research showed that FOTO methods had great potential to finely characterize different vegetation strata organization at large scale with only few synthetic indices. Best regression results were obtained for ligneous and ground strata fragmentation with coefficient of determination greater than 0.7.
Published: 2018

40. Coupling Sentinel-1 and Sentinel-2 images for operational soil moisture mapping

Author: Nicolas Baghdadi, Hassan Bazzi, Mehrez Zribi, Mohammad El Hajj, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Synthetic aperture radar, 010504 meteorology & atmospheric sciences, Noise measurement, Artificial neural network, 0211 other engineering and technologies, 02 engineering and technology, 15. Life on land, 01 natural sciences, C-BAND, Normalized Difference Vegetation Index, law.invention, law, Soil water, [SDE]Environmental Sciences, Environmental science, Radar, Image resolution, Water content, SENTINEL 2, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, SENTINEL 1
Abstract: International audience; The objective of the present paper is to develop an operational approach for soil moisture mapping in agricultural areas at a high spatial resolution over bare soils, as well as soils with vegetation cover. The developed approach is based on the synergic use of radar and optical data and uses the neural network technique to invert the radar signal. Three inversion SAR (Synthetic Aperture Radar) configurations were tested: (1) VV polarization, (2) VH polarization, and (3) both VV and VH polarization, all in addition to the NDVI information extracted from optical images. Neural networks were developed and validated using synthetic and real databases. The results showed that the soil moisture could be estimated in agricultural areas with an accuracy of approximately 5 vol.%.
Published: 2018

41. Performances of GNSS-R GLORI data over Lande forest

Author: Jean-Pierre Wigneron, Dominique Guyon, Mehrez Zribi, Nazzareno Pierdicca, Erwan Matte, Pascal Fanise, Nicolas Baghdadi, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), and Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome]
Subjects: Ground truth, ABOVE GROUND BIOMASS, GIRONDE, AIRBORNE, 010504 meteorology & atmospheric sciences, FRANCE SUD OUEST, GNSS-R, Pine forest, 0211 other engineering and technologies, Diameter at breast height, 02 engineering and technology, 15. Life on land, Polarization (waves), 01 natural sciences, Reflectivity, GNSS applications, High elevation, [SDE]Environmental Sciences, Environmental science, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract: International audience; The GLORI Campaign performed in June-July 2015 to investigate the sensitivity of airborne GNSS-R measurements to land parameters is presented. In this paper data obtained on forest areas are analyzed. Ground truth measurements of tree height, density and diameter at breast height, AGB etc were measured over 100 maritime pine forest plots of various ages. The correlation between forest parameters and GNSS reflectivity in LHCP polarization or polarization ratio (PR) yields to high sensitivity for high elevation angles (70°-90°). Results show that PR illustrates highest potential than the reflectivity in LHCP.
Published: 2018

42. L-band UAVSAR tomographic imaging in dense forest: AfriSAR results

Author: Nicolas Baghdadi, Jalal Jomaah, Chadi Abdallah, Ibrahim El Moussawi, Olivier Strauss, Dinh Ho Tong Minh, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), National Center For Remote Sensing [CNRS-L], National Council for Scientific Research = Conseil national de la recherche scientifique du Liban [Lebanon] (CNRS-L), Lebanese University [Beirut] (LU), Image & Interaction (ICAR), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
Subjects: L band, Lidar, Tomographic reconstruction, 010504 meteorology & atmospheric sciences, Light detection, Small footprint, TomoSAR, AfriSAR campaign, 0211 other engineering and technologies, 02 engineering and technology, 15. Life on land, Dense forest, 01 natural sciences, Phase Calibration, Tropical forest, [SDE]Environmental Sciences, Calibration, Tomography, Geology, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract: International audience; This paper presents tomographic analysis using L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope Park on February 2016. Prior to tomographic imaging, a phase residual correction methodology based on Sum Kronecker Product have been implemented. The estimated vertical structure of the forest extracted from the correct tomographic data is validated with small footprint light detection collected during the AfriSAR campaign on July 2015. The result demonstrates that L-band tomographic imaging can now be carried out even in the dense tropical forest.
Published: 2018

43. Estimation of rice height and biomass using multi-temporal SAR Sentinel-1 for Camargue, southern France

Author: Hai Thu Dang Nguyen, Laure Hossard, Dinh Ho Tong Minh, Dominique Courault, Emile Ndikumana, Nicolas Baghdadi, Ibrahim El Moussawi, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), and Minh, Dinh Ho Tong
Subjects: Synthetic aperture radar, 010504 meteorology & atmospheric sciences, Correlation coefficient, Science, IMAGE SATELLITE, 0211 other engineering and technologies, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Support Vector Regression, Camargue, 02 engineering and technology, 01 natural sciences, law.invention, rice dry biomass, satellites, law, Radar imaging, Linear regression, SURVEILLANCE, rice height, multiple linear regression, support vector regression, random forest, sentinel-1, tomoSAR platform, camargue, southern france, Radar, SATELLITE, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, 2. Zero hunger, Random Forest, BIOMASSE, biomass, rice, Speckle noise, 15. Life on land, Random forest, CULTURE DE PLEIN CHAMP, monitoring, 13. Climate action, Multiple Linear Regression, Sentinel-1, TomoSAR platform, souther France, REGRESSION LINEAIRE, [SDE]Environmental Sciences, General Earth and Planetary Sciences, Paddy field, Environmental science, RIZ, radar
Abstract: The research and improvement of methods to be used for crop monitoring are currently major challenges, especially for radar images due to their speckle noise nature. The European Space Agency’s (ESA) Sentinel-1 constellation provides synthetic aperture radar (SAR) images coverage with a 6-day revisit period at a high spatial resolution of pixel spacing of 20 m. Sentinel-1 data are considerably useful, as they provide valuable information of the vegetation cover. The objective of this work is to study the capabilities of multitemporal radar images for rice height and dry biomass retrievals using Sentinel-1 data. To do this, we train Sentinel-1 data against ground measurements with classical machine learning techniques (Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Random Forest (RF)) to estimate rice height and dry biomass. The study is carried out on a multitemporal Sentinel-1 dataset acquired from May 2017 to September 2017 over the Camargue region, southern France. The ground in-situ measurements were made in the same period to collect rice height and dry biomass over 11 rice fields. The images were processed in order to produce a radar stack in C-band including dual-polarization VV (Vertical receive and Vertical transmit) and VH (Vertical receive and Horizontal transmit) data. We found that non-parametric methods (SVR and RF) had a better performance over the parametric MLR method for rice biophysical parameter retrievals. The accuracy of rice height estimation showed that rice height retrieval was strongly correlated to the in-situ rice height from dual-polarization, in which Random Forest yielded the best performance with correlation coefficient R 2 = 0.92 and the root mean square error (RMSE) 16% (7.9 cm). In addition, we demonstrated that the correlation of Sentinel-1 signal to the biomass was also very high in VH polarization with R 2 = 0.9 and RMSE = 18% (162 g·m − 2 ) (with Random Forest method). Such results indicate that the highly qualified Sentinel-1 radar data could be well exploited for rice biomass and height retrieval and they could be used for operational tasks.
Published: 2018

44. Soil moisture and irrigation mapping in a semi-arid region, based on the synergetic use of Sentinel-1 and Sentinel-2 data

Author: Safa Bousbih, Mehrez Zribi, Mohammad El Hajj, Nicolas Baghdadi, Zohra Lili-Chabaane, Qi Gao, Pascal Fanise, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), CHAAMS, ERANET-MED 03-62 CHAAMS, TOSCA/CNES, ASCAS projects, ESA-ESTEC ITT project : AO/1-8845/16/CT, Allocation de Recherche pour une These au Sud (ARTS) by the Institut de Recherche pour le Developpement (IRD), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: NDVI, Science, irrigation, modelling, remote sensing, cartography, TELEDETECTION, semiarid zones, CARTOGRAPHIE, agriculture, INDICE DE VEGETATION, RADAR A SYNTHESE D'OUVERTURE, Water Cloud, HUMIDITE DU SOL, soil moisture, Sentinel-1, Sentinel-2, Water Cloud Model, soil water content, MODELISATION, ZONE SEMI ARIDE, [SDE]Environmental Sciences, Model, synthetic aperture radar
Abstract: [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOS [ADD1_IRSTEA]Dynamiques spatiales d'anthropisation; International audience; This paper presents a technique for the mapping of soil moisture and irrigation, at the scale of agricultural fields, based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). The Kairouan plain, a semi-arid region in central Tunisia (North Africa), was selected as a test area for this study. Firstly, an algorithm for the direct inversion of the Water Cloud Model (WCM) was developed for the spatialization of the soil water content between 2015 and 2017. The soil moisture retrieved from these observations was first validated using ground measurements, recorded over 20 reference fields of cereal crops. A second method, based on the use of neural networks, was also used to confirm the initial validation. The results reported here show that the soil moisture products retrieved from remotely sensed data are accurate, with a Root Mean Square Error (RMSE) of less than 5% between the two moisture products. In addition, the analysis of soil moisture and Normalized Difference Vegetation Index (NDVI) products over cultivated fields, as a function of time, led to the classification of irrigated and rainfed areas on the Kairouan plain, and to the production of irrigation maps at the scale of individual fields. This classification is based on a decision tree approach, using a combination of various statistical indices of soil moisture and NDVI time series. The resulting irrigation maps were validated using reference fields within the study site. The best results were obtained with classifications based on soil moisture indices only, with an accuracy of 77%. This article belongs to the Special Issue soil moisture retrieval using radar remote sensing sensors.
Published: 2018

45. A novel approach for mapping wheat areas using high resolution Sentinel-2 images

Author: T. Darwish, Hatem Belhouchette, Mario Mhawej, Ghaleb Faour, Nicolas Baghdadi, Salem Darwich, Ali Nasrallah, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), National Center For Remote Sensing [Beirut], National Council for Scientific Research [Lebanon] (CNRS-L), Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Université Libanaise, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), National Center For Remote Sensing [CNRS-L], National Council for Scientific Research = Conseil national de la recherche scientifique du Liban [Lebanon] (CNRS-L), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)
Subjects: INDICATORS, Winter cereal, 010504 meteorology & atmospheric sciences, IMAGE SATELLITE, 0211 other engineering and technologies, PRODUCTION AGRICOLE, 02 engineering and technology, Agricultural engineering, lcsh:Chemical technology, 01 natural sciences, Biochemistry, Analytical Chemistry, MEASUREMENT, wheat, WHEATS, LIBAN, lcsh:TP1-1185, MESURE, Instrumentation, INDICE DE VEGETATION, 2. Zero hunger, RADAR A SYNTHESE D'OUVERTURE, Ground truth, Food security, [SDE.IE]Environmental Sciences/Environmental Engineering, CULTURE D'HIVER, Staple food, CARTOGRAPHY, Triticale, Atomic and Molecular Physics, and Optics, WINTER CROPS, BEKAA, Geography, BLE, DONNEE DE PRODUCTION, IMAGING TECHNIQUES, SURFACE, NDVI, Context (language use), Normalized Difference Vegetation Index, Article, PRODUCTION DATA, CLASSIFICATION, REMOTE SENSING, tree-like approach, INDICATEUR, [SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture, SURVEILLANCE, APPROCHE ARBORESCENTE, CAPTEUR, Electrical and Electronic Engineering, AIDE A LA DECISION, TELEDETECTION, CARTOGRAPHIE, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, business.industry, LEBANON, crop classification, 15. Life on land, SURFACE AREA, DECISION SUPPORT, Agriculture, Sentinel-2, business
Abstract: Global wheat production reached 754.8 million tons in 2017, according to the FAO database. While wheat is considered as a staple food for many populations across the globe, mapping wheat could be an effective tool to achieve the SDG2 sustainable development goal&mdash, End Hunger and Secure Food Security. In Lebanon, this crop is supported financially, and sometimes technically, by the Lebanese government. However, there is a lack of statistical databases, at both national and regional scales, as well as critical information much needed in the subsidy and compensation system. In this context, this study proposes an innovative approach, named Simple and Effective Wheat Mapping Approach (SEWMA), to map the winter wheat areas grown in the Bekaa plain, the primary wheat production area in Lebanon, in the years of 2016 and 2017. The proposed methodology is a tree-like approach relying on the Normalized Difference Vegetation Index (NDVI) values of four-month period that coincides with several phenological stages of wheat (i.e., tillering, stem extension, heading, flowering and ripening). The usage of the freely available Sentinel-2 imageries, with a high spatial (10 m) and temporal (5 days) resolutions, was necessary, particularly due to the small sized and overlapped plots encountered in the study area. Concerning the wheat areas, results show that there was a decrease from 11,063 &plusmn, 1309 ha in 2016 to 7605 &plusmn, 1184 in 2017. When SEWMA was applied using 2016 ground truth data, the overall accuracy reached 87.0% on 2017 data, whereas, when implemented using 2017 ground truth data, the overall accuracy was 82.6% on 2016 data. The novelty resides in executing early classification output (up to six weeks before harvest) as well as distinguishing wheat from other winter cereal crops with similar NDVI yearly profiles (i.e., barley and triticale). SEWMA offers a simple, yet effective and budget-saving approach providing early-season classification information, very crucial to decision support systems and the Lebanese government concerning, but not limited to, food production, trade, management and agricultural financial support.
Published: 2018

46. Detection of frozen soil using Sentinel-1 SAR data

Author: Mehrez Zribi, Nicolas Baghdadi, Hassan Bazzi, Mohammad El Hajj, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: Synthetic aperture radar, 010504 meteorology & atmospheric sciences, soil temperature, AGRICULTURE, C band, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], Science, IMAGE SATELLITE, 0211 other engineering and technologies, DONNEE DE SATELLITE, Soil science, 02 engineering and technology, Dielectric, 01 natural sciences, complex mixtures, law.invention, remote sensing, frozen soils, law, SURVEILLANCE, Radar, C-band, TELEDETECTION, Water content, Physics::Atmospheric and Oceanic Physics, satellite data, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Soil map, Moisture, [SDE.IE]Environmental Sciences/Environmental Engineering, Astrophysics::Instrumentation and Methods for Astrophysics, IEM, RADAR, monitoring, TEMPERATURE DU SOL, 13. Climate action, dielectric constant, Soil water, Physics::Accelerator Physics, General Earth and Planetary Sciences, Environmental science, Sentinel-1, SAR
Abstract: [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOS [ADD1_IRSTEA]Dynamiques spatiales d'anthropisation; International audience; The objective of this paper is to evaluate the potential of Sentinel-1 Synthetic Aperture Radar "SAR" data (C-band) for monitoring agricultural frozen soils. First, investigations were conducted from simulated radar signal data using a SAR backscattering model combined with a dielectric mixing model. Then, Sentinel-1 images acquired at a study site near Paris, France were analyzed using temperature data to investigate the potential of the new Sentinel-1 SAR sensor for frozen soil mapping. The results show that the SAR backscattering coefficient decreases when the soil temperature drops below 0 °C. This decrease in signal is the most important for temperatures that ranges between 0 and -5 °C. A difference of at least 2 dB is observed between unfrozen soils and frozen soils. This difference increases under freezing condition when the temperature at the image acquisition date decreases. In addition, results show that the potential of the C-band radar signal for the discrimination of frozen soils slightly decreases when the soil moisture decreases (simulated data were used with soil moisture contents of 20 and 30 vol%). The difference between the backscattering coefficient of unfrozen soil and the backscattering coefficient of frozen soil decreases by approximately 1 dB when the soil moisture decreases from 30 to 20 vol%). Finally, the results show that both VV and VH allow a good detection of frozen soils but the sensitivity of VH is higher by approximately 1.5 dB. In conclusion, this study shows that the difference between a reference image acquired without freezing and an image acquired under freezing conditions is a good tool for detecting frozen soils.
Published: 2018

47. Evaluation of SMOS, SMAP, ASCAT and Sentinel-1 soil moisture products at sites in southwestern France

Author: Nicolas Baghdadi, Jean-Christophe Calvet, Amen Al-Yaari, Clément Albergel, Nemesio Rodriguez-Fernandez, Mohammad El Hajj, Mehrez Zribi, Ahmad Al Bitar, Jean-Pierre Wigneron, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Interactions Sol Plante Atmosphère (ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Centre national de recherches météorologiques (CNRM), Météo France-Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Interactions Sol Plante Atmosphère (UMR ISPA), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), and Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS)
Subjects: scatteromètre, 010504 meteorology & atmospheric sciences, télédétection, Science, in situ soil moisture, IMAGE SATELLITE, 0211 other engineering and technologies, Root mean square difference, DONNEE DE SATELLITE, southwestern France, 02 engineering and technology, Atmospheric sciences, 01 natural sciences, remote sensing, Satellite data, humidité du sol, SMOS, SMAP, ASCAT, Sentinel-1, cartography, TELEDETECTION, Water content, satellite data, CARTOGRAPHIE, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, CYCLE DE L'EAU, HUMIDITE DU SOL, soil water content, Scatterometer, analyse comparative, Active passive, 13. Climate action, [SDE]Environmental Sciences, General Earth and Planetary Sciences, Environmental science, sud ouest france
Abstract: [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOS [ADD1_IRSTEA]Équiper l'agriculture; International audience; This study evaluates the accuracy of several recent remote sensing Surface Soil Moisture (SSM) products at sites in southwestern France. The products used are Soil Moisture Active Passive "SMAP" (level 3: 36 km x 36 km, level 3 enhanced: 9 km x 9 km, and Level 2 SMAP/Sentinel-1: 1 km x 1km), Advanced Scatterometer "ASCAT" (level 2 with three spatial resolution 25 km x 25 km, 12.5 km x 12.5 km, and 1 km x 1 km), Soil Moisture and Ocean Salinity "SMOS" (SMOS INRA-CESBIO "SMOS-IC", SMOS Near-Real-Time "SMOS-NRT", SMOS Centre Aval de Traitement des Données SMOS level 3 "SMOS-CATDS", 25 km x 25 km) and Sentinel-1(S1) (25 km x 25 km, 9 km x 9 km, and 1 km x 1 km). The accuracy of SSM products was computed using in situ measurements of SSM observed at a depth of 5 cm. In situ measurements were obtained from the SMOSMANIA ThetaProbe (Time Domaine reflectometry) network (7 stations between 1 January 2016 and 30 June 2017) and additional field campaigns (near Montpellier city in France, between 1 January 2017 and 31 May 2017) in southwestern France. For our study sites, results showed that (i) the accuracy of the Level 2 SMAP/Sentinel-1 was lower than that of SMAP-36 km and SMAP-9 km; (ii) the SMAP-36 km and SMAP-9 km products provide more precise SSM estimates than SMOS products (SMOS-IC, SMOS-NRT, and SMOS-CATDS), mainly due to higher sensitivity of SMOS to RFI (Radio Frequency Interference) noise; and (iii) the accuracy of SMAP-36 km and SMAP-9 km products was similar to that of ASCAT (ASCAT-25 km, ASCAT-12.5 km and ASCAT-1 km) and S1 (S1-25 km, S1-9 km, and S1-1 km) products. The accuracy of SMAP, Sentinel-1 and ASCAT SSM products calculated using the average of statistics obtained on each site is defined by a bias of about -3.2 vol. %, RMSD (Root Mean Square Difference) about 7.6 vol. %, ubRMSD (unbiased Root Mean Square Difference) about 5.6 vol. %, and R coefficient about 0.57. For SMOS products, the station average bias, RMSD, ubRMSD, and R coefficient were about -10.6 vol. %, 12.7 vol. %, 5.9 vol. %, and 0.49, respectively.
Published: 2018

48. Deep Recurrent Neural Network for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France

Author: Dominique Courault, Laure Hossard, Emile Ndikumana, Dinh Ho Tong Minh, Nicolas Baghdadi, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), ANR-10-EQPX-20, and Minh, Dinh Ho Tong
Subjects: 010504 meteorology & atmospheric sciences, Computer science, IMAGE SATELLITE, 0211 other engineering and technologies, Camargue, 02 engineering and technology, 01 natural sciences, law.invention, long-short term memory, remote sensing, law, Radar, lcsh:Science, agriculture, 2. Zero hunger, Random forest, classification, [SDE]Environmental Sciences, France, SAR, Synthetic aperture radar, agricultural land cover map, gated recurrent unit, Radar imaging, active learning, cartography, K nearest neighbors, TELEDETECTION, Spatial analysis, APPRENTISSAGE ACTIF, CARTOGRAPHIE, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, sentinel-1, recurrent neural network, K nearest neighbor, random forest, vector support machine, camargue, france, business.industry, Deep learning, rice, Sentinel-1, vector support machines, Speckle noise, 15. Life on land, Support vector machine, General Earth and Planetary Sciences, lcsh:Q, Artificial intelligence, business, RIZ, radar
Abstract: International audience; The development and improvement of methods to map agricultural land cover are currently major challenges, especially for radar images. This is due to the speckle noise nature of radar, leading to a less intensive use of radar rather than optical images. The European Space Agency Sentinel-1 constellation, which recently became operational, is a satellite system providing global coverage of Synthetic Aperture Radar (SAR) with a 6-days revisit period at a high spatial resolution of about 20 m. These data are valuable, as they provide spatial information on agricultural crops. The aim of this paper is to provide a better understanding of the capabilities of Sentinel-1 radar images for agricultural land cover mapping through the use of deep learning techniques. The analysis is carried out on multitemporal Sentinel-1 data over an area in Camargue, France. The data set was processed in order to produce an intensity radar data stack from May 2017 to September 2017. We improved this radar time series dataset by exploiting temporal filtering to reduce noise, while retaining as much as possible the fine structures present in the images. We revealed that even with classical machine learning approaches (K nearest neighbors, random forest, and support vector machines), good performance classification could be achieved with F-measure/Accuracy greater than 86% and Kappa coefficient better than 0.82. We found that the results of the two deep recurrent neural network (RNN)-based classifiers clearly outperformed the classical approaches. Finally, our analyses of the Camargue area results show that the same performance was obtained with two different RNN-based classifiers on the Rice class, which is the most dominant crop of this region, with a F-measure metric of 96%. These results thus highlight that in the near future these RNN-based techniques will play an important role in the analysis of remote sensing time series.This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications
Published: 2018

49. LiDAR Measurements and Applications in Coastal and Continental Waters

Author: Jean-Stéphane Bailly, Martin A. Montes-Hugo, Nicolas Baghdadi, Yves Pastol, Laboratoire d'étude des interactions entre sols, agrosystèmes et hydrosystèmes (LISAH), Institut National de la Recherche Agronomique (INRA), AgroParisTech, Service Hydrographique et Océanographique de la Marine (SHOM), Ministère de la Défense, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Programme France-Quebec Samuel de Champlain, Nicolas Baghdadi and Mehrez Zribi, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)
Subjects: depth measurement, Photomultiplier, 010504 meteorology & atmospheric sciences, Meteorology, [SDE.MCG]Environmental Sciences/Global Changes, atmospheric aerosols, Photodetector, 01 natural sciences, scattering layers, spaceborne lidar, law.invention, 010309 optics, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], law, 0103 physical sciences, Bathymetry, 14. Life underwater, [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology, polarization lidar, 0105 earth and related environmental sciences, Remote sensing, Avalanche photodiode, Laser, wave-forms, Lidar, Geography, Underwater imaging, oceanographic lidar, airborne lidar, river bathymetry, [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, Soviet union, spectral-resolution lidar
Abstract: International audience; The first LiDAR systems applied to aquatic environments were developed for underwater imaging by the \{US\} navy, This pioneer LiDAR system was developed by Radio Corporation of America (RCA) for conducting moreefficient military operations and improving the safety of divers. This primitive LiDAR was characterized by a blue-green laser with a laser pulse a pulse repetition smaller than 0.01 Hz. In the late 60s, LiDAR technologies underwent major progress due to the development of scanners and advanced photodetectors (Avalanche Photo Diode (APD) or Photomultiplier Tubes (PMT) type). It was during this period that detection of submerged targets based on LiDAR systems was first attempted. This capability was originally evaluated in the United States, Canada, Australia and Sweden and was later exapanded to other countries such as China and the Soviet Union.
Published: 2016

50. Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands

Author: Mehrez Zribi, Mohammad El Hajj, Safa Bousbih, and Nicolas Baghdadi
Subjects: Synthetic aperture radar, 010504 meteorology & atmospheric sciences, C band, 0211 other engineering and technologies, Soil science, 02 engineering and technology, 01 natural sciences, Normalized Difference Vegetation Index, law.invention, law, Surface roughness, Radar, C-band, lcsh:Science, Water content, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Moisture, grasslands, water cloud model, integral equation model, SAR, crops, soil moisture, 15. Life on land, Soil water, General Earth and Planetary Sciences, Environmental science, lcsh:Q
Abstract: In a perspective to develop an inversion approach for estimating surface soil moisture of crop fields from Sentinel-1/2 data (radar and optical sensors), the Water Cloud Model (WCM) was calibrated from C-band Synthetic Aperture Radar (SAR) data and Normalized Difference Vegetation Index (NDVI) values collected over crops fields and grasslands. The soil contribution that depends on soil moisture and surface roughness (in addition to SAR instrumental parameters) was simulated using the physical backscattering model IEM (Integral Equation Model). The vegetation descriptor used in the WCM is the NDVI because it can be directly calculated from optical images. A large dataset consisting of radar backscattered signal in Vertical transmit and Vertical receive (VV) and Vertical transmit and Horizontal receive (VH) polarizations with wide range of incidence angle, soil moisture, surface roughness, and NDVI-values was used. It was collected over two agricultural study sites. Results show that the soil contribution to the total radar backscattered signal is lower in VH than in VV because VH is more sensitive to vegetation cover. Thus, the use of VH alone or in addition to VV for retrieving the soil moisture is not advantageous in presence of well-developed vegetation cover.
Published: 2017
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