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Land cover land use (LCLU) products provide essential information for numerous environmental and human studies. Here, we assess the accuracy of eleven global and regional products over the conterminous U.S. using 25,000 high-confidence randomly distributed samples. Results show that in general, the National Land Cover Database (NLCD) and the Land Change Monitoring, Assessment and Projection (LCMAP) outperform other multi-class products, both in terms of higher individual class accuracy and with accuracy variability across classes. More specifically, F1 accuracy comparisons between the best performing USGS and non-USGS products indicate: (i) similar performance for the water class, (ii) USGS product outperformance in the developed (+1.3%), grass/shrub (+3.2%) and tree cover (+4.2%) classes, and (iii) non-USGS product (WorldCover) gains in the cropland (+5.1%) class. The NLCD and LCMAP also outperformed specialized single-class products, such as the Hansen Global Forest Change, the Cropland Data Layer and the Global Artificial Impervious Areas, while offering comparable results to the Global Surface Water Dynamics product. Spatial visualizations also allowed accuracy comparisons across different geographic areas. In general, the NLCD and LCMAP have disagreements mainly in the middle and southeastern part of conterminous U.S. while Esri, WorldCover and Dynamic World have most errors in the western U.S. Comparisons were also undertaken on a subset of the reference data, called spatial edge samples, that identifies samples surrounded by neighboring samples of different class labels, thus excluding easy-to-classify homogenous areas. There, the WorldCover product offers higher accuracies for the highly dynamic grass/shrub (+4.4%) and cropland (+8.1%) classes when compared to the NLCD and LCMAP products. An important conclusion while looking at these challenging samples is that except for the tree class (78%), the best performing products per class range in accuracy between 55% and 70%, which suggests that there is substantial room for improvement. [ABSTRACT FROM AUTHOR]

The Plutonium Attribute Test (PAT) is a software program originally developed for determining the category of a plutonium sample. The premise for its development was that, to assign Pu samples to particular categories, it is not necessary to determine the abundance of all its isotopes. Rather, it is sufficient to determine only 239Pu abundance, which is universally used to establish a category assignment, i.e., >90 wt% 239Pu is low burnup (LBPu), <70 wt% is high burnup (HBPu), and the abundances in between could be considered medium burnup (MBPu) plutonium grade. In contrast to previous Pu isotopic analysis programs which analyse spectra taken with HPGe detectors, the algorithms developed in PAT analyses use spectra taken with medium resolution detectors such as LaBr 3 (Ce) and CZT detectors. As demonstrated by initial tests, PAT was found capable of not only assigning the material category, but also accurately determining 239Pu abundance, with several per cent typical relative combined standard uncertainty. Furthermore, an advanced algorithm (A-PAT) was added to allow full isotopic characterization of samples. This paper presents further enhancements of the PAT/A-PAT algorithms towards analysis of heavily shielded samples, samples with varied size and age, as well as mixed oxide (MOX) materials. [ABSTRACT FROM AUTHOR]

Farmland is a crucial resource for the survival and evolution of humans. The accurate evaluation of farmland production capacity (FPC) is of great significance for planting structure optimization, the improvement of low-yield farmland and sustainable utilization. The objective of this study is to quantitatively evaluate the FPC at the county scale using time series remote sensing (RS) images. Taking winter wheat as a benchmark crop, the relations between annual yield and the Normalized Difference Vegetation Index (NDVI) were established by a multiple linear regression algorithm. The mean and standard deviations (SD) of the multi-year yield of winter wheat were used to evaluate FPC and its instability using the farmland parcels as the basic unit. The results show that the estimation model for annual winter wheat yield performed best in 2011. The R2 of the modeling sample was 0.93, and the RMSE of the testing sample was 368.1 kg/ha. The FPC grades in the south and north of the study area were relatively high with a good stability, while those in the center were low with poor stability. There was a certain correlation between FPC and soil organic matter (SOM), and the correlation coefficient was 0.525 (p < 0.01). In this study, taking the farmland parcel as a basic unit instead of a pixel, long time series of multi-source RS images with medium resolution were used to monitor the per unit yield of benchmark crops and then evaluate the FPC. This can provide a method for the rapid evaluation of FPC at the county scale. [ABSTRACT FROM AUTHOR]

Land cover land use (LCLU) products provide essential information for numerous environmental and human studies. Here, we assess the accuracy of eleven global and regional products over the conterminous U.S. using 25,000 high-confidence randomly distributed samples. Results show that in general, the National Land Cover Database (NLCD) and the Land Change Monitoring, Assessment and Projection (LCMAP) outperform other multi-class products, both in terms of higher individual class accuracy and with accuracy variability across classes. More specifically, F1 accuracy comparisons between the best performing USGS and non-USGS products indicate: (i) similar performance for the water class, (ii) USGS product outperformance in the developed (+1.3%), grass/shrub (+3.2%) and tree cover (+4.2%) classes, and (iii) non-USGS product (WorldCover) gains in the cropland (+5.1%) class. The NLCD and LCMAP also outperformed specialized single-class products, such as the Hansen Global Forest Change, the Cropland Data Layer and the Global Artificial Impervious Areas, while offering comparable results to the Global Surface Water Dynamics product. Spatial visualizations also allowed accuracy comparisons across different geographic areas. In general, the NLCD and LCMAP have disagreements mainly in the middle and southeastern part of conterminous U.S. while Esri, WorldCover and Dynamic World have most errors in the western U.S. Comparisons were also undertaken on a subset of the reference data, called spatial edge samples, that identifies samples surrounded by neighboring samples of different class labels, thus excluding easy-to-classify homogenous areas. There, the WorldCover product offers higher accuracies for the highly dynamic grass/shrub (+4.4%) and cropland (+8.1%) classes when compared to the NLCD and LCMAP products. An important conclusion while looking at these challenging samples is that except for the tree class (78%), the best performing products per class range in accuracy between 55% and 70%, which suggests that there is substantial room for improvement.

Chinese pine has tremendous applications in many fields. Mapping the distribution of Chinese pine is of great importance for government decision-making and forest management. In order to extract Chinese pine on a large scale, efficient algorithms and open remote-sensing datasets are needed. It is widely believed that machine learning algorithms and medium-resolution remote-sensing datasets can work well for this purpose. Unfortunately, their performance for Chinese pine extraction has remained unclear until now. Therefore, this study aims to explore the ability of the different machine learning algorithms and open remote-sensing datasets for Chinese pine extraction over large areas on Google Earth Engine (GEE). So, based on the combination of three typical machine learning algorithms, namely deep neural network (DNN), support vector machine (SVM), random forest (RF), and three open medium-resolution remote-sensing datasets, namely Sentinel-2, Gaofen-1, and Landsat-8 OLI, 27 models are constructed and GEE, with its powerful computing ability, is used. The main findings are as follows: (1) DNN has the highest accuracy for Chinese pine extraction, followed by SVM and RF; DNN is more sensitive to spatial geometric information, while SVM and RF algorithms are more sensitive to spectral information. (2) Spectral indexes are helpful for improving the extraction accuracy of Chinese pine. The extraction accuracy by using Gaofen-1 dataset increases 7.6% after adding spectral indexes, while the accuracies by using Sentinel-2 and Landsat-8 datasets increase 1.8% and 1.9% after adding spectral indexes, respectively. (3) The extraction accuracy by using DNN and Sentinel-2 dataset with spectral indexes is the highest, with an overall accuracy of 94.4%. (4) The area of Chinese pine is 153.73 km2, accounting for 5.06% of the administrative area of Karaqin Banner, and it is convenient to extract Chinese pine on a large scale by using GEE. [ABSTRACT FROM AUTHOR]

This article elaborates a processing chain devised to recognize the ships existing on medium resolution multispectral imageries (MSI). The chain consists of the following three steps. Firstly, an adaptive local saliency mapping technique is instigated on open ocean regions to obtain all floating objects. Secondly, to extract the ship candidates, two-step verification is applied based on specific spectral and geometric information of the ships. Lastly, a calculation to determine the properties of the ships, including their length, breadth, and heading, is then carried out. Furthermore, we propose a novel method for correcting miscalculated ship heading; by combining wake segmentation and Radon Transform (RT) approaches to locate the position and estimate the length of the wake generated by the ships. With the detected wake length, ship velocity can also be assessed. The developed chain is then tested using imageries acquired by LAPAN-A3 microsatellite, and the results are compared to those reported by the Automatic Identification System (AIS). Experimental results indicate that the proposed chain achieves higher detection performance and can produce better heading information compared to the existing methods.

Determination of actinide isotopic composition is one of the most important tasks accomplished under IAEA safeguards treaties for verification and inspection activities. Spectrometers based on HPGe detectors have traditionally been the detectors of choice for such systems with a large array of various data analysis codes developed specifically to suit the performance of these detectors. Over the past decade in this domain there has been interest in using medium resolution room temperature detectors based on CdZnTe and LaBr3(Ce) crystals. Moreover, progress in analytical tools that could be applied to analyze worsened statistical quality spectral data has opened a wide range of possible applications where such compact detectors and absence of cryogenics are advantageous. In this paper we aim to provide a review on the recent advances in the domain of isotopic composition determination systems used as with traditional HPGe detectors as well as with medium resolution ones. We limit ourselves to the achievements in the European Union and divide our review in three parts. First, we provide an update on the current status of the French code IGA aimed at applications with traditional HPGe systems. Second, we present the recent results in the domain of isotopic composition determination codes development for medium resolution detectors, namely the codes MCSIGMA and CAMILA developed in the framework of a PhD project in Belgium. Third, we give an outlook on the current research activities conducted at CEA List Saclay in the domain of isotopic composition determination algorithms applied to medium resolution detectors. [ABSTRACT FROM AUTHOR]

Farmland is a crucial resource for the survival and evolution of humans. The accurate evaluation of farmland production capacity (FPC) is of great significance for planting structure optimization, the improvement of low-yield farmland and sustainable utilization. The objective of this study is to quantitatively evaluate the FPC at the county scale using time series remote sensing (RS) images. Taking winter wheat as a benchmark crop, the relations between annual yield and the Normalized Difference Vegetation Index (NDVI) were established by a multiple linear regression algorithm. The mean and standard deviations (SD) of the multi-year yield of winter wheat were used to evaluate FPC and its instability using the farmland parcels as the basic unit. The results show that the estimation model for annual winter wheat yield performed best in 2011. The R2 of the modeling sample was 0.93, and the RMSE of the testing sample was 368.1 kg/ha. The FPC grades in the south and north of the study area were relatively high with a good stability, while those in the center were low with poor stability. There was a certain correlation between FPC and soil organic matter (SOM), and the correlation coefficient was 0.525 (p < 0.01). In this study, taking the farmland parcel as a basic unit instead of a pixel, long time series of multi-source RS images with medium resolution were used to monitor the per unit yield of benchmark crops and then evaluate the FPC. This can provide a method for the rapid evaluation of FPC at the county scale.

Building on the availability of high revisit frequency Earth Observation satellites at medium spatial resolution (250 m), this study investigates the feasibility of temporal monitoring of water bodies at a continental scale with MODIS. A 2004–2010 time series of twice-daily observations covering the whole African continent was systematically processed using a surface water detection method to derive 10-day indicators describing the location, the intra- and inter-annual variability as well as the temporal characterization of water bodies (i.e. seasonal or permanent water and maximum extent). The multispectral surface reflectance transformation in the HSV color space allows a per-pixel identification of surface water. The water aggregation time indicator provides the water occurrence for each 10-day period built from the seven years of observations. The cartographic products were successfully cross-validated with already existing maps and water products. The validation of the water body maximum extent map estimates the commission error at less than 6% and the seasonality information was also found to be consistent with the Köppen climatic classification.

It is well known that there are geometric distortions in synthetic aperture radar (SAR) images when the terrain undulates. Layover is the most common one, which brings challenges to the application of SAR remote sensing. This study proposes a novel detection method that is mainly aimed at the layover caused by mountains and can be performed with only medium-resolution SAR images and no other auxiliary data. The detection includes the following four stages: initial processing, difference image calculation and rough and fine layover detection. Initial processing mainly obtains the potential layover areas, which are mixed with the built-up areas after classification. Additionally, according to the analysis of the backscatter coefficient (BC) of various ground objects with different polarization images, the layover areas are detected step-by-step from the mixed areas, in which the region-based FCM segmentation algorithm and spatial relationship criteria are used. Taking the Danjiangkou Reservoir area as the study area, the relevant experiments with Sentinel-1A SAR images were conducted. The quantitative analysis of detection results adopted the figure of merit (FoM), and the highest accuracy was up to 87.6% of one selected validation region. Experiments in the South Taihang area also showed the satisfactory effect of layover detection, and the values of FoM were all above 85%. These results show that the proposed method can do well in the layover detection caused by mountains. Its simplicity and effectiveness are helpful in removing the influence of layover on SAR image applications to a certain extent and improving the development of SAR remote sensing technology.

Room temperature semiconductor detectors of CdZnTe type have been proposed as possible alternatives to spectrometers based on HPGe and NaI detectors. Attractive spectroscopic performance and portability, as well as continuous improvements in their design and availability in different sizes make them especially competitive in various radiation measurement applications where absence of cooling, small size and significantly better resolution compared to other room temperature detectors are an asset. Among these applications are nuclear safeguards that include uranium and plutonium isotopic composition determination tasks. This paper focuses on the investigation of possibilities and limits of a net peak area based methodology used for the determination of the uranium enrichment without use of calibration standards. Tests are conducted on a room temperature medium resolution spectrometer based on a 500 mm3 CdZnTe sensor of a quasi-hemispherical design using which spectra of different statistical quality are obtained from certified uranium standards. Gamma-ray peaks in the 143–1001 keV energy range are used as uranium gamma-ray signatures. Performance assessment of an intrinsic calibration of the counting efficiency curve is conducted. A hybrid methodology based on a combination of the peak ratios method with Monte Carlo simulations of the counting efficiency curve is proposed for poor quality spectra. Implemented algorithms and analysis routines are described in detail and presented. • CdZnTe detectors are a promising alternative to HPGe and NaI detectors. • Net peak area based methods are widely used for uranium enrichment tasks. • Performance limits and possibilities of a 500 mm3 CdZnTe detector are investigated. • Statistical quality and shape of gamma-ray peaks in spectra are limiting factors. • Hybrid methodology with a Monte Carlo based counting efficiency curve is proposed. [ABSTRACT FROM AUTHOR]

Monitoring surface albedo at medium-to-fine resolution (

Associating a radar scatterer to a physical object is crucial for the correct interpretation of interferometric synthetic aperture radar measurements. Yet, especially for medium-resolution imagery, this is notoriously difficult and dependent on the accurate 3-D positioning of the scatterers. Here, we investigate the 3-D positioning capabilities of ENVISAT medium-resolution data. We find that the data are perturbed by range-and-epoch-dependent timing errors and calibration offsets. Calibration offsets are estimated to be about 1.58 m in azimuth and 2.84 m in range and should be added to ASAR products to improve geometric calibration. The timing errors involve a bistatic offset, atmospheric path delay, solid earth tides, and local oscillator drift. This way, we achieve an unbiased positioning capability in 2-D, while in 3-D, a scatterer was located at a distance of 28 cm from the true location. 3-D precision is now expressed as an error ellipsoid in local coordinates. Using the Bhattacharyya metric, we associate radar scatterers to real-world objects. Interpreting deformation of individual infrastructure is shown to be feasible for this type of medium-resolution data. [ABSTRACT FROM AUTHOR]

The Sentinel-2 optical satellites provide a global coverage of land surfaces with a 5-day revisit time at the Equator. We investigate the ability of these freely available optical images to detect precursory motions before rapid landslides. A 9-month time-series of displacement is derived from Sentinel-2 data over a major landslide in the French Alps, which exhibited a sudden reactivation in June 2016. This analysis reveals a 7-month period of low activity (≤1 m), followed by a sudden acceleration of 3.2 ± 1.2 m in 3 days, before the failure of a mass of about 2 to 3.6 10 6  m 3 . The location of this precursory motion is consistent with that of the slow motions occurring since 2001 (about 1 m/year), as revealed by aerial photographs and LiDAR analysis. This change in activity over a very short period of time (days) emphasizes the value of the frequent revisit time of Sentinel-2, despite its medium resolution of 10 m. We finally simulate the ability of Sentinel-2 for detecting these precursory patterns before a rapid landslide occurs, based on typical Voight's laws for creeping materials, characterized by a power law exponent α . Based on this analysis and on global cloud cover maps, we compute the probability to detect pre-failure motions of landslides using the Sentinel-2 constellation. This probability is highly heterogeneous at the global scale, affected by the revisit time of the satellite and the cloud cover. However the main factors controlling this detection ability are the properties of the landslide itself (its size and the α parameter), with almost 100% of detection probability for α  = 1.3 and 0% for α  = 1.8. Despite all these limitations, probability to detect a motion before a landslide failure often reaches 50% for classical landslide parameters. These results open new perspectives for the early warning of large landslide motion from global and open source remote sensing data. [ABSTRACT FROM AUTHOR]

Time series of vegetation biophysical variables (leaf area index (LAI), fraction canopy cover (FCOVER), fraction of absorbed photosynthetically active radiation (FAPAR), canopy chlorophyll content (CCC), and canopy water content (CWC)) were estimated from interpolated Sentinel-2 (S2-LIKE) surface reflectance images, for an agricultural region located in central Canada, using the Simplified Level 2 Product Prototype Processor (SL2P). S2-LIKE surface reflectance data were generated by blending clear-sky Sentinel-2 Multispectral Imager (S2-MSI) images with daily BRDF-adjusted Moderate Resolution Imaging Spectrometer images using the Prediction Smooth Reflectance Fusion Model (PSFRM), and validated using thirteen independent S2-MSI images (RMSE ≤ 6%). The uncertainty of S2-LIKE surface reflectance data increases with the time delay between the prediction date and the closest S2-MSI image used for training PSFRM. Vegetation biophysical variables from S2-LIKE products are validated qualitatively and quantitatively by comparison to the corresponding vegetation biophysical variables from S2-MSI products (RMSE = 0.55 for LAI, ~10% for FCOVER and FAPAR, and 0.13 g/m2 for CCC and 0.16 kg/m2 for CWC). Uncertainties of vegetation biophysical variables derived from S2-LIKE products are almost linearly related to the uncertainty of the input reflectance data. When compared to the in situ measurements collected during the Soil Moisture Active Passive Validation Experiment 2016 field campaign, uncertainties of LAI (0.83) and FCOVER (13.73%) estimates from S2-LIKE products were slightly larger than uncertainties of LAI (0.57) and FCOVER (11.80%) estimates from S2-MSI products. However, equal uncertainties (0.32 kg/m2) were obtained for CWC estimates using SL2P with either S2-LIKE or S2-MSI input data.

We developed a land cover and land use mapping framework specifically designed for agricultural systems of the Sudan-Sahel region. The mapping approach extracts information from inter- and intra-annual vegetation dynamics from dense stacks of Landsat 8 images. We applied this framework to create a 30 m spatial resolution land use map with a focus on agricultural landscapes of northern Nigeria for 2015. This map provides up-to-date information with a higher level of spatial and thematic detail resulting in a more precise characterization of agriculture in the region. The map reveals that agriculture is the main land use in the region. Arable land represents on average 52.5% of the area, higher than the reported national average for Nigeria (38.4%). Irrigated agriculture covers nearly 2.2% of the total area, reaching nearly 20% of the cultivated land when traditional floodplain agriculture systems are included, above the reported national average (0.63%). There is significant variability in land use within the region. Cultivated land in the northern section can reach values higher than 75%, most land suitable for agriculture is already under cultivation and there is limited land for future agricultural expansion. Marginal lands, not suitable for permanent agriculture, can reach 30% of the land at lower altitudes in the northeast and northwest. In contrast, the southern section presents lower land use intensity that results in a complex landscape that intertwines areas farms and larger patches of natural vegetation. This map improves the spatial detail of existing sources of LCLU information for the region and provides updated information of the current status of its agricultural landscapes. This study demonstrates the feasibility of multi temporal medium resolution remote sensing data to provide detailed and up-to-date information about agricultural systems in arid and sub arid landscapes of the Sahel region.

In change detection (CD) of medium-resolution remote sensing images, the threshold and clustering methods are two kinds of the most popular ones. It is found that the threshold method of the expectation maximum (EM) algorithm usually generates a CD map including many false alarms but almost detecting all changes, and the fuzzy local information c-means algorithm (FLICM) obtains a homogeneous CD map but with some missed detections. Therefore, we aim to design a framework to improve CD results by fusing the advantages of threshold and clustering methods. Experimental results indicate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A two stage burned scar detection approach is applied to produce a burned scar map for Mediterranean Europe using IRS-AWiFS imagery acquired at the end of the 2009 fire season. The first stage identified burned scar seeds based on a learning algorithm (Artificial Neural Network) coupled with a bootstrap aggregation process. The second stage implemented a region growing process to extend the area of the burned scars. Several ancillary datasets were used for the accuracy assessment and a final visual check was performed to refine the burned scar product. Training data for the learning algorithm were obtained from MODIS-based polygons, which were generated by the Rapid Damage Assessment module of the European Forest Fire Information System. The map produced from this research is the first attempt to increase the spatial detail of current burned scar maps for the Mediterranean region. The map has been analyzed and compared to existing burned area polygons from the European Forest Fire Information System. The comparison showed that the IRS-AWiFS-based burned scar map improved the delineation of burn scars; in addition the process identified a number of small burned scars that were not detected on lower resolution sensor data. Nonetheless, the results do not clearly support the improved capability for the detection of smaller burned scars. A number of reasons can be provided for the under-detection of burned scars, these include: the lack of a full coverage and cloud free imagery, the time lag between forest fires and image acquisition date and the occurrence of fires after the image acquisition dates. On the other hand, the limited spectral information combined with the presence of undetected cloud shadows and shaded slopes are reasons for the over-estimation of small burned scars.

Nowadays, there is interest in developing gamma-ray measuring devices based on the room temperature operated medium resolution detectors such as semiconductor detectors of the CdZnTe type and scintillators of the LaBr3 type. This is true also for safeguards applications and the International Atomic Energy Agency (IAEA) has launched a project devoted to the assessment of medium resolution gamma-ray spectroscopy for the verification of the isotopic composition of U and Pu bearing samples. This project is carried out within the Non-Destructive Assay Working Group of the European Safeguards Research and Development Association (ESARDA). In this study we analyze medium resolution spectra of U and Pu standards with the aim to develop an isotopic composition determination algorithm, particularly suited for these types of detectors. We show how the peak shape of a CdZnTe detector is influenced by the instrumentation parameters. The experimental setup consisted of a 500 mm3 CdZnTe detector, a 2×2 inch LaBr3 detector, two types of measurement instrumentation – an analogue one and a digital one, and a set of certified samples – a 207Bi point source and U and Pu CBNM standards. The results of our measurements indicate that the lowest contribution to the peak asymmetry and thus the smallest impact on the resolution of the 500 mm3 CdZnTe detector was achieved with the digital MCA. Analysis of acquired spectra allowed to reject poor quality measurement runs and produce summed spectra files with the least impact of instrumentation instabilities. This work is preliminary to further studies concerning the development of an isotopic composition determination algorithm particularly suited for CZT and LaBr3 detectors for safeguards applications.

Abstract: A two stage approach for burnt scar detection from single date multispectral medium spatial resolution optical remote sensing data (AWIFS) has been developed. The approach includes first an identification of burnt scar seeds based on a learning algorithm followed by a region growing process. An Artificial Neural Network (ANN) and a Classification tree (CT) were tested as learning algorithms. Both learning algorithms were coupled with a bootstrap aggregation. Training data for the classifiers were obtained from MODIS-based polygons generated by the Rapid Damage Assessment (RDA) module of the European Forest Fire Information System (EFFIS), to which different levels of filtering were applied. The outcomes were validated against datasets generated from visual interpretation of ETM+ scenes. The method was tested in two locations of Portugal and Greece. Both ANN and CT alternatives produced similar results, with kappa coefficients close to 0.80 in the Greek location and higher than 0.70 in the Portuguese location. In test sites, more than 80% and 90% of burnt areas larger than 10ha and 50ha respectively were detected. The results show that filtering the training dataset reduces the overestimation of burnt areas and produce higher accuracies. [Copyright &y& Elsevier]

In modern remote sensing applications the use of automatic image processing codes is becoming increasingly common. When dealing with satellite time series or multi-source data, image co-registration is a time-consuming, but necessary, pre-processing step. This study shows how a medium resolution satellite multitemporal dataset can be effectively processed using automatic data processing. Results on real and transformed ASTER data showed that, without human interaction, the automatic ground control points extraction (AGE) technique, developed at Politecnico di Milano, is able to obtain accuracies generally sufficient for practical applications and similar to those reported in other studies. [ABSTRACT FROM AUTHOR]

Abstract: This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow. [Copyright &y& Elsevier]

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It is well known that there are geometric distortions in synthetic aperture radar (SAR) images when the terrain undulates. Layover is the most common one, which brings challenges to the application of SAR remote sensing. This study proposes a novel detection method that is mainly aimed at the layover caused by mountains and can be performed with only medium-resolution SAR images and no other auxiliary data. The detection includes the following four stages: initial processing, difference image calculation and rough and fine layover detection. Initial processing mainly obtains the potential layover areas, which are mixed with the built-up areas after classification. Additionally, according to the analysis of the backscatter coefficient (BC) of various ground objects with different polarization images, the layover areas are detected step-by-step from the mixed areas, in which the region-based FCM segmentation algorithm and spatial relationship criteria are used. Taking the Danjiangkou Reservoir area as the study area, the relevant experiments with Sentinel-1A SAR images were conducted. The quantitative analysis of detection results adopted the figure of merit (FoM), and the highest accuracy was up to 87.6% of one selected validation region. Experiments in the South Taihang area also showed the satisfactory effect of layover detection, and the values of FoM were all above 85%. These results show that the proposed method can do well in the layover detection caused by mountains. Its simplicity and effectiveness are helpful in removing the influence of layover on SAR image applications to a certain extent and improving the development of SAR remote sensing technology. [ABSTRACT FROM AUTHOR]

Land surface phenology (LSP) is a consistent and sensitive indicator of climate change effects on Earth's vegetation. Existing methods of estimating LSP require time series densities that, until recently, have only been available from coarse spatial resolution imagery such as MODIS (500 m) and AVHRR (1 km). LSP products from these datasets have improved our understanding of phenological change at the global scale, especially over the MODIS era (2001-present). Nevertheless, these products may obscure important finer scale spatial patterns and longer-term changes. Therefore, we have developed a Bayesian hierarchical model to retrieve complete annual sequences of LSP from Landsat imagery (1984-present), which has medium spatial resolution (30 m) but relatively sparse temporal frequency. Our approach uses Markov Chain Monte Carlo (MCMC) sampling to quantify individual phenometric uncertainty, which is especially important when considering long time series with variable observation quality and density, but has rarely been demonstrated. The estimated spring LSP had strong agreement with ground phenology records at Harvard Forest (R2 = 0.87) and Hubbard Brook Experimental Forest (R2 = 0.67). The estimated LSP were consistent with the recently released 30 m LSP product, MSLSP30NA, in its time period of 2016 to 2018 (R2 of 0.86 and 0.73 for spring and autumn phenology, respectively). Our Bayesian hierarchical model is an important step forward in extending medium resolution LSP records back in time as it accomplishes both critical goals of retrieving annual LSP from sparse time series and accurately estimating uncertainty. • Developed a new Bayesian hierarchical model for land surface phenology retrieval. • The model can retrieve land surface phenology from sparse Landsat time series. • The model provides over 30 years of complete annual land surface phenology. • The model provides pixel-wise uncertainty on estimated phenometrics. [ABSTRACT FROM AUTHOR]

Time series of vegetation biophysical variables (leaf area index (LAI), fraction canopy cover (FCOVER), fraction of absorbed photosynthetically active radiation (FAPAR), canopy chlorophyll content (CCC), and canopy water content (CWC)) were estimated from interpolated Sentinel-2 (S2-LIKE) surface reflectance images, for an agricultural region located in central Canada, using the Simplified Level 2 Product Prototype Processor (SL2P). S2-LIKE surface reflectance data were generated by blending clear-sky Sentinel-2 Multispectral Imager (S2-MSI) images with daily BRDF-adjusted Moderate Resolution Imaging Spectrometer images using the Prediction Smooth Reflectance Fusion Model (PSFRM), and validated using thirteen independent S2-MSI images (RMSE ≤ 6%). The uncertainty of S2-LIKE surface reflectance data increases with the time delay between the prediction date and the closest S2-MSI image used for training PSFRM. Vegetation biophysical variables from S2-LIKE products are validated qualitatively and quantitatively by comparison to the corresponding vegetation biophysical variables from S2-MSI products (RMSE = 0.55 for LAI, ~10% for FCOVER and FAPAR, and 0.13 g/m2 for CCC and 0.16 kg/m2 for CWC). Uncertainties of vegetation biophysical variables derived from S2-LIKE products are almost linearly related to the uncertainty of the input reflectance data. When compared to the in situ measurements collected during the Soil Moisture Active Passive Validation Experiment 2016 field campaign, uncertainties of LAI (0.83) and FCOVER (13.73%) estimates from S2-LIKE products were slightly larger than uncertainties of LAI (0.57) and FCOVER (11.80%) estimates from S2-MSI products. However, equal uncertainties (0.32 kg/m2) were obtained for CWC estimates using SL2P with either S2-LIKE or S2-MSI input data. [ABSTRACT FROM AUTHOR]

We developed a land cover and land use mapping framework specifically designed for agricultural systems of the Sudan-Sahel region. The mapping approach extracts information from inter- and intra-annual vegetation dynamics from dense stacks of Landsat 8 images. We applied this framework to create a 30 m spatial resolution land use map with a focus on agricultural landscapes of northern Nigeria for 2015. This map provides up-to-date information with a higher level of spatial and thematic detail resulting in a more precise characterization of agriculture in the region. The map reveals that agriculture is the main land use in the region. Arable land represents on average 52.5% of the area, higher than the reported national average for Nigeria (38.4%). Irrigated agriculture covers nearly 2.2% of the total area, reaching nearly 20% of the cultivated land when traditional floodplain agriculture systems are included, above the reported national average (0.63%). There is significant variability in land use within the region. Cultivated land in the northern section can reach values higher than 75%, most land suitable for agriculture is already under cultivation and there is limited land for future agricultural expansion. Marginal lands, not suitable for permanent agriculture, can reach 30% of the land at lower altitudes in the northeast and northwest. In contrast, the southern section presents lower land use intensity that results in a complex landscape that intertwines areas farms and larger patches of natural vegetation. This map improves the spatial detail of existing sources of LCLU information for the region and provides updated information of the current status of its agricultural landscapes. This study demonstrates the feasibility of multi temporal medium resolution remote sensing data to provide detailed and up-to-date information about agricultural systems in arid and sub arid landscapes of the Sahel region. [ABSTRACT FROM AUTHOR]