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[Display omitted] • Inclusion of temporal data can improve detection F 1 -Score by 8% • Infrared bands provide about 4% more F 1 -Score than visible bands despite lower resolution • Using over 80 features provides an increase of about 12% F 1 -Score over using less than 10. • The proposed method successfully detects all test plots with nearly 90% F 1 -Score • A dataset of recent manure application, verified through on-site validation, is made public. The use of manure in agricultural fields during the wet season can lead to environmental pollution by releasing nitrates into nearby water sources. To address this issue, authorities may impose closed periods during which manure application is prohibited. However, ensuring compliance with these regulations can be challenging, as it is difficult to monitor all fields in a country. To tackle this problem, a solution has been proposed that involves employing machine learning techniques in conjunction with satellite imagery to automatically identify freshly manured fields. This paper investigates the relationship and effectiveness of the Sentinel-2 satellite bands and 51 frequently utilized multispectral indices in the context of precision agriculture, by exploring different feature selection methods. The proposed method achieves nearly 90% F 1 -Score and detects all test plots of the northern Spanish region, showing its potential for large-scale use in precision agriculture and environmental monitoring. This method incorporates temporal data, resulting in an 8% improvement in the detection F 1 -Score. Despite their lower spatial resolution, infrared bands have proven to be more effective than visible bands, enhancing the F 1 -Score by 4%. Furthermore, the use of over 80 features contributes to a 12% increase in the F 1 -Score compared to using fewer than 10 features. For further research and future studies, a dataset of recently manured plots, verified on-site, has been developed and made publicly available. [ABSTRACT FROM AUTHOR]

Deformation monitoring is a common practice in most of dams to ensure their structural health and safety status. Systematic monitoring is frequently carried out by means of geotechnical sensors and geodetic techniques that, although very precise an accurate, can be time-consuming and economically costly. Remote sensing techniques are proved to be very effective in assessing deformation. Changes in the structure, shell or associated infrastructures of dams, including adjacent slopes, can be efficiently recorded by using satellite Synthetic Aperture Radar Inteferometry (InSAR) techniques, in particular, Muti-Temporal InSAR time-series analyses. This is a mature technology nowadays but not very common as a routine procedure for dam monitoring. Today, thanks to the availability of spaceborne satellites with high spatial resolution SAR images and short revisit times, this technology is a powerful cost-effective way to monitor millimeter-level displacements of the dam structure and its surroundings. What is more, the potential of the technique is increased since the Copernicus C-band SAR Sentinel-1 satellites are in orbit, due to the high revisit time of 6 days and the free data availability. ReMoDams is a Spanish research project devoted to provide the deformation monitoring of several embankments dams using advances time-series InSAR techniques. One of these dams is The Arenoso dam, located in the province of Cordova (southern Spain). This dam has been monitored using Sentinel-1 SAR data since the beginning of the mission in 2014. In this paper, we show the processing of 382 SLC SAR images both in ascending and descending tracks until March 2019. The results indicate that the main displacement of the dam in this period is in the vertical direction with a rate in the order of -1 cm/year in the central part of the dam body. [ABSTRACT FROM AUTHOR]

Granitic boulders are widespread geomorphological elements, particularly found in humid granitic areas. Although they seldom represent a hazard for people or infrastructures, sometimes their location in steep or natural slopes may jeopardise their stability and potentially affect people or infrastructures. In addition, their complex geometry makes the instability mechanisms difficult to identify, so it is even more difficult to compute factors of safety regarding their stability. In this paper, the authors analyse potential failure mechanisms of granite boulders based on analytical mechanic calculations and physical tilt tests, permitting understanding the phenomena under scrutiny. Then, they study the stability of one of these boulders: the Pena do Equilibrio or equilibrium rock. To do that they resort to standard geotechnical characterization and advanced geometrical characterization derived from UAV photogrammetric and 3D Laser Scanning of the boulder. The presented results exemplify how the application of these recently available topographic technologies, in combination with rock mechanics approaches, enable a rigorous analysis of the stability of granite boulders. • Failure mechanisms of granite boulders have been analysed through analytical calculations and tilt tests in laboratory. • Pena do Equilibrio boulder, located in NW Spain, has been selected for the presented analysis. • UAS and Terrestrial Laser Scanner have been resorted for geometrical calculations like volume and position of the centroid. • The stability of the granitic boulder has been assessed through rock mechanics approaches combined with remote sensing techniques. • A 3D-printed 1:50-scale plastic replica of the boulder has been utilized for analyzing results in laboratory. [ABSTRACT FROM AUTHOR]

Highlights • Satellite images have been utilized to estimate crop coefficients. • The estimated and the theoretical crop coefficients have been compared. • The comparison was made among field plots, municipalities and irrigation districts. • A set of water use efficiency indicators have been calculated. Abstract Irrigation in the Duero river basin accounts for 75% of total water resources. Nevertheless, irrigation in this area is paramount to maintain agricultural production. Within this context, this paper is aimed at assessing the water use efficiency (WUE), for the major crop (corn), among four irrigation districts (IDs) from 2014 to 2017. Since the WUE indicators base upon crop water requirements, these were calculated by estimating the reference evapotranspiration and the local crop coefficients Kc. First, they were estimated by satellite images and then, compared with the recommendation from the Regional Irrigation Advisory Service. Finally, the gross irrigation requirements were compared with the irrigation supply. WUE indicators varied among IDs depending upon the hydrologic year, the water available for irrigation (superficial and subsurface water), and the corn sowing date. Usually, the IDs performed deficit irrigation in the dry years, and either full irrigation or over irrigation in the wet years. The rainfall only fulfilled less than 10% of corn requirements. Small differences in Kc were observed within municipalities (in each ID) and/or among IDs. The pattern of the Kc fitted curves varied among IDs and was affected by sowing dates. Although, the local Kc values were close to the general coefficients recommended by the Irrigation Advisory Service, they affected the determination of gross irrigation requirements which were significant different among IDs. Moreover, it was observed that the FAO recommendation for the duration of the corn growth stages fitted better the estimated Kc than the one from the Advisory Center. The gross irrigation requirements were similar among IDs and years, varying between 6476 and 7646 m3/ha. The local Kc estimation could help irrigation managers to adapt the irrigation supply to the actual corn needs. [ABSTRACT FROM AUTHOR]

Forest fires play a critical role in landscape transformation, vegetation succession, soil degradation and air quality. Improvements in fire risk estimation are vital to reduce the negative impacts of fire, either by lessen burn severity or intensity through fuel management, or by aiding the natural vegetation recovery using post-fire treatments. This paper presents the methods to generate the input variables and the risk integration developed within the Firemap project (funded under the Spanish Ministry of Science and Technology) to map wildland fire risk for several regions of Spain. After defining the conceptual scheme for fire risk assessment, the paper describes the methods used to generate the risk parameters, and presents proposals for their integration into synthetic risk indices. The generation of the input variables was based on an extensive use of geographic information system and remote sensing technologies, since the project was intended to provide a spatial and temporal assessment of risk conditions. All variables were mapped at 1km2 spatial resolution, and were integrated into a web-mapping service system. This service was active in the summer of 2007 for semi-operational testing of end-users. The paper also presents the first validation results of the danger index, by comparing temporal trends of different danger components and fire occurrence in the different study regions. [Copyright &y& Elsevier]

The great urban and industrial development of the towns surrounding Madrid has caused an irreversible and rapid soil loss. In this paper, soil sealed evolution in Guadalajara province during the past 15 years, mainly in the capital, industrial district and small municipalities near Madrid, has been studied. This province has experimented a sharp reduction of the most fertile soils, located on the Henares valley, due to the action of new models of local and regional development. The main goal of this paper is the mapping of the covered soils in a pilot area of Spain. Supervised classification of Landsat images from 1989 to 2002 has been carried out. Geo-referred image analysis allows us to detect precisely the amount of covered soils and where this phenomenon has taken place. [Copyright &y& Elsevier]

This paper discusses the combined use of tools from dynamical systems theory and remote sensing techniques and shows how they are effective instruments which may greatly contribute to the decision making protocols of the emergency services for the real-time management of oil spills. This work presents the successful interplay of these techniques for a recent situation, the sinking of the Oleg Naydenov fishing ship that took place in Spain, close to the Canary Islands, in April 2015. [ABSTRACT FROM AUTHOR]

• Eco-hydrological distributed models can validate spatial patterns of satellite SSM. • Differences in temporal and spatial dynamics of SSM products were found. • Multi-objective calibration (Q and SSM) increases the robustness of the modelling. • DISPATCH algorithm provided best spatial agreement compared to TETIS results. Reliable distributed hydrological modeling, especially in semi-arid areas, must consider the inclusion of surface soil moisture (SSM) spatial information during the calibration process. This variable plays a key role in the evapotranspiration processes that determine the hydrological cycle. The coarse resolution of the SSM estimates by satellite remote sensing has restricted the application of this approach to only large basins, focusing most of the studies in the consideration of simply the temporal dynamics of this variable. The growing efforts in providing higher spatial resolution through disaggregating methodologies or new sensor estimates facilitates the application of this spatial approach to small basins. This paper explores the applicability of the currently available satellite surface soil moisture estimates for distributed eco-hydrological modelling in Mediterranean forest basins. On one hand, this study contributes to fill the existing research gap on the use of remote sensing SSM spatial patterns within the distributed hydrological modelling framework in small basins. On the other hand, it serves as an indirect validation method for the spatial performance of satellite SSM products. To achieve this goal, we implemented the eco-hydrological model TETIS in three case studies named: Hozgarganta (southern Spain), Ceira (western Portugal) and Carraixet (eastern Spain). The SSM estimates selected for comparison were Sentinel-1 SSM provided by the Copernicus Global Land Services (CGLS), SMAP SSM disaggregated using Sentinel-1 (SPL2SMAP_S) provided by the National Aeronautics and Space Administration (NASA), SMOS SSM provided by the Barcelona Expert Center (BEC), and SMOS and SMAP SSM disaggregated using the DISPATCH algorithm provided by Lobelia Earth. The methodology employed involved a multi-objective and multi-variable calibration in terms of remote sensing SSM spatial patterns and in-situ streamflow, using the Spatial Efficiency Metric (SPAEF) and the Nash-Sutcliffe efficiency index (NSE) respectively. Before model calibration a sensitivity analysis of the most influent variables was performed. The temporal and spatial comparison of the reference SSM products revealed inconsistencies amongst products. The disaggregating methodology determined the spatial agreement to a greater degree than the sensor itself (i.e. SMAP, SMOS). In spite of the differences amongst products, the multi-objective calibration approach proposed increased the robustness of the hydrological modelling. [ABSTRACT FROM AUTHOR]

This paper applies remote sensing techniques and 3D point cloud (3DPC) analysis to the study of historical quarries and the relationship between old quarry landscapes and the natural fracture systems of rock massifs. Ancient quarry landscapes present particular characteristics and different features from those of modern quarries. Consequently, specific considerations are needed when historical extractive places are studied. The proposed method is based on terrestrial 3D laser scanners and the structure from motion technique with remotely piloted aircraft systems. Additional specific fieldwork is required to identify and characterise quarry faces from old, blurry outcrops. A case study of the 18th century "Rambla Honda" quarries in La Romana (Alicante, SE Spain) is presented. The comparison between the fieldwork and 3DPC analysis enables the numbers and orientations of both (1) the discontinuity sets within the rock mass facilitating extraction and (2) the planes resulting from block extraction (traditional quarry faces) to be determined. The comparative analysis reveals how the stonecutters managed the extraction. The results show that structural and stratigraphic discontinuities act as planes of weakness for block extraction and determine the optimum orientation of the quarry face for minimising efforts and rock waste. The final quarry landscape is formed from the superposition of natural and artificial surfaces, but currently they appear eroded and partially covered by silting and plants. The proposed methodology contributes to distinguishing both natural and artificial discontinuities and to achieving a comprehensive knowledge of these cultural places. • The analysis of 3DPCs permits the extraction of discontinuity planes in a quarry. • Natural and artificial fractures are distinguished following the proposed workflow via a historical quarry as a case study. • Natural fractures strongly controlled the extraction of stone blocks in historical quarries. • Stonecutters developed artificial extraction surfaces to obtain optimally shaped stone blocks. [ABSTRACT FROM AUTHOR]

Abstract: Water deficit indices based on the spatial relationship between surface temperature (Ts) and NDVI, known as triangle approaches, are widely used for drought monitoring. However, their application has been recently questioned when the main factor limiting evapotranspiration is energy. Even though water is the main control in dryland ecosystems, these can also undergo periods of energy and temperature limitation. In this paper we aimed to: (i) evaluate the TVDI (Temperature–Vegetation Dryness Index) to estimate water deficits (e.g. ratio between actual and potential evapotranspiration), and heat surface fluxes using MODIS data; and (ii) provide insights about the factors most affecting the accuracy of results. Factors considered included the type of climatic control on evapotranspiration, λE, (i.e. water-limited vs. energy-limited), the quality of Tair estimates, the heterogeneity of land cover types and climatic variables in the region, or the algorithm to extract hydrological boundaries from the images. The TVDI was compared with eddy covariance (EC) data from two shrublands with different climatic controls for λE in South Spain. Evaluations showed that it could be used to estimate the water deficit when water was the main limiting factor (R=0.81–0.88; Mean Average Error, MAE=0.16–0.17) but not in energy-limited situations (R<0.2; MAE=0.10–0.2). Spatial heterogeneity in climatic variables also had a different impact on accuracy depending on limiting factors. Relative humidity was significant at the water-limited site while solar irradiance and air temperature were more important at the energy-limited site. The skill of the TVDI to estimate surface fluxes at the water-limited site was confirmed for the dominant sensible heat flux, H (R 2 =0.93; Mean Absolute Percentage Error, MAPE=12.85%) but not for λE (R 2 =0.01, MAPE=115.22%) as λE fluxes at this site are just slightly above the error of the eddy covariance system. At the energy-limited site, λE (R 2 =0.74; MAPE=31.83%) and H estimates (R 2 =0.80; MAPE=26.85%) were better than those from the SEBAL (Surface Energy Balance Algorithm for Land) or the PML (Penman–Monteith–Leuning) models. However, the skill to predict surface fluxes in this case was due to the net radiation inputs and not by the TVDI input. Our analyses also suggest: (1) to apply the TVDI excluding energy-limited sites/periods based on climatic knowledge of limiting factors on λE; (2) the best conditions for TVDI performance correspond to the situation when the controlling factors are less limiting, e.g. during the growing season (higher SWC and lower VPD); and (3) to account better for the role of vegetation controls on transpiration. [Copyright &y& Elsevier]

• Predictive modelling benchmark of Nitrate Vulnerable Zones using Random Forest. • Extrinsic driving forces to groundwater were selected as environmental predictors. • Phenological features derived from remote sensing were included as novel features. • Feature selection methods revealed good performance predicting nitrate pollution. • Phenology and manure production from livestock farms as most important features. Nitrate leaching losses from arable lands into groundwater were a main driver in designating Nitrate Vulnerable Zones (NVZs) according to the Nitrates Directive, with a view to enhancing their water quality. Despite this, developing common strategies for effective water quality control in these areas remains a challenge in the European Union. This paper evaluates the performance of the Random Forest (RF) machine learning algorithm combined with Feature Selection (FS) techniques in predicting nitrate pollution in NVZs groundwater bodies in different periods and using updated environmental features in Andalusia, Spain. A set of forty-four features extrinsic to groundwater bodies were used as environmental predictors, with an aim to make this methodology exportable to other regions. Phenological features obtained through remote-sensing techniques were included to measure the dynamics of agricultural activity. In addition, other dynamic features derived from weather and livestock effluents were included to analyse seasonal and interannual changes in nitrate pollution. Three feature stacks and two nitrate databases were used in the predictive modelling: Period 1 (2009), with 321 nitrate samples for training; Period 2 (2010), with 282 nitrate samples for validation and initial spatial prediction; and Period 3 (2017), to assess the changes in the probability of groundwater nitrate content exceeding 50 mg/L. Random Forest as a wrapper with four sequential search methods was considered: sequential backward selection (SBS), sequential forward selection (SFS), sequential forward floating selection (SFFS) and sequential backward floating selection (SBFS). From among all the Feature Selection methods applied, Random Forest with SFS had the best performance (overall accuracy = 0.891 and six predictor features) and linked the highest probability of nitrate pollution with three dynamic features: the Normalized Difference Vegetation Index (NDVI) base level, NDVI value for the end of the growing season and accumulated manure production of livestock farms; and three static features: slope, sediment depositional areas and valley depth. [ABSTRACT FROM AUTHOR]

Abstract: We present in this paper the Generic RAdiative traNsfer AnD non-LTE population Algorithm (GRANADA). This model is able to compute non-LTE populations for vibrational, rotational, spin (i.e., NO and OH), and electronic (i.e., O2) states in a given planetary atmosphere. The model is very flexible and can be used for computing very accurate non-LTE populations or for calculating reasonably accurate but at high speed non-LTE populations in order to implement it into non-LTE remote sensing retrievals. We describe the model in detail and present an update of the non-LTE collisional processes and their rate coefficients for the most important molecules in Earth''s atmosphere. In addition, we have applied the model to the most important atmospheric infrared emitters including 13 species (H2O, CO2, O3, N2O, CO, CH4, O2, NO, NO2, HNO3, OH, N2, and HCN) and 460 excited vibrational or electronic energy levels. Non-LTE populations for all these energy levels have been calculated for 48 reference atmospheres expanding from the surface up to 200km, including seasonal (January, April, July and October), latitudinal (75°S, 45°S, 10°S, 10°N, 45°N, 75°N) and diurnal (day and night) coverages. The effects of the most recent updates of the non-LTE collisional parameters on the non-LTE populations are briefly described. This climatology is available online to the community and it can be used for estimating non-LTE effects at specific conditions and for testing and validation studies. [Copyright &y& Elsevier]

Abstract: Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning. In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level. [Copyright &y& Elsevier]

Abstract: Land use and land covers (LULC) maps are remote sensing products that are used to classify areas into different landscapes. Data fusion for remote sensing is becoming an important tool to improve classical approaches. In addition, artificial intelligence techniques such as machine learning or evolutive computation are often applied to improve the final LULC classification. In this paper, a hybrid artificial intelligence method based on an ensemble of multiple classifiers to improve LULC map accuracy is shown. The method works in two processing levels: first, an evolutionary algorithm (EA) for label-dependent feature weighting transforms the feature space by assigning different weights to every attribute depending on the class. Then a statistical raster from LIDAR and image data fusion is built following a pixel-oriented and feature-based strategy that uses a support vector machine (SVM) and a weighted k-NN restricted stacking. A classical SVM, the original restricted stacking (R-STACK) and the current improved method (EVOR-STACK) are compared. The results show that the evolutive approach obtains the best results in the context of the real data from a riparian area in southern Spain. [Copyright &y& Elsevier]

Abstract: The southwestern area of Spain, by its geographical and climatological conditions, is a key location for the characterization of atmospheric aerosol properties. The present study is aimed at evaluating the reliability of satellite-based aerosol climatologies, as inferred from level 2 standard aerosol products such as the Terra-MODIS (Moderate Resolution Imaging Radiometer) MOD04 aerosol product, with an application over this region during the period 2000–2008. This evaluation is carried out by means of comparison with ground-based data from the AERONET station of El Arenosillo (Spain, 37.1N, 6.7W), which has been providing continuous data since 2000. The focus of this paper is the climatology of two aerosol optical parameters: the aerosol optical depth (AOD) and the Ångström exponent. AERONET ground-based measurements give an annual mean value of 0.16±0.12 and a median of 0.12 for the AOD, and a mean value of 1.20±0.47 for the Ångström exponent. The seasonal pattern is characterized by two maxima, the most important maximum occurs in summer months, and the other one in late-winter/early-spring. Lowest values appear in fall and winter, however, a local minimum is observed in July which is only detected with the long-term data series. The mean climatological AOD based on AERONET exhibits complex seasonal patterns (i.e. with multiple local extrema), which are not always captured by MODIS-based climatology. MODIS only reproduces low values of the AOD in winter and high values in summer, as well as the local minimum of July which is sharper when using over-land retrievals. The time series of the AOD retrieved from MODIS both over land and ocean are in relatively good agreement with the ground-based measurements, with a monthly overestimation of about 30% on average, and higher differences in spring. Seasonal patterns from MODIS are better reproduced over land than over ocean. The agreement between daily AERONET and MODIS, as assessed by linear regression, gives correlation coefficients above 80% and an intercept bias below 0.03. [Copyright &y& Elsevier]

This paper presents an algorithm for rapid and accurate burn mapping that is applied here to the forest fires occurred in Galicia (northwest Spain) in August 2006, when nearly 930km2 were almost entirely burned over the course of eight days. The algorithm synergistically combines remotely-sensed reflectance and active fire data as measured by the MODIS (MODerate resolution Imaging Spectrometer) sensor on board Terra and Aqua NASA (National Aeronautics and Space Administration) satellites. Burned area data collected from this work was compared to official fires statistics from the Spanish Ministry of Environment and to perimeters that were derived using a high spatial resolution satellite image. In a later step, burn patches area analyzed using information from the National Forest Map (1:50,000) and some Geographical Information Systems (GIS) tools. [Copyright &y& Elsevier]

Abstract: The objectives of this study were to determine onion water requirements with a sprinkler irrigation system, the most usual irrigation method in Spain. A weighing lysimeter was used to measure single (Kc) and dual (Kcb+Ke) crop coefficient curves and obtain the relationship between Kc-ground cover (GC) and Kcb-GC. Experimental work was carried out in 2005 at “Las Tiesas” farm, located in Albacete (Central Spain). To determine actual onion evapotranspiration (ETc), we used a weighing lysimeter with continuous electronic data recording. Daily measured ETc values obtained by the lysimeter were compared to calculated ETc values obtained through the standard FAO methodology [Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. FAO Irrig. and Drain. Paper 56. Rome, Italy]. Seasonal evapotranspiration measured in the lysimeter (893.34mm) was higher than the seasonal ETc calculated by FAO-56 method (832.90mm). The percentage of GC was found through the supervised classification technique of digital photographic images with the maximum probability algorithm [Calera, A., Martínez, C., Melia, J., 2001. A procedure for obtaining green plant cover: relation to NDVI in a case study for barley. Int. J. Remote Sensing, 22, 3357–3362]. The values derived from lysimetric measurements are Kc ini: 0.65, Kc mid: 1.20 and Kc end: 0.75, similar to values given in FAO-56. Lysimetric measurements showed that the evaporative component was high during the growing season, due to the high frequency of irrigation and the fact that the onion crop does not completely cover the ground; maximum GC was 72%. Therefore, the dual crop coefficient was calculated, which allowed differentiation between crop transpiration (basal crop coefficient, Kcb) and evaporation from the soil (evaporation coefficient, Ke). With the aim of facilitating extrapolation of the results to other areas, Kc and Kcb were linearly correlated to fractional GC. [Copyright &y& Elsevier]

Abstract: The hydrology of the Bay of Biscay was investigated using the regional ocean model MARS3D (Model for Application at Regional Scale). The simulated hydrology is compared to a set of various data encompassing monthly climatology, remote sensing SST, CTD casts, and coastal salinity measurements. Special focus was put on the validation over the continental shelf. This paper reports that despite some misfits, the climatological hydrology and its seasonal variability are correctly simulated. Various statistics computed over the period from 1999–2004 highlight different aspects of the hydrology. The biases and root mean square errors (RMSE) remain very weak at all depths when comparing salinity (<0.1 and <0.6psu respectively). The predicted temperature shows a global overestimation of temperature (bias of around 0.8°C) and the maximum errors are located near the thermocline (rmse of 1°C at 20–40m). The model is shown to properly reproduce the annual dynamics of sea surface temperature, as well as the dynamics of large river plumes observed by high frequency time series from coastal salinity gauges. The misfits highlighted by these various comparisons between model and observations are attributed to heat fluxes and mixing parameterisation. [Copyright &y& Elsevier]

Abstract: The validation of coarse satellite-derived products from field measurements generally relies on up-scaling the field data to the corresponding satellite products. This question is commonly addressed through the generation of a reference high-resolution map of an area covering several moderate resolution pixels. This paper describes a method by which reference leaf area index (LAI) maps can be generated from ground-truth LAI measurements. The method is based on a multivariate ordinary least squares (OLS) algorithm which uses an iteratively re-weighted least squares (IRLS) algorithm. It provides an empirical relationship (i.e. a transfer function) between in situ measurements and concomitant radiance values from high-resolution HRVIR2/SPOT4 imagery. The band combination composed of green (G), red (R) and near infrared (NIR) proved to be appropriate for LAI prediction, with a cross-validation error lower than 0.7. The convex hull technique has been proposed to assess the extrapolation error of the up-scaling model. Although the model showed to be sensitive to pixels outside the convex hull, the error associated with extrapolated pixels was moderate (i.e. root mean square error (RMSE)≈0.3). A close agreement was found between LAI maps derived from different field based datasets despite the notable differences in the sampling constraints imposed by the use of different instruments. It confirms the validity of a stratified random sampling strategy and suggests the suitability of using around 50 elementary sampling units (ESUs) to characterize the study area. A sensitivity analysis has confirmed that this method is suited to capture the variability across the site extent and, at the same time, minimize field efforts. Knowledge gained from this work provides guidance for an efficient sampling strategy of LAI in a cropland area. [Copyright &y& Elsevier]

Burn severity is a key factor in post-fire assessment and its estimation is traditionally restricted to field work and empirical fitting from remotely sensed data. However, the first method is limited in terms of spatial coverage and cost effectiveness and the second is site-and data-specific. Since alternative approaches based on radiative transfer models (RTM) have been usefully applied in retrieving several biophysical plant parameters (leaf area index, water and dry matter content, chlorophyll), this paper has applied the inversion of a simulation model to estimate burn severity in terms of the Composite Burn Index (CBI). The performance of the model inversion method was compared to standard empirical techniques. The study area chosen was a large forest fire in central Spain which occurred in July 2005. The model inversion showed the most accurate estimation for high severity levels (for CBI>2.7, RMSE=0.30) and for unburned areas (CBI<0.5, RMSE=0). In both methodologies, the error associated to CBI from 0.5 to 2.7 was not acceptable (RMSE>0.7), because it is higher than 25% of the total range of the index. Finally, burn severity maps from both methods were compared. [Copyright &y& Elsevier]

This paper presents the hourly evolution of a severe Saharan dust outbreak, (SDO), affecting Central Spain over July 23–24, 2004 measured with a laser remote sensing device at a location close to the Guadarrama mountain range foothills and its impact on PM10 levels (particles with an aerodynamic diameter below 10μm) recorded at four contrasting monitoring stations located in the upper and lower Spanish plateau, some 170km apart. During the period of study the Saharan dust layer, (SDL), presented significant hourly variability in height (3600–1500m), depth (1500–700m) and aerosol dust loading (extinction coefficient, EC, 0.22–1.28km−1). Overnight layering was generally observed whereas a well mixed layer prevailed in the afternoon. The (SDO) impact on the lower levels took place approximately 12h after the (SDL) was initially observed and triggered by a descending dust enriched, evidencing the important role of subsidence over the presence of dust in lower altitudes. During the event, PM10 levels at all the stations exceeded EU air quality daily mean standards, 50μgm−3, on 2–4days. The maximum values ranged from 185 to 245μgm−3 depending on the monitoring station. The impact on PM10 spread from 2days in the upper plateau to 3–4 in the lower plateau, in agreement with the geographical location of the monitoring stations with respect to the southwest origin of the intrusion. The impact was even more dramatic on PM10 hourly concentrations, leading to maximum hourly peaks ranging from 322 to 598μgm−3 again depending on the monitoring station. Correlations between EC vertical profiles and PM10 hourly concentrations at the monitoring stations showed the great influence of the (SDO) on surface concentrations. The best linear fits corresponded to the extinction coefficients in the lower altitudes (close to the lower range of the device 500–650m), EC2, yielding satisfactory correlation coefficients ranging from 0.68 to 0.71. The low variability of the slope of each individual linear fit, 19.2%, shows the similar impact of (SDO) on the PM10 hourly concentrations recorded in the area under study. [Copyright &y& Elsevier]

Abstract: The spaceborne differential interferometric SAR (DInSAR) technique may play an important role in the measurement of land deformation phenomena, especially in urban, suburban and industrial areas. In order to fully exploit its capabilities and increase its operational use, this paper proposes two complementary levels of analysis. The first one is a comprehensive low-cost screening, in order to detect unknown subsidence phenomena over large areas. This analysis, which provides a first estimation of deformations, has to be performed using a limited set of SAR images. The second level includes an in-depth quantitative analysis based on large image stacks, which requires more image acquisition and data processing resources. This type of analysis can be typically used to study deformation phenomena of special relevance, where an effective support to the decision-makers requires a fully quantitative estimation of deformations. The two levels of analysis were proved over a test area of about 2100 km2, located in Catalonia (Spain). Without any a priori information, seven deformation phenomena were detected using a reduced set of interferograms. Furthermore, the quantitative analysis of a subsidence of small spatial extent, which was based on ascending and descending datasets, confirmed the capability of DInSAR to quantitatively assess deformation phenomena. [Copyright &y& Elsevier]

Abstract: Agricultural subsidies can only be effectively allocated if there is a suitable information system both at the level of subsidy providers – the government – and recipients – agricultural organizations. Governments have to set up complex schemes to control subsidies using techniques such as remote sensing or photogrammetry. If these techniques do not help, technical staff has to visit the plots. This paper explains the development and implementation of a mobile application to make fieldwork easier. The application uses a number of devices in a Smartphone such as a GPS or a camera to collect information and broadcast it to an office in real time. This application, called GeoFoto, computers was tested in real scenarios in the Spanish region of Andalusia. This provided information about its advantages and disadvantages, and the final results are highly encouraging. [Copyright &y& Elsevier]

• Two problems of the T-V method in determining soil moisture index are proved. • A new long-term T-V scatter plot is proposed. • A new temperature-vegetation index is proposed. • A new soil moisture index is designed which is named SM iSEE. • SM iSEE is studied on two networks and compared with three other well-known indices. The estimation of soil moisture content on the global scale is a key research issue in the field of remote sensing and, to date, a range of methods have been developed to achieve this. The temperature-vegetation (T-V) technique, which is perhaps one of the most common and successful, suffers from being inaccurate in some cases, especially when the daily T-V scatter plots are not able to define the wet and dry edges in a reliable way. A further challenge is the scale inconsistency between the results obtained on different days, which is a serious concern when monitoring soil moisture changes over periods of time. To address these restrictions, this paper introduces a new soil moisture index, called SM iSEE , driven by a new long-term T-V scatter plot that is established from data covering a 1-year period. The normalized NDVI is set as the horizontal axis and three different temperature factors are suggested as the vertical axis in the proposed 1-year scatter plot. From these three temperature factors, the results proved that "LST day -Ta day (10:30 am)" is the most appropriate choice. In contrast to most linear co-distance indexes reported in the literature, the proposed SM iSEE is a novel non-linear soil moisture index that defines the locus of co-moisture points in the scatter plot as unequal distance curves. To investigate the proposed method, two different soil moisture observation networks with different climate and vegetation conditions, namely, SMAPVEX12 in Canada and REMEDHUS in Spain, were applied. The results demonstrated that the efficiency of the proposed 1-year scatter plot, which was even applicable to, and improved upon, other indexes in the literature. In addition, SM iSEE outperformed SEE, iTVDI and φas the most recent similar indexes, achieving correlation coefficients of 0.65 and 0.74 for the SMAPVEX12 and REMEDHUS networks, respectively. These results appear to be promising, especially for the vegetated area in the SMAPVEX12 network. [ABSTRACT FROM AUTHOR]