Modeling actual water use under different irrigation regimes at district scale: Application to the FAO-56 dual crop coefficient method

The modeling of irrigation in land surface models are generally based on two soil moisture parameters SMthreshold and SMtarget at which irrigation automatically starts and stops, respectively. Typically, both parameters are usually set to optimal values allowing to fill the soil water reservoir with just the estimated right amount and to avoid crop water excess at all times. The point is that agricultural practices greatly vary according to many factors (climatological, crop, soil, technical, human, etc.). To fill the gap, we propose a new calibration method of SMthreshold and SMtarget to represent the irrigation water use in any (optimal, deficit or even over) irrigation regime. The approach is tested using the dual-crop coefficient FAO-56 model implemented at the field scale over an 8100 ha irrigation district in northeastern Spain where the irrigation water use is precisely monitored at the district scale. Both irrigation parameters are first retrieved at monthly scale from the irrigation observations of year 2019. The irrigation simulated by the FAO-56 model is then evaluated against observations at district and weekly scale over 5 years (2017–2021) separately. The performance of the newly calibrated irrigation module is also assessed by comparing it against three other modules with varying configurations including default estimates for SMthreshold and SMtarget. The proposed irrigation module obtains systematically the best performance for each of the 5 years with an overall correlation coefficient of 0.95 ± 0.02 and root-mean square error of 0.27 ± 0.07 hm3/week (0.64 ± 0.17 mm/day). Unlike the three irrigation modules used as benchmark, the new irrigation module is able to reproduce the farmers’ practices throughout the year, and especially, to simulate the actual water use in the deficit and excess irrigation regimes occurring in the study area in spring and summer, respectively.This study was supported by the IDEWA project ( ANR-19-P026-003 ) of the Partnership for research and innovation in the Mediterranean area ( PRIMA ) program and by the Horizon 2020 ACCWA project (grant agreement # 823965 ) in the context of Marie Sklodowska-Curie Research and Innovation Staff Exchange (RISE) program. The authors wish to acknowledge the "Comunitat de Regants Canal Algerri Balaguer" and the Ebro Hydrographic Confederation (SAIH Ebro) for providing the observation irrigation data used in this study

Suivi des ressources en eau des cultures irriguées par télédétection multi-spectrales optique/thermique

Irrigated agriculture is an important pressure on water resources, consuming more than 70% of the mobilized freshwater resources at global scale. However, the information on irrigation, which is crucial for the sustainability of water resources in agricultural regions, is often unavailable. Therefore, monitoring and quantifying the crop water budget over extended areas is critical. This PhD thesis aims to integrate optical/thermal remote sensing data into a simplified crop water balance model for monitoring the water budget of irrigated agricultural areas. For this purpose, an innovative and stepwise approach is developed to estimate simultaneously the irrigation, the evapotranspiration (ET) and the root-zone soil moisture (RZSM) at crop field scale (100 m resolution) on a daily basis. In a first step, a feasibility study is carried out using in situ optical/thermal measurements collected over a winter wheat field of the Haouz plain, Morocco. A crop water stress coefficient (Ks) derived from the land surface temperature (LST) and vegetation index (NDVI) is first translated into RZSM diagnostic estimates, which is then used to estimate irrigation amounts and dates along the season. Next, the retrieved irrigations allow forcing the dual crop coefficient FAO-56 model (FAO- 2Kc) to re-analyze the daily ET and RZSM. The re-analyzed RZSM is significantly improved with respect to RZSM diagnostic estimates, reaching the same accuracy as that obtained by using actual irrigations (RMSE = 0.03 m3m-3 and R2 = 0.7). However, the approach needs to be tested using satellite data in order to demonstrate its real applicability. The next step consists in adapting the previous approach to spatially integrated but temporally sparse Landsat NDVI/LST data. For this purpose, a contextual method is first used to derive Landsat-derived estimates (crop coefficients and RZSM), which are used to re-initialize a FAO-based model and propagate this information daily throughout the season. Then, the retrieved pixel-scale irrigations are aggregated to the crop field-scale. The approach is applied to three agricultural areas (12 km by 12 km) in the semi-arid region of Haouz Plain, and validated over five winter wheat fields with different irrigation techniques (drip-, flood- and no-irrigation). The results show that the seasonal irrigation amounts over all the sites and seasons is accurately estimated (RMSE = 44 mm and R = 0.95), regardless of the irrigation techniques. Acceptable errors (RMSE = 27 mm and R = 0.52) are obtained for irrigations cumulated over 15 days, but poor agreements at daily to weekly scales are found in terms of irrigation. However, the daily RZSM and ET are accurately estimated using the retrieved irrigation and are very close to those estimated using actual irrigations (overall RMSE equal to 0.04 m3m-3 and 0.83 mm.d-1 for RZSM and ET, respectively). In a final step, an operational LST disaggregation method based on NDVI/LST and Landsat/MODIS relationships is implemented for enhancing the spatio-temporal resolution of LST as input to the irrigation retrieval approach. The disaggregation method is tested over an arid region of Chile and our study area in the Haouz Plain. Combining both disaggregated LST and Landsat LST data sets, thanks to the increase in the temporal frequency of LST data, results in a better detection of irrigation events and amounts. The overall RMSE of cumulated irrigation at different time scales is decreased from 46 to 34 mm, while the R is increased from 0.50 to 0.64. Consistently, the RZSM estimated using the disaggregated LST in addition to Landsat LST as input is improved by 26% and 14% in terms of RMSE and R, respectively.L'agriculture est une pression importante sur les ressources en eau, consommant plus de 70% de l'eau douce mobilisée à l'échelle mondiale. Cependant, les informations sur l'irrigation, pourtant cruciales pour assurer une durabilité de la ressource, sont souvent indisponibles. Par conséquent, il est essentiel d'estimer les différents termes du bilan d'eau des cultures à grande échelle. Cette thèse vise à intégrer les données de télédétection optique/thermique dans un modèle simplifié de bilan d'eau des cultures pour le suivi du bilan d'eau des zones agricoles irriguées. Une approche innovante est développée pour estimer simultanément l'irrigation, l'évapotranspiration (ET) et l'humidité en zone racinaire (RZSM) journalières à l'échelle de parcelle (ou à 100 m de résolution). Dans une première partie, une étude de faisabilité est réalisée à l'aide de mesures optiques/thermiques in situ collectées sur une parcelle de blé d'hiver dans la plaine du Haouz, au Maroc. En pratique, un coefficient de stress hydrique (Ks) dérivé de la température de surface (LST) et d'un indice de végétation (NDVI) est d'abord traduit en une première approximation de RZSM, qui est utilisée pour estimer les quantités et les dates d'irrigation au cours de la saison. Les irrigations obtenues permettent ensuite de forcer le modèle FAO-56 à coefficient cultural double (FAO-2Kc) et de fournir des ré-analyses ET et RZSM journalières. La RZSM ré-analysée est significativement améliorée par rapport aux premières estimations de RZSM, atteignant la même précision que celle obtenue en utilisant les irrigations réelles (RMSE=0,03 m3m-3 et R2=0,7). Toutefois, l'approche doit encore être testée avec des données satellitaires afin de démontrer son applicabilité dans le cas réel. La deuxième partie consiste à adapter l'approche précédente aux données optiques/thermiques Landsat à faible fréquence temporelle. Une méthode contextuelle est utilisée pour obtenir des estimations dérivées de Landsat (coefficients de culture et RZSM), qui sont utilisées pour réinitialiser un modèle basé sur le FAO-2Kc et propager ces informations à l'échelle journalière tout au long de la saison. Ensuite, les irrigations obtenues à l'échelle des pixels sont agrégées à la parcelle pour ré-analyser l'ET et la RZSM journalières. L'approche est appliquée sur trois zones agricoles (12 km x 12 km) de la région semi-aride de la plaine du Haouz et validée sur cinq parcelles de blé d'hiver avec différentes techniques d'irrigation (goutte à goutte, gravitaire et sans irrigation). Les résultats montrent que l'irrigation saisonnière sur l'ensemble des sites et des saisons est estimée avec une bonne précision (RMSE=44 mm et R=0,95), et ce quelque soit la technique d'irrigation. Des erreurs acceptables (RMSE=27 mm et R=0,52) sont obtenues pour des irrigations cumulées sur 15 jours, mais les erreurs sont beaucoup plus importants à l'échelle journalière et hebdomadaire. Cependant, les RZSM et ET journalières sont estimées avec précision à l'aide de des irrigations inversées et sont même très proches de celles estimées à l'aide des irrigations réelles (RMSE=0,04 m3m-3 pour RZSM et RSME=0,83 mm.d-1 pour ET). Dans la troisième partie, une méthode opérationnelle de désagrégation des données de LST basée sur les relations NDVI/LST et Landsat/MODIS est mise en œuvre pour améliorer la résolution spatio-temporelle de la LST utilisée en entrée de l'approche d'estimation de l'irrigation. La méthode de désagrégation est testée sur une région aride du Chili et sur notre zone d'étude dans la plaine du Haouz. La combinaison des données deLST Landsat et des données de LST désagrégées permet, grâce au gain en résolution temporelle, une meilleure détection des événements et des quantités d'irrigation. Le RMSE global de l'irrigation cumulée à différentes échelles de temps est réduite de 46 à 34 mm, tandis que le R passe de 0,50 à 0,64

Identifying relationships between biophysical variables of the rainfed coastal landscape of maule region and the surface energy balance components, by using remote sensing

Memoria para optar al título profesional de: Ingeniero en Recursos Naturales RenovablesPara la planificación y gestión de los recursos hídricos se requiere conocer no sólo la cantidad de agua que llega a la superficie terrestre, sino también el agua que sale de la superficie como evapotranspiración (ET). Esta variable es el factor más importante en el intercambio de energía y agua entre la superficie de la tierra y la atmósfera, la cual puede estimarse a través del balance energético superficial (BES). De esta manera, la estimación de los flujos energéticos superficiales permite avanzar en el conocimiento del paisaje de un territorio para su posterior planificación. En este estudio se estimó la distribución espacial de los flujos energéticos superficiales en un sector caracterizado por limitaciones hídricas, el secano costero de la Región del Maule, con el objetivo de identificar relaciones entre las variables biofísicas del paisaje y los componentes del BES. Para esto se calibró el modelo S-SEBI en el área de estudio y estimaron los componentes del BES a través de dos imágenes satelitales del sensor ASTER y datos de temperaturas máximas y mínimas diarias. Luego, para analizar los patrones espaciales en el paisaje, los componentes del BES se compararon con las variables biofísicas del paisaje asociadas a: índice de vegetación NDVI obtenido de las imágenes ASTER; cobertura de uso de suelo, clases de textura y profundidad del suelo, obtenidos a partir de estudios cartográficos de la Región del Maule; y variables topográficas de altitud, pendiente y exposición obtenidas de un Modelo Digital de Elevación (DEM). Las comparaciones fueron realizadas en base a diagramas de cajas entre las clases del material cartográfico para cada componente del BES, para lo cual se aplicaron pruebas de contrastes entre clases con el test estadístico Kruskal-Wallis y posteriormente con el test Mann-Whitney. Las variables topográficas fueron comparadas en base a las distribuciones de frecuencias para cada variable y componente. A partir de esta metodología se obtuvieron los componentes del BES en ambas escenas, donde se encontraron que la mayoría de las clases mostraron diferencias significativas. Mientras que las coberturas boscosas tienen las mayores tasas de ET, los terrenos agrícolas se sitúan muy por debajo con tasas similares a las praderas y matorrales. En cuanto al NDVI se encontró una alta correlación lineal con la ET, explicando en más del 75% las tasas encontradas para ambas escenas. Finalmente, se pudo concluir que el modelo S-SEBI permite estimar los componentes del BES con un mínimo de datos meteorológicos, y sus patrones espaciales observados en ambas escenas pueden ser explicados por las variables biofísicas estudiadas.The planning and management of water resources requires not only to know the amount of water that reaches Earth's surface, but also the amount of water that leaves the surface in the evapotranspiration process (ET). This variable is the most important factor in the energy and water exchange between Earth's surface and the atmosphere and it can be estimated with the Surface Energy Balance Model (SEB). The estimation of surface's energy fluxes spatial distribution improves the knowledge of an area's landscape thus, allowing a better planning and management of such area. The purpose of this study was to estimate the spatial distribution of surface energy fluxes in a sector with limited water resources, the rained coastal of Region del Maule, Chile, in order to identify the relationships between the biophysical variables present it the landscape and the the SEB model components. To achieve this, the S-SEBI model was calibrated to estimate the components of the SEB model by using data (2 scenes) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and data of the maximum and minimum daily temperatures. Then, to analyze the spatial patterns present in the landscape, SEB components were compared with landscape biophysical variables associated to: NDVI index obtained from the ASTER images; land use, soil texture and soil depth classes (obtained from Region del Maule's Geographic Information System (GIS); and the elevation, slope and aspect obtained from a Digital Elevation Model. Comparisons were made based on the analysis of box-plots between GIS classes of each component of the SEB model, applying contrast test between the classes with the Kruskal-Wallis and Mann-Whitney statistical tests. The topographic variables were compared based on the frequency distributions of each variable and component. Then, the SEB model components were obtained for both of the ASTER scenes showing that the majority of the classes showed significant differences. While the forest cover had the highest ET rates, agricultural land rates are similar of those of the grassland and shrubland classes. A high linear correlation was found between the ET and NDVI, explaining more than the 75% found for both of the scene. Finally, it was concluded that S-SEBI model can estimate the components of the SEB model using minimal meteorological data and that the spatial patterns observed in both scenes are in fact explained by the studied biophysical variables

Monitoring the water budget of irrigated crops from multi-spectral optical/thermal remote sensing data

L'agriculture est une pression importante sur les ressources en eau, consommant plus de 70% de l'eau douce mobilisée à l'échelle mondiale. Cependant, les informations sur l'irrigation, pourtant cruciales pour assurer une durabilité de la ressource, sont souvent indisponibles. Par conséquent, il est essentiel d'estimer les différents termes du bilan d'eau des cultures à grande échelle. Cette thèse vise à intégrer les données de télédétection optique/thermique dans un modèle simplifié de bilan d'eau des cultures pour le suivi du bilan d'eau des zones agricoles irriguées. Une approche innovante est développée pour estimer simultanément l'irrigation, l'évapotranspiration (ET) et l'humidité en zone racinaire (RZSM) journalières à l'échelle de parcelle (ou à 100 m de résolution). Dans une première partie, une étude de faisabilité est réalisée à l'aide de mesures optiques/thermiques in situ collectées sur une parcelle de blé d'hiver dans la plaine du Haouz, au Maroc. En pratique, un coefficient de stress hydrique (Ks) dérivé de la température de surface (LST) et d'un indice de végétation (NDVI) est d'abord traduit en une première approximation de RZSM, qui est utilisée pour estimer les quantités et les dates d'irrigation au cours de la saison. Les irrigations obtenues permettent ensuite de forcer le modèle FAO-56 à coefficient cultural double (FAO-2Kc) et de fournir des ré-analyses ET et RZSM journalières. La RZSM ré-analysée est significativement améliorée par rapport aux premières estimations de RZSM, atteignant la même précision que celle obtenue en utilisant les irrigations réelles (RMSE=0,03 m3m-3 et R2=0,7). Toutefois, l'approche doit encore être testée avec des données satellitaires afin de démontrer son applicabilité dans le cas réel. La deuxième partie consiste à adapter l'approche précédente aux données optiques/thermiques Landsat à faible fréquence temporelle. Une méthode contextuelle est utilisée pour obtenir des estimations dérivées de Landsat (coefficients de culture et RZSM), qui sont utilisées pour réinitialiser un modèle basé sur le FAO-2Kc et propager ces informations à l'échelle journalière tout au long de la saison. Ensuite, les irrigations obtenues à l'échelle des pixels sont agrégées à la parcelle pour ré-analyser l'ET et la RZSM journalières. L'approche est appliquée sur trois zones agricoles (12 km x 12 km) de la région semi-aride de la plaine du Haouz et validée sur cinq parcelles de blé d'hiver avec différentes techniques d'irrigation (goutte à goutte, gravitaire et sans irrigation). Les résultats montrent que l'irrigation saisonnière sur l'ensemble des sites et des saisons est estimée avec une bonne précision (RMSE=44 mm et R=0,95), et ce quelque soit la technique d'irrigation. Des erreurs acceptables (RMSE=27 mm et R=0,52) sont obtenues pour des irrigations cumulées sur 15 jours, mais les erreurs sont beaucoup plus importants à l'échelle journalière et hebdomadaire. Cependant, les RZSM et ET journalières sont estimées avec précision à l'aide de des irrigations inversées et sont même très proches de celles estimées à l'aide des irrigations réelles (RMSE=0,04 m3m-3 pour RZSM et RSME=0,83 mm.d-1 pour ET). Dans la troisième partie, une méthode opérationnelle de désagrégation des données de LST basée sur les relations NDVI/LST et Landsat/MODIS est mise en œuvre pour améliorer la résolution spatio-temporelle de la LST utilisée en entrée de l'approche d'estimation de l'irrigation. La méthode de désagrégation est testée sur une région aride du Chili et sur notre zone d'étude dans la plaine du Haouz. La combinaison des données deLST Landsat et des données de LST désagrégées permet, grâce au gain en résolution temporelle, une meilleure détection des événements et des quantités d'irrigation. Le RMSE global de l'irrigation cumulée à différentes échelles de temps est réduite de 46 à 34 mm, tandis que le R passe de 0,50 à 0,64.Irrigated agriculture is an important pressure on water resources, consuming more than 70% of the mobilized freshwater resources at global scale. However, the information on irrigation, which is crucial for the sustainability of water resources in agricultural regions, is often unavailable. Therefore, monitoring and quantifying the crop water budget over extended areas is critical. This PhD thesis aims to integrate optical/thermal remote sensing data into a simplified crop water balance model for monitoring the water budget of irrigated agricultural areas. For this purpose, an innovative and stepwise approach is developed to estimate simultaneously the irrigation, the evapotranspiration (ET) and the root-zone soil moisture (RZSM) at crop field scale (100 m resolution) on a daily basis. In a first step, a feasibility study is carried out using in situ optical/thermal measurements collected over a winter wheat field of the Haouz plain, Morocco. A crop water stress coefficient (Ks) derived from the land surface temperature (LST) and vegetation index (NDVI) is first translated into RZSM diagnostic estimates, which is then used to estimate irrigation amounts and dates along the season. Next, the retrieved irrigations allow forcing the dual crop coefficient FAO-56 model (FAO- 2Kc) to re-analyze the daily ET and RZSM. The re-analyzed RZSM is significantly improved with respect to RZSM diagnostic estimates, reaching the same accuracy as that obtained by using actual irrigations (RMSE = 0.03 m3m-3 and R2 = 0.7). However, the approach needs to be tested using satellite data in order to demonstrate its real applicability. The next step consists in adapting the previous approach to spatially integrated but temporally sparse Landsat NDVI/LST data. For this purpose, a contextual method is first used to derive Landsat-derived estimates (crop coefficients and RZSM), which are used to re-initialize a FAO-based model and propagate this information daily throughout the season. Then, the retrieved pixel-scale irrigations are aggregated to the crop field-scale. The approach is applied to three agricultural areas (12 km by 12 km) in the semi-arid region of Haouz Plain, and validated over five winter wheat fields with different irrigation techniques (drip-, flood- and no-irrigation). The results show that the seasonal irrigation amounts over all the sites and seasons is accurately estimated (RMSE = 44 mm and R = 0.95), regardless of the irrigation techniques. Acceptable errors (RMSE = 27 mm and R = 0.52) are obtained for irrigations cumulated over 15 days, but poor agreements at daily to weekly scales are found in terms of irrigation. However, the daily RZSM and ET are accurately estimated using the retrieved irrigation and are very close to those estimated using actual irrigations (overall RMSE equal to 0.04 m3m-3 and 0.83 mm.d-1 for RZSM and ET, respectively). In a final step, an operational LST disaggregation method based on NDVI/LST and Landsat/MODIS relationships is implemented for enhancing the spatio-temporal resolution of LST as input to the irrigation retrieval approach. The disaggregation method is tested over an arid region of Chile and our study area in the Haouz Plain. Combining both disaggregated LST and Landsat LST data sets, thanks to the increase in the temporal frequency of LST data, results in a better detection of irrigation events and amounts. The overall RMSE of cumulated irrigation at different time scales is decreased from 46 to 34 mm, while the R is increased from 0.50 to 0.64. Consistently, the RZSM estimated using the disaggregated LST in addition to Landsat LST as input is improved by 26% and 14% in terms of RMSE and R, respectively

Irrigation retrieval from Landsat optical/thermal data integrated into a crop water balance model: A case study over winter wheat fields in a semi-arid region

International audienceMonitoring irrigation is essential for an efficient management of water resources in arid and semi-arid regions. We propose to estimate the timing and the amount of irrigation throughout the agricultural season using optical and thermal Landsat-7/8 data. The approach is implemented in four steps: i) partitioning the Landsat land surface temperature (LST) to derive the crop water stress coefficient (Ks), ii) estimating the daily root zone soil moisture (RZSM) from the integration of Landsat-derived Ks into a crop water balance model, iii) retrieving irrigation at the Landsat pixel scale and iv) aggregating pixel-scale irrigation estimates at the crop field scale. The new irrigation retrieval method is tested over three agricultural areas during four seasons and is evaluated over five winter wheat fields under different irrigation techniques (drip, flood and no-irrigation). The model is very accurate for the seasonal accumulated amounts (R ~ 0.95 and RMSE ~ 44 mm). However, lower agreements with observed irrigations are obtained at the daily scale. To assess the performance of the irrigation retrieval method over a range of time periods, the daily predicted and observed 2 irrigations are cumulated from 1 to 90 days. Generally, acceptable errors (R = 0.52 and RMSE = 27 mm) are obtained for irrigations cumulated over 15 days and the performance gradually improves by increasing the accumulation period, depicting a strong link to the frequency of Landsat overpasses (16 days or 8 days by combining Landsat-7 and-8). Despite the uncertainties in retrieved irrigations at daily to weekly scales, the daily RZSM and evapotranspiration simulated from the retrieved daily irrigations are estimated accurately and are very close to those estimated from actual irrigations. This research demonstrates the utility of high spatial resolution optical and thermal data for estimating irrigation and consequently for better closing the water budget over agricultural areas. We also show that significant improvements can be expected at daily to weekly time scales by reducing the revisit time of high-spatial resolution thermal data, as included in the TRISHNA future mission requirements

Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach

Monitoring agricultural crops is of paramount importance for preserving water resources and increasing water efficiency over semi-arid areas. This can be achieved by modelling the water resources all along the growing season through the coupled water–surface energy balance. Surface albedo is a key land surface variable to constrain the surface radiation budget and hence the coupled water–surface energy balance. In order to capture the hydric status changes over the growing season, optical remote sensing becomes impractical due to cloud cover in some periods, especially over irrigated winter crops in semi-arid regions. To fill the gap, this paper aims to generate cloudless surface albedo product from Sentinel-1 data that offers a source of high spatio-temporal resolution images. This can help to better capture the vegetation development along the growth season through the surface radiation budget. Random Forest (RF) algorithm was implemented using Sentinel-1 backscatters as input. The approach was tested over an irrigated semi-arid zone in Morocco, which is known by its heterogeneity in term of soil conditions and crop types. The obtained results are evaluated against Landsat-derived albedo with quasi-concurrent Landsat/Sentinel-1 overpasses (up to one day offset), while a further validation was investigated using in situ field scale albedo data. The best model-hyperparameters selection was dependent on two validation approaches (K-fold cross-validation ‘k = 10’, and holdout). The more robust and accurate model parameters are those that represent the best statistical metrics (root mean square error ‘RMSE’, bias and correlation coefficient ‘R’). Coefficient values ranging from 0.70 to 0.79 and a RMSE value between 0.0002 and 0.00048 were obtained comparing Landsat and predicted albedo by RF method. The relative error ratio equals 4.5, which is acceptable to predict surface albedo

Estimating the water budget components of irrigated crops: Combining the FAO-56 dual crop coefficient with surface temperature and vegetation index data

International audienceThe FAO-56 dual crop coefficient (FAO-2Kc) model has been extensively used at the field scale to estimate the crop water requirements by means of the simulated evapotranspiration (ET) and its two components evaporation (E) and transpiration (T). Given that the main limitation of FAO-2Kc for operational irrigation management over large areas is the unavailability (over most irrigated areas) of irrigation data, this study investigates the feasibility 1) to constrain the FAO-2Kc ET from LST and VI data, 2) to retrieve irrigation amounts and dates from LST and VI data and 3) to estimate the root-zone soil moisture (RZSM) at the daily scale. In practice, the vegetation and soil temperatures retrieved from LST/VI data are used to estimate the FAO-2Kc vegetation stress coefficient (Ks) and soil evaporation reduction coefficient (Kr), respectively. The modeling and remote sensing combined approach is tested over a wheat crop field in central Morocco, and results are evaluated in terms of ET, irrigation and RZSM estimates. ET is estimated with a RMSE of 0.68 mm day-1 compared to 0.84 mm day-1 for the standard (without using LST data) FAO-2Kc based on tabulated values for the parameters. The total irrigation depth (67 mm) is correctly estimated and is very close to the actual effective irrigation (69.8 mm) applied by the farmer. Daily RZSM is estimated with an R2 value of 0.68 (0.42) and a RMSE value of 0.034 (0.061) m3 m-3 by forcing FAO-2Kc using the retrieved irrigation (from LST-derived estimates and precipitation only). Since spaceborne LST data are currently not available at both high-spatial and high-temporal resolution, a sensitivity analysis is finally undertaken to assess the potential and applicability of the proposed methodology to temporally sparse thermal data

Calibration and evaluation of an optical-passive microwave approach to estimate soil moisture over several land cover types

International audienceSoil moisture is one of the biosphere's most essential climatic variables. However, its periodical monitoring at regional scales using in-situ measurements is complex. In this context, L-band microwaves observations from passive remote sensors appear as an important tool for the periodical estimation of soil moisture at regional scales. In this work, a semi-empirical model to estimate soil moisture values from L-band observation was calibrated and evaluated over several land cover classes in a heterogeneous study area in Chile. For this, 3 years (2010, 2011 and 2012) of brightness temperature data from SMOS, soil temperature and volumetric water content from ERA-Interim, and NDVI from MODIS were used. Results showed an increase in the average r2 when a vegetation index was used in the calibration of the approach. These increases ranged from a 3% for Crops, to a 49% for Closed Shrublands. The ubRMSD showed a decrease in its value of up to 1% m3/m3 for Woodlands, Open Shrublands and Woody Shrublands and of up to 2% m3/m3 for Closed Shrublands

Correcting land surface temperature data for elevation and illumination effects in mountainous areas: A case study using ASTER data over a steep-sided valley in Morocco

International audienceThe remotely sensed land surface temperature (LST) is a key parameter to monitor surface energy and water fluxes but the strong impact of topography on LST has limited its use to mostly flat areas. To fill the gap, this study proposes a physically-based method to normalize LST data for topographic-namely illumination and elevation-effects over mountainous areas. Both topographic effects are first quantified by inverting a dual-source soil/vegetation energy balance (EB) model forced by 1) the instantaneous solar radiation simulated by a 3D radiative transfer model named DART (Discrete Anisotropic Radiative Transfer) that uses a digital elevation model (DEM), 2) a satellite-derived vegetation index, and 3) local meteorological (air temperature, air relative humidity and wind speed) data available at a given location. The satellite LST is then normalized for topography by simulating the LST using both pixel-and image-scale DART solar radiation and elevation data. The approach is tested on three ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) overpass dates over a steep-sided 6 km by 6 km area in the Atlas Mountain in Morocco. The mean correlation coefficient and root mean square difference (RMSD) between EB-simulated and ASTER LST is 0.80 and 3 • C, respectively. Moreover, the EB-based method is found to be more accurate than a more classical approach based on a multi-linear regression with DART solar radiation and elevation data. The EB-simulated LST is also evaluated against an extensive ground dataset of 135 autonomous 1-cm depth temperature sensors deployed over the study area. While the mean RMSD between 90 m resolution ASTER LST and localized ibutton measurements is 6.1 • C, the RMSD between EB-simulated LST and ibut-ton soil temperature is 5.4 and 5.3 • C for a DEM at 90 m and 8 m resolution, respectively. The proposed topographic normalization is self-calibrated from (LST, DEM, vegetation index and in situ meteorological data) data available over large extents. As a significant perspective this approach opens the path to using normalized LST as input to evapotranspiration retrieval methods based on LST

Retrieving the irrigation actually applied at district scale: Assimilating high-resolution Sentinel-1-derived soil moisture data into a FAO-56-based model

International audienc