Apports de données radar pour l'estimation des paramètres biophysiques des surfaces agricoles

Les travaux de thèse s'inscrivent au sein du chantier Sud-Ouest, dont le principal objectif est de contribuer à la compréhension et à la modélisation du fonctionnement des surfaces continentales à l'échelle du paysage. Ces travaux visent à améliorer les capacités de suivi et d'analyses de surfaces fortement anthropisées : les agrosystèmes. A la fois acteurs et spectateurs vis-à-vis du changement climatique, ces surfaces sont également dédiées à la production alimentaire. La problématique vise donc à concilier durabilité des ressources et niveau de production suffisant, en identifiant des outils comme la télédétection utiles à la prise de décision à des échelles allant de la parcelle au territoire. Dans ce contexte, les radars à synthèse d'ouverture (RSO) embarqués au sein de satellites, présentent le double avantage d'être sensibles à différents paramètres des surfaces continentales (en lien avec le sol, ou la végétation), et la capacité d'observation par condition nuageuse (à l'inverse des capteurs opérant dans le visible). Depuis les années 90, différentes études basées sur des images acquises avec la technologie RSO ont montré l'intérêt des données micro-ondes pour le suivi des surfaces continentales. Ces dernières années, l'émergence de missions satellites dans les bandes de fréquence X et L vient enrichir les possibilités d'étude autrefois limitées à la seule bande C. Ces couples capteurs-satellites fournissent aujourd'hui des produits à haute résolution spatiale (allant jusqu'au mètre), avec des possibilités de revisite hebdomadaire, critères nécessaires pour le suivi des zones hétérogènes, associées à de fortes dynamiques temporelles. Les travaux effectués dans le cadre de cette thèse visent à établir la complémentarité entre les données radars (TerraSAR-X, Radarsat-2 et Alos, dans les bandes spectrales X, C et L) et optiques (Formosat-2, Spot-4/5) acquises par satellites pour le suivi des agrosytèmes. Ils s'articulent autour de trois axes complémentaires : - Le premier consiste en la mise en oeuvre d'une campagne expérimentale basée sur l'acquisition d'un jeu de données (satellitaire et de terrain), nécessaire au développement de nouvelles approches pour l'analyse du paysage. La zone suivie, caractérisée par une forte anthropisation, est située à 50 km au sud-ouest de Toulouse. Les images satellitaires regroupent trois séries temporelles radar (bandes X, C et L), auxquelles s'ajoutent des acquisitions réalisées dans l'optique (Formosat-2, Spot-4/5). Avec un total d'une centaine d'images acquises dans les hyperfréquences, la zone commune aux différentes scènes couvre une surface de 10×10 km². Conjointement, les protocoles de mesures de terrain ont permis de considérer de manière indépendante les deux éléments clés de la surface : le sol et la culture. En complément des stations météorologiques installées dans le cadre du chantier, des mesures qualitatives et quantitatives ont été réalisés de manière synchrone avec les acquisitions satellites, sur un total de 387 parcelles. Cinq cultures sont principalement étudiées : blé, colza, tournesol, mais et soja. - Les signatures temporelles de chacune des cultures sont ensuite établies à chaque longueur d'onde d'acquisition satellitaire (optique et radar) à travers une approche originale de normalisation angulaire des signaux radar (combinaison de l'information radar et optique). Les résultats obtenus durant le cycle phénologique des cultures d'hiver (blé et colza) et d'été (maïs, soja et tournesol) montrent clairement la complémentarité des approches multi-capteurs, et la spécificité des signaux radars (en lien avec les états de polarisations et les fréquences considérées). Deux paramètres biophysiques relatifs à la végétation sont enfin estimés (LAI et hauteur), les données micro-ondes montrant à la fois une importante sensibilité et de bonnes performances. - La modélisation électromagnétique sur sol nu a tout d'abord permis d'évaluer différents formalismes, à savoir : les modèles de Dubois et d'Oh (1992 et 2004) ayant comme caractéristiques communes une description simplifiée des processus. Ils sont confrontés à un modèle reposant sur des bases physiques, le modèle IEM (Integral Equation Model). L'application des modèles dans les différentes bandes spectrales (X, C et L), montre des résultats très hétérogènes, les meilleures performances étant obtenue en bande X, avec le modèle d'Oh 1992. Par la suite, l'amélioration des modèles tire parti de l'analyse des résidus (vis-à-vis des variables d'entrée), afin de réduire la dispersion observée. Les modèles testés sont optimisés et validés selon une approche de type résidus. Une forte amélioration est observée pour la plupart des modèles. Les résultats mettent en évidence l'intérêt des données multi-capteurs pour le suivi des surfaces dédiées à l'agriculture. Dans un futur proche, les missions spatiales telles que Tandem-X, Sentinel-1/-2, Radarsat Constellation ou Alos-2 devraient pérenniser l'accès à ces données, et préciser ainsi les résultats obtenus dans le cadre de cette thèse.The thesis fall within the "SudOuest" project, whose main objective is to contribute to the understanding and the modeling of the land surface functioning, at the landscape scale. This work aims to improve the capacity of monitoring and analysis of highly anthropic surfaces: agrosystems. Both actors and audience to climate change, these surfaces are also dedicated to the food production. So the problem is to reconcile sustainability of resources and sufficient level of production, identifying tools, such as remote sensing, useful in making decision at scales ranging from plot to land. In this context, the Synthetic Aperture Radar (SAR) embedded in satellites have the twofold advantages of being sensitive to different parameters of the land surface (related to soil, and vegetation), and the ability to observe by cloudy condition (unlike sensors operating in the visible). Since the 90s, several studies based on images acquired with SAR technology have shown the interest of microwave data for the monitoring of land surface. In recent years, the emergence of satellite missions at X- and L-bands enriches study opportunities once only limited to the C-band. These sensor/satellite couples now provide products with high spatial resolution (up to a meter), with the possibility of weekly revisits, necessary criteria for the monitoring of heterogeneous areas associated with high temporal dynamics. Works done in this thesis aim to establish the complementarities between the radar (TerraSAR-X, Radarsat-2 and Alos, at X-, C- and L-bands) and optical data (Formosat-2, Spot-4/-5) acquired by satellites for the monitoring of agrosystems. They revolve around three complementary areas: - The first is the implementation of an experimental campaign based on the acquisition of a set of data (satellite and ground), necessary for the development of new approaches to landscape analysis. The studied area, characterized by a strong human impact, is located near Toulouse (at 50 km in the South West). Satellite images include three radar time series acquired at X-, C- and L-bands, and images acquired in the optical (Formosat-2, Spot-4/-5). With a total of one hundred images acquired in the microwave domain, the common area to the different scenes covering a region of 10×10 km². Together, the protocols used for field measurements consider independently the two key elements of the surface: the soil and the culture. In addition to the weather stations (part of the "SudOuest" project), qualitative and quantitative measurements are performed synchronously with the satellite acquisitions, on a total of 387 plots. Five crops are mainly studied: wheat, rapeseed, sunflower, corn and soybean. - The temporal signatures of these crops are then established for each satellite wavelength (optical and radar), through an original approach based on an angular normalization of radar signals (combining the optical and radar information). The results obtained during the phenological cycle of winter (wheat and rapeseed) and summer crops (corn, soybean and sunflower) clearly show the complementarity of multi-sensor approaches and the specificity of radar signals (associated with the considered polarization states and frequencies). Two biophysical parameters related to vegetation are finally estimated (leaf area index and height), the microwave data showing both high sensitivity and good performances. - The electromagnetic modeling of bare soil is first used to evaluate different formalisms, namely Dubois and Oh (1992 and 2004) models, with common characteristics, a simplified description of the process. They are confronted with a model based on the physical laws, the IEM (Integral Equation Model). The application of models in different spectral bands (X, C and L), shows very mixed results; the best performances are obtained at X-band with Oh 1992 model. Thereafter, the enhancement of the models takes advantage of the residue analysis (as a function of the input variables), to reduce the observed dispersion. The tested models are optimized and validated using an approach such residues. A significant improvement is observed for most models. The results highlight the interest of multi-sensor data for the monitoring of continental surfaces dedicated to agriculture. In the near future, satellite missions such as Tandem -X, Sentinel-1/-2, Radarsat Constellation or Alos-2 should sustain access to these data, and define the results obtained in this thesis

Evaluation of Multiorbital SAR and Multisensor Optical Data for Empirical Estimation of Rapeseed Biophysical Parameters

This article aims to evaluate the potential of multitemporal and multiorbital remote sensing data acquired both in the microwave and optical domain to derive rapeseed biophysical parameters (crop height, dry mass, fresh mass, and plant water content). Dense temporal series of 98 Landsat-8 and Sentinel-2 images were used to derive normalized difference vegetation index (NDVI), green fraction cover (fCover), and green area index (GAI), while backscattering coefficients and radar vegetation index (RVI) were obtained from 231 mages acquired by synthetic aperture radar (SAR) onboard Sentinel-1 platform. Temporal signatures of these remote sensing indicators (RSI) were physically interpreted, compared with each other to ground measurements of biophysical parameters acquired over 14 winter rapeseed fields throughout the 2017–2018 crop season. We introduced new indicators based on the cumulative sum of each RSI that showed a significant improvement in their predictive power. Results particularly reveal the complementarity of SAR and optical data for rapeseed crop monitoring throughout its phenological cycle. They highlight the potential of the newly introduced indicator based on the VH polarized backscatter coefficient to estimate height (R2 = 0.87), plant water content (R2 = 0.77, from flowering to harvest), and fresh mass (R2 = 0.73) and RVI to estimate dry mass (R2 = 0.82). Results also demonstrate that multiorbital SAR data can be merged without significantly degrading the performance of SAR-based relationships while strongly increasing the temporal sampling of the monitoring. These results are promising in view of assimilating optical and SAR data into crop models for finer rapeseed monitoring

Synergistic integration of optical and microwave satellite data for crop yield estimation

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor. Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD. This study considers two statistical methods, a regularized linear regressionand its nonlinear extension called kernel ridge regression to directly estimate the county-level surveyed total production, as well as individual yields of the major crops grown in the region: corn, soybean and wheat. The study area includes the US Corn Belt, and we use agricultural survey data from the National Agricultural Statistics Service (USDA-NASS) for year 2015 for quantitative assessment. Results show that (1) the proposed EVI-VOD lag metric correlates well with crop yield and outperforms common single-sensor metrics for crop yield estimation; (2) the statistical (machine learning) models working directly with the time series largely improve results compared to previously reported estimations; (3) the combined exploitation of information from the optical and microwave data leads to improved predictions over the use of single sensor approaches with coefficient of determination R 2 ≥ 0.76; (4) when models are used for within-season forecasting with limited time information, crop yield prediction is feasible up to four months before harvest (models reach a plateau in accuracy); and (5) the robustness of the approach is confirmed in a multi-year setting, reaching similar performances than when using single-year data. In conclusion, results confirm the value of using both EVI and VOD at the same time, and the advantage of using automatic machine learning models for crop yield/production estimation

Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes

The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications. Persistent clouds over agricultural fields can mask key stages of crop growth, leading to unreliable yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery which can potentially overcome this limitation, but given its high and distinct sensitivity to different surface properties, the fusion of SAR and optical data still remains an open challenge. In this work, we propose the use of Multi-Output Gaussian Process (MOGP) regression, a machine learning technique that learns automatically the statistical relationships among multisensor time series, to detect vegetated areas over which the synergy between SAR-optical imageries is profitable. For this purpose, we use the Sentinel-1 Radar Vegetation Index (RVI) and Sentinel-2 Leaf Area Index (LAI) time series over a study area in north west of the Iberian peninsula. Through a physical interpretation of MOGP trained models, we show its ability to provide estimations of LAI even over cloudy periods using the information shared with RVI, which guarantees the solution keeps always tied to real measurements. Results demonstrate the advantage of MOGP especially for long data gaps, where optical-based methods notoriously fail. The leave-one-image-out assessment technique applied to the whole vegetation cover shows MOGP predictions improve standard GP estimations over short-time gaps (R 2 of 74% vs 68%, RMSE of 0.4 vs 0.44 [m 2 m −2 ]) and especially over long-time gaps (R 2 of 33% vs 12%, RMSE of 0.5 vs 1.09 [m 2 m −2 ])

Apports des données radar pour l’estimation des paramètres biophysiques des surfaces agricoles

The thesis fall within the “SudOuest” project, whose main objective is to contribute to the understanding and the modeling of the land surface functioning, at the landscape scale. This work aims to improve the capacity of monitoring and analysis of highly anthropic surfaces: agrosystems. Both actors and audience to climate change, these surfaces are also dedicated to the food production. So the problem is to reconcile sustainability of resources and sufficient level of production, identifying tools, such as remote sensing, useful in making decision at scales ranging from plot to land.In this context, the Synthetic Aperture Radar (SAR) embedded in satellites have the twofold advantages of being sensitive to different parameters of the land surface (related to soil, and vegetation), and the ability to observe by cloudy condition (unlike sensors operating in the visible). Since the 90s, several studies based on images acquired with SAR technology have shown the interest of microwave data for the monitoring of land surface. In recent years, the emergence of satellite missions at X- and L-bands enriches study opportunities once only limited to the C-band. These sensor/satellite couples now provide products with high spatial resolution (up to a meter), with the possibility of weekly revisits, necessary criteria for the monitoring of heterogeneous areas associated with high temporal dynamics.Works done in this thesis aim to establish the complementarities between the radar (TerraSAR-X, Radarsat-2 and Alos, at X-, C- and L-bands) and optical data (Formosat-2, Spot-4/-5) acquired by satellites for the monitoring of agrosystems. They revolve around three complementary areas:- The first is the implementation of an experimental campaign based on the acquisition of a set of data (satellite and ground), necessary for the development of new approaches to landscape analysis. The studied area, characterized by a strong human impact, is located near Toulouse (at 50 km in the South West). Satellite images include three radar time series acquired at X-, C- and L-bands, and images acquired in the optical (Formosat-2, Spot-4/-5). With a total of one hundred images acquired in the microwave domain, the common area to the different scenes covering a region of 10×10 km². Together, the protocols used for field measurements consider independently the two key elements of the surface: the soil and the culture. In addition to the weather stations (part of the “SudOuest” project), qualitative and quantitative measurements are performed synchronously with the satellite acquisitions, on a total of 387 plots. Five crops are mainly studied: wheat, rapeseed, sunflower, corn and soybean.- The temporal signatures of these crops are then established for each satellite wavelength (optical and radar), through an original approach based on an angular normalization of radar signals (combining the optical and radar information). The results obtained during the phenological cycle of winter (wheat and rapeseed) and summer crops (corn, soybean and sunflower) clearly show the complementarity of multi-sensor approaches and the specificity of radar signals (associated with the considered polarization states and frequencies). Two biophysical parameters related to vegetation are finally estimated (leaf area index and height), the microwave data showing both high sensitivity and good performances.- The electromagnetic modeling of bare soil is first used to evaluate different formalisms, namely Dubois and Oh (1992 and 2004) models, with common characteristics, a simplified description of the process. They are confronted with a model based on the physical laws, the IEM (Integral Equation Model). The application of models in different spectral bands (X, C and L), shows very mixed results; the best performances are obtained at X-band with Oh 1992 model. Thereafter, the enhancement of the models takes advantage of the residue analysis (as a function of the input variables), to reduce the observed dispersion. The tested models are optimized and validated using an approach such residues. A significant improvement is observed for most models.The results highlight the interest of multi-sensor data for the monitoring of continental surfaces dedicated to agriculture. In the near future, satellite missions such as Tandem -X, Sentinel-1/-2, Radarsat Constellation or Alos-2 should sustain access to these data, and define the results obtained in this thesis.Les travaux de thèse s‘inscrivent au sein du chantier Sud-Ouest, dont le principal objectif est de contribuer à la compréhension et à la modélisation du fonctionnement des surfaces continentales à l‘échelle du paysage. Ces travaux visent à améliorer les capacités de suivi et d‘analyses de surfaces fortement anthropisées : les agrosystèmes. A la fois acteurs et spectateurs vis-à-vis du changement climatique, ces surfaces sont également dédiées à la production alimentaire. La problématique vise donc à concilier durabilité des ressources et niveau de production suffisant, en identifiant des outils comme la télédétection utiles à la prise de décision à des échelles allant de la parcelle au territoire.Dans ce contexte, les radars à synthèse d‘ouverture (RSO) embarqués au sein de satellites, présentent le double avantage d‘être sensibles à différents paramètres des surfaces continentales (en lien avec le sol, ou la végétation), et la capacité d‘observation par condition nuageuse (à l‘inverse des capteurs opérant dans le visible). Depuis les années 90, différentes études basées sur des images acquises avec la technologie RSO ont montré l‘intérêt des données micro-ondes pour le suivi des surfaces continentales. Ces dernières années, l‘émergence de missions satellites dans les bandes de fréquence X et L vient enrichir les possibilités d‘étude autrefois limitées à la seule bande C. Ces couples capteurs-satellites fournissent aujourd’hui des produits à haute résolution spatiale (allant jusqu‘au mètre), avec des possibilités de revisite hebdomadaire, critères nécessaires pour le suivi des zones hétérogènes, associées à de fortes dynamiques temporelles.Les travaux effectués dans le cadre de cette thèse visent à établir la complémentarité entre les données radars (TerraSAR-X, Radarsat-2 et Alos, dans les bandes spectrales X, C et L) et optiques (Formosat-2, Spot-4/5) acquises par satellites pour le suivi des agrosytèmes. Ils s‘articulent autour de trois axes complémentaires :- Le premier consiste en la mise en oeuvre d‘une campagne expérimentale basée sur l‘acquisition d‘un jeu de données (satellitaire et de terrain), nécessaire au développement de nouvelles approches pour l‘analyse du paysage. La zone suivie, caractérisée par une forte anthropisation, est située à 50 km au sud-ouest de Toulouse. Les images satellitaires regroupent trois séries temporelles radar (bandes X, C et L), auxquelles s‘ajoutent des acquisitions réalisées dans l‘optique (Formosat-2, Spot-4/5). Avec un total d‘une centaine d‘images acquises dans les hyperfréquences, la zone commune aux différentes scènes couvre une surface de 10×10 km². Conjointement, les protocoles de mesures de terrain ont permis de considérer de manière indépendante les deux éléments clés de la surface : le sol et la culture. En complément des stations météorologiques installées dans le cadre du chantier, des mesures qualitatives et quantitatives ont été réalisés de manière synchrone avec les acquisitions satellites, sur un total de 387 parcelles. Cinq cultures sont principalement étudiées : blé, colza, tournesol, mais et soja.- Les signatures temporelles de chacune des cultures sont ensuite établies à chaque longueur d‘onde d‘acquisition satellitaire (optique et radar) à travers une approche originale de normalisation angulaire des signaux radar (combinaison de l‘information radar et optique). Les résultats obtenus durant le cycle phénologique des cultures d‘hiver (blé et colza) et d‘été (maïs, soja et tournesol) montrent clairement la complémentarité des approches multi-capteurs, et la spécificité des signaux radars (en lien avec les états de polarisations et les fréquences considérées). Deux paramètres biophysiques relatifs à la végétation sont enfin estimés (LAI et hauteur), les données micro-ondes montrant à la fois une importante sensibilité et de bonnes performances.- La modélisation électromagnétique sur sol nu a tout d‘abord permis d‘évaluer différents formalismes, à savoir : les modèles de Dubois et d‘Oh (1992 et 2004) ayant comme caractéristiques communes une description simplifiée des processus. Ils sont confrontés à un modèle reposant sur des bases physiques, le modèle IEM (Integral Equation Model). L‘application des modèles dans les différentes bandes spectrales (X, C et L), montre des résultats très hétérogènes, les meilleures performances étant obtenue en bande X, avec le modèle d‘Oh 1992. Par la suite, l‘amélioration des modèles tire parti de l‘analyse des résidus (vis-à-vis des variables d‘entrée), afin de réduire la dispersion observée. Les modèles testés sont optimisés et validés selon une approche de type résidus. Une forte amélioration est observée pour la plupart des modèles.Les résultats mettent en évidence l‘intérêt des données multi-capteurs pour le suivi des surfaces dédiées à l‘agriculture. Dans un futur proche, les missions spatiales telles que Tandem-X, Sentinel-1/-2, Radarsat Constellation ou Alos-2 devraient pérenniser l‘accès à ces données, et préciser ainsi les résultats obtenus dans le cadre de cette thèse

Estimation of Sunflower Yields at a Decametric Spatial Scale—A Statistical Approach Based on Multi-Temporal Satellite Images

Recent advances in sensors onboard harvesting machines allow accessing the intra-plot variability of yields, spatial scale fully compatible with numerous on-going satellite missions. The aim of this study is to estimate the sunflower yield at the intra-plot spatial scale using the multi-temporal images provided by the Landsat-8 and Sentinel-2 missions. The proposed approach is based on a statistical algorithm, testing different sampling strategies to partition the dataset into independent training and testing sets: A random selection (testing different ratio), a systematic selection (focusing on different plots) and a forecast procedure (using an increasing number of images). Emphasis is put on the use of high spatial and temporal resolution satellite data acquired throughout two agricultural seasons, on a study site located in southwestern France. Ground measurements consist in intra-plot yields collected by a surveying harvesting machine with GPS system on track mode. The forecast of yield throughout the agricultural season provides early accurate estimation two months before the harvest, with R2 equal to 0.59 or 0.66 and root mean square error (RMSE) of 4.7 or 3.4 q ha−1, for the agricultural seasons 2016 and 2017 respectively. Results obtained with the random selection or the systematic selection will be developed later, in a longer paper

Estimation of Crop Production and CO2 Fluxes Using Remote Sensing: Application to a Winter Wheat/Sunflower Rotation

To meet the incoming growth of the world’s food needs, and the demands of climate change, the agricultural sector will be forced to adapt its practices. To do so, the contribution of agricultural fields to greenhouse gas emissions, as well as the impact—on soil, climate and productions—of certain agricultural practices have to be known. In this study, the SAFY-CO2 crop model is driven by remote sensing products in order to estimate CO2 fluxes on the main crop rotation observed in the study area, i.e., winter wheat followed by sunflower. Different modeling scenarios are tested, particularly for intercropping periods, the approach being validated locally, thanks to eddy covariance flux measurements, and then applied regionally. Results showed that the model was able to reproduce crop production with high accuracy (rRMSE of 21% and 24% for winter wheat and sunflower yield, respectively) as well as daily net CO2 flux (RMSE of 1.29 and 0.97 gC.m−2.d−1 for winter wheat and sunflower respectively). Moreover, the tested modeling scenarios highlight the importance of taking the regrowth events into account for assessing accurate carbon budgets. In a perspective of large-scale application, the model was upscaled over more than 100 plots, allowing discussion of the effect of regrowth on carbon uptake

Forecast of wheat yield throughout the agricultural season using optical and radar satellite images

International audienceThe aim of this study is to estimate the capabilities of forecasting the yield of wheat using an artificial neural network combined with multi-temporal satellite data acquired at high spatial resolution throughout the agricultural season in the optical and/or microwave domains. Reflectance (acquired by Formosat-2, and Spot 4-5 in the green, red, and near infrared wavelength) and multi-configuration backscattering coefficients (acquired by TerraSAR-X and Radarsat-2 in the X- and C-bands, at co- (abbreviated HH and VV) and cross-polarization states (abbreviated HV and VH)) constitute the input variable of the artificial neural networks, which are trained and validated on the successively acquired images, providing yield forecast in near real-time conditions. The study is based on data collected over 32 fields of wheat distributed over a study area located in southwestern France, near Toulouse. Among the tested sensor configurations, several satellite data appear useful for the yield forecasting throughout the agricultural season (showing coefficient of determination (R2) larger than 0.60 and a root mean square error (RMSE) lower than 9.1 quintals by hectare (q ha-1)): CVH, CHV, or the combined used of XHH and CHH, CHH and CHV, or green reflectance and CHH. Nevertheless, the best accurate forecast (R2 = 0.76 and RMSE = 7.0 q ha-1) is obtained longtime before the harvest (on day 98, during the elongation of stems) using the combination of co- and cross-polarized backscattering coefficients acquired in the C-band (CVV and CVH). These results highlight the high interest of using synthetic aperture radar (SAR) data instead of optical ones to early forecast the yield before the harvest of wheat