Soil Moisture Estimation Over Cereal Fields Based on Sar ALOS-2 Data
2021
Zribi, Mehrez | Kassouk, Zeineb | Lili Chabaane, Zohra | Bousbih, Safa | Baghdadi, Nicolas | Centre d'études spatiales de la biosphère (CESBIO) ; Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) ; Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Institut National Agronomique de Tunisie (INAT) | Université de Carthage (Tunisie) = University of Carthage (UCAR) | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
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Mostrar más [+] Menos [-]Inglés. In this paper, we discuss the potential of L-band Advanced Land Observing Satellite-2 (ALOS-2) images for retrieving soil moisture over cereal fields in a semi -arid area (Merguellil- Tunisia). SAR signal sensitivity was studied as function of in-situ measurements: roughness and soil moisture. Sensitivity to soil moisture was illustrated for three classes of Normalized Difference Vegetation Index (NDVI). Results reveal the impact of soil moisture on L-band data even in dense vegetation class (NDVI > 0.6). High correlations characterize linear relationships between radar signal and vegetation biophysical properties (Leaf Area Index, vegetation height and Vegetation Water Content). Signal modeling over bare soils was evaluated through empirical equation, modified Dubois model (Dubois-B) and modified Integral Equation Model (IEM-B). For covered fields, Water Cloud Model (WCM) was parametrized for HH and HV polarizations (with and without soil-vegetation interactions component) coupled with the best accuracy bare soil backscattering models: IEM-B for co-polarization and empirical models for the entire dataset. WCM coupled to IEM - B illustrates the best performance to estimate soil water content in HH polarization. The integration of soil-vegetation interaction component provides a stable accuracy of soil moisture estimation in HH polarization and improve soil moisture accuracy in HV polarization mode.
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