Soil texture estimation using radar and optical data from Sentinel-1 and Sentinel-2
2019
Bousbih, Safa | Zribi, Mehrez | Pelletier, C. | Gorrab, Azza | Lili Chabaâne, Z. | Baghdadi, Nicolas | Aissa, N.B. | Mougenot, Bernard | 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) | Monash University [Melbourne] | Institut National Agronomique de Tunisie (INAT) | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS) | CHAAMS project ; ERANET-MED 03-62 ; CHAAMS TOSCA/CNES project | ANR-17-NMED-0002,CHAAMS,Global Change: Assessment and Adaptation to Mediterranean Region Water Scarcity(2017)
[Notes_IRSTEA]1520 [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [ADD1_IRSTEA]Dynamiques spatiales d'anthropisation
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Mostrar más [+] Menos [-]Inglés. This paper discusses the combined use of remotely sensed optical and radar data for the estimation and mapping of soil texture. The study is based on Sentinel-1 (S-1) and Sentinel-2 (S-2) data acquired between July and early December 2017, on a semi-arid area about 3000 km2 in central Tunisia. In addition to satellite acquisitions, texture measurement samples were taken in several agricultural fields, characterized by a large range of clay contents (between 13% and 60%). For the period between July and August, various optical indicators of clay content Short-Wave Infrared (SWIR) bands and soil indices) were tested over bare soils. Satellite moisture products, derived from combined S-1 and S-2 data, were also tested as an indicator of soil texture. Algorithms based on the support vector machine (SVM) and random forest (RF) methods are proposed for the classification and mapping of clay content and a three-fold cross-validation is used to evaluate both approaches. The classifications with the best performance are achieved using the soil moisture indicator derived from combined S-1 and S-2 data, with overall accuracy (OA) of 63% and 65% for the SVM and RF classifications, respectively.
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