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Open AccessArticle

Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2

1
CESBIO (CNRS/UPS/IRD/CNES/INRA), 18 Avenue Edouard Belin, 31401 Toulouse CEDEX9, France
2
Université de Carthage/INAT/LR GREEN-TEAM, 43 Avenue Charles Nicolle, Tunis 1082, Tunisia
3
Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
4
IRSTEA, University of Montpellier, UMR TETIS, 34093 Montpellier CEDEX 5, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1520; https://doi.org/10.3390/rs11131520
Received: 20 May 2019 / Revised: 20 June 2019 / Accepted: 25 June 2019 / Published: 27 June 2019
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. View Full-Text
Keywords: Sentinel-1; Sentinel-2; Soil Moisture; Texture; Clay; SVM; Random Forest Sentinel-1; Sentinel-2; Soil Moisture; Texture; Clay; SVM; Random Forest
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Bousbih, S.; Zribi, M.; Pelletier, C.; Gorrab, A.; Lili-Chabaane, Z.; Baghdadi, N.; Ben Aissa, N.; Mougenot, B. Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2. Remote Sens. 2019, 11, 1520.

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