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Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes

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Institute of Electronics and Telecommunications of Rennes IETR, UMR CNRS 6164, University of Rennes, 35000 Rennes, France
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Littoral-Environnement-Télédétection-Géomatique LETG UMR 6554, University of Rennes, 35 000 Rennes, France
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Internal Research Unit Forests & Societies, Centre de Coopération Internationale en Recherche Agronomique pour le Développement CIRAD, 34 398 Montpellier, France
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L’unité mixte de recherche Biodiversité, AGroécologie et Aménagement du Paysage UMR BAGAP, Institut National De La Recherche Agronomique, INRA, 35 000 Rennes, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(1), 37; https://doi.org/10.3390/rs11010037
Received: 20 November 2018 / Revised: 13 December 2018 / Accepted: 20 December 2018 / Published: 27 December 2018
Monitoring vegetation cover during winter is a major environmental and scientific issue in agricultural areas. From an environmental viewpoint, the presence and type of vegetation cover in winter influences the transport of pollutants to water resources. From a methodological viewpoint, characterizing spatio-temporal dynamics of land cover and land use at the field scale is challenging due to the diversity of farming strategies and practices in winter. The objective of this study was to evaluate the respective advantages of Sentinel optical and SAR time-series to identify land use in winter. To this end, Sentinel-1 and -2 time-series were classified using Support Vector Machine and Random Forest algorithms in a 130 km² agricultural area. From the classification, the Sentinel-2 time-series identified winter land use more accurately (overall accuracy (OA) = 75%, Kappa index = 0.70) than that of Sentinel-1 (OA = 70%, Kappa = 0.66) but a combination of the Sentinel-1 and -2 time-series was the most accurate (OA = 81%, Kappa = 0.77). Our study outlines the effectiveness of Sentinel-1 and -2 for identify land use in winter, which can help to change agricultural practices. View Full-Text
Keywords: agricultural monitoring; earth observing sensors; multi-temporal classification; optical and SAR time-series; Random Forest algorithm; Support Vector Machine algorithm agricultural monitoring; earth observing sensors; multi-temporal classification; optical and SAR time-series; Random Forest algorithm; Support Vector Machine algorithm
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MDPI and ACS Style

Denize, J.; Hubert-Moy, L.; Betbeder, J.; Corgne, S.; Baudry, J.; Pottier, E. Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes. Remote Sens. 2019, 11, 37. https://doi.org/10.3390/rs11010037

AMA Style

Denize J, Hubert-Moy L, Betbeder J, Corgne S, Baudry J, Pottier E. Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes. Remote Sensing. 2019; 11(1):37. https://doi.org/10.3390/rs11010037

Chicago/Turabian Style

Denize, Julien, Laurence Hubert-Moy, Julie Betbeder, Samuel Corgne, Jacques Baudry, and Eric Pottier. 2019. "Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes" Remote Sensing 11, no. 1: 37. https://doi.org/10.3390/rs11010037

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