Evaluation of using sentinel-1 and -2 time-series to identify winter land use in agricultural landscapes

Denize Julien, Hubert-Moy Laurence, Betbeder Julie, Corgne Samuel, Baudry Jacques, Pottier Eric. 2019. Evaluation of using sentinel-1 and -2 time-series to identify winter land use in agricultural landscapes. Remote Sensing, 11 (1):37, 18 p.

https://doi.org/10.3390/rs11010037

Article de revue ; Article de recherche ; Article de revue à facteur d'impact Revue en libre accès total

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Quartile : Q2, Sujet : REMOTE SENSING

Résumé : 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.

Mots-clés Agrovoc : paysage agricole, hiver, utilisation des terres, couverture végétale, méthode statistique

Classification Agris : E11 - Économie et politique foncières
P40 - Météorologie et climatologie
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 5 (2019-) - Territoires

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