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

J. Denize (1) , Laurence Hubert-Moy (2) , J. Betbeder (3) , Samuel Corgne (2) , J. Baudry (4) , E. Pottier (1)

1 IETR - Institut d'Électronique et des Technologies du numéRique
2 LETG - Nantes - Littoral, Environnement, Télédétection, Géomatique
3 Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement
4 UMR BAGAP - Biodiversité agroécologie et aménagement du paysage

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 km(2) 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.

Keywords: Agricultural monitoring; Random Forest algorithm; Support Vector Machine algorithm; Multi-temporal classification; Optical and SAR time-series; Earth observing sensors

Domains: Engineering Sciences [physics]

J. Denize, Laurence Hubert-Moy, J. Betbeder, Samuel Corgne, J. Baudry, et al.. Evaluation of using sentinel-1 and -2 time-series to identifywinter land use in agricultural landscapes. Remote Sensing, 2019, 11 (1), pp.37. ⟨10.3390/rs11010037⟩. ⟨hal-02042533⟩