Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions

Remote Sensing

Silvia Valero⁴  David Morin⁴  Jordi Inglada⁴  Guadalupe Sepulcre³  Marcela Arias⁴  Olivier Hagolle⁴  Gérard Dedieu⁴  Sophie Bontemps³  Pierre Defourny³  Benjamin Koetz²  Anton Vrieling¹  Yoshio Inoue¹ 

[1] CESBIO-CNES, CNRS (UMR 5126), IRD, Université de Toulouse, 31401 Toulouse Cedex 9, FranceESRIN D/EOP-SEP, European Space Agency, Via Galileo Galilei, 00044 Frascati, Italy;Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium;CESBIO-CNES, CNRS (UMR 5126), IRD, Université de Toulouse, 31401 Toulouse Cedex 9, France;

关键词: cropland mapping;    satellite image time series;    Sentinel-2;    dynamic classification;    Random Forests;   

DOI  :  10.3390/rs8010055

来源: mdpi

【 摘 要 】

The exploitation of new high revisit frequency satellite observations is an important opportunity for agricultural applications. The Sentinel-2 for Agriculture project S2Agri (http://www.esa-sen2agri.org/SitePages/Home.aspx) is designed to develop, demonstrate and facilitate the Sentinel-2 time series contribution to the satellite EO component of agriculture monitoring for many agricultural systems across the globe. In the framework of this project, this article studies the construction of a dynamic cropland mask. This mask consists of a binary “annual-cropland/no-annual-cropland” map produced several times during the season to serve as a mask for monitoring crop growing conditions over the growing season. The construction of the mask relies on two classical pattern recognition techniques: feature extraction and classification. One pixel- and two object-based strategies are proposed and compared. A set of 12 test sites are used to benchmark the methods and algorithms with regard to the diversity of the agro-ecological context, landscape patterns, agricultural practices and actual satellite observation conditions. The classification results yield promising accuracies of around 90% at the end of the agricultural season. Efforts will be made to transition this research into operational products once Sentinel-2 data become available.

【 授权许可】

CC BY   
© 2016 by the authors; licensee MDPI, Basel, Switzerland.