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Remote Sens. 2016, 8(1), 55; doi:10.3390/rs8010055

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

1
CESBIO-CNES, CNRS (UMR 5126), IRD, Université de Toulouse, 31401 Toulouse Cedex 9, France
2
Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
3
ESRIN D/EOP-SEP, European Space Agency, Via Galileo Galilei, 00044 Frascati, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Anton Vrieling, Yoshio Inoue and Prasad S. Thenkabail
Received: 3 June 2015 / Revised: 8 December 2015 / Accepted: 16 December 2015 / Published: 11 January 2016
View Full-Text   |   Download PDF [14015 KB, uploaded 11 January 2016]   |  

Abstract

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. View Full-Text
Keywords: cropland mapping; satellite image time series; Sentinel-2; dynamic classification; Random Forests cropland mapping; satellite image time series; Sentinel-2; dynamic classification; Random Forests
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Valero, S.; Morin, D.; Inglada, J.; Sepulcre, G.; Arias, M.; Hagolle, O.; Dedieu, G.; Bontemps, S.; Defourny, P.; Koetz, B. Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions. Remote Sens. 2016, 8, 55.

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