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Remote Sens. 2015, 7(10), 13208-13232; doi:10.3390/rs71013208

An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series

1
Earth and Life Institute, Université Catholique de Louvain, Croix du Sud 2, 1348 Louvain-la-Neuve, Belgium
2
Centre d'Etudes Spatiales de la BIOSphère, Unité Mixte CNES-CNRS-UPS-IRD, Toulouse 31401, France
3
European Space Agency, European Space Research Institute, Via Galileo Galilei, Casella Postale 64, 00044 Roma, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad S. Thenkabail
Received: 1 June 2015 / Revised: 15 September 2015 / Accepted: 17 September 2015 / Published: 6 October 2015
View Full-Text   |   Download PDF [5904 KB, uploaded 6 October 2015]   |  

Abstract

Cropland mapping relies heavily on field data for algorithm calibration, making it, in many cases, applicable only at the field campaign scale. While the recently launched Sentinel-2 satellite will be able to deliver time series over large regions, it will not really be compatible with the current mapping approach or the available in situ data. This research introduces a generic methodology for mapping annual cropland along the season at high spatial resolution with the use of globally available baseline land cover and no need for field data. The methodology is based on cropland-specific temporal features, which are able to cope with the diversity of agricultural systems, prior information from which mislabeled pixels have been removed and a cost-effective classifier. Thanks to the JECAM network, eight sites across the world were selected for global cropland mapping benchmarking. Accurate cropland maps were produced at the end of the season, showing an overall accuracy of more than 85%. Early cropland maps were also obtained at three-month intervals after the beginning of the growing season, and these showed reasonable accuracy at the three-month stage (>70% overall accuracy) and progressive improvement along the season. The trimming-based method was found to be key for using spatially coarse baseline land cover information and, thus, avoiding costly field campaigns for prior information retrieval. The accuracy and timeliness of the proposed approach shows that it has substantial potential for operational agriculture monitoring programs. View Full-Text
Keywords: agriculture monitoring; cropland; timeliness; high resolution time series; Sen2Agri; Sentinel-2; SPOT 4 (Take 5); JECAM agriculture monitoring; cropland; timeliness; high resolution time series; Sen2Agri; Sentinel-2; SPOT 4 (Take 5); JECAM
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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

Matton, N.; Canto, G.S.; Waldner, F.; Valero, S.; Morin, D.; Inglada, J.; Arias, M.; Bontemps, S.; Koetz, B.; Defourny, P. An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series. Remote Sens. 2015, 7, 13208-13232.

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