The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 83–89, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-83-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 83–89, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-83-2020

  21 Aug 2020

21 Aug 2020

EXPLORING SENTINEL-2 FOR LAND COVER AND CROP MAPPING IN PORTUGAL

I. Hernandez2, P. Benevides1, H. Costa1,2, and M. Caetano1,2 I. Hernandez et al.
  • 1Direção-Geral do Território, 1099-052 Lisbon, Portugal
  • 2NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisbon, Portugal

Keywords: Land Cover Mapping, Crop Mapping, Sentinel-2, Multi-temporal datasets, Supervised Classification, Random Forest, Portugal

Abstract. Land cover information is fundamental for a wide range of fields, such as research and policymaking. Remote sensing has historically been a source of data on land cover and recognized as the only practical systematic and wall-to-wall source for crop mapping. The European Copernicus programme and its free data policy for Sentinel-2 made accessible large volumes of imagery for frequent mapping and updating, generating new challenges. One such challenge is timely mapping through supervised image classification. The need for a prompt classification workflow requires training to become automatic, which typically relies on samples collected manually via fieldwork or image interpretation. Another challenge is to map land cover classes that traditionally have been troublesome to identify when satellite observations were sparse. For instance, crops have a spectral response that changes substantially throughout the year or during narrow time windows, which cannot be observed with few image acquisitions. This paper presents research ongoing in Portugal to develop a methodology for automatic image classification using training samples labelling with no human intervention. Rather, auxiliary datasets are used to randomly extract labelled points from large training samples to produce a land cover and crop map in raster format at 10 m spatial resolution using 2018 Sentinel-2 images. The proposed methodology was tested with the Random Forest classifier achieving an overall accuracy of 76%. These results are promising and support the idea of refining the methodology to move towards an annual land cover map production at the national scale.