Annual Crop Classification Experiments in Portugal Using Sentinel-2

Benevides, P., Costa, H., Moreira, F. D., Moraes, D., & Caetano, M. (2021). Annual Crop Classification Experiments in Portugal Using Sentinel-2. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium: Proceedings (pp. 5838-5841). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9555009 --------------------------- This work has been supported by projects IPSTERS (DSAIPA/AI/0100/2018), foRESTER (PCIF/SSI/0102/2017), and SCAPEFIRE (PCIF/MOS/0046/2017), and by Centro de Investigação em Gestão de Informação (MagIC), all funded by the Portuguese Foundation for Science and Technology (FCT). Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The satellite image pre-processing uses algorithms developed by Theia's Scientific Expertise Centers. SIP validation data was kindly provided by Instituto de Financiamento da Agricultura e Pescas.

This paper presents an experimental crop classification of the 10 most abundant annual crop types in Portugal, using a study area located in Alentejo region. This region has great diversity of land uses as well as multiple crop types. Sentinel-2 2018 intra-annual time-series imagery is considered in the experiment. The Portuguese Land Parcel Identification System (LPIS) is used to extract automatic training samples. LPIS information is automatically processed with the help of auxiliary datasets to filter out crop areas more likely to have been mislabeled. Classification is obtained using random forest. Validation is performed using an independent dataset also based on LPIS. A global accuracy of 76% is obtained. The novelty of the methodology here presented shows that LPIS can be used together with auxiliary data for crop type mapping, helping to characterize the agriculture land diversity in Portugal.