Mapping annual crops in Portugal with Sentinel-2 data

Benevides, P., Costa, H., Moreira, F. D., & Caetano, M. (2022). Mapping annual crops in Portugal with Sentinel-2 data. In C. M. U. Neale, & A. Maltese (Eds.), Proceedings of SPIE.Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV (Vol. 12262). SPIE. Society of Photo-Optical Instrumentation Engineers. https://doi.org/10.1117/12.2636125---Funding Information: The work has been supported by projects foRESTER (PCIF/SSI/0102/2017), and SCAPE FIRE (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. A special thanks to IFAP for ceding LPIS dataset of controlled parcels.

This paper presents an annual crop classification exercise considering the entire area of continental Portugal for the 2020 agricultural year. The territory was divided into landscape units, i.e. areas of similar landscape characteristics for independent training and classification. Data from the Portuguese Land Parcel Identification System (LPIS) was used for training. Thirty-one annual crops were identified for classification. Supervised classification was undertaken using Random Forest. A time-series of Sentinel-2 images was gathered and prepared. Automatic processes were applied to auxiliary datasets to improve the training data quality and lower class mislabeling. Automatic random extraction was employed to derive a large amount of sampling units for each annual crop class in each landscape unit. An LPIS dataset of controlled parcels was used for results validation. An overall accuracy of 85% is obtained for the map at national level indicating that the methodology is useful to identify and characterize most of annual crop types in Portugal. Class aggregation of the annual crop types by two types of growing season, autumn/winter and spring/summer, resulted in large improvements in the accuracy of almost all annual crops, and an overall accuracy improvement of 2%. This experiment shows that LPIS dataset can be used for training a supervised classifier based on machine learning with high-resolution remote sensing optical data, to produce a reliable crop map at national level.