Sentinel 1 and Sentinel 2 for cropland mapping with special emphasis on the usability of textural and vegetation indices

Abstract

Regional as well as global cropland database at a high spatial resolution like 10 m is not available. This study aims to integrate the Sentinel-1 synthetic aperture radar (SAR) data and Sentinel-2 optical data in a more robust way to map the croplands at 10 m spatial resolution. It also examines the effectiveness of different derived products from the SAR and multispectral data. The VH and VV polarizations of the SAR data were used to generate the 80 textural parameters at four different kernel sizes. The multispectral bands of the optical data were used to create nine different spectral indices. The SAR and optical data were classified separately and integrated with the other derived products, using the random forest classifier, to identify the most potential way to use the two datasets for LULC classification. The classification accuracy was the least when only the Sentinel 1 data was used, with an overall accuracy of 59.48% and a kappa value of 0.51. The best accuracy (97.94% overall accuracy and 0.98 kappa value) was achieved by combining the Sentinel 1 and 2 data and their optimal textures and spectral indices. Overall, this study suggests the suitability of the SAR data along with optical data to map the croplands with higher accuracy.