Polarimetric information content of Sentinel-1 for land cover mapping: An experimental case study using quad-pol data synthesized from complementary repeat-pass acquisitions

Abstract

This work presents a technique to merge two Sentinel-1 image products of complementary polarimetric information (HH/HV and VH/VV) to derive pseudo-polarimetric features, such as polarimetric covariance, but also model-based and eigenvalue-based decompositions and an unsupervised Wishart classification of scattering types. The images were acquired within a 6-day period over Southern Germany and have been processed to mimic an actual quad-pol product. This was analyzed statistically, visually and within several classification processes to get an understanding of how well such a dataset depicts scattering mechanisms and other polarimetric features as inputs for land use and land cover mapping. A systematic comparison with the original dual-polarization product showed an increase in information content and largely feasible polarimetric features. Yet, especially the average Alpha angle was found to be biased and too high for some of the compared surfaces. Despite these inaccuracies, the polarimetric features turned out to improve potential land cover mapping as compared with backscatter intensities and dual-polarization features of the input products alone. Among the most significant variables related to land use and cover reported by an independent dataset, Entropy, the co-polarization ratio and the C22 element of the covariance matrix generated the strongest impact on the class separability, although misclassifications between physically related classes remain. Yet, the findings are encouraging concerning further investigation of the polarimetric potential to combine repeat-pass acquisitions of Sentinel-1 for a better description of more specific types of land cover.