Spectral Unmixing for Bare Soil Detection: A Step Toward Soil Organic Carbon Estimation

Abstract

This study presents a benchmark for the detection of bare soil from EnMAP hyperspectral imagery, addressing the challenge of mixed pixels, a known limitation in satellitebased soil property estimation. A reference bare soil mask was constructed using a histogram separation method applied to spectral indices of a multitemporal Sentinel-2 composite. Using this higher resolution reference, we evaluate two classification approaches on EnMAP data: spectral index thresholding and linear spectral unmixing. The latter achieves higher classification performance, indicating improved reliability under heterogeneous surface conditions. The benchmark dataset created for this study is publicly available.