High-resolution soil erosion mapping in croplands via Sentinel-2 bare soil imaging and a two-step classification approach

作者：Lulu Qi, Yue Zhou, Kristof Van Oost, Jiamin Ma, Bas van Wesemael, Pu Shi · 发表于：Geoderma · 年份：2024 · DOI：10.1016/j.geoderma.2024.116905 · 被引用次数：17 · 研究领域：Soil erosion and sediment transport、Remote Sensing and LiDAR Applications、Soil Geostatistics and Mapping

Erosion-induced lateral soil redistribution leads to spatially heterogenous soil composition, which can be captured through the distinctive spectral reflectance of soils under varying levels of erosion influence. This points to the potential of using remotely sensed soil spectra to detect severe erosion and deposition hotspots in exposed croplands and, importantly, further differentiate the intra-class spectral variability of moderate erosion that often occupies the largest proportion. Here, we aim to develop a two-step erosion classification and mapping approach based on multitemporal compositing of Sentinel-2 bare soil images of a typical agricultural region (11,500 km2) of northeast China. A random forest classifier was firstly trained against the ground-truth data derived from very high resolution (VHR) imagery in Google Earth, with an overall accuracy of 91 % that allowed for clear delineation of severe erosion and deposition areas based on their distinct topographic and spectral features particularly in the red and red-edge bands. In the second step, the remaining area of moderate erosion (60.30 %) was further differentiated using Iterative Self-Organizing cluster unsupervised classification to yield a five-class soil erosion map at 10 m spatial resolution. The accuracy of the predicted map was successfully validated by independent Caesium-137 (137Cs) and soil organic carbon observations at catchment and regional scales, as revealed by significant inter-class difference...