A new framework for retrieving bare soil information using multi-temporal Sentinel-2 images across China

Detailed soil spatial information is a worldwide need for monitoring soil quality, especially in agricultural region. Remote sensing technology has evolved as a powerful tool for characterizing spectral reflectance of bare soil, which is the perquisite for retrieving soil information. However, existing methodologies were mostly designed to extract bare soil information from single satellite image, which is prone to cloud contamination and phenological variation. Although some composite algorithms based upon multitemporal images were proposed for soil mapping, they were all designed for coarse-resolution satellite dataset; besides, their generalization ability over a large scale (e.g., national) remains poorly explored. To fill the knowledge gap, we proposed a new framework, namely Two-Dimensional Bare Soil Separation (TDBSS), for extracting continuous bare soil information at 10-m spatial resolution based on multi-temporal Sentinel-2 images for cropland across China. The TDBSS used Soil Adjusted Vegetation Index and Green-Red Vegetation Index as two-dimensional indicators. The optimal thresholds for these two indicators were further obtained across two dimensions based upon ecoregion-specific samples. These thresholds were further applied for nine primary agricultural zones in China and subsequently adapted for the entire country. We also compared the framework with three widely used bare soil detecting algorithms (i.e., Geospatial Soil Sensing System (GEOS3), soil composite processor (SCMaP), and Barest Pixel Composite (BPC)) using the spatial accuracy. The TDBSS performed the best with an overall accuracy (OA = 78.28%), while SCMaP showed the lowest OA of 29.25%. The results showed the TDBSS was an effective method for a large-area mapping of bare soil. The resultant bare soil composite map holds great significance for further retrieving soil properties for Chinese cropland. TDBSS is computationally efficient and readily applied for a broad spatial scale, which is practically crucial to the food security, land management, and precision agriculture policymaking.