A Landsat-Based Framework for Long-Term Mapping of Topsoil Sand Content in Croplands

Topsoil sand content (TSC) is a critical indicator of soil degradation in black soil regions, yet its long-term dynamics remain poorly quantified. To address this, we developed an automated Landsat-based framework on Google Earth Engine (GEE) for mapping cropland TSC across the Northeast China Black Soil Region (NCBSR) from 1984 to 2023. The methodology integrates a hierarchical bare-soil extraction strategy using the Normalized Difference Bare Soil Index (NDBSI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Tillage Index (NDTI) with a Random Forest (RF) model optimized by three-band spectral indices and a “prediction-first” compositing workflow. Results demonstrate that the bare-soil extraction achieved an overall accuracy of 96%, while the TSC retrieval model maintained robust performance with a coefficient of determination (R²) of 0.80 and a root mean square error (RMSE) of 9.68%, together with satisfactory temporal transferability. Long-term mapping revealed a significant biphasic evolutionary trajectory: 23.4% of croplands experienced soil coarsening predominantly before 2000, followed by a partial reversal and stabilization in later decades. This framework provides a high-resolution, multi-decadal baseline for monitoring soil physical degradation and supports sustainable agricultural management in global black soil regions.