Integrating transformer-based learning and Sentinel-2 bare soil composites for soil organic carbon mapping in the black soil region of Northeast China.

ABSTRACT: Accurate assessment of soil organic carbon (SOC) is essential for sustainable cropland management and carbon sequestration monitoring. However, high-resolution SOC mapping remains challenging due to two persistent limitations: (1) the difficulty of extracting true bare-soil reflectance-especially when single-date imagery is used and spectral signals remain influenced by vegetation, residue, and soil moisture; and (2) reliance on models that require large training datasets and may underperform in typical small-sample soil survey settings. To address these challenges, we developed an approach that integrates multi-temporal Sentinel-2 bare-soil composites with a transformer-based foundation model-Tabular Prior-data Fitted Network (TabPFN)-for SOC prediction in the black soil region of Northea

SUBMITTER: Chen N 

PROVIDER: S-EPMC12852939 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature