Mapping soil organic carbon in fragmented agricultural landscapes: The efficacy and interpretability of multi-category remote sensing variables

Accurately mapping the spatial distribution of soil organic carbon (SOC) is crucial for guiding agricultural management and improving soil carbon sequestration, especially in fragmented agricultural landscapes. Although remote sensing provides spatially continuous environmental information about heterogeneous agricultural landscapes, its relationship with SOC remains unclear. In this study, we hypothesized that multi-category remote sensing-derived variables can enhance our understanding of SOC variation within complex landscape conditions. Taking the Qilu Lake watershed in Yunnan, China, as a case study area and based on 216 topsoil samples collected from irrigation areas, we applied the extreme gradient boosting (XGBoost) model to investigate the contributions of vegetation indices (VI), brightness indices (BI), moisture indices (MI), and spectral transformations (ST, principal component analysis and tasseled cap transformation) to SOC mapping. The results showed that ST contributed the most to SOC prediction accuracy, followed by MI, VI, and BI, with improvements in R² of 29.27, 26.83, 19.51, and 14.43%, respectively. The dominance of ST can be attributed to the fact that it contains richer remote sensing spectral information. The optimal SOC prediction model integrated soil properties, topographic factors, location factors, and landscape metrics, as well as remote sensing-derived variables, and achieved RMSE and MAE of 15.05 and 11.42 g kg^–1, and R² and CCC of 0.57 and 0.72, respectively. The Shapley additive explanations deciphered the nonlinear and threshold effects that exist between soil moisture, vegetation status, soil brightness and SOC. Compared with traditional linear regression models, interpretable machine learning has advantages in prediction accuracy and revealing the influences of variables that reflect landscape characteristics on SOC. Overall, this study not only reveals how remote sensing-derived variables contribute to our understanding of SOC distribution in fragmented agricultural landscapes but also clarifies their efficacy. Through interpretable machine learning, we can further elucidate the causes of SOC variation, which is important for sustainable soil management and agricultural practices.