Estimation and spatial distribution of soil organic carbon content in farmland using unmanned aerial vehicle hyperspectral remote sensing technology

Accurately estimating soil organic carbon (SOC) is essential for evaluating soil quality and supporting precision farmland management. Traditional laboratory methods provide reliable results but are labor-intensive and spatially limited. UAV hyperspectral remote sensing offers high-resolution data that can improve SOC estimation, yet its performance under heterogeneous farmland conditions requires further verification. In this study, three typical farmland areas in the Huangshui River Basin, Qinghai Province, were selected to investigate the potential of UAV hyperspectral imagery for SOC estimation and mapping. A total of 296 soil samples were collected, and laboratory spectra were resampled to match UAV spectral characteristics. Seven spectral transformations were tested, and the combination of multiplicative scatter correction (MSC) and first-order derivative achieved the highest correlation with SOC (r = 0.91). Five regression models were evaluated, and the random forest model performed best (R² = 0.90, RPD = 3.11). The optimal model was applied to UAV images to generate fine-scale SOC distribution maps, which showed clear spatial heterogeneity across the three farmland areas and were consistent with measured SOC values. These results demonstrate the feasibility of using UAV hyperspectral imagery for high-resolution SOC estimation and provide methodological reference for precision agriculture.