[Hyperspectral Inversion of Soil Organic Matter Content in Mountainous Farmland Based on ResNet-MHAM Model]

In response to the lack of accuracy and generalization challenges in predicting soil organic matter （SOM） content in the karst mountainous agricultural soils of the Guizhou Province using hyperspectral remote sensing, a one-dimensional hyperspectral reflectance data model, termed ResNet-MHAM, was proposed. First, soil samples from 188 locations across 13 counties and districts in Guizhou were collected, and their spectral information was analyzed. Second, the ResNet structure was optimized in combination with MHAM across different layers （34, 50, 101, and 152 layers） to construct the model presented in this study. Finally, model validation was conducted using 30% of the dataset and 10-fold cross-validation. Experimental results demonstrated that the optimized version of the model combining 50-layer ResNet structure with MHAM achieved a coefficient of determination （R²） of 0.917 2 and a root mean square error （RMSE） of 7.454 9 g·kg^-1, showcasing superior accuracy and generalization capabilities compared to commonly used models such as BPNN, SVM, PLSR, GPR, and RF. These findings provide a novel and effective approach for hyperspectral prediction of SOM content in the mountainous regions of Guizhou.