Estimation of Soil Organic Carbon Content in Gannan Grassland Based on SSA Optimized CatBoost - Ma, Zi-Ming; Zhang, Mei-Ling; Liu, Xing-Yu (Huanjing kexue, 2025)
Estimating the content of soil organic carbon （SOC） in Gannan Tibetan Autonomous Prefecture, studying its spatial distribution characteristics, and clarifying the main influencing factors of SOC are of great significance for improving grassland quality, optimizing management, regulating climate, and maintaining ecosystem functions. Taking the grassland in Gannan Tibetan Autonomous Prefecture of Gansu Province as the research object, multi-feature factor data were constructed by integrating data such as soil properties, meteorological factors, elevation, and vegetation index, and 24 significant feature factors were screened out using Pearson correlation analysis. Then, the normalized contribution degree was obtained according to the SHAP value. The machine learning model was used to divide the 8∶2 training set and test set, and the results were obtained by ten-fold cross-validation. According to the evaluation models such as MAE, RMSE, and , the sparrow search algorithm （SSA） and whale optimization algorithm （