Explainable machine learning-based fractional vegetation cover inversion and performance optimization – A case study of an alpine grassland on the Qinghai-Tibet Plateau

作者：Xinhong Li, Jianjun Chen, Zizhen Chen, Yanping Lan, Ming Ling, Qinyi Huang, Hucheng Li, Xiaowen Han, Shuhua Yi · 发表于：Ecological Informatics · 年份：2024 · DOI：10.1016/j.ecoinf.2024.102768 · 被引用次数：28 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Land Use and Ecosystem Services

Fractional Vegetation Cover (FVC) serves as a crucial indicator in ecological sustainability and climate change monitoring. While machine learning is the primary method for FVC inversion, there are still certain shortcomings in feature selection, hyperparameter tuning, underlying surface heterogeneity, and explainability. Addressing these challenges, this study leveraged extensive FVC field data from the Qinghai-Tibet Plateau. Initially, a feature selection algorithm combining genetic algorithms and XGBoost was proposed. This algorithm was integrated with the Optuna tuning method, forming the GA-OP combination to optimize feature selection and hyperparameter tuning in machine learning. Furthermore, comparative analyses of various machine learning models for FVC inversion in alpine grassland were conducted, followed by an investigation into the impact of the underlying surface heterogeneity on inversion performance using the NDVI Coefficient of Variation (NDVI-CV). Lastly, the SHAP (Shapley Additive exPlanations) method was employed for both global and local interpretations of the optimal model. The results indicated that: (1) GA-OP combination exhibited favorable performance in terms of computational cost and inversion accuracy, with Optuna demonstrating significant potential in hyperparameter tuning. (2) Stacking model achieved optimal performance in FVC inversion for alpine grassland among the seven models (R2 = 0.867, RMSE = 0.12, RPD = 2.552, BIAS = −0.0005, VAR = 0.014),...