地理空间信息2026，Vol.24Issue(3)：92-98,7.DOI:10.3969/j.issn.1672-4623.2026.03.019
基于SHAP可解释特征优选的乌克兰作物制图研究
Research on Ukrainian Crop Mapping Based on SHAP Explainable Feature Selection
摘要
Abstract
The precise and efficient delineation of distribution of winter wheat,corn,and soybeans is of vital importance for agricultural resource management,crop planting planning,and agricultural policy formulation.Focused on the agricultural regions of Ukraine,based on Sentinel-2 remote sensing images,we combined the feature selection method of Spearman correlation coefficient with SHAP,to optimize the mapping process of major crops such as winter wheat,corn,and soybeans.We explored the effectiveness of seven combination strategies with three machine learning models in distinguishing major crop types.The results showed that①the feature selection method combining Spearman correlation coefficient with SHAP demonstrated the best performance.②Based on the optimal features derived from Spearman correlation coefficient and SHAP,the LightGBM algorithm achieved the highest classification accuracy with overall accuracy and Kappa coefficient of 95.42%and 90.83%for winter wheat mapping,and overall accuracy and Kappa coefficient of 96.19%and 94.28%for corn,soybeans mapping,respectively.The classification results demonstrated high consistency with statistical yearbook data,achieving area extraction accuracy of 97.31%for winter wheat,97.55%for soybean,and 99.61%for corn.关键词
Sentinel-2/机器学习/SHAP/特征优选/作物制图Key words
Sentinel-2/machine learning/SHAP/feature selection/crop mapping分类
天文与地球科学引用本文复制引用
刘腾,庞新华,朱秀芳,曹建荣,姬忠林..基于SHAP可解释特征优选的乌克兰作物制图研究[J].地理空间信息,2026,24(3):92-98,7.基金项目
国家重点研发计划资助项目(2023YFB3906201). （2023YFB3906201）
