草业学报2025，Vol.34Issue(2)：149-162,14.DOI:10.11686/cyxb2024110
基于机器学习的高精度耕地识别模型构建
Construction of a high-precision cultivated land identification model based on machine learning-using Zhangye City,Gansu Province as an example
摘要
Abstract
Cultivated land is a vital foundation resource for agricultural production and ensuring food security.Accurate identification of cultivated land is of great significance for the conservation of cultivable land resources and the sustainable development of agricultural production.In order to construct a high-precision cultivated land identification model,this study used Sentinel-1/2 data together with the spatial cloud computing platform and built combinations of different feature types.Through feature importance analysis,cultivated land identification features were then evaluated to identify the optimal feature set.Random Forest(RF),support vector machine(SVM),and classification and regression tree(CART)models were employed to identify the cultivated land in Zhangye City,Gansu Province for the year 2021.Simultaneously,the classification accuracy of each classifier was compared and analyzed.The results show that using a combination of vegetation index features,radar features,and topographic features improved the classification accuracy to 91.32%;Features that performed well in cultivated land identification in the study area included elevation,radar polarization channel VH,and normalized difference water index(NDWI).In the cultivated land identification of Zhangye City,RF algorithm demonstrates clear advantages,with an overall accuracy of 90.04%and a Kappa coefficient of 0.79.Based on the RF model,the cultivated land area associated with Zhangye City is estimated to be 585000 ha,accounting for 15.4%of the total area.The methodology developed in this study achieves accurate identification of cultivated land in Zhangye City and offers a tool for cultivated land mapping in the region.关键词
耕地识别/机器学习/随机森林/哨兵卫星Key words
identification of cultivated land/machine learning/random forest/Sentinel引用本文复制引用
麦晶晶,冯琦胜,王瑞泾,封森耀,金哲人,张忠雪,梁天刚,金加明..基于机器学习的高精度耕地识别模型构建[J].草业学报,2025,34(2):149-162,14.基金项目
财政部和农业农村部:国家现代农业产业技术体系(CARS-34),甘肃省林业和草原局科技创新项目(kjcx2022010),2023年提前批中央财政林业改革发展资金草原科技支撑项目(甘林草发[2023]211号)和近自然恢复技术在退化草地修复中的应用与示范项目资助. （CARS-34）
