【摘要】 土壤水分对草原生态环境具有十分重要的意义，其影响着放牧季节的长短，草地生长速度和植被的养分吸收。基于合成孔径雷达(SAR)的土壤水分监测往往受到植被覆盖度和地表粗糙度的影响，机器学习方法不受物理参数的制约，具有很强的非线性拟合能力。文章分别利用三种特征选择方法，基于随机森林进行草原土壤水分反演。通过构建草原土壤水分反演应用示范系统，根据土壤水分反演结果进行土壤水分的旱情监测与报告。更多还原

【Abstract】 Information about soil moisture content is important for ecological environment in prairies because it impacts the length of the grazing season,the grass growth rate and nutrient uptake.The conventional synthetic aperture radar (SAR) based soil moisture monitoring is often influenced by vegetation cover and surface roughness.The machine learning methods are not constrained by physical parameters and have high nonlinear fitting capabilities.Three feature selection methods are used to retrieve grassland soil moisture using random forest.The application demonstration system on retrieval of grassland soil moisture is constructed.The drought monitoring and reporting of soil moisture are carried out according to the soil moisture inversion results.更多还原