水资源保护2026，Vol.42Issue(2)：100-106,7.DOI:10.3880/j.issn.1004-6933.2026.02.011
基于CNN的流域多源土壤湿度数据降尺度研究
Study on downscaling of multi-source soil moisture data in basins based on CNN
摘要
Abstract
To obtain high-precision soil moisture data in basins,three types of soil moisture data from SMAP,AMSR2,and CLDAS were integrated.Considering the effects of precipitation,normalized difference vegetation index,slope,and other factors on soil moisture,a convolutional neural network(CNN)downscaling model for soil moisture that can consider the spatial neighborhood relationship of auxiliary factors was established based on CNN,and the soil moisture data with a spatial resolution of 1 km were obtained.The model was applied to the basin from Pushi County to Wuqiangxi Dam site in Hunan Province,and the results show that the spatial distribution characteristics of soil moisture data before and after downscaling are consistent,and the soil moisture data after downscaling can correctly reflect the response of soil moisture to flood events and add more spatial distribution details.Compared with the measured soil moisture data at soil moisture stations,the mean bias,mean absolute error,and root mean square error of the downscaled soil moisture are-0.061,0.086,and 0.099 cm3/cm3.The CNN downscaling model also shows better stability in comparison with the results of the random forest downscaling model.关键词
土壤湿度/降尺度/卷积神经网络/深度学习/浦市—五强溪坝址区间流域Key words
soil moisture/downscaling/convolutional neural network/deep learning/the basin from Pushi County to Wuqiangxi Dam site引用本文复制引用
李巧玲,李晓梅,仇娟娟,刘兴文,谭忠成..基于CNN的流域多源土壤湿度数据降尺度研究[J].水资源保护,2026,42(2):100-106,7.基金项目
山西省水利技术推广与应用项目(2025ZF15) （2025ZF15）
