云南水力发电2026，Vol.42Issue(7)：24-27,4.DOI:10.3969/j.issn.1006-3951.2026.07.006
基于卷积神经网络的土壤水分反演研究
Research on Soil Moisture Retrieval Based on Convolutional Neural Network
摘要
Abstract
This study utilizes Sentinel-1 SAR and Sentinel-2 optical imagery data.After correcting for vegetation canopy interference using the Water Cloud Model,a 4D spatiotemporal feature vector was constructed and input into a Convolutional Neural Network(CNN)model to retrieve surface soil moisture(0-20 cm depth).The results indicate:1)The model achieved high retrieval accuracy on the test set,with PCC=0.75,NRMSE=0.24,and SMAPE=11.40％;2)Spatially,SM levels in the northwestern region were generally higher than those in the southeastern region.Temporally,overall SM exhibited a declining trend from October 2024 to January 2025,although localized increases occurred at specific monitoring sites due to an irrigation event in November 2024 and snowmelt in February 2025.The proposed CNN model effectively enhances the accuracy of soil moisture retrieval over the Yunnan-Guizhou Plateau region and holds significant potential for application in refined agricultural drought early warning and water resource management.关键词
土壤水分反演/卷积神经网络/时空特征/水云模型Key words
soil moisture retrieval/convolutional neural network/spatiotemporal feature/water cloud model分类
天文与地球科学引用本文复制引用
黄浩然,易珍言,刘蛟,李雪..基于卷积神经网络的土壤水分反演研究[J].云南水力发电,2026,42(7):24-27,4.基金项目
西南科技大学素质类教改(青年发展研究)专项(22SZJG15) （青年发展研究）
西南科技大学人才引进项目(21zx7154) （21zx7154）
