干旱区地理2026，Vol.49Issue(6)：1192-1202,11.DOI:10.12118/j.issn.1000-6060.2025.314
基于多源遥感特征参数与ACNN的土壤湿度反演研究
Soil moisture inversion based on multi-source remote sensing feature parameter and ACNN
摘要
Abstract
Soil moisture is a key parameter in agriculture,meteorology,and hydrology.Enhancing the accuracy of its inversion can provide essential support for precise irrigation in arid regions.This study presents a soil mois-ture inversion method that combines multi-source feature parameter fusion with an adaptive convolutional neural network(ACNN).We extracted 11 basic feature parameters from Sentinel-1/2 data and performed 29 feature fu-sion operations,selecting 6 parameters using the Pearson correlation coefficient and Interquartile Range methods.The ACNN model's accuracy was compared with that of other models and then used to invert the spatial distribu-tion of soil moisture in Yongning County,Yinchuan City.The findings reveal that(1)The necessity of the 6 multi-source fusion feature parameters is confirmed through RF modeling and ablation experiments,and the importance of single-source feature parameters is also assessed.Notably,the dual polarization ratio logarithmic parameter sig-nificantly enhances the model inversion,highlighting the central role of the microwave polarization ratio parame-ter.(2)An accuracy comparison of soil moisture inversion was conducted among 4 models,namely,BP,GABP,RF,and ACNN,using different training and test sets.The ACNN model achieved superior inversion accuracy(R2=0.947,RMSE=1.263,MAE=0.840)compared to the other models.(3)Evaluating the inversion accuracy of 40 feature parameters,11 basic parameters,and 6 optimized parameters within the ACNN framework revealed that the six optimized parameters had the highest R2 and the lowest RMSE and MAE.The model demonstrated that fewer feature parameters led to better accuracy and shorter computation times,outperforming both the basic and all-parameter approaches.(4)The measured and inverted soil moisture values at sampling sites showed minimal differences,and the inverted values at larger spatial scales aligned well with actual measurements.This consisten-cy supports effective monitoring of crop growth conditions and irrigation scheduling.In addition,the soil mois-ture inversion results align with the conclusion of moisture briefing.Thus,the spatial distribution and Gaussian characteristics of soil moisture derived from multi-source remote sensing parameters and ACNN inversion can in-form regional crop irrigation decisions.This study validates the feasibility of integrating multi-source remote sensing feature parameters with deep learning,offering a technical solution for managing agricultural water re-sources in arid areas.关键词
多源遥感/特征参数/土壤湿度/反演/自适应卷积神经网络Key words
multi-source remote sensing/feature parameters/soil moisture/inversion/ACNN引用本文复制引用
李占虎,郭中华,马嘉强,李蕾蕾..基于多源遥感特征参数与ACNN的土壤湿度反演研究[J].干旱区地理,2026,49(6):1192-1202,11.基金项目
国家自然科学基金项目(62365016)资助 （62365016）
