Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere.This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau,China...Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere.This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau,China,as well as in the related ecological and hydrological processes.However,the region's complex terrain and extreme climatic conditions result in low-accuracy soil moisture estimations using traditional remote sensing techniques.Thus,this study considered parameters of the backscatter coefficient of Sentinel-1A ground range detected(GRD)data,the polarization decomposition parameters of Sentinel-1A single-look complex(SLC)data,the normalized difference vegetation index(NDVI)based on Sentinel-2B data,and the topographic factors based on digital elevation model(DEM)data.By combining these parameters with a machine learning model,we established a feature selection rule.A cumulative importance threshold was derived for feature variables,and those variables that failed to meet the threshold were eliminated based on variations in the coefficient of determination(R^(2))and the unbiased root mean square error(ubRMSE).The eight most influential variables were selected and combined with the CatBoost model for soil moisture inversion,and the SHapley Additive exPlanations(SHAP)method was used to analyze the importance of these variables.The results demonstrated that the optimized model significantly improved the accuracy of soil moisture inversion.Compared to the unfiltered model,the optimal feature combination led to a 0.09 increase in R^(2)and a 0.7%reduction in ubRMSE.Ultimately,the optimized model achieved a R²of 0.87 and an ubRMSE of 5.6%.Analysis revealed that soil particle size had significant impact on soil water retention capacity.The impact of vegetation on the estimated soil moisture on the Qinghai-Xizang Plateau was considerable,demonstrating a significant positive correlation.Moreover,the microtopographical features of hummocks interfered with soil moisture estimation,indicating that such terrain effects warrant increased attention in future studies within the permafrost regions.The developed method not only enhances the accuracy of soil moisture retrieval in the complex terrain of the Qinghai-Xizang Plateau,but also exhibits high computational efficiency(with a relative time reduction of 18.5%),striking an excellent balance between accuracy and efficiency.This approach provides a robust framework for efficient soil moisture monitoring in remote areas with limited ground data,offering critical insights for ecological conservation,water resource management,and climate change adaptation on the Qinghai-Xizang Plateau.展开更多
基金supported by the Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology(13230550)the Coal Industry Engineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring,Anhui University of Science and Technology(KSXTJC202305)+1 种基金the State Key Laboratory of Geodesy and Earth's Dynamics,Innovation Academy for Precision Measurement Science and Technology(SKLGED2023-5-1)the China Postdoctoral Science Foundation(2023M733604).
文摘Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere.This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau,China,as well as in the related ecological and hydrological processes.However,the region's complex terrain and extreme climatic conditions result in low-accuracy soil moisture estimations using traditional remote sensing techniques.Thus,this study considered parameters of the backscatter coefficient of Sentinel-1A ground range detected(GRD)data,the polarization decomposition parameters of Sentinel-1A single-look complex(SLC)data,the normalized difference vegetation index(NDVI)based on Sentinel-2B data,and the topographic factors based on digital elevation model(DEM)data.By combining these parameters with a machine learning model,we established a feature selection rule.A cumulative importance threshold was derived for feature variables,and those variables that failed to meet the threshold were eliminated based on variations in the coefficient of determination(R^(2))and the unbiased root mean square error(ubRMSE).The eight most influential variables were selected and combined with the CatBoost model for soil moisture inversion,and the SHapley Additive exPlanations(SHAP)method was used to analyze the importance of these variables.The results demonstrated that the optimized model significantly improved the accuracy of soil moisture inversion.Compared to the unfiltered model,the optimal feature combination led to a 0.09 increase in R^(2)and a 0.7%reduction in ubRMSE.Ultimately,the optimized model achieved a R²of 0.87 and an ubRMSE of 5.6%.Analysis revealed that soil particle size had significant impact on soil water retention capacity.The impact of vegetation on the estimated soil moisture on the Qinghai-Xizang Plateau was considerable,demonstrating a significant positive correlation.Moreover,the microtopographical features of hummocks interfered with soil moisture estimation,indicating that such terrain effects warrant increased attention in future studies within the permafrost regions.The developed method not only enhances the accuracy of soil moisture retrieval in the complex terrain of the Qinghai-Xizang Plateau,but also exhibits high computational efficiency(with a relative time reduction of 18.5%),striking an excellent balance between accuracy and efficiency.This approach provides a robust framework for efficient soil moisture monitoring in remote areas with limited ground data,offering critical insights for ecological conservation,water resource management,and climate change adaptation on the Qinghai-Xizang Plateau.
