Efficient soil moisture estimation on the Qinghai- Xizang Plateau via machine learning and optimized feature selection

doi:10.1007/s40333-025-0084-9

Journal of Arid Land

2025, Vol. 17

Issue (8): 1147-1167 DOI: 10.1007/s40333-025-0084-9

Research article

Efficient soil moisture estimation on the Qinghai- Xizang Plateau via machine learning and optimized feature selection

JIA Shichao¹^,², SUN Wen³^,^*(

), WEI Sihao⁴, SUN Rui¹

¹School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China
²Institute of Land Surveying and Spatial Geographic Information, Anhui University of Science and Technology, Huainan 232001, China
³Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
⁴School of Atmospheric Sciences, Sun Yat-sen University, and Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai 519082, China

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Abstract

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 (R²) 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 R² and a 0.7% reduction in ubRMSE. Ultimately, the optimized model achieved a R² of 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.

Key words： soil moisture machine learning feature selection radar and optical remote sensing polarization decomposition CatBoost model Qinghai-Xizang Plateau

Received: 17 December 2024 Published: 31 August 2025

Corresponding Authors: ^*SUN Wen (E-mail: wensun@itpcas.ac.cn)

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Cite this article:

JIA Shichao, SUN Wen, WEI Sihao, SUN Rui. Efficient soil moisture estimation on the Qinghai- Xizang Plateau via machine learning and optimized feature selection. Journal of Arid Land, 2025, 17(8): 1147-1167.

URL:

http://jal.xjegi.com/10.1007/s40333-025-0084-9 OR http://jal.xjegi.com/Y2025/V17/I8/1147

Fig. 1 Overview of the location of the study area on the Qinghai-Xizang Plateau (a) and overall distribution of in-situ measurement sites (b) as well as the local distribution of in-situ measurement sites (c and d). The base imagery was provided by LocaSpace Viewer (https://www.tuxingis.com). TDR, time-domain reflectometry; CR, cutting ring.

Fig. 2 Comparison of TDR-based soil moisture (SM_TDR) and CR-based soil moisture (SM_CR). R², coefficient of determination; ubRMSE, unbiased root mean square error.

Fig. 3 Surface roughness investigations in the Beilu River area (a) and Tuotuo River area (b)

Table 1 Specific parameter information of satellite data used in this study

Fig. 4 Flowchart of the CatBoost model driven by source remote sensing datasets. GRD, ground range detected; SLC, single-look complex; MSIL2A, MultiSpectral Instrument Level-2A; NDVI, normalized difference vegetation index; DEM, digital elevation model; SHAP, SHapley Additive exPlanations.

Fig. 5 Prediction scores given by eight different machine learning models over five independent runs. PR, polynomial regression; RR, ridge regression; LR, lasso regression; DTR, decision tree regression; SVR, support vector machine; KNN, K-nearest neighbors; RF, random forest.

Fig. 6 Importance of 32 feature variables for inverting soil moisture based on the CatBoost model. TWI, topographic wetness index. Interpretations for other variables can be found in Table S1.

Table 2 Feature variable filtering and verification information

Fig. 7 Variable importance and SHAP values after five rounds of filtering (a) and verification of final model using the optimal combination (b)

Fig. 8 Inversion distribution map of surface soil moisture in the study area

Fig. 9 Correlations of soil particle size with soil moisture (a) and NDVI (b) and particle size distribution of samples from the Beilu River and Tuotuo River areas (c). BLH-14 and TTH-2 represent the 14^th measured sample and the 2^nd measured sample in the Beilu River area and Tuotuo River area, respectively. The light blue area (symmetric on both sides of the fitting line) represents the 95% confidence interval.

Fig. 10 Soil moisture statistics of different vegetation types on the Qinghai-Xizang Plateau. The purple box plot represents soil moisture, and the green box plot represents NDVI. The dot represents the mean and the solid line within the box represent the median. The lower and upper boxes represent the first quartile (Q1, 25^th percentile) and third quartile (Q3, 75^th percentile), respectively, indicating the interquartile range (IQR) that encompasses the middle 50.0% of the data. The whiskers extend from the edges of the box and indicate the overall spread of the data.

Feature variable	Interpretation	Physical significance
H	Scattering entropy, indicating the randomness of the scattering process and ranging from 0 to 1	Measuring target heterogeneity (0=homogeneous, 1=random)
α	Average angle of the polarization scattering mechanism, indicating the dominant scattering type and ranging from 0° to 90°	Indicating dominant scattering mechanism type (surface/volume)
Anisotropy	Representing the relative importance of secondary scattering mechanism and ranging from 0 to 1	Measuring relative importance of secondary scattering components
c_1mH1mA	Combination of entropy (H) and anisotropy (A), defined as (1-H)×(1-A)	Enhancing separation of surface types
c_1mHA	Defined as 1-(H×A)	Improving vegetation masking capability
c_H1mA	Defined as H×(1-A)	Assisting in mixed scattering characterization
c_HA	Defined as H×A	Supporting land cover classification
l1	Largest eigenvalue (λ₁) from eigenvalue decomposition	Representing dominant scattering power
l2	Second-largest eigenvalue (λ₂)	Representing secondary scattering power
delta	Scattering mechanism type	Distinguishing odd/even bounce scattering
delta1	Scattering symmetry	Identifying directional scattering patterns
delta2	Scattering randomness	Evaluating surface/volume scattering mix
lambda	Eigenvalue set	Characterizing scattering energy distribution
p1	First eigenvector parameter	Containing dominant scattering phase information
p2	Second eigenvector parameter	Containing secondary scattering phase information
span	Total scattered power	Measuring overall backscatter intensity
C₁₁	Representing VV polarization channel power or intensity	Directly reflecting the surface dielectric constant (higher C₁₁ in moist soil)
C₂₂	Representing VH polarization channel power or intensity	Characterization of depolarization effect (higher C₂₂ in vegetation or rough terrain)
zdr_11_22	Polarization ratio (ZDR), C₁₁/C₂₂, representing the ratio of VV to VH polarization intensities	Surface roughness indicator
zdr_22_11	Inverse polarization ratio, C₂₂/C₁₁, reciprocal of zdr_11_22	Vegetation structure indicator
Stokes_cont	Stokes continuity parameter	Polarimetric continuity indicator
Stokes_DoLP	Degree of linear polarization	Measuring polarization purity
Stokes_g0	Stokes vector element 0	Fundamental polarization intensity
Stokes_g1	Stokes vector element 1	Linear polarization component
Stokes_phi	Stokes phase angle	Changes in surface scattering mechanisms
Stokes_tau	Stokes ellipticity angle	Polarization ellipse parameter

Table S1 Polarimetric parameter information from Sentinel-1A dual-polarization data


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