Application potential and spatiotemporal uncertainty assessment of multi-layer soil moisture estimation in different climate zones using multi-source data

Abstract

Accurately estimating multi-layer soil moisture (SM) through remote sensing methods presents inherent challenges and limitations. Multi-layer SM provides valuable insights into the intricate interactions within the “soil-vegetation-atmosphere” system. This study explored the temporal dynamics of multi-layer SM in the Shandian River Basin, China, from 2019 to 2020. Through sensitivity analysis, we demonstrated the feasibility of using multi-source data for estimating multi-layer SM, including dual polarization radar data, optical vegetation descriptors, terrain factors, soil parameters, and meteorological indices. Initially, surface soil moisture (SSM) at depths of 3 cm and 5 cm was estimated using the modified change detection (MCD) model, which reduces the impact of vegetation. Incorporating constraints from soil parameters during the solving process improved the estimation accuracy of multi-layer SM. Subsequently, the water balance model, involving precipitation and evaporation, was applied to further correct the estimation results of SSM. Based on this, the infiltration process was considered to estimate deeper SM, including near-surface soil moisture (NSSM) at depths of 10 cm and 20 cm, and root zone soil moisture (RZSM) at depths of 40–50 cm. Under this framework, the estimation errors for multi-layer SM were satisfactory (RMSE = 0.041–0.045 cm³/cm³). Finally, we explored the upper limits of multi-layer SM estimation using multi-input and multi-output machine learning regression (MLR) algorithms. With the incorporation of multi-source data, advanced MLR algorithms achieved higher estimation accuracy (RMSE = 0.015–0.022 cm³/cm³) and showed potential for cross-temporal transfer (RMSE = 0.030–0.037 cm³/cm³). Moreover, spatiotemporal robustness revalidation of multi-layer SM was conducted across 17 observation networks distributed cross different climatic zones in China. The results shown that the MCD model achieved satisfactory results in estimating multi-layer SM (RMSE = 0.053–0.064 cm³/cm³), whereas the regression models displayed higher accuracy (RMSE = 0.039–0.051 cm³/cm³). Both the MCD and MLR models yielded similar conclusions, indicating that the estimation accuracy of NSSM and RZSM surpassed that of SSM, primarily due to the relatively lower variability of the former and their strong coupling with vegetation productivity. This study also specifically discussed the influence of factors such as radar incidence angles, soil texture types, and vegetation types on the estimation accuracy of multi-layer SM. This study introduced a novel concept and framework for regional multi-layer and profile SM estimation and real-time prediction through multi-source data, exhibiting high potential for practical applications.