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

•Modified change detection models for estimating multi-layer soil moisture were proposed.•Soil parameter constrained solutions and time-delay effects were introduced to improve the estimation accuracy.•Multi-outputs and multi-inputs machine learning regression algorithms demonstrated higher upper limits of estimation and transfer accuracy.•Estimation accuracy for deep soil moisture surpassed that of surface soil moisture.•The proposed framework had been validated across different climate zones in China. 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