Estimating root zone soil moisture in farmland by integrating multi-source remote sensing data based on the water balance equation

Existing remote sensing approaches for estimating root zone soil moisture are limited by their dependence on initial conditions, sensitivity to model parameters, and high computational costs. This study proposes a lightweight model for predicting root zone soil moisture at the irrigation district scale. The model is developed based on the soil water balance equation and incorporates multi-source remote sensing data. A random forest algorithm is employed as the core predictive framework. The model is validated in the Jiefangzha Irrigation District. Results show: (1) The model achieves satisfactory accuracy, with site-level R values of 0.43–0.72 and RMSE of 0.007–0.01; for scattered locations, R values range from 0.53 to 0.66 and RMSE from 0.005 to 0.01; (2) Downscaling methods effectively resolve spatial scale mismatches, allowing substitution of water balance equation features and high-resolution simulations. Downscaling errors range from 12.56 %–16.60 % for RH, 3.18–3.61 mm for PET, 0.03–0.05 for kNDVI, 1.76–4.74 °C for LST, and 0.08–0.11 m³ /m³ for SSM; (3) Annual average AWF in 2018 and 2019 remains stable at ∼0.12, with daily variations mainly from late July to early September; (4) Initial soil moisture has minor impact on long-term simulations, with convergence after ∼40 days; (5) The relative importance of influencing factors is: AWFt-1, SMAPt-1, RH, kNDVI, SMAPt, precipitation, and PET. The proposed model reduces sensitivity and computational burden, enabling accurate root zone soil moisture prediction at the irrigation district scale. • Multi-source remotely sensed data were used as a proxy for features in the water balance equation. • Time-series iterative models were constructed to estimate daily root zone soil moisture. • The effects of initial model values on soil moisture simulation results are discussed.