Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Agricultural Water Management

DOI: 10.1016/j.agwat.2025.109544