Farm-Scale Retrieval of Subsurface Fluxes Using Joint Assimilation of Land Surface Temperature and Soil Moisture

Root zone soil moisture (RZSM) and subsurface fluxes such as capillary rise, root water uptake, and deep percolation are critical for effective irrigation planning and groundwater recharge assessment. However, direct measurement of these variables is spatially limited and costly to implement at scale. This study presents a computationally efficient, reduced-order variational data assimilation (RA-VDA) framework that combines satellite-based land surface temperature (LST) and surface soil moisture (SSM) observations with a physically-based soil moisture–temperature model (HYDRUS-1D). The approach leverages the complementary strengths of LST—sensitive to surface energy fluxes—and SSM—directly linked to near-surface soil water content—to improve RZSM and flux retrievals. Synthetic experiments are conducted under three scenarios: SSM-only, LST-only, and joint assimilation. Results show that SSM constrains moisture in deeper layers, while LST significantly enhances the estimation of water and energy fluxes, including evaporation and infiltration. Joint assimilation yields the best overall performance, reducing root-mean-square error in both state variables and fluxes. Importantly, the framework enables accurate estimation of capillary rise and deep percolation, which are essential for evaluating recharge potential and optimizing irrigation timing. The RA-VDA method uses Proper Orthogonal Decomposition (POD) to reduce the dimensionality of the optimization space, enabling scalable implementation without compromising accuracy. This integration of satellite remote sensing and physical modeling offers a practical, low-cost solution for soil moisture monitoring in data-scarce regions. The framework provides a strong foundation for advancing operational water management tools that support agricultural sustainability and groundwater conservation.