Sensitivity-informed Parameter Selection for Improved Soil Moisture Estimation from Remote Sensing Data

Accurate soil moisture estimation is essential for advancing closed-loop irrigation. Central to this task are soil hydraulic parameters, which are rarely known precisely and must be inferred from moisture measurements. Inferring these parameters for large-scale agricultural fields presents practical difficulties due to the sparse and noisy nature of moisture measurements. To address this challenge, a framework is developed that combines sensitivity analysis and orthogonal projection to identify parameters that are most reliably estimable from the measurements. The selected parameters, together with soil moisture states, are estimated by assimilating remotely sensed soil moisture observations into the Richards equation using an extended Kalman filter. Numerical simulations and field experiments conducted on a large-scale site in Lethbridge, Alberta, Canada, demonstrate improvements of 24%-43% in soil moisture estimation accuracy and a 50% enhancement in predictive performance. Furthermore, the estimated parameters, particularly saturated hydraulic conductivity, show good agreement with experimental measurements.