A Top-Down Approach for Estimating Effective Soil Hydraulic Parameters from Space

The estimation of effective soil hydraulic parameters and their uncertainties is a critical step in all large-scale hydrologic and climatic model applications. In this study, a scale-dependent (top-down) parameter estimation (inverse modeling) scheme called the Noisy Monte Carlo Genetic Algorithm (NMCGA) was developed and tested for estimating these effective soil hydraulic parameters and their uncertainties. We tested our method using three case studies involving a synthetic pixel, an airborne remote sensing (RS) footprint, and a satellite RS footprint. In the synthetic case studies with pure (one soil texture) and mixed-pixel (multiple soil textures) conditions, we found that the NMCGA performed well in estimating the effective soil hydraulic parameters even with the complexities of various soil types and land management practices. Using airborne or satellite remote sensing soil moisture data, the NMCGA was found to be suitable for estimating the effective soil hydraulic properties that could mimic large-scale soil moisture time-series if used in forward stochastic simulation models. The results also showed that the effective soil water retention curve è(h) tend to scale down (smaller mean) at the larger satellite remote sensing pixel compared to air-borne remote sensing pixel. This finding, however, did not generally imply that every effective soil hydraulic parameters have to be scaled down like the soil water retention curve. The Mualem- van Genuchten soil hydraulic parameters á and n tend to increase (in mean and spread) as the parameter search spaces were relaxed progressively in our satellite remote sensing studies. The scaling down of the soil hydraulic parameters was observed to be more profound in èsat than that of the other scale parameters such as Ksat and ères. Overall, the NMCGA framework was found to be very promising in the inverse modeling of remotely sensed near-surface soil moisture for estimating the effective soil hydraulic parameters and their uncertainties at the remote sensing footprint/climate model grid.