Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithms: 2. Using airborne remote sensing during SGP97 and SMEX02
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Pixelbased effective soil hydraulic parameters are crucial inputs for largescale hydroclimatic modeling. In this paper, we extend/apply a genetic algorithm (GA) approach for estimating these parameters at the scale of an airborne remote sensing (RS) footprint. To estimate these parameters, we used a time series of nearsurface RS soil moisture data to invert a physically based soilwateratmosphereplant (SWAP) model with a (multipopulated) modifiedmicroGA. Uncertainties in the solutions were examined in two ways: (1) by solving the inverse problem under various combinations of modeling conditions in a respective way; and (2) the same as the first method but the inverse solutions were determined in a collective way aimed at finding the robust solutions for all the modeling conditions (ensembles). A cross validation of the derived soil hydraulic parameters was done to check their effectiveness for all the modeling conditions used. For our case studies, we considered three electronically scanned thinned array radiometer (ESTAR) footprints in Oklahoma and four polarimetric scanning radiometer (PSR) footprints in Iowa during the Southern Great Plains 1997 (SGP97) Hydrology Experiment and Soil Moisture Experiment 2002 (SMEX02) campaigns, respectively. The results clearly showed the promising potentials of nearsurface RS soil moisture data combined with inverse modeling for determining average soil hydrologic properties at the footprint scale. Our cross validation showed that parameters derived by method 1 under water table (bottom boundary) conditions are applicable also for freedraining conditions. However, parameters derived under freedraining conditions generally produced too wet nearsurface soil moisture when applied under water table conditions. Method 2, on the other hand, produced robust parameter sets applicable for all modeling conditions used. These results were validated using distributed in situ soil moisture and soil hydraulic properties measurements, and texturebased data from the UNSODA database. In this study, we conclude that inverse modeling of RS soil moisture data is a promising approach for parameter estimation at large measurement support scale. Nevertheless, the derived effective soil hydraulic parameters are subject to the uncertainties of remotely sensed soil moisture data and from the assumptions used in the soilwateratmosphereplant modeling. Method 2 provides a flexible framework for accounting these sources of uncertainties in the inverse estimation of largescale soil hydraulic properties. We have illustrated this flexibility by combining multiple data sources and various modeling conditions in our largescale inverse modeling.
