Constraining a physically based Soil-Vegetation-Atmosphere Transfer model with surface water content and thermal infrared brightness temperature measurements using a multiobjective approach

This article reports on a multiobjective approach which is carried out on the physically based Soil-Vegetation-Atmosphere Transfer (SVAT) model. This approach is designed for (1) analyzing the model sensitivity to its input parameters under various environmental conditions and (2) assessing input parameters through the combined assimilation of the surface water content and the thermal infrared brightness temperature. To reach these goals, a multiobjective calibration iterative procedure (MCIP) is applied on the Simple Soil Plant Atmosphere Transfer-Remote Sensing (SiSPAT-RS) model. This new multiobjective approach consists of performing successive contractions of the feasible parameter space with the multiobjective generalized sensitivity analysis algorithm. Results show that the MCIP is an original and pertinent approach both for improving model calibration (i.e., reducing the a posteriori preferential ranges) and for driving a detailed SVAT model using various calibration data. The usefulness of the water content of the upper 5 cm and the thermal infrared brightness temperature for retrieving quantitative information about the main input surface parameters is also underlined. This study opens perspectives in the combined assimilation of various multispectral remotely sensed observations, such as passive microwaves and thermal infrared signals.