![]() ![]() In particular, it is now well established that L-band (1.4 GHz) passive microwave remotely sensed measurements are highly related to the surface soil moisture and canopy water amount. Alternative approaches based on remote sensing techniques may be able to provide indirect large-scale information about surface properties. Same difficulties occurred for most of the other surface properties (vegetation surface conductance, thermal properties of soil, aerodynamic roughness), all of them being difficult to assess in situ. However, these functions were developed from a very limited number of soils and the correlations are often weak and location specific. Pedo-transfer functions, relating the soil hydraulic properties to soil data available from soil surveys, were alternatively developed to estimate empirically soil water retention and conductivity. The experimental techniques to assess soil hydraulic properties are time consuming, expensive and generally limited by the large spatial variability of these properties. For example, SVAT models based on Darcian flow require information about the soil hydraulic characteristics that define the relationships between hydraulic conductivity, soil matric potential, and soil water content. Most of these properties vary in time and space and are often assessed through in situ experiments. They require a large set of input parameters and initial conditions describing surface properties that must be correctly specified for providing accurate assessment of the energy and water fluxes. ![]() Models designed for simulating these exchanges are the so-called Soil-Vegetation-Atmosphere Transfer (SVAT) models. Monitoring energy and water exchanges between the soil, the vegetation and the atmosphere is important for meteorological, agronomical and hydrological purposes. This study opens perspectives in the combined assimilation of various multispectral remotely sensed observations, such as passive microwaves and thermal infrared signals. 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. 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. This new multiobjective approach consists of performing successive contractions of the feasible parameter space with the multiobjective generalized sensitivity analysis algorithm. 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 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. This article reports on a multiobjective approach which is carried out on the physically based Soil-Vegetation-Atmosphere Transfer (SVAT) model. ![]()
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