Conditioning a multiple-patch SVAT model using uncertain time-space estimates of latent heat fluxes as inferred from remotely sensed data

It has been shown that the calibration of soil vegetation-atmosphere transf er (SVAT) models is inherently uncertain, even when data are available over a relatively limited homogeneous area. The representation of subgrid-scale variability of fluxes is not easily achieved because of the lack of inform ation available about appropriate parameter distributions and their covaria nce. However, remote sensing of thermal surface responses offers the possib ility of obtaining distributed estimates of surface fluxes. In this paper, multiple Landsat-Thematic Mapper (TM) images of the First International Sat ellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) si te are used to derive uncertain estimates of the land surface-atmosphere se nsible and latent fluxes over a period of time. Employing a framework based on fuzzy set theory, the parameter space representing all feasible paramet erizations of a SVAT model are examined with respect to these image estimat es. Areal weightings for a number of functional types of flux behavior are then derived through which the temporal evolution of surface fluxes can be estimated.