Analyses of hyperspectral directional data from CHRIS/PROBA using land surface models

Hyperspectral, directional remote sensing data as provided from the CHRIS sensor on PROBA open new facilities for land applications, since this kind of optical data allows quantitative analyses using physically based models both for radiative transfer in the atmosphere and canopy, as well as for land surface processes. It is demonstrated how hyperspectral and directional information can deliver required information for an autonomous atmospheric correction based on MODTRAN 4 simulations. From the satellite images themselves the atmospheric properties on visibility and water vapor content are retrieved. The soil-leaf-canopy reflectance model SLC is further used to interpret the spectral and directional signatures measured by CHRIS. SLC simulates the radiative transfer in leaves and canopies. A non-Lambertian soil BRDF submodel for the soil reflectance and its variation with moisture is incorporated. Using SLC in an inverse mode, bio- geophysical land surface properties like LAI and surface soil moisture are retrieved from CHRIS data of Tunisia. These are in a next step translated into land use and soil classes. The model based approach is followed even more consequently in agricultural applications. The SLC model together with the CHRIS data is used to provide information on leaf area, fraction of senescent material and canopy structure. The combination with the growth model PROMET-V additionally provides information on phenological development, biomass and yield.