Spatial heterogeneity of satellite derived land surface parameters and energy flux densities for LITFASS-area
1Institute for Hydrology and Meteorology, Dresden, Germany
2Deutscher Wetterdienst, Meteorologisches Observatorium Lindenberg, Germany
*now at: Institute for Oceanography, Hamburg, Germany
Abstract. Remote sensing data provide area integrated information of surface properties in different spatial or temporal resolutions according to different sensor features. Landsat ETM+, Terra MODIS and NOAA-AVHRR surface temperature and spectral reflectance were used to infer further surface parameters and radiant- and energy flux densities for LITFASS-area, a 20×20 km2 heterogeneous area in Eastern Germany, mainly characterized by the land use types forest, crop, grass and water. Based on the Penman-Monteith-approach the actual latent heat flux (L.E), as key quantity of the hydrological cycle, is determined for each sensor in the accordant spatial resolution with an improved parametrization. However, using three sensors, significant discrepancies between the inferred parameters can cause flux distinctions resultant from differences of the sensor filter response functions or atmospheric correction methods. The approximation of MODIS- and AVHRR- derived surface parameters to the reference parameters of ETM (via regression lines and histogram stretching, respectively), further the use of accurate land use classifications (CORINE and a new Landsat-classification), and a consistent parametrization for the three sensors were realized to obtain a uniform base for investigations of the spatial variability. For the target area the spatial heterogeneity is analysed investigating frequency distribution functions (PDF) for surface parameters and energy fluxes. PDF is the most promising way to describe subgrid heterogeneity due to the given data in different spatial resolution. Aim of this study is to find typical distribution pattern of parameters (albedo, NDVI) for the determination of L.E determined from the highly resolved ETM data within pixel on coarser scale (MODIS, AVHRR). The analyses for 4 scenes in 2002 and 2003 showed that clear distribution-pattern for forest for NDVI and albedo are found. Grass and crop distributions show higher variability and differ significantly to each other in NDVI but only marginal in albedo. Regarding NDVI-distribution functions NDVI was found to be the key variable for L.E-determination.