Abstract:

Mesoscale modelling of large watersheds lacks data on the spatial heterogeneity of soil moisture because methods to determine the spatial patterns of soil moisture are not yet available. The goal of this study is to use ERS-1/2 data for the determination of surface soil moisture patterns in large watersheds. To provide mesoscale data the ERS-data were degraded to a resolution of approx. 1 km, which can be expected for future radar systems. A simple soil moisture index (SMI) was developed to describe the spatial variation of the soil moisture conditions. The approach is based upon the assumption, that the radar backscatter depends on land use dependent surface roughness and soil moisture. Factors influencing the surface roughness other than crop type are assumed to average out on a mesoscale pixel. Thus given the land use, the roughness for each land use class within each pixel, the spatial heterogeneity due to surface roughness differences can be normalised to a reference roughness or crop type, respectively. Once the whole image is normalised to a reference roughness, the spatial patterns of the radar backscatter can be attributed to soil moisture differences. The land use in the test site was determined from a LANDSAT TM classification. The SMI-images show an increase of SMI from north to south with a moist spot in the centre of the test site. This corresponds well with the precipitation pattern, which shows an increase from north west to south east. The moist spot belongs to an area of loess with increased moisture. Additional research is needed to not only normalise the roughness within an image, but to also allow to correct for temporal variability of the roughness.

Keywords: ESA European Space Agency - Agence spatiale europeenne, observation de la terre, earth observation, satellite remote sensing, teledetection, geophysique, altimetrie, radar, chimique atmospherique, geophysics, altimetry, radar, atmospheric chemistry