Soil moisture estimation in hydrological mesoscale modelling using ERS SAR data

Herrmann-Gregor Mendel                       Federal Institute of Hydrology
Felix Portmann                          Kaiserin-Augusta-Anlagen 15-17
                                   D-56072 Koblenz, F.R.G.
                                   Phone:    +49 261 1306-217 / -218
                                   Fax: +49 261 1306-280

Abstract
The moisture in the top soil layer is considered as a key element in the 
runoff process. Facilitation and improvements of the computation of runoff, 
particularly in flood events, and water balance is readily acknowledged if 
this value were known for the whole catchment or at least for representative 
sub-areas. Direct measurements on the ground at all sites are impossible 
because of the unjustifiable expenditures, and indirect estimates are 
inacceptable because of their high degree of uncertainty. Remote sensing 
from satellite, especially radar satellites like ERS, being indedepdent from 
weather conditions, is expected to offer an alternative. Therefore, the 
derivation of soil moisture from satellite synthetic aperture radar (SAR) 
data is expected to be a key element in a state-of-the-art runoff process 
modelling. Yet, the derivation of the volumetric soil moisture is, up to 
now, readily available to a certain extent only for bare soil. The framework 
conditions, e.g. inclination of the terrain, surface texture / roughness, 
flooding of an area increase the incertainty in the respective derivative 
function. Ground-truth information is needed for the calibration of that 
function. For a flat area in Northern Germany, a relationship for bare soil 
conditions between the backscattering coefficient sigma zero and volumetric 
soil moisture could be established, resulting in an R squared of 0.7 without 
outliers. Outlying values have to be discussed individually with respect to 
derivation due to soil probing accuracy, vegetation cover/texture, specular 
reflection. Their inclusion significantly reduces the goodness of fit to an 
R squared of 0.3.

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