Validating Soil Moisture Estimates from Polarimetric Radar Using GIS Models : further results from the 1993 AIRSAR mission to Australia

The capability of active microwave remote sensing to measure soil moisture was validated via the use of GIS techniques incorporating soil landscape unit assessment and terrain wetness. The work was carried out over an 80 km region in the Mount lofty Ranges in South Australia. Search for atypically wet sites (ie. wet areas in a dry landscape, which were non-irrigated) was completed using analysis of co-registered visible infra-red imagery from Landsat. Soil dielectric constant (a surrogate for volumetric soil moisture) was derived from L Band polarimetric AIRSAR data acquired in the 1993 mission to Australia. Results from this dielectric modelling, which have been previously reported by Bruce (1996), showed areas of wet soil in the landscape. At the sub-catchment scale ( 2 km) reasonable correlation with ground measurements of soil moisture was observable and, moreover, correlation with other data sets indicating soil wetness was encouraging. These data sets, reported in more detail by Fitzpatrick et al (1999), consisted of a Topographic Index (TI), derived from contributing surface area and slope, a Discharge Index, derived from EM31 ground measurements, a Vegetation Colour Index, derived from multi season aerial photography and a potential waterlogging attribute, extracted from a Soil Landscape Units (SLU) GIS data set derived from mapping at 1 : 50,000 scale. The strength of the spatial correlation between these data sets led to a comparison, at the regional scale, of the soil moisture as inferred from polarimetric radar, with the SLU and TI. This comparison showed that indeed L band Polarimetric radar can be processed to estimate broad categories of soil wetness and, when combined with co-registered Landsat TM data, can be used to locate unusually wet sites where ground water discharges at the surface. In a related component of the research, it was found that soil dielectric/wetness derived from the polarimetric SAR assisted the prediction of soil salinity at both the catchment and regional scales. At the latter scale soil dielectric was combined with geology, TI and SLU to create a best estimate of potential salinity. Soil Moisture Estimation Using AIRSAR Soil dielectric constant has been derived from the use of Synthetic Aperture Radar (SAR) multi-polarisation C-, Land Pband data from the NASA/JPL airborne synthetic aperture radar (AIRSAR) instrument which acquired imagery over the study area for early spring, 1993. The processed data for the site was received from JPL as a 12 band composite image with pixel size of approximately 6.7 m × 8.3 m. The characteristic effects associated with data of this type, ie. nearrange compression, brightness fall-off and radar speckle were all present in the image and it was necessary to correct for some of these geometric and radiometric errors prior to processing for soil moisture determination. In addition the image was re-sampled to a 10 m pixel size for effective comparison with the other data sets. Dubois et al. (1995) suggested an empirical approach for the modelling of radar backscatter (σ) from the variables of incidence angle (θ), soil dielectric constant (ε), wave number (k), surface roughness (s) and wavelength (λ). The model, which utilises co-polarised data and is expressed in the following relationships: