Speaker
Description
Soil moisture plays an important role in land surface processes such as water and energy fluxes. To derive soil moisture in high spatial and temporal resolution SAR remote sensing technologies has proven to be very suitable. Over the last decades different retrieval approaches, from empirical (Water Cloud Model) over semi-empirical (Oh et al., Dubois et al.) to more physical based (Integral Equation Method) radiative transfer (RT) models, have been proposed. In the past all these RT-models have been widely used on different test-sites and studies. Nevertheless, a clear validation site and tool for the comparison of different models among themselves and in different model combinations for the different backscatter contribution of soil and vegetation is missing. The Copernicus Sentinel missions and especially the Sentinel 1A and 1B satellites offer new opportunities in terms of spatial and temporal resolution to analyze and validate the outputs of the different radiative transfer models
This study presents a comparative analysis of different RT-model combinations for soil moisture retrieval over agricultural fields. The tested RT-models consist of a vegetation and a soil scattering component. Therefore, soil models with different complexity for estimating the soil scattering contribution (Oh et al., Dubois et al., Integral Equation Method) are coupled with vegetation models which are calculating the vegetation scattering contribution (Water Cloud Model, Single Scattering Radiative Transfer) of the total radar backscatter. The RT-models are driven with in-situ data on field basis which has been acquired at the Munich-North-Isar (MNI) test-site located in the south of Germany. The retrieved backscatter is compared to available Sentinel 1 time series. In addition, an outlook on how these models can be used in a variational land-data-assimilation scheme is given.
