Speaker
Description
Soil moisture plays a key role in land surface processes such as water, carbon and energy fluxes. SAR satellites, like Sentinel-1, have proven to be particularly useful to derive high spatio-temporal soil moisture estimates. Thus, microwave radiative transfer models are often used to derive soil moisture estimates from SAR satellites. But, as the derivation of soil moisture, especially over vegetated agricultural areas, is complicated, additional information about the vegetation state is usually required. Luckily, information about the vegetation can be derived by optical satellites like Sentinel-2. Other challenges in the retrieval process of high spatial (field scale) soil moisture patterns are the correct estimation in terms of absolute values and reproducing the temporal dynamics. Thus, existing medium resolution (1km x 1km) soil moisture products can be used as a priori information to enhance the quality of high spatio-temporal soil moisture estimates.
This study presents a data assimilation approach to downscale a medium resolution (1km x 1km) soil moisture product (RADOLAN API) to field scale (max. 10m x 10m). Within the data assimilation scheme, a radiative transfer model framework (Oh04 + SSRT) is run by data from Sentinel-1 and Sentinel-2 and constraint by the medium resolution soil moisture product (prior information). Thus, in the end soil moisture estimates at field scale (10m-100m) can be retrieved. The retrieval approach is validated with in-situ data acquired at the Munich-North-Isar (MNI) test site (southern Germany, Bavaria) during the vegetation periods of 2017 and 2018.
