The results indicate that the sensor network data provide valuable information to calibrate the mobile CRNS rover, and to optimize the vegetation removal within the polarimetric SAR retrieval algorithm. The individual approaches are evaluated and brought into synergy for a 9 ha grassland and several other locations within the catchment. In this study, the performance and synergistic potential of these complementary methods is investigated for the determination of soil moisture within a 55 km 2 Alpine foothill river catchment in Southern Germany. With airborne synthetic aperture radar (SAR) remote sensing, it is possible to cover regional scales, but the method is limited to the topmost soil layer and sensitive to vegetation parameters. At this scale, also cosmic ray neutron sensing (CRNS) has become an established method to derive volume-averaged, root zone soil moisture over several tens of hectometers, but the signal is often biased due to biomass water. While in situ measurements are trusted at the point scale, distributed sensor networks extend the areal representation to the field scale.
Several measurement methods exist at distinct scales, each of which is challenging in terms of data processing, removal of vegetation and surface effects, and calibration. The consistent determination of soil moisture across scales is a persistent challenge in hydrology.
