Synergies for Soil Moisture Retrieval Across Scales From Airborne Polarimetric SAR, Cosmic Ray Neutron Roving, and an In Situ Sensor Network

The consistent determination of soil moisture across scales is a persistent challenge in hydrology. 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. While in situ measurements are trusted at the point scale, distributed sensor networks extend the areal representation to the field scale. 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. 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. In this study, the performance and synergistic potential of these complementary methods is investigated for the determination of soil moisture within a 55 km2 Alpine foothill river catchment in Southern Germany. The individual approaches are evaluated and brought into synergy for a 9 ha grassland and several other locations within the catchment. 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 root‐mean‐square errors for polarimetric SAR soil permittivity are 9.32 with the standard agriculture approach, 4.29 with the semi‐stand‐alone approach, and 0.31 with the sensor network optimized approach. Furthermore, the CRNS soil moisture product was improved by considering the remotely sensed cross‐polarized backscatter product as a biomass water proxy. Key Points Soil moisture data from a local sensor network, airborne PolSAR, and CRNS rover surveys are evaluated across scales for potential synergism The vegetation bias correction in the PolSAR algorithm benefits from in situ soil moisture data Cosmic ray rover data benefit from the cross‐polarized backscatter product of PolSAR by accounting for biomass and the elongated footprint