Fine-Scale SAR Soil Moisture Estimation in the Subarctic Tundra

• Main Authors: Zwieback, Simon, Berg, Aaron
• Format: Report
• Language: unknown
• Published: EarthArXiv 2018
• Subjects: Physical Sciences and Mathematics; Environmental Sciences; Environmental Monitoring; permafrost; Subarctic; Tundra
• Online Access: https://dx.doi.org/10.17605/osf.io/kp5xd; https://eartharxiv.org/kp5xd/

In the subarctic tundra, soil moisture information can benefit permafrost monitoring and ecological studies, but fine-scale remote sensing approaches are lacking. We explore the suitability of C-band SAR, paying attention to two challenges soil moisture retrieval faces. First, the microtopography and the heterogeneous organic soils impart unique microwave scattering properties, even in absence of noteworthy shrub cover. Empirically, we find the polarimetric response is highly random (entropies > 0.7). The randomness precludes the application of purely polarimetric approaches to soil moisture estimation, as it causes a tailor-made decomposition to break down. For comparison, the L-band scattering response is much more surface-like (entropies of 0.1-0.2), also in terms of its angular characteristics. The second challenge concerns the large spatial but small temporal variability of soil moisture. Accordingly, the Radarsat-2 C-band backscatter has a limited dynamic range (approx. 2 dB). However, contrary to polarimetric indicators, it shows a clear soil moisture signal. To account for the small dynamic range while retaining a 100 m spatial resolution, we embed an empirical time-series model in a Bayesian framework. This framework adaptively pools information from neighboring grid cells, thus increasing the precision. The retrieved soil moisture index achieves correlations of 0.3-0.5 with in-situ network data and, upon calibration, RMSEs of < 0.04 m3m-3. As this approach is applicable to Sentinel-1 data, it can potentially provide frequent soil moisture estimates across large regions. In the long term, L-band data hold greater promise for operational retrievals.