Multifrequency Subsurface Soil Moisture Retrieval for Forest Flows: A Case Study in Te Hiku, New Zealand

A subsurface soil moisture retrieval algorithm, using a pathfinder simultaneously acquired P- and L-band radar data over forested areas, is proposed in this article. It employs a generalized radar backscattering model for forests and a second-order polynomial function for soil moisture profile. The inversion uses a hybrid simulated annealing method. The proposed multifrequency retrieval algorithm has been evaluated using synthetic data and showed good performance. Furthermore, the proposed algorithm was applied to actual radar data from the forest flows airborne campaign in Te Hiku, New Zealand, in April 2022. The multifrequency inversion results revealed that the root mean squared error between the retrieved and measured soil moisture profiles ranged from 0.019 to 0.048 $\mathbf {m^{3}/m^{3}}$, with an overall RMSE of 0.032 $\mathbf {m^{3}/m^{3}}$. In addition, comparing multifrequency and single P-band retrievals indicated a reduction in RMSE with the multifrequency approach, particularly noted during the Te Hiku dry season.