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Retrieval of Subsurface Soil Moisture and Vegetation Water Content from Multi-frequency SoOp-Reflectometry: Sensitivity Analysis
  • Seho Kim ,
  • James Garrison ,
  • Mehmet Kurum
James Garrison
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Mehmet Kurum
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Signals of opportunity reflectometry (SoOp-R), the re-utilization of non-cooperative satellite transmissions for communication and navigation, is a promising approach to remote sensing of root-zone soil moisture (RZSM). Satellite transmissions in the frequency ranges of 137-138, 240-270, and 360-380 MHz are of interest due to the increased penetration depth. These can be combined with Global Navigation Satellite System Reflectometry (GNSS-R) in L-band (1575.42 MHz) to estimate the subsurface SM profile. The objective is to define requirements (e.g. frequency and polarization combinations, observation error, and temporal coincidence of multi-source observations) for satellite-based remote sensing of RZSM. Our approach is to use synthetic observations generated from multi-year time series of in-situ SM measurements from seven United States Climate Reference Network (USCRN) sites and dynamic vegetation structure based on a simple scaling method. A multi-frequency/polarimetric retrieval algorithm is developed and applied to these synthetic observations and used to predict retrieval errors for a range of changes in system parameters. We found that the use of both high and low frequencies improves retrieval accuracy by limiting uncertainties from vegetation and surface SM and providing sensitivity to deeper layers. Moreover, the retrieval errors were found to increase linearly with the reflectivity error and inter-frequency time delays. A bivariate model derived from this linear relationship will be useful for developing requirements on reflectivity precision based upon science requirements for SM/VWC retrievals. Although orbits of specific transmitter constellations were used to generate realistic distributions of incidence angle combinations, the method and results could be applied more generally.
2023Published in IEEE Transactions on Geoscience and Remote Sensing volume 61 on pages 1-18. 10.1109/TGRS.2023.3284800