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.

Recent proof-of-concept experiments have demonstrated the potential utility of Signals of Opportunity (SoOp) in remote sensing. SoOp methods involve the re-use of existing satellite transmissions as sources in bistatic radar, applying fundamental physical principles to estimate surface and scattered medium properties from reflectivity and phase observables in the reflected signal. Through utilizing signals intended for communications, SoOp methods can make these observables using frequencies that are not allocated or protected for scientific use. Two promising applications in hydrology have been studied: Sub-canopy root-zone soil moisture (RZSM) using satellite communications signals below 500 MHz and snow water equivalent (SWE) retrieval from the observed phase different through propagation through the snow layer. Signals of Opportunity P-band Investigation (SNOOPI) is a NASA Cubesat technology demonstration mission to test forward scattering models and validate a prototype instrument for SoOp reflectometry in 250-380 MHz range. Contribution to the panel discussion will focus on the expected contributions of the SoOp techniques validated in the SNOOPI mission and the existing challenges in the full utilization of SoOp methods for both RZSM and SWE remote sensing. Multiple frequencies are required in order to solve the inverse problem and estimating a sub-surface profile. In the case of SoOp, this may require combining observations with diverse geometry due to the different orbits of the potential sources. This presents new challenges in the development of retrieval algorithms and may possibly require the integration of additional data sources. Another important challenge for SWE retrieval is the need for repetitive coverage to extract phase differences between subsequent passes, coupled with orbit determination for the non-cooperative sources. In contrast to GNSS reflectometry (in which high-precision orbits are publicly available for use in positioning), communication satellite orbits are not known to the required meter-level accuracy. Even geostationary sources frequently have a small inclination which results in motion relative to the surface of the Earth. Finally, antenna calibration is a substantial contribution to the error budget.