Daily 9-km Soil Moisture Retrieval With Reconstructed GNSS-R Observations

Abstract

Spaceborne global navigation satellite system-reflectometry (GNSS-R) observations are effective for extensive monitoring of soil moisture (SM). Reliance on gridded data from other remote-sensing systems is the general way of GNSS-R SM retrieval. Increasing the grid size improves temporal coverage but decreases spatial resolution, which may mask changes in SM within the grid. Conversely, finer grids result in higher spatial resolution but less temporal resolution, leading to discontinuities in SM temporal dynamics. Therefore, GNSS-R observation gridding cannot achieve high spatiotemporal resolutions simultaneously. To leverage the potential of GNSS-R technology in SM retrieval with a high spatiotemporal resolution, this study introduced a reconstruction method for spaceborne GNSS-R observations to generate gridded observations with both high temporal and spatial resolution. This method was used to build a pixel-by-pixel multivariate temporal fitting model that incorporates the characteristics of GNSS-R observations and the associated driving factors. Specifically, this study utilizes cyclone GNSS (CYGNSS) sliding average observations to supplement daily trend observations and integrates ancillary data from other satellite missions as adjustments to daily variability, which generates spatiotemporally seamless CYGNSS observations. The reconstructed CYGNSS observations were then utilized to provide daily quasi-global SM retrievals on a 9-km grid. The root-mean-square error (RMSE) of the SM retrievals was 0.052 $\mathrm{c}{{\mathrm{m}}^{3}}{\rm{/c}}{{\mathrm{m}}^{3}}$, which are comparable with the accuracy of the SM retrievals conducted prior to reconstruction. Similarly, independent evaluations of local in situ sites demonstrated that the RMSE and correlation (R) of the SM retrieval were 0.051 $\mathrm{c}{{\mathrm{m}}^{3}}{\rm{/c}}{{\mathrm{m}}^{3}}$and 0.77, respectively. The SM from the reconstructed CYGNSS observations also captured local wet and dry dynamics, which are consistent with the performance of the SM retrieved from the original CYGNSS observations. Notably, the temporal resolution was improved by 258% over the original CYGNSS observations at 9 km. Therefore, we argue that the method used in this study addresses the problem of mutual constraints between spatial and temporal resolutions in quasi-global CYGNSS SM retrieval without the loss of SM retrieval accuracy. Furthermore, the observation reconstruction method developed in this study may be a promising reference for monitoring other geophysical parameters with high spatiotemporal resolution in the GNSS-R domain.