Assessment of Interpolation Errors of CYGNSS Soil Moisture Estimations

Abstract

High spatiotemporal soil moisture (SM) is essential for many meteorological, hydrological, and agricultural applications and studies. Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) provides a promising opportunity for high-resolution SM retrievals. NASA's Cyclone Global Navigation Satellite System (CYGNSS) is a recent GNSS-R application that offers relatively high spatial and temporal resolution observations from earth's surface. However, the quasi-random sampling of land surface by the CYGNSS constellation circumvents obtaining fully observed daily SM predictions at high spatial resolutions. Spatial interpolation techniques may fill this gap and provide a fully covered high-resolution daily SM estimation. However, the spatial interpolation errors need to be assessed when applied to the quasi-random 9-km CYGNSS-based SM estimations. In this article, we conduct interpolation error analysis using the Soil Moisture Active Passive (SMAP) Enhanced L3 Radiometer Global Daily 9-km product, sampled at the CYGNSS observation locations. The results indicate that the overall interpolation error (RMSE) was 0.013 m^3 m^{-3} over SMAP's recommended grids. In addition, sparse CYGNSS SM observations are directly interpolated. The achieved results show that interpolated and observed CYGNSS SM values have similar performance metrics when validated with the SMAP 9-km gridded SM product as well as sparse SM networks.