Assimilation of GPS soil moisture data from CYGNSS into land surface models

The moisture in the topsoil layer governs the interaction between land and atmosphere since the amount of surface soil moisture controls the partitioning of outgoing energy flux into latent and sensible heat fluxes. Thus, accurate soil moisture measurements are critical to researchers in many different research fields. However, estimations of soil moisture taken from sun-synchronous satellites are limited due to the coverage of current satellite-based soil moisture observation systems, which is neither spatially nor temporally continuous (e.g., the Soil Moisture Active Passive (SMAP) revisit time is 2 to 3 days). This limitation creates data gaps in current data assimilation systems, hampering our understanding of the fundamental processes that control the surface hydrologic cycle and land-atmosphere interaction across time and space domains. In this study, we have introduced a new source of soil moisture data obtained from the Cyclone Global Navigation Satellite System (CYGNSS): in 2017, NASA launched eight microsatellites, CYGNSS, to predict cyclone paths. We have found that while the CYGNSS satellites are predicting cyclone paths, they can simultaneously measure changes in soil moisture around 5 times per day. In the present study, we used the Land Information System (LIS) to assimilate CYGNSS-based soil moisture estimates into various land surface models (LSMs). The present research shows that CYGNSS-based soil moisture estimates have a strong potential to improve modeling capabilities by filling the gap in observations made by satellites in sun-synchronous orbits.