A New Method for Generating the SMOPS Blended Satellite Soil Moisture Data Product without Relying on a Model Climatology

Soil moisture operational product system (SMOPS) is developed by National Oceanic and Atmospheric Administration (NOAA) to provide the real-time blended soil (SM) for numeric weather prediction national water model applications. However, all individual satellite SM data ingested into current SMOPS are scaled global land assimilation (GLDAS) 0–10 cm climatology before combination. As a result, useful information from original microwave retrievals could be lost, GLDAS errors brought final product. In this paper, we propose scale active passive (SMAP) through building regression models. The rescaled SMAP observations then have similar dynamics, which allows producing SMOPScdr (distinguishing with SMOPSopr) using an equal-weight averaging approach. With respect in situ measurements, more successful tracking surface status than products significantly decreased errors. proposed method also preserves of reference period when not available, allowing us produce long-term