Knowledge of snow water equivalent (SWE) magnitude and spatial distribution are keys to improving snowmelt flood predictions. Since the 1980s, the operational National Oceanic and Atmospheric Administration's (NOAA) airborne gamma radiation soil moisture (SM) and SWE survey has provided over 20,000 SWE observations to regional National Weather Service (NWS) River Forecast Centers (RFCs). Because the gamma SWE algorithm is based on the difference in natural gamma emission measurements from the soil between bare (fall) and snow-covered (winter) conditions, it requires a baseline fall SM for each flight line. The operational approach assumes the fall SM remains constant throughout that winter's SWE survey. However, early-winter snowmelt and rainfall events after the fall SM surveys have the potential to introduce large biases into airborne gamma SWE estimates. In this study, operational airborne gamma radiation SWE measurements were improved by updating the baseline fall SM with Soil Moisture Active Passive (SMAP) enhanced SM measurements immediately prior to winter onset over the north-central and eastern United States and southern Canada from September 2015 to April 2018. The operational airborne gamma SM had strong agreement with the SMAP SM (Pearson's correlation coefficient, R = 0.69, unbiased root mean square difference, ubRMSD = 0.057 m³/m³), compared to the Advanced Microwave Scanning Radiometer 2 (AMSR2) SM (R = 0.45, ubRMSD = 0.072 m³/m³) and the North American Land Data Assimilation System Phase 2 (NLDAS-2) Mosaic SM products (R = 0.53, ubRMSD = 0.069 m³/m³) in non-forested regions. The SMAP-enhanced gamma SWE was evaluated with satellite-based SWE (R = 0.57, ubRMSD = 34 mm) from the Special Sensor Microwave Imager Sounder (SSMIS) and in-situ SWE (R = 0.71–0.96) from the Soil Climate Analysis Network and United States Army Corps of Engineer (USACE) St. Paul District, which had better agreement than the operational gamma SWE (R = 0.48, ubRMSD = 36 mm for SSMIS and R = 0.65–0.75 for in-situ SWE). The results contribute to improving snowmelt flood predictions as well as the accuracy of the NOAA SNOw Data Assimilation System.
