气候变化领域集成服务门户(CCPortal): Improvement of operational airborne gamma radiation snow water equivalent estimates using SMAP soil moisture

CCPortal

DOI	10.1016/j.rse.2020.111668
	Improvement of operational airborne gamma radiation snow water equivalent estimates using SMAP soil moisture
	Cho E.; Jacobs J.M.; Schroeder R.; Tuttle S.E.; Olheiser C.
发表日期	2020
ISSN	00344257
卷号	240
英文摘要	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. © 2020 Elsevier Inc.
英文关键词	Airborne gamma radiation; AMSR2; NLDAS-2; SMAP; Snow water equivalent; Soil moisture; SSMIS
语种	英语
scopus关键词	Correlation methods; Floods; Forecasting; Gamma rays; Image enhancement; Microwave sensors; Snow; Snow melting systems; Soil moisture; AMSR2; Nldas-2; SMAP; Snow water equivalent; SSMIS; Soil surveys; algorithm; gamma radiation; NOAA satellite; rainfall; snow water equivalent; snowmelt; soil moisture; spatial analysis; spatial distribution; SSM-I; Canada; Minnesota; Saint Paul; United States
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179403
作者单位	Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, United States; Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, United States; Mount Holyoke College, South Hadley, MA, United States; NOAA National Weather Service Office of Water Prediction, Chanhassen, MN, United States
推荐引用方式 GB/T 7714	Cho E.,Jacobs J.M.,Schroeder R.,et al. Improvement of operational airborne gamma radiation snow water equivalent estimates using SMAP soil moisture[J],2020,240.
APA	Cho E.,Jacobs J.M.,Schroeder R.,Tuttle S.E.,&Olheiser C..(2020).Improvement of operational airborne gamma radiation snow water equivalent estimates using SMAP soil moisture.Remote Sensing of Environment,240.
MLA	Cho E.,et al."Improvement of operational airborne gamma radiation snow water equivalent estimates using SMAP soil moisture".Remote Sensing of Environment 240(2020).