Paper Info
Title
Using GNSS-IR Snow Depth Estimation to Monitor the 2022 Early February Snowstorm over Southern China
Abstract
Snow depth is an essential meteorological indicator for monitoring snow disasters. The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has been proven to be a practical approach to retrieving snow depth. This study presents a case study to explore utilizing the GNSS-IR-derived snow depth to monitor the 2022 early February snowstorm over southern China. A snow depth retrieval framework considering data quality control and specific ground surface substances was developed using 8-day data from 13 operational GNSS/Meteorology stations. The daily snow depths retrieved from different ground surfaces, i.e., dry grass, wet grass, and concrete, agreed well with the measured snow depth, with Mean Absolute Error (MAE) of 2.79 cm, 3.36 cm, and 2.53 cm, respectively. The percentage MAE when snow depths > 5 cm for the three ground surface substances was 26.8%, 53.7%, and 35.0%, respectively. The 6 h snow depth results also showed a swift and significant response to the snowfall event. This study proves the potential of GNSS-IR, used as a new operational tool in the automatic meteorological system, to monitor snow disasters over southern China, particularly as an efficient and cost-effective framework for real-time and accurate monitoring.
Year
DOI
Venue
2022
10.3390/rs14184530
REMOTE SENSING
Keywords
DocType
Volume
snowstorm, snow depth, GNSS interferometric reflectometry, southern China
Journal
14
Issue
ISSN
Citations 
18
2072-4292
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jie Zhang107.44
Shanwei Liu211.05
Hong Liang300.34
Wei Wan401.01
Zhizhou Guo500.68
Baojian Liu600.34