{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:40:49Z","timestamp":1768344049396,"version":"3.49.0"},"reference-count":29,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T00:00:00Z","timestamp":1662768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC) project","award":["41971377"],"award-info":[{"award-number":["41971377"]}]},{"name":"National Natural Science Foundation of China (NSFC) project","award":["SY2020005"],"award-info":[{"award-number":["SY2020005"]}]},{"name":"National Natural Science Foundation of China (NSFC) project","award":["NODAOP2021002"],"award-info":[{"award-number":["NODAOP2021002"]}]},{"name":"observing experiment project of Meteorological Observation Center of China Meteorological Administration","award":["41971377"],"award-info":[{"award-number":["41971377"]}]},{"name":"observing experiment project of Meteorological Observation Center of China Meteorological Administration","award":["SY2020005"],"award-info":[{"award-number":["SY2020005"]}]},{"name":"observing experiment project of Meteorological Observation Center of China Meteorological Administration","award":["NODAOP2021002"],"award-info":[{"award-number":["NODAOP2021002"]}]},{"name":"open fund of the National Earth Observation Data Center","award":["41971377"],"award-info":[{"award-number":["41971377"]}]},{"name":"open fund of the National Earth Observation Data Center","award":["SY2020005"],"award-info":[{"award-number":["SY2020005"]}]},{"name":"open fund of the National Earth Observation Data Center","award":["NODAOP2021002"],"award-info":[{"award-number":["NODAOP2021002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 &gt; 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.<\/jats:p>","DOI":"10.3390\/rs14184530","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T04:05:41Z","timestamp":1663041941000},"page":"4530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Using GNSS-IR Snow Depth Estimation to Monitor the 2022 Early February Snowstorm over Southern China"],"prefix":"10.3390","volume":"14","author":[{"given":"Jie","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266555, China"}]},{"given":"Shanwei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266555, China"}]},{"given":"Hong","family":"Liang","sequence":"additional","affiliation":[{"name":"Center of Meteorological Observation, China Meteorological Administration, Beijing 100044, China"}]},{"given":"Wei","family":"Wan","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}]},{"given":"Zhizhou","family":"Guo","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2390-2889","authenticated-orcid":false,"given":"Baojian","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.ijdrr.2016.09.007","article-title":"Analysis and assessment of the risk of snow and freezing disaster in China","volume":"19","author":"Gao","year":"2016","journal-title":"Int. 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