A New Geostationary Satellite-Based Snow Cover Recognition Method for FY-4A AGRI

Snow cover is an important component of the cryosphere. Clouds have a large influence on optical remote sensing satellites when recognizing snow cover. Geostationary satellites, due to their high-frequency observations over coverage areas, can effectively compensate for the drawback of snow cover re...

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Autores principales:	Haiwei Qiao, Ping Zhang, Zhen Li, Chang Liu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	China Fengyun-4A (FY-4A) advanced geostationary radiation imager (AGRI) geostationary satellite snow cover Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/78a8bbf84d644b53ad38c73616c10ea9
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id	oai:doaj.org-article:78a8bbf84d644b53ad38c73616c10ea9
record_format	dspace
spelling	oai:doaj.org-article:78a8bbf84d644b53ad38c73616c10ea92021-11-19T00:00:19ZA New Geostationary Satellite-Based Snow Cover Recognition Method for FY-4A AGRI2151-153510.1109/JSTARS.2021.3125015https://doaj.org/article/78a8bbf84d644b53ad38c73616c10ea92021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9600871/https://doaj.org/toc/2151-1535Snow cover is an important component of the cryosphere. Clouds have a large influence on optical remote sensing satellites when recognizing snow cover. Geostationary satellites, due to their high-frequency observations over coverage areas, can effectively compensate for the drawback of snow cover recognition from polar orbit optical satellites under cloud-covered conditions. However, past geostationary satellites have relatively few band settings to produce sensitive factors for snow cover recognition. The FY-4A Advanced Geostationary Radiation Imager (AGRI) satellite has the advantage of high temporal resolution with a wealth of bands, which highlights its potential in reducing the impact of clouds and accurately obtaining snow cover information. Based on the advantages of FY-4A AGRI data and the flow characteristics of clouds, we developed an improved maximum brightness temperature image synthesis algorithm, which can greatly reduce the probability of cloud and snow cover misclassification. Combining the features of FY-4A AGRI data, we reorganized the snow cover recognition factor and developed a new snow cover recognition method. The results show that the proposed method can reduce cloud cover by 57.172% compared with MOD10A1 data. After evaluating the proposed method using meteorological ground observation datasets and MOD10A1 data, we found that the overall accuracy of the proposed method can reach 94.11% and 98.55%, respectively, and the F-score (FS) can reach 73.05% and 85.40%, respectively.Haiwei QiaoPing ZhangZhen LiChang LiuIEEEarticleChinaFengyun-4A (FY-4A) advanced geostationary radiation imager (AGRI)geostationary satellitesnow coverOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11372-11385 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	China Fengyun-4A (FY-4A) advanced geostationary radiation imager (AGRI) geostationary satellite snow cover Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	China Fengyun-4A (FY-4A) advanced geostationary radiation imager (AGRI) geostationary satellite snow cover Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Haiwei Qiao Ping Zhang Zhen Li Chang Liu A New Geostationary Satellite-Based Snow Cover Recognition Method for FY-4A AGRI
description	Snow cover is an important component of the cryosphere. Clouds have a large influence on optical remote sensing satellites when recognizing snow cover. Geostationary satellites, due to their high-frequency observations over coverage areas, can effectively compensate for the drawback of snow cover recognition from polar orbit optical satellites under cloud-covered conditions. However, past geostationary satellites have relatively few band settings to produce sensitive factors for snow cover recognition. The FY-4A Advanced Geostationary Radiation Imager (AGRI) satellite has the advantage of high temporal resolution with a wealth of bands, which highlights its potential in reducing the impact of clouds and accurately obtaining snow cover information. Based on the advantages of FY-4A AGRI data and the flow characteristics of clouds, we developed an improved maximum brightness temperature image synthesis algorithm, which can greatly reduce the probability of cloud and snow cover misclassification. Combining the features of FY-4A AGRI data, we reorganized the snow cover recognition factor and developed a new snow cover recognition method. The results show that the proposed method can reduce cloud cover by 57.172% compared with MOD10A1 data. After evaluating the proposed method using meteorological ground observation datasets and MOD10A1 data, we found that the overall accuracy of the proposed method can reach 94.11% and 98.55%, respectively, and the F-score (FS) can reach 73.05% and 85.40%, respectively.
format	article
author	Haiwei Qiao Ping Zhang Zhen Li Chang Liu
author_facet	Haiwei Qiao Ping Zhang Zhen Li Chang Liu
author_sort	Haiwei Qiao
title	A New Geostationary Satellite-Based Snow Cover Recognition Method for FY-4A AGRI
title_short	A New Geostationary Satellite-Based Snow Cover Recognition Method for FY-4A AGRI
title_full	A New Geostationary Satellite-Based Snow Cover Recognition Method for FY-4A AGRI
title_fullStr	A New Geostationary Satellite-Based Snow Cover Recognition Method for FY-4A AGRI
title_full_unstemmed	A New Geostationary Satellite-Based Snow Cover Recognition Method for FY-4A AGRI
title_sort	new geostationary satellite-based snow cover recognition method for fy-4a agri
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/78a8bbf84d644b53ad38c73616c10ea9
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A New Geostationary Satellite-Based Snow Cover Recognition Method for FY-4A AGRI

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