干旱区生态环境知识资源中心(Arid): Retrieving Land Surface Temperature From Chinese FY-3D MERSI-2 Data Using an Operational Split Window Algorithm

Arid

DOI	10.1109/JSTARS.2021.3075698
	Retrieving Land Surface Temperature From Chinese FY-3D MERSI-2 Data Using an Operational Split Window Algorithm
	Tang, Kai; Zhu, Hongchun; Ni, Ping; Li, Ruibo; Fan, Cheng
通讯作者	Zhu, HC (corresponding author), Shandong Univ Sci & Technol, Sch Coll Geodesy & Geomat, Qingdao 266590, Peoples R China.
来源期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN	1939-1404
EISSN	2151-1535
出版年	2021
卷号	14 页码:6639-6651
英文摘要	Medium resolution spectral imager II (MERSI-2) is a payload for the Chinese meteorological satellite Feng Yun 3 (FY-3). China's satellite remote sensing observation capabilities such as climate change research can be improved during MERSI-2's operation in orbit, and the sensor is the world's first imaging instrument that can obtain a global infrared split-window data with a spatial resolution of 250 m. We developed an operational spilt-window (SW) algorithm to retrieve land surface temperature (LST) accurately from the MERSI-2 data. The SW algorithm coefficients were derived from a simulation dataset that was established with the Moderate spectral resolution atmospheric Transmittance model version 5.2 and the thermodynamic initial guess retrieval dataset. In the practical retrieval, the precise algorithm coefficients were determined by view zenith angle and atmospheric water vapor content (WVC), the atmospheric WVC were obtained from the ERA5 dataset, and the land surface emissivity was dynamically estimated using the advanced spaceborne thermal emission and reflection radiometer global emissivity dataset, considering the fractional of vegetation cover and snow cover. The retrieved LST compared with in situ LST, which was highly consistent with the in situ LST and that the root-mean-square error of the two is within 3 K. The retrieved LST was compared with the MYD11_L2 and MYD21_L2 LST products, and the results indicated that MERSI-2 LST was more consistent with the MYD21 LST. The operational SW algorithm for FY-3D MERSI-2 developed in this study could retrieve LST accurately and has a wide range of popularization and application values.
英文关键词	Land surface temperature Atmospheric modeling Satellites Land surface Spatial resolution Remote sensing MODIS Feng Yun (FY)-3D medium resolution spectral imager II (MERSI-2) land surface emissivity (LSE) land surface temperature (LST) operational split-window (SW) algorithm validation
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000673442300021
WOS关键词	EMISSIVITY SEPARATION ALGORITHM ; ATMOSPHERIC CORRECTION ; GROUND MEASUREMENTS ; ARID AREA ; VALIDATION ; PRODUCTS ; VIIRS ; ASTER ; FY-3C/VIRR ; IMAGERY
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/363561
作者单位	[Tang, Kai; Zhu, Hongchun; Ni, Ping; Li, Ruibo] Shandong Univ Sci & Technol, Sch Coll Geodesy & Geomat, Qingdao 266590, Peoples R China; [Li, Ruibo; Fan, Cheng] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Aerosp Informat Res Inst, Beijing 100101, Peoples R China; [Fan, Cheng] Sch Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Tang, Kai,Zhu, Hongchun,Ni, Ping,et al. Retrieving Land Surface Temperature From Chinese FY-3D MERSI-2 Data Using an Operational Split Window Algorithm[J],2021,14:6639-6651.
APA	Tang, Kai,Zhu, Hongchun,Ni, Ping,Li, Ruibo,&Fan, Cheng.(2021).Retrieving Land Surface Temperature From Chinese FY-3D MERSI-2 Data Using an Operational Split Window Algorithm.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,6639-6651.
MLA	Tang, Kai,et al."Retrieving Land Surface Temperature From Chinese FY-3D MERSI-2 Data Using an Operational Split Window Algorithm".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):6639-6651.