A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data

doi:10.1016/j.rse.2017.04.008

	A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data
	Duan, Si-Bo 1; Li, Zhao-Liang 1,2; Leng, Pei 1
刊名	REMOTE SENSING OF ENVIRONMENT
	2017-06-15
卷号	195 页码:107-117
关键词	Land surface temperature All-weather Thermal infrared Passive microwave Subsurface temperature
ISSN号	0034-4257
DOI	10.1016/j.rse.2017.04.008
通讯作者	Li, Zhao-Liang(lizhaoliang@caas.cn)
英文摘要	Land surface temperature (LST) is an important parameter associated with the land-atmosphere interface. Satellite remote sensing is the most effective method of measuring LST at regional and global scales. Satellite thermal infrared (TIR) measurements are widely used to retrieve LST with high accuracy and high spatial resolution but are limited to cloud-free conditions due to their inability to penetrate clouds. By contrast, satellite passive microwave (PMW) measurements are capable of penetrating clouds and providing data regardless of the cloud conditions. However, PMW measurements have limitations, such as a low spatial resolution and low temperature retrieval accuracy. Furthermore, temperature retrieval from PMW measurements yields the subsurface temperature, which differs from the LST retrieved from TIR measurements (skin temperature). This study proposes a framework for the retrieval of all-weather LST at a high spatial resolution by combining the advantages of TIR and PMW measurements. Compared to the MODIS LST product, the all-weather LST reflects the spatial variations in LST accurately. In situ LST measurements at four sites in an arid area of northwest China were used to evaluate the accuracy of the all-weather LST. The root mean square error of the LST under cloud-free conditions was approximately 2 K, whereas that of the LST under cloudy conditions varied from 3.5 K to 4.4 K. The results indicate that the all-weather LST retrieval algorithm can provide an IST dataset with reasonable accuracy and a high spatial resolution under clear and cloudy conditions. (C) 2017 Elsevier Inc. All rights reserved.
资助项目	National Natural Science Foundation of China[41231170] ; National Natural Science Foundation of China[41501406]
WOS关键词	SPLIT-WINDOW ALGORITHM ; MODIS DATA ; RECONSTRUCTION ; EMISSIVITY ; PRODUCT ; WATER ; LST ; DISAGGREGATION ; VALIDATION ; CYCLE
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000402355700009
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/63561]
专题	中国科学院地理科学与资源研究所
通讯作者	Li, Zhao-Liang
作者单位	1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Minist Agr, Key Lab Agr Remote Sensing, Beijing 100081, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Duan, Si-Bo,Li, Zhao-Liang,Leng, Pei. A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data[J]. REMOTE SENSING OF ENVIRONMENT,2017,195:107-117.
APA	Duan, Si-Bo,Li, Zhao-Liang,&Leng, Pei.(2017).A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data.REMOTE SENSING OF ENVIRONMENT,195,107-117.
MLA	Duan, Si-Bo,et al."A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data".REMOTE SENSING OF ENVIRONMENT 195(2017):107-117.