干旱区生态环境知识资源中心(Arid): Estimating Land Surface Temperature from Landsat-8 Data using the NOAA JPSS Enterprise Algorithm

Arid

DOI	10.3390/rs11020155
	Estimating Land Surface Temperature from Landsat-8 Data using the NOAA JPSS Enterprise Algorithm
	Meng, Xiangchen 1,2; Cheng, Jie 1,2; Zhao, Shaohua 3; Liu, Sihan 3; Yao, Yunjun 1,2
通讯作者	Cheng, Jie
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2019
卷号	11 期号:2
英文摘要	Land surface temperature (LST) is one of the key parameters in hydrology, meteorology, and the surface energy balance. The National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Enterprise algorithm is adapted to Landsat-8 data to obtain the estimate of LST. The coefficients of the Enterprise algorithm were obtained by linear regression using the analog data produced by comprehensive radiative transfer modeling. The performance of the Enterprise algorithm was first tested by simulation data and then validated by ground measurements. In addition, the accuracy of the Enterprise algorithm was compared to the generalized split-window algorithm and the split-window algorithm of Sobrino et al. (1996). The validation results indicate the Enterprise algorithm has a comparable accuracy to the other two split-window algorithms. The biases (root mean square errors) of the Enterprise algorithm were 1.38 (3.22), 1.01 (2.32), 1.99 (3.49), 2.53 (3.46), and -0.15 K (1.11 K) at the SURFRAD, HiWATER_A, HiWATER_B, HiWATER_C sites and BanGe site, respectively, whereas those values were 1.39 (3.20), 1.0 (2.30), 1.93 (3.48), 2.53 (3.35), and -0.35 K (1.16 K) for the generalized split-window algorithm, 1.45 (3.39), 1.08 (2.41), 2.16 (3.67), 2.52 (3.58), and 0.02 K (1.12 K) for the split-window algorithm of Sobrino, respectively. This study provides an alternative method to estimate LST from Landsat-8 data.
英文关键词	Landsat8 Enterprise LST SURFRAD HiWATER TIPEX-III
类型	Article
语种	英语
国家	Peoples R China
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000457939400052
WOS关键词	SPLIT-WINDOW ALGORITHM ; RADIATION BUDGET NETWORK ; FOREST-FIRE RISK ; EMISSIVITY SEPARATION ; ARID AREA ; RETRIEVAL ; SATELLITE ; VALIDATION ; DERIVATION ; SURFRAD
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	北京师范大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/218332
作者单位	1.Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China; 2.Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Fac Geog Sci, Beijing 100875, Peoples R China; 3.Minist Environm Protect, Satellite Environm Ctr, Beijing 100094, Peoples R China
推荐引用方式 GB/T 7714	Meng, Xiangchen,Cheng, Jie,Zhao, Shaohua,et al. Estimating Land Surface Temperature from Landsat-8 Data using the NOAA JPSS Enterprise Algorithm[J]. 北京师范大学,2019,11(2).
APA	Meng, Xiangchen,Cheng, Jie,Zhao, Shaohua,Liu, Sihan,&Yao, Yunjun.(2019).Estimating Land Surface Temperature from Landsat-8 Data using the NOAA JPSS Enterprise Algorithm.REMOTE SENSING,11(2).
MLA	Meng, Xiangchen,et al."Estimating Land Surface Temperature from Landsat-8 Data using the NOAA JPSS Enterprise Algorithm".REMOTE SENSING 11.2(2019).