干旱区生态环境知识资源中心(Arid): Adjustment From Temperature Annual Dynamics for Reconstructing Land Surface Temperature Based on Downscaled Microwave Observations

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DOI	10.1109/JSTARS.2020.3021386
	Adjustment From Temperature Annual Dynamics for Reconstructing Land Surface Temperature Based on Downscaled Microwave Observations
	Li, Kangning; Chen, Yunhao; Xia, Haiping; Gong, Adu; Guo, Zheng
通讯作者	Chen, YH
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	2020
卷号	13 页码:5272-5283
英文摘要	Land surface temperature (LST) is crucial to wide varieties of environmental issues, whereas its low tolerance to cloud contamination greatly challenges its applications. Passive microwave (PMW) measurements are employed to retrieve LST due to its great capability of penetrating clouds. Despite great efforts from previous studies, their further applications are limited by coarse resolution of PMW, uncertainty from cloudy coverage, and less attention on large-scale applications. To address these problems, this article proposes a method combining adjustment from annual temperature circle to reconstruct LST based on downscaled microwave observations over mainland China. In order to conduct a comprehensive and solid accuracy evaluation, the proposed method is validated according to three strategies namely comparing with moderate resolution imaging spectroradiometer (MODIS) LST, ground surface temperature, and in situ observations from the Heihe Watershed Allied Telemetry Experimental Research, respectively. In the strategy 1, mean root mean square error (RMSE) of predicted LST is 2.39 K and 1.33 K at day and night. According to the strategy 2, mean RMSE of MODIS, predicted and merged LST is 4.97 K, 5.07 K, and 4.93 K at daytime, and 2.32 K, 4.57 K, and 3.39 K at nighttime. In the strategy 3, they are 3.75 K, 3.90 K, and 2.87 K at day, and 2.80 K, 3.61 K, and 2.79 K at night. Furthermore, adjustments from annual temperature cycle are discussed. The proposed method is promising in future research for reconstructing LST with high spatiotemporal resolution at large scales thanks to its simple process, high accuracy, and seamless reconstruction.
英文关键词	Land surface temperature Clouds Spatiotemporal phenomena MODIS Meteorology Temperature measurement Land surface Annual temperature cycle (ATC) downscaling land surface temperature (LST) passive microwave (PMW)
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:000571725700002
WOS关键词	SIMPLE RETRIEVAL METHOD ; URBAN HEAT-ISLAND ; UNITED-STATES ; ARID AREA ; VALIDATION ; WEATHER ; WATER ; REFINEMENTS ; PREDICTION ; CHINA
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
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/2XILL650/326428
作者单位	[Li, Kangning; Chen, Yunhao; Xia, Haiping; Gong, Adu] Beijing Normal Univ, Beijing Key Lab Environm Remote Sensing & Digital, Fac Geog Sci, Beijing 100875, Peoples R China; [Li, Kangning; Chen, Yunhao; Xia, Haiping; Gong, Adu] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Fac Geog Sci, Beijing 100875, Peoples R China; [Guo, Zheng] Natl Satellite Meteorol Ctr, Beijing 100081, Peoples R China
推荐引用方式 GB/T 7714	Li, Kangning,Chen, Yunhao,Xia, Haiping,et al. Adjustment From Temperature Annual Dynamics for Reconstructing Land Surface Temperature Based on Downscaled Microwave Observations[J]. 北京师范大学,2020,13:5272-5283.
APA	Li, Kangning,Chen, Yunhao,Xia, Haiping,Gong, Adu,&Guo, Zheng.(2020).Adjustment From Temperature Annual Dynamics for Reconstructing Land Surface Temperature Based on Downscaled Microwave Observations.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,13,5272-5283.
MLA	Li, Kangning,et al."Adjustment From Temperature Annual Dynamics for Reconstructing Land Surface Temperature Based on Downscaled Microwave Observations".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 13(2020):5272-5283.