An improved method for separating soil and vegetation component temperatures based on diurnal temperature cycle model and spatial correlation

doi:10.1016/j.rse.2020.111979

	An improved method for separating soil and vegetation component temperatures based on diurnal temperature cycle model and spatial correlation
	Liu, Xiangyang 1,2; Tang, Bo-Hui 1,2; Li, Zhao-Liang 1,2,3; Zhou, Chenghu 1,2; Wu, Wenbin 3; Rasmussen, Mads Olander 4
刊名	REMOTE SENSING OF ENVIRONMENT
	2020-10-01
卷号	248 页码:13
关键词	Component temperature separation Land surface temperature (LST) Diurnal temperature cycle (DTC) Spatial weight
ISSN号	0034-4257
DOI	10.1016/j.rse.2020.111979
通讯作者	Tang, Bo-Hui(tangbh@igsnrr.ac.cn) ; Wu, Wenbin(wuwenbin@caas.cn)
英文摘要	This paper proposed an improved method for separating soil and vegetation component temperatures from one pixel land surface temperature (LST) using multi-pixel and multi-temporal data. The two main features of the method are (1) the use of a diurnal temperature cycle (DTC) model to describe component temperatures and (2) the application of a spatial weighting matrix to consider the spatial correlation of component temperatures. The proposed method was evaluated using an extensive simulated dataset with five component temperature types, three LST errors and 69 fractional vegetation cover (FVC) types, and field measurements with a high temporal frequency. Due to the time extendibility of DTC model, the possibility for retrieving component temperatures at any time was analyzed. Correspondingly, the schemes for selecting the best observations for four representative periods, i.e., 10:00-12:00, 09:00-18:00, 18:00-03:00 (on the next day) and 09:00-03:00, were determined. The validations showed satisfactory accuracies, and it was found that the errors were significantly influenced by the original LST retrieval error. In addition, the difference between the ideal temperature pattern from the DTC model and the actual temperature variation also affected the accuracy of the temporally extended component temperatures. Furthermore, sensitivity analyses indicated that the separation accuracy was independent of the uncertainty of the component emissivity but was influenced by FVC. Specifically, the retrieval accuracy was sensitive to the size and variation of FVC, and the latter had a more significant influence, but the result was less sensitive to the retrieval error and angular effect of FVC. Considering its accuracy, operability and robustness, the proposed method is effective for separating soil and vegetation component temperatures.
资助项目	National Natural Science Foundation of China[41871244] ; Innovation Project of LREIS[O88RA801YA]
WOS关键词	LAND-SURFACE TEMPERATURE ; REMOTE-SENSING MODEL ; IN-SITU ; VALIDATION ; CROP ; ANISOTROPY ; RETRIEVAL ; ALGORITHM ; PRODUCTS ; COVER
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:000568715700001
资助机构	National Natural Science Foundation of China ; Innovation Project of LREIS
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/156927]
专题	中国科学院地理科学与资源研究所
通讯作者	Tang, Bo-Hui; Wu, Wenbin
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 3.Chinese Acad Agr Sci, Key Lab Agr Remote Sensing, Minist Agr & Rural Affairs, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China 4.DHI GRAS AS, DK-2970 Horsholm, Denmark
推荐引用方式 GB/T 7714	Liu, Xiangyang,Tang, Bo-Hui,Li, Zhao-Liang,et al. An improved method for separating soil and vegetation component temperatures based on diurnal temperature cycle model and spatial correlation[J]. REMOTE SENSING OF ENVIRONMENT,2020,248:13.
APA	Liu, Xiangyang,Tang, Bo-Hui,Li, Zhao-Liang,Zhou, Chenghu,Wu, Wenbin,&Rasmussen, Mads Olander.(2020).An improved method for separating soil and vegetation component temperatures based on diurnal temperature cycle model and spatial correlation.REMOTE SENSING OF ENVIRONMENT,248,13.
MLA	Liu, Xiangyang,et al."An improved method for separating soil and vegetation component temperatures based on diurnal temperature cycle model and spatial correlation".REMOTE SENSING OF ENVIRONMENT 248(2020):13.