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

文献类型：期刊论文

作者Liu, Xiangyang3,4; Tang, Bo-Hui3,4; Li, Zhao-Liang1,3,4; Zhou, Chenghu3,4; Wu, Wenbin1; Rasmussen, Mads Olander2
刊名REMOTE SENSING OF ENVIRONMENT
出版日期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
DOI10.1016/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.
WOS关键词LAND-SURFACE TEMPERATURE ; REMOTE-SENSING MODEL ; IN-SITU ; VALIDATION ; CROP ; ANISOTROPY ; RETRIEVAL ; ALGORITHM ; PRODUCTS ; COVER
资助项目National Natural Science Foundation of China[41871244] ; Innovation Project of LREIS[O88RA801YA]
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000568715700001
出版者ELSEVIER SCIENCE INC
资助机构National Natural Science Foundation of China ; Innovation Project of LREIS
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 Agr Sci, Key Lab Agr Remote Sensing, Minist Agr & Rural Affairs, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China
2.DHI GRAS AS, DK-2970 Horsholm, Denmark
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
4.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		 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.

入库方式： OAI收割

来源：地理科学与资源研究所

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