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Evaluating Four Remote Sensing Methods for Estimating Surface Air Temperature on a Regional Scale

文献类型：期刊论文

作者Liu, Suhua1,2; Su, Hongbo3; Tian, Jing1; Zhang, Renhua1; Wang, Weizhen4; Wu, Yueru4
刊名JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
出版日期2017-03-01
卷号56期号:3页码:803-814
ISSN号1558-8424
DOI10.1175/JAMC-D-16-0188.1
通讯作者Su, Hongbo(hongbo@ieee.org) ; Tian, Jing(tianj.04b@igsnrr.ac.cn)
英文摘要Surface air temperature is a basic meteorological variable to monitor the environment and assess climate change. Four remote sensing methods-the temperature-vegetation index (TVX), the univariate linear regression method, the multivariate linear regression method, and the advection-energy balance for surface air temperature (ADEBAT)-have been developed to acquire surface air temperature on a regional scale. To evaluate their utilities, they were applied to estimate the surface air temperature in northwestern China and were compared with each other through regressive analyses, t tests, estimation errors, and analyses on estimations of different underlying surfaces. Results can be summarized into three aspects: 1) The regressive analyses and t tests indicate that the multivariate linear regression method and the ADEBAT provide better accuracy than the other two methods. 2) Frequency histograms on estimation errors show that the multivariate linear regression method produces the minimum error range, and the univariate linear regression method produces the maximum error range. Errors of the multivariate linear regression method exhibit a nearly normal distribution and that of the ADEBAT exhibit a bimodal distribution, whereas the other two methods display negative skewness distributions. 3) Estimates on different underlying surfaces show that the TVX and the univariate linear regression method are significantly limited in regions with sparse vegetation cover. The multivariate linear regression method has estimation errors within 1 degrees C and without high levels of errors, and the ADEBAT also produces high estimation errors on bare ground.
WOS关键词ENERGY-BALANCE ; AVHRR DATA ; SPARSE VEGETATION ; VAPOR-PRESSURE ; DAILY MAXIMUM ; MODIS DATA ; HEAT-FLUX ; EVAPOTRANSPIRATION ; AFRICA ; RESISTANCE
资助项目National Key Research and Development Program of China[2016YFA0602501] ; National Natural Science Foundation of China[41571356] ; National Natural Science Foundation of China[41671354] ; National Natural Science Foundation of China[41671373] ; National Basic Research Program of China[2013CB733406] ; Key Project of National Natural Science Foundation of China[41301363]
WOS研究方向Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:000397873400017
出版者AMER METEOROLOGICAL SOC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; National Basic Research Program of China ; Key Project of National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/64805]  
专题中国科学院地理科学与资源研究所
通讯作者Su, Hongbo; Tian, Jing
作者单位1.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Florida Atlantic Univ, Dept Civil Environm & Geomat Engn, Boca Raton, FL 33431 USA
4.Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Heihe Remote Sensing Expt Res Stn, Lanzhou, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Suhua,Su, Hongbo,Tian, Jing,et al. Evaluating Four Remote Sensing Methods for Estimating Surface Air Temperature on a Regional Scale[J]. JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY,2017,56(3):803-814.
APA Liu, Suhua,Su, Hongbo,Tian, Jing,Zhang, Renhua,Wang, Weizhen,&Wu, Yueru.(2017).Evaluating Four Remote Sensing Methods for Estimating Surface Air Temperature on a Regional Scale.JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY,56(3),803-814.
MLA Liu, Suhua,et al."Evaluating Four Remote Sensing Methods for Estimating Surface Air Temperature on a Regional Scale".JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY 56.3(2017):803-814.

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来源：地理科学与资源研究所

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