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
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| 出版日期 | 2017-03-01 |
| 卷号 | 56期号:3页码:803-814 |
| ISSN号 | 1558-8424 |
| DOI | 10.1175/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. |
| 收录类别 | SCI |
| WOS关键词 | ENERGY-BALANCE ; AVHRR DATA ; SPARSE VEGETATION ; VAPOR-PRESSURE ; DAILY MAXIMUM ; MODIS DATA ; HEAT-FLUX ; EVAPOTRANSPIRATION ; AFRICA ; RESISTANCE |
| WOS研究方向 | Meteorology & Atmospheric Sciences |
| WOS类目 | Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:000397873400017 |
| 出版者 | AMER METEOROLOGICAL SOC |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2557595 |
| 专题 | 寒区旱区环境与工程研究所 |
| 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：寒区旱区环境与工程研究所
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