Global Land Surface Evapotranspiration Estimation From Meteorological and Satellite Data Using the Support Vector Machine and Semiempirical Algorithm
文献类型：期刊论文
| 作者 | Liu, Meng1,2; Tang, Ronglin1,2; Li, Zhao-Liang1,3; Yao, Yunjun4; Yan, Guangjian4 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2018-02-01 |
| 卷号 | 11期号:2页码:513-521 |
| 关键词 | Evapotranspiration (ET) support vector machine (SVM) |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2017.2788462 |
| 通讯作者 | Tang, Ronglin(trl_wd@163.com) |
| 英文摘要 | Evapotranspiration (ET) is the combination process of the surface evaporation and plant transpiration, which occur simultaneously, and it links the terrestrial water cycles, carbon cycles, and energy exchange. In this study, based on the observations from 242 global FLUXnet sites, with daily mean temperature, relative humidity, net radiation, wind speed, incoming shortwave radiation, maximum temperature, minimum temperature, normalized difference vegetation index, altitude, difference in temperature, and observed ET as input data, we used a support vector machine and a semiempirical algorithm to estimate the land surface daily ET at nine different vegetation- type sites. Subsequently, based on the meteorological reanalysis data combined with remote sensing data, we estimated regional land surface ET of China during 1982-2010. The results showed that, for all vegetation-type sites, when the predicted ET was validated with the eddy covariance measurements, the support vector machine algorithm undervalued ET while the semiempirical algorithm overvalued ET. When five indicators and the second classification method were selected, the semiempirical algorithm probably could explain 56%-76% of the land surface ET change, whereas the support vector machine algorithm probably could explain 71%-85%. The regional values of annual daily average ET varied from 5.8 to 110.5 W/m(2), and the land surface ET overall trend decreased from the southeast to the northwest in China. |
| WOS关键词 | LATENT-HEAT FLUX ; CHINA ; EVAPORATION ; MODELS ; ASSIMILATION ; NETWORKS ; WATER |
| 资助项目 | National Natural Science Foundation of China[41571351] ; National Natural Science Foundation of China[41571367] ; National Natural Science Foundation of China[41401659] ; International Science and Technology Cooperation Program of China[2014DFE10220] |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000425661700015 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; International Science and Technology Cooperation Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/56976]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Tang, Ronglin |
| 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, Beijing 100101, Peoples R China 3.Chinese Acad Agr Sci, Minist Agr, Key Lab Agriinformat, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China 4.Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Meng,Tang, Ronglin,Li, Zhao-Liang,et al. Global Land Surface Evapotranspiration Estimation From Meteorological and Satellite Data Using the Support Vector Machine and Semiempirical Algorithm[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2018,11(2):513-521. |
| APA | Liu, Meng,Tang, Ronglin,Li, Zhao-Liang,Yao, Yunjun,&Yan, Guangjian.(2018).Global Land Surface Evapotranspiration Estimation From Meteorological and Satellite Data Using the Support Vector Machine and Semiempirical Algorithm.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,11(2),513-521. |
| MLA | Liu, Meng,et al."Global Land Surface Evapotranspiration Estimation From Meteorological and Satellite Data Using the Support Vector Machine and Semiempirical Algorithm".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 11.2(2018):513-521. |
入库方式： OAI收割
来源：地理科学与资源研究所
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