An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping
文献类型：期刊论文
| 作者 | Wang, Tong1,2; Tang, Ronglin1,2 ; Li, Zhao-Liang3,4; Jiang, Yazhen2,3; Liu, Meng1,2; Niu, Lu1,2
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| 刊名 | REMOTE SENSING
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| 出版日期 | 2019-04-01 |
| 卷号 | 11期号:7页码:21 |
| 关键词 | evapotranspiration fusion multi-source satellite data Landsat 8 MODIS SADFAET |
| ISSN号 | 2072-4292 |
| DOI | 10.3390/rs11070761 |
| 通讯作者 | Tang, Ronglin(tangrl@lreis.ac.cn) |
| 英文摘要 | Continuous high spatio-temporal resolution monitoring of evapotranspiration (ET) is critical for water resource management and the quantification of irrigation water efficiency at both global and local scales. However, available remote sensing satellites cannot generally provide ET data at both high spatial and temporal resolutions. Data fusion methods have been widely applied to estimate ET at a high spatio-temporal resolution. Nevertheless, most fusion methods applied to ET are initially used to integrate land surface reflectance, the spectral index and land surface temperature, and few studies completely consider the influencing factor of ET. To overcome this limitation, this paper presents an improved ET fusion method, namely, the spatio-temporal adaptive data fusion algorithm for evapotranspiration mapping (SADFAET), by introducing critical surface temperature (the corresponding temperature to decide soil moisture), importing the weights of surface ET-indicative similarity (the influencing factor of ET, which is estimated from remote sensing data) and modifying the spectral similarity (the differences in spectral characteristics of different spatial resolution images) for the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). We fused daily Moderate Resolution Imaging Spectroradiometer (MODIS) and periodic Landsat 8 ET data in the SADFAET for the experimental area downstream of the Heihe River basin from April to October 2015. The validation results, based on ground-based ET measurements, indicated that the SADFAET could successfully fuse MODIS and Landsat 8 ET data (mean percent error: -5%), with a root mean square error of 45.7 W/m(2), whereas the ESTARFM performed slightly worse, with a root mean square error of 50.6 W/m(2). The more physically explainable SADFAET could be a better alternative to the ESTARFM for producing ET at a high spatio-temporal resolution. |
| WOS关键词 | LAND-SURFACE TEMPERATURE ; MODIS ; RESOLUTION ; MODEL ; FIELD ; SOIL ; GEOSTATIONARY ; REFLECTANCE ; MODULATION ; MANAGEMENT |
| 资助项目 | National Key R&D Program of China[2018YFA0605401] ; Beijing Municipal Science and Technology Project[Z181100005318003] ; Youth Innovation Promotion Association CAS[2015039] ; National Natural Science Foundation of China[41571351] ; National Natural Science Foundation of China[41571367] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000465549300026 |
| 出版者 | MDPI |
| 资助机构 | National Key R&D Program of China ; Beijing Municipal Science and Technology Project ; Youth Innovation Promotion Association CAS ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/59787]  |
| 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, Coll Resources & Environm, Beijing 100049, Peoples R China 3.CNRS, ICube, UdS, 300 Blvd Sebastien Brant,CS10413, F-67412 Illkirch Graffenstaden, France 4.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing, Minist Agr, Beijing 100081, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Tong,Tang, Ronglin,Li, Zhao-Liang,et al. An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping[J]. REMOTE SENSING,2019,11(7):21. |
| APA | Wang, Tong,Tang, Ronglin,Li, Zhao-Liang,Jiang, Yazhen,Liu, Meng,&Niu, Lu.(2019).An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping.REMOTE SENSING,11(7),21. |
| MLA | Wang, Tong,et al."An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping".REMOTE SENSING 11.7(2019):21. |
入库方式： OAI收割
来源：地理科学与资源研究所
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