A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data
文献类型：SCI/SSCI论文
| 作者 | Li Z. L. ; Tang R. L. ; Wan Z. M. ; Bi Y. Y. ; Zhou C. H. ; Tang B. H. ; Yan G. J. ; Zhang X. Y. |
| 发表日期 | 2009 |
| 关键词 | remote sensing evapotranspiration methodology review temporal scaling surface-energy balance large-aperture scintillometer southern great-plains thermal infrared data latent-heat flux 4-dimensional variational assimilation radiometric temperature observations hydrologic data assimilation atmospheric boundary-layer ensemble kalman filter |
| 英文摘要 | An overview of the commonly applied evapotranspiration (ET) models using remotely sensed data is given to provide insight into the estimation of ET on a regional scale from satellite data. Generally, these models vary greatly in inputs, main assumptions and accuracy of results, etc. Besides the generally used remotely sensed multi-spectral data from visible to thermal infrared bands, most remotely sensed ET models, from simplified equations models to the more complex physically based two-source energy balance models, must rely to a certain degree on ground-based auxiliary measurements in order to derive the turbulent heat fluxes on a regional scale. We discuss the main inputs, assumptions, theories, advantages and drawbacks of each model. Moreover, approaches to the extrapolation of instantaneous ET to the daily values are also briefly presented. In the final part, both associated problems and future trends regarding these remotely sensed ET models were analyzed to objectively show the limitations and promising aspects of the estimation of regional ET based on remotely sensed data and ground-based measurements. |
| 出处 | Sensors |
| 卷 | 9 |
| 期 | 5 |
| 页 | 3801-3853 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 1424-8220 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/23571]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Li Z. L.,Tang R. L.,Wan Z. M.,et al. A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data. 2009. |
入库方式： OAI收割
来源：地理科学与资源研究所
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