A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data
Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie · Institute of Geographic Sciences and Natural Resources Research · +8 more institutions
Abstract
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…
Citation impact
- FWCI
- 14.25
- Percentile
- 100%
- References
- 228
Authors
8- ZLZhao-Liang LiCorresponding
Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie, Institute of Geographic Sciences and Natural Resources Research
- RTRonglin Tang
TGS (United Kingdom), Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie, Institute of Geographic Sciences and Natural Resources Research, University of Chinese Academy of Sciences
- ZWZhengming Wan
University of California, Santa Barbara, Engineering Service Center und Handel (Germany)
- YBYuyun Bi
Twitter (United States), Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie, Institute of Agricultural Resources and Regional Planning
- CZChenghu Zhou
University of California, Santa Barbara, Institute of Geographic Sciences and Natural Resources Research
Topics & keywords
- Extrapolation
- Evapotranspiration
- Remote sensing
- Scale (ratio)
- Satellite
- Environmental science
- Computer science
- Estimation
- Affordable and clean energy