Improving the Evapotranspiration Estimation under Cloudy Condition by Extending the Ts-VI Triangle Model

Evapotranspiration (ET) of soil-vegetation system is the main process of the water and energy exchange between the atmosphere and the land surface. Spatio-temporal continuous ET is vitally important to agriculture and ecological applications. Surface temperature and vegetation index (Ts-VI) triangle...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 13; no. 8; p. 1516
Main Authors Li, Boyang, Cui, Yaokui, Geng, Xiaozhuang, Li, Huan
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2021
Subjects
Algorithms
Atmosphere
Availability
Clouds
Datasets
Estimates
Evapotranspiration
Hydrology
Irrigation
Land surface temperature
Missing data
Precipitation
Radiation
Remote monitoring
Remote sensing
River basins
robust regression
spatio-temporal continuity
Ts-VI ET model
Vegetation
Vegetation index
Heihe River
Gansu China
China
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Summary:Evapotranspiration (ET) of soil-vegetation system is the main process of the water and energy exchange between the atmosphere and the land surface. Spatio-temporal continuous ET is vitally important to agriculture and ecological applications. Surface temperature and vegetation index (Ts-VI) triangle ET model based on remote sensing land surface temperature (LST) is widely used to monitor the land surface ET. However, a large number of missing data caused by the presence of clouds always reduces the availability of the main parameter LST, thus making the remote sensing-based ET estimation unavailable. In this paper, a method to improve the availability of ET estimates from Ts-VI model is proposed. Firstly, continuous LST product of the time series is obtained using a reconstruction algorithm, and then, the reconstructed LST is applied to the estimate ET using the Ts-VI model. The validation in the Heihe River Basin from 2009 to 2011 showed that the availability of ET estimates is improved from 25 days per year (d/yr) to 141 d/yr. Compared with the in situ data, a very good performance of the estimated ET is found with RMSE 1.23 mm/day and R2 0.6257 at point scale and RMSE 0.32 mm/day and R2 0.8556 at regional scale. This will improve the understanding of the water and energy exchange between the atmosphere and the land surface, especially under cloudy conditions.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs13081516