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Open AccessArticle

Actual Evapotranspiration Estimates in Arid Cold Regions Using Machine Learning Algorithms with In Situ and Remote Sensing Data

1
Departamento de Ingeniería Hidráulica y Ambiental, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
2
Centro de Desarrollo Urbano Sustentable (CEDEUS), Santiago 7820436, Chile
3
Centro de Excelencia en Geotermia de los Andes (CEGA), Santiago 7820436, Chile
*
Author to whom correspondence should be addressed.
Academic Editor: Josef Tanny
Water 2021, 13(6), 870; https://doi.org/10.3390/w13060870
Received: 15 February 2021 / Revised: 18 March 2021 / Accepted: 19 March 2021 / Published: 23 March 2021
(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)
Actual evapotranspiration (ETa) estimations in arid regions are challenging because this process is highly dynamic over time and space. Nevertheless, several studies have shown good results when implementing empirical regression formulae that, despite their simplicity, are comparable in accuracy to more complex models. Although many types of regression formulae to estimate ETa exist, there is no consensus on what variables must be included in the analysis. In this research, we used machine learning algorithms—through implementation of empirical linear regression formulae—to find the main variables that control daily and monthly ETa in arid cold regions, where there is a lack of available ETa data. Meteorological data alone and then combined with remote sensing vegetation indices (VIs) were used as input in ETa estimations. In situ ETa and meteorological data were obtained from ten sites in Chile, Australia, and the United States. Our results indicate that the available energy is the main meteorological variable that controls ETa in the assessed sites, despite the fact that these regions are typically described as water-limited environments. The VI that better represents the in situ ETa is the Normalized Difference Water Index, which represents water availability in plants and soils. The best performance of the regression equations in the validation sites was obtained for monthly estimates with the incorporation of VIs (R2 = 0.82), whereas the worst performance of these equations was obtained for monthly ETa estimates when only meteorological data were considered. Incorporation of remote-sensing information results in better ETa estimates compared to when only meteorological data are considered. View Full-Text
Keywords: evapotranspiration; remote sensing; machine learning; arid regions evapotranspiration; remote sensing; machine learning; arid regions
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MDPI and ACS Style

Mosre, J.; Suárez, F. Actual Evapotranspiration Estimates in Arid Cold Regions Using Machine Learning Algorithms with In Situ and Remote Sensing Data. Water 2021, 13, 870. https://doi.org/10.3390/w13060870

AMA Style

Mosre J, Suárez F. Actual Evapotranspiration Estimates in Arid Cold Regions Using Machine Learning Algorithms with In Situ and Remote Sensing Data. Water. 2021; 13(6):870. https://doi.org/10.3390/w13060870

Chicago/Turabian Style

Mosre, Josefina; Suárez, Francisco. 2021. "Actual Evapotranspiration Estimates in Arid Cold Regions Using Machine Learning Algorithms with In Situ and Remote Sensing Data" Water 13, no. 6: 870. https://doi.org/10.3390/w13060870

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