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Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem.

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Title: Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem.
Authors: Reyes Rojas, Luis A.1 (AUTHOR) lreyesrojas@uchile.cl, Moletto-Lobos, Italo2 (AUTHOR) italo.moletto@um.uchile.cl, Corradini, Fabio3 (AUTHOR) fabio.corradini@inia.cl, Mattar, Cristian2 (AUTHOR) cmattar@uchile.cl, Fuster, Rodrigo1 (AUTHOR) rfuster@uchile.cl, Escobar-Avaria, Cristián1 (AUTHOR) crescobar@uchile.cl
Source: Remote Sensing. Oct2021, Vol. 13 Issue 20, p4105-4105. 1p.
Database: Academic Search Ultimate
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  Data: Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Oct2021, Vol. 13 Issue 20, p4105-4105. 1p.
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        Value: 10.3390/rs13204105
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      – Code: eng
        Text: English
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      – TitleFull: Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem.
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              Text: Oct2021
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              Y: 2021
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