Machine learning models to predict daily actual evapotranspiration of citrus orchards under regulated deficit irrigation

Antonino Pagano; Federico Amato; Matteo Ippolito; Dario De Caro; Daniele Croce; Antonio Motisi; Giuseppe Provenzano; Ilenia Tinnirello

doi:10.1016/j.ecoinf.2023.102133

journal article Open Access Sep 01, 2023

Machine learning models to predict daily actual evapotranspiration of citrus orchards under regulated deficit irrigation

Antonino Pagano Federico Amato

Matteo Ippolito

Dario De Caro Daniele Croce

Antonio Motisi Giuseppe Provenzano Ilenia Tinnirello

Ecological Informatics Vol. 76 pp. 102133 · Elsevier BV

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Assessing the performance of the Gaussian Process Regression algorithm to fill gaps in the time-series of daily actual evapotranspiration of different crops in temperate and continental zones using ground and remotely sensed data

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54

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References

Details

Published: Sep 01, 2023
Vol/Issue: 76
Pages: 102133
License: View

Authors

Department of Engineering, University of Palermo, Palermo, Italy

Funding

European Commission

Ministero dell’Istruzione, dell’Università e della Ricerca

Cite This Article

Antonino Pagano, Federico Amato, Matteo Ippolito, et al. (2023). Machine learning models to predict daily actual evapotranspiration of citrus orchards under regulated deficit irrigation. Ecological Informatics, 76, 102133. https://doi.org/10.1016/j.ecoinf.2023.102133

Machine learning models to predict daily actual evapotranspiration of citrus orchards under regulated deficit irrigation

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