Prediction of Evapotranspiration in the Pampean Plain from CERES Satellite Products and Machine Learning Techniques
A key aspect in agricultural zones, such as the Pampean Plain of Argentina, is to accurately estimate evapotranspiration rates to optimize crops and irrigation requirements and the floods and droughts prediction. In this sense, we evaluate six machine learning approaches to estimate the reference an...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/225298 |
| Acceso en línea: | http://hdl.handle.net/11336/225298 |
| Access Level: | acceso abierto |
| Palabra clave: | EVAPOTRANSPIRACIÓN MACHINE LEARNING CERES REMOTE SENSING https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 |
| Sumario: | A key aspect in agricultural zones, such as the Pampean Plain of Argentina, is to accurately estimate evapotranspiration rates to optimize crops and irrigation requirements and the floods and droughts prediction. In this sense, we evaluate six machine learning approaches to estimate the reference and actual evapotranspiration (ET0 and ETa) through CERES satellite products data. The results obtained applying machine learning techniques were compared with values obtained from ground-based information. After training and validating the algorithms, we observed that Support Vector machine-based Regressor (SVR) showed the best accuracy. Then, with an independent dataset, the calibrated SVR were tested. For predicting the reference evapotranspiration, we observed statistical errors of MAE = 0.437 mm d-1, and RMSE = 0.616 mm d-1, with a determination coefficient, R2, of 0.893. Regarding actual evapotranspiration modelling, we observed statistical errors of MAE = 0.422 mm d-1, and RMSE = 0.599 mm d-1, with a R2 of 0.614. Comparing the results obtained with the machine learning models developed another studies in the same field, we understand that the results are promising and represent a baseline for future studies. Combining CERES data with information from other sources may generate more specific evapotranspiration products, considering the different land covers. |
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