Predicting the fundamental fluxes of an eddy-covariance station using machine learning methods
Monitoring tools are needed to maximise living systems' ability to mitigate emissions and adapt to changing environmental conditions. Therefore, it is important to be able to predict the fundamental fluxes in crops, in this case vineyards, such as sensible heat flux (H), latent heat flux (LE) and ca...
| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/20.500.11939/8965 https://www.sciencedirect.com/science/article/pii/S1574954124001808 |
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