Comparison of Climate Reanalysis and Remote-Sensing Data for Predicting Olive Phenology through Machine-Learning Methods
| Autor: | S. Marchi, Izar Azpiroz, Igor G. Olaizola, Noelia Oses, Marco Quartulli, D. Guidotti |
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| Rok vydání: | 2021 |
| Předmět: |
Decision support system
010504 meteorology & atmospheric sciences Phenology machine learning remote sensing vegetation indices ERA5 growing degree days Terrain 04 agricultural and veterinary sciences Growing degree-day 01 natural sciences Reflectivity Environmental data 13. Climate action Remote sensing (archaeology) Sustainable management 040103 agronomy & agriculture 0401 agriculture forestry and fisheries General Earth and Planetary Sciences Environmental science 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Remote Sensing; Volume 13; Issue 6; Pages: 1224 Remote Sensing |
| ISSN: | 2072-4292 |
| Popis: | Machine-learning algorithms used for modelling olive-tree phenology generally and largely rely on temperature data. In this study, we developed a prediction model on the basis of climate data and geophysical information. Remote measurements of weather conditions, terrain slope, and surface spectral reflectance were considered for this purpose. The accuracy of the temperature data worsened when replacing weather-station measurements with remote-sensing records, though the addition of more complete environmental data resulted in an efficient prediction model of olive-tree phenology. Filtering and embedded feature-selection techniques were employed to analyze the impact of variables on olive-tree phenology prediction, facilitating the inclusion of measurable information in decision support frameworks for the sustainable management of olive-tree systems. |
| Databáze: | OpenAIRE |
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