Analysis of Copernicus’ ERA5 Climate Reanalysis Data as a Replacement for Weather Station Temperature Measurements in Machine Learning Models for Olive Phenology Phase Prediction
| Autor: | S. Marchi, Igor G. Olaizola, Noelia Oses, D. Guidotti, Izar Azpiroz, Marco Quartulli |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
BBCH scale Climate Change olive phenology modeling Climate change lcsh:Chemical technology Machine learning computer.software_genre 01 natural sciences Biochemistry Temperature measurement Article Analytical Chemistry Weather station BBCH-scale Benchmark (surveying) Olea lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Weather 0105 earth and related environmental sciences business.industry Phenology phenophase Temperature 04 agricultural and veterinary sciences Growing degree-day base temperature Atomic and Molecular Physics and Optics Tree (data structure) machine learning Italy 13. Climate action 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Environmental science Artificial intelligence Seasons business computer |
| Zdroj: | Sensors Volume 20 Issue 21 Sensors (Basel, Switzerland) Sensors, Vol 20, Iss 6381, p 6381 (2020) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s20216381 |
| Popis: | Knowledge of phenological events and their variability can help to determine final yield, plan management approach, tackle climate change, and model crop development. THe timing of phenological stages and phases is known to be highly correlated with temperature which is therefore an essential component for building phenological models. Satellite data and, particularly, Copernicus&rsquo ERA5 climate reanalysis data are easily available. Weather stations, on the other hand, provide scattered temperature data, with fragmentary spatial coverage and accessibility, as such being scarcely efficacious as unique source of information for the implementation of predictive models. However, as ERA5 reanalysis data are not real temperature measurements but reanalysis products, it is necessary to verify whether these data can be used as a replacement for weather station temperature measurements. The aims of this study were: (i) to assess the validity of ERA5 data as a substitute for weather station temperature measurements, (ii) to test different machine learning models for the prediction of phenological phases while using different sets of features, and (iii) to optimize the base temperature of olive tree phenological model. The predictive capability of machine learning models and the performance of different feature subsets were assessed when comparing the recorded temperature data, ERA5 data, and a simple growing degree day phenological model as benchmark. Data on olive tree phenology observation, which were collected in Tuscany for three years, provided the phenological phases to be used as target variables. The results show that ERA5 climate reanalysis data can be used for modelling phenological phases and that these models provide better predictions in comparison with the models trained with weather station temperature measurements. |
| Databáze: | OpenAIRE |
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