{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T21:09:01Z","timestamp":1781039341271,"version":"3.54.1"},"reference-count":39,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020","doi-asserted-by":"publisher","award":["857202"],"award-info":[{"award-number":["857202"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.3390\/s20216381","type":"journal-article","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T14:10:41Z","timestamp":1605017441000},"page":"6381","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Analysis of Copernicus\u2019 ERA5 Climate Reanalysis Data as a Replacement for Weather Station Temperature Measurements in Machine Learning Models for Olive Phenology Phase Prediction"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7317-5184","authenticated-orcid":false,"given":"Noelia","family":"Oses","sequence":"first","affiliation":[{"name":"Vicomtech Foundation Basque Research and Technology Alliance (BRTA), 20000 Donostia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0401-8139","authenticated-orcid":false,"given":"Izar","family":"Azpiroz","sequence":"additional","affiliation":[{"name":"Vicomtech Foundation Basque Research and Technology Alliance (BRTA), 20000 Donostia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susanna","family":"Marchi","sequence":"additional","affiliation":[{"name":"Agricolus s.r.l., 06100 Perugia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diego","family":"Guidotti","sequence":"additional","affiliation":[{"name":"Agricolus s.r.l., 06100 Perugia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5735-2072","authenticated-orcid":false,"given":"Marco","family":"Quartulli","sequence":"additional","affiliation":[{"name":"Vicomtech Foundation Basque Research and Technology Alliance (BRTA), 20000 Donostia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9965-2038","authenticated-orcid":false,"given":"Igor","family":"G. Olaizola","sequence":"additional","affiliation":[{"name":"Vicomtech Foundation Basque Research and Technology Alliance (BRTA), 20000 Donostia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1016\/j.agrformet.2011.03.003","article-title":"Assessing the effects of climate change on the phenology of European temperate trees","volume":"151","author":"Vitasse","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105103","DOI":"10.1016\/j.compag.2019.105103","article-title":"A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques","volume":"168","author":"Lee","year":"2020","journal-title":"Comput. Electron. 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