Vicomtech Foundation Basque Research and Technology Alliance (BRTA), 20000 Donostia, Spain.
Agricolus s.r.l., 06100 Perugia, Italy.
Sensors (Basel). 2020 Nov 9;20(21):6381. doi: 10.3390/s20216381.
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' 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.
物候事件及其可变性的知识有助于确定最终产量、制定管理方法、应对气候变化和模拟作物发育。物候阶段和时期的时间与温度高度相关，因此温度是构建物候模型的必要组成部分。卫星数据，特别是哥白尼的 ERA5 气候再分析数据，易于获取。相比之下，气象站提供的是分散的温度数据，空间覆盖范围和可访问性支离破碎，因此作为实施预测模型的唯一信息源几乎没有效果。然而，由于 ERA5 再分析数据不是真实的温度测量值，而是再分析产品，因此有必要验证这些数据是否可以替代气象站的温度测量值。本研究的目的是：（i）评估 ERA5 数据作为气象站温度测量值替代品的有效性；（ii）在使用不同特征集的情况下，测试不同的机器学习模型用于预测物候阶段；（iii）优化油橄榄物候模型的基础温度。通过将记录的温度数据、ERA5 数据和简单的生长度日物候模型（作为基准）进行比较，评估了机器学习模型的预测能力和不同特征子集的性能。油橄榄物候观测数据是在托斯卡纳收集的三年数据，这些数据被用来作为目标变量。结果表明，ERA5 气候再分析数据可用于模拟物候阶段，并且这些模型提供的预测结果优于使用气象站温度测量值训练的模型。
