Climatic Drivers of the Complex Phenology of the Mediterranean Semi-Deciduous Shrub Phlomis fruticosa Based on Satellite-Derived EVI

A 21-year Enhanced Vegetation Index (EVI) time-series produced from MODIS satellite images was used to study the complex phenological cycle of the drought semi-deciduous shrub Phlomis fruticosa and additionally to identify and compare phenological events between two Mediterranean sites with different microclimates. In the more xeric Araxos site, spring leaf fall starts earlier, autumn revival occurs later, and the dry period is longer, compared with the more favorable Louros site. Accordingly, the control of climatic factors on phenological events was examined and found that the Araxos site is mostly influenced by rain related events while Louros site by both rain and temperature. Spring phenological events showed significant shifts at a rate of 1-4.9 days per year in Araxos, which were positively related to trends for decreasing spring precipitation and increasing summer temperature. Furthermore, the climatic control on the inter-annual EVI fluctuation was examined through multiple linear regression and machine learning approaches. For both sites, temperature during the previous 2-3 months and rain days of the previous 3 months were identified as the main drivers of the EVI profile. Our results emphasize the importance of focusing on a single species and small-spatial-scale information in connecting vegetation responses to the climate crisis.

Automated summary

In this study, a 21-year time-series of Enhanced Vegetation Index (EVI) derived from MODIS satellite images was used to analyze the phenological cycle of the drought semi-deciduous shrub Phlomis fruticosa and compare phenological events between two Mediterranean sites. The results showed that climatic factors, particularly rain and temperature, have different influences on phenological events in different sites. The study also identified temperature and rain days as the main drivers of EVI fluctuations through regression and machine learning methods.