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Vegetation phenology dynamics across ecoregions of Iberian Peninsula from MODIS NDVI time series: 2001-2017

Vegetation phenology, the study of the timing of biological cycles of plants and their relation with environmental factors, is considered an important ecological indicator of climate change. Different ecological processes, such as water and carbon cycles, energy fluxes or species interactions, can be altered by changes in the phenological patterns of plants. Furthermore, these changes could also have important consequences on economy (e. g. agriculture, forestry). Iberian Peninsula is one of the regions with the greatest diversity of ecosystems in European continent. It is therefore an excellent natural laboratory for monitoring vegetation dynamics. The goal of this study was to monitor the vegetation phenology dynamics across ecoregions of the Iberian Peninsula for the period between 2001 and 2017. 782 8-day composites of NDVI (Normalised Difference Vegetation Index) images were produced from the surface reflectance product MOD09Q1 at a spatial resolution of 250 meters. A Savitzky-Golay filter was applied to smooth the NDVI time series and a threshold-based method was used to extract three phenometrics: the start of the growing season (SOS), the end of growing season (EOS) and the length of the growing season (LOS). Results of this research showed how both SOS and EOS are significantly different between the northern and southern ecoregions. Cantabrian mixed forests and Pyrenees conifer and mixed forests presented the latest SOS and EOS. Iberian conifer forests, Northwest Iberian montane forests and Northeastern Spain and Southern France Mediterranean forests showed the highest internal variability in the phenological dates, which may be related with the behaviour of different land covers (e. g., phenology of natural vegetation vs. crop phenology) and the altitude effects on climatic conditions (e. g. increases in precipitation, decrease of the temperature). Phenological patterns of the Iberian ecoregions could contribute to improve the understanding of the potential environmental effects of climate change.