Assessing Heatwave Impacts on Western Greece and Peloponnese Vineyards Through Remote Sensing

The Mediterranean region is widely recognized as a climate change hotspot. Extreme heat events are becoming more frequent and present increasing risks to vineyards and other cultivated crops. Satellite remote sensing allow us to monitor over large areas and long time periods vegetation responses to such events. However, it is important to understand spatial scaling effects (i.e., due to vegetation crown architecture and satellite pixel resolution) and lagged temporal dynamics (i.e., legacy effects in vegetation functioning). Here, we focus on three selected viticultural regions in the Peloponnese and Western Greece and we synthesized remote sensing data and environmental variables to address the above-mentioned issues. The regional climatic conditions at each vineyard, were identified sing ERA5-Land reanalysis dataset, accessed via the Copernicus Climate Data Store. With respect to spatial scale, we analysed NDVI time series derived from Landsat (30 m), Sentinel-2 (10 m), and PlanetScope (3 m) imagery. These data are used to assess spatiotemporal heterogeneities in vineyard canopy responses to extreme heat events. Moreover, we analysed NDVI responses during recent heatwave events, characterized by their duration, timing within the growing season, and maximum temperature, and quantified vegetation recovery following each event. The results showed consistent NDVI reductions associated with heatwaves, with differences in the intensity and duration of the response across regions, vineyard varieties, irrigation practices, and events. Landsat data provide a stable long-term reference, while Sentinel-2 and PlanetScope improve the detection of short-term changes and spatial variability. Despite differences in spatial resolution, all three datasets capture similar temporal NDVI patterns. These results demonstrate that multi-sensor NDVI time series can be used to detect and compare vineyard vegetation responses to extreme heat events. The proposed approach provides a simple and widely applicable framework for monitoring agricultural impacts of climate extremes using operational openly-available satellite data.