Monitoring functional traits of complex temperate forests using Sentinel-2 data during a severe drought period

Forest ecosystems are crucial for preserving biodiversity and providing ecosystem services. The Ticino Park is a temperate mixed forest, one of the few natural ecosystems in northern Italy, which is facing increasing natural and anthropogenic disturbances exacerbated by climate change. Remote sensing is a cost-effective tool for the indirect estimation of forest status. However, it has typically relied on indirect proxies that often have coarse spatio-temporal resolution. This study investigates the possibility of deriving high temporal resolution time series of forest traits to identify drought-induced anomalies and relate them to differences in forest type and environmental factors. Sentinel-2 images from 2017 to 2022 were analysed, with 2022 being characterised by a severe drought. Leaf area index (LAI), canopy chlorophyll content (CCC), and canopy water content (CWC) were retrieved from Sentinel-2 time series through the S2 Toolbox and validated using measurements collected during an intensive field campaign in 2022. A positive and statistically significant correlation was found for all traits. The best results were obtained for LAI (R² = 0.75, nRMSE = 11.49 %) and CCC (R² = 0.82, nRMSE = 13.56 %), while slightly worse results were obtained for CWC (R² = 0.64, nRMSE = 8.84 %). The accurate retrieval of LAI, CCC and CWC enabled the analysis of the temporal and spatial variations of the daily standardised anomalies (DSA). CCC reached the most negative DSA values, highlighting its higher sensitivity in detecting the effects of water shortage compared to CWC and LAI. The statistical analysis showed that DSA was linked to forest types. Pine and black cherry exhibited the highest stress response, while hygrophilic black alder and chestnut were the least impacted. These results highlight the species-specific responses to drought and the importance of incorporating species information in forest monitoring. The developed methodology provides a cost-effective approach for monitoring forest status and supporting effective management strategies.