Towards a remote-sensing driven model of isoprene emissions from Alpine Tundra.

This study investigates isoprene emissions in a high-latitude Alpine tundra ecosystem, focusing on using near-field remote sensing of surface temperatures, the Photochemical Reflectance Index (PRI) and Normalized Difference Vegetation Index (NDVI), and meteorological measurements to model these emissions. Isoprene is a key biogenic volatile organic compound (BVOC) emitted by select plants, and which can impact atmospheric chemistry and climate. Increased temperatures, particularly in high latitudes, may enhance isoprene emissions due to extended growing seasons and heightened plant stress. The research was conducted in Finse, Norway, where isoprene and CO₂ fluxes were measured with eddy-covariance alongside spectral-, meteorological data, and surface temperature. A Random Forest (RF) model was developed to predict isoprene fluxes, considering the variable importance of different environmental factors. Results showed that surface temperature and CO₂ flux were consistently important predictors, across three differential temporal data aggregations (hourly, daily, weekly) while PRI demonstrated low predictive power, possibly due to the heterogeneous vegetation and variable light conditions. NDVI was more effective than anticipated, likely linked to phenological changes in vegetation. Model performance varied with temporal resolution, with weekly data achieving the highest predictive accuracy (R² up to 0.76). The RF model accurately reflected seasonal emission patterns but underestimated short-term peaks, suggesting potential in combining machine-learning with process-based modeling. This research highlights the promise of proxy data from remote sensing for scaling BVOC emission models to regional levels, essential for understanding climate impacts in Arctic ecosystems.