Mapping tree dieback using Sentinel-2 time series and generalized regression-based unmixing models reveals species-specific patterns across Germany

Temperate forests are experiencing widespread crown dieback and increasing mortality from combined biotic and abiotic stressors. While satellite-based remote sensing is increasingly used to monitor forest health, most studies rely on spectral greenness indices, rather than directly quantifying dieback. Fractional cover of non-photosynthetic vegetation (NPV) provides a more direct measurement of disturbance impact but has not been applied for broad-scale dieback mapping. We present an approach to map sub-pixel tree dieback using Sentinel-2 imagery and regression-based unmixing, without requiring extensive training data. We combined triangular feature space concepts, aerial imagery, and tree species maps to compile a multi-temporal spectral library for Germany and generate synthetic training data. We trained ensembles of Support Vector Regression models to predict NPV, photosynthetic vegetation, and soil fractions for every Sentinel-2 image, and evaluated model scalability across broad-scale forest gradients (species, topography, time). Finally, we mapped annual dieback from the intra-annual NPV time series and compared dieback rates between dominant tree species. Our models generalized well across forest types and environmental gradients at national scale (Mean Absolute Errors ≤13.6%). NPV-based dieback rates were consistent with the German Forest Crown Condition Survey ( r = 0.8) and revealed species-specific patterns from 2019 to 2022: spruce exhibited the highest dieback (2.7%), followed by pine (1.5%), oak (0.7%), and beech (0.5%). Our study shows that tree dieback mapping is possible at national scale using spectral unmixing, without requiring extensive training data. The approach emphasizes the value of intra-annual NPV fraction time series and advances national- and regional-scale monitoring of forest health by directly quantifying disturbance impacts beyond greenness indices. • Unmixing forest fraction time series (NPV, PV, soil) using synthetic training data. • Models generalized well across forest gradients at national scale (MAE NPV = 13.3%). • Annual tree dieback rates estimated from intra-annual NPV time series. • NPV-based dieback reveals species-specific patterns (spruce > pine > oak > beech). • Dieback rates aligned with field-based mortality rates at national scale ( r = 0.8).