Mapping tree species fractions in temperate mixed forests using Sentinel-2 time series and synthetically mixed training data

Monitoring and mapping of forest stands, including tree species composition can support forest protection and management. Sentinel-2 imagery provide a viable data source due to their high spectral, temporal, and spatial resolution. However, especially in temperate mixed forests challenges with tree species classification persist, mainly due to the high mixing ratio of tree species, which cannot be fully resolved even with the 10 m resolution of Sentinel-2 data. Additional challenges are associated with the commonly low number of reference data for rare tree species, resulting in low classification accuracy for these species. This study proposes an approach to map sub-pixel tree species fractions in mixed temperate forests by combining dense annual multi-spectral Sentinel-2 time series to target differences between species in phenological relevant periods with synthetically mixed training data. This allows for a limited number of pure training samples per tree species, which serves as basis for randomized linear mixing to compute a synthetic spectral library. An artificial neural network is trained for regression for tree species fractions per pixel. To enhance model robustness and stabilize predictions, we implemented this library generation and artificial neural network regression as an ensemble approach. We effectively mapped tree species fractions for the federal state of Rhineland-Palatinate, Germany, with Mean Absolute Errors of 2.76% to 16.05% and R 2 values up to 0.92 – when validated against forest planning data. We show that the data augmentation through synthetic mixing allows for a sample size as small as 30 pure pixels per class, to sufficiently distinguish nine tree species and one ‘other species’ class, hence substantially increasing the operational potential for deployment when reference data for rare species are limited – while simultaneously generating accurate and information-rich tree species distributions over large areas of mixed forest. • Dense Sentinel-2 time series allow mapping sub-pixel tree species mixtures. • Use of synthetically mixed training data with neural network regression. • A small number of pure training points are needed for accurate results. • High potential for operational deployment.