Spatially explicit and detailed information on tree species composition is critical for forest management, nature conservation and the assessment of forest ecosystem services. In many countries, forest attributes are monitored regularly through sample-based forest inventories. In combination with satellite imagery, data from such forest inventories have a great potential for developing large-area tree species maps. Here, the high temporal resolution of Sentinel-1 and Sentinel-2 has been useful for extracting vegetation phenology, information that may also be valuable for improving forest tree species mapping. The objective of this study was to map the main tree species in Germany using combined Sentinel-1 and Sentinel-2 time series, and to identify and address challenges related to the use of National Forest Inventory (NFI) data in remote sensing applications. We generated cloud free time series with 5-day intervals from Sentinel-2 imagery and combine those with monthly Sentinel-1 backscatter composites. Further, we incorporate information on topography, meteorology, and climate to account for environmental gradients. To use NFI data for training machine learning models, we address the following challenges: 1) link satellite pixels with variable radius NFI plots, for which the precise area is unknown, and 2) efficiently utilize mixed-species NFI plots for model training and validation. In the past, accuracies for pixel-level species maps were often estimated solely for homogeneous pure-species stands. In this study, we assess how well pixel-level maps generalize to mixed plot conditions. Our results show the potential of combined Sentinel-2 and Sentinel-1 time series with NFI data for tree species mapping in large, environmentally diverse landscapes. Classification accuracy in pure stands ranged between 72% and 97% (F1-score) for five dominant species, while mapping less frequent species remained challenging. When including mixed forest stands in the accuracy assessment, accuracy decreased by 4–14 percentage points for the most dominant species groups. Our study highlights the importance of including mixed-forest stands when training and validating tree species maps. Based on these results, we discuss potentials and remaining challenges for tree species mapping at the national level. Our findings allow to further improve national-level tree species mapping with medium to high resolution data and provide guidance for similar approaches in other countries where ground-based inventory data are available. Keywords: Temperate forests; Time series; Large-area mapping; Optical remote sensing; SAR; Mixed forests; Environmental conditions
