Title : 
 Dominant Tree Species for Germany ( 2017/2018 ) 

 Abstract : 
 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 and spatial resolution of Sentinel-1 and Sentinel-2 has been useful for extracting vegetation phenology , information that may also be useful for improving forest tree species mapping . Thus , we generate cloud free time series with 5 - day intervals from Sentinel-2 imagery and combine those with monthly Sentinel-1 backscatter composites to map the dominant tree species throughout Germany . Further , we incorporate information on topography , meteorology , and climate to account for environmental gradients in Germany . We use the German National Forest Inventory ( NFI ) as source for training data for a random forest classifier and for validation data to assess map accuracy . The optical Sentinel-2 data were downloaded , processes and structured in an analysis - ready data cube with the open - source software FORCE ( Framework for operational radiometric correction for environmental monitoring ; Frantz , D. , 2019 , doi.org/10.3390/rs11091124 ; https://force-eo.readthedocs.io/en/latest/ last accessed : 02 . Nov. 2022 ) . The preprocessed Sentinel-1 SAR data was obtained through the CODE - DE platform ( https://code-de.org/en/portfolio/?q=Sentinel-1 last accessed : 02 . Nov. 2022 ) and included in the ARD cube along with the environmental data . We used Sentinel-2 , Sentinel-1 and environmental data from the two years 2017 and 2018 to generate the map with 11 tree species groups , including two broader deciduous tree classes ( ODH : other deciduous trees with high life expectancy , ODL : other deciduous trees with low life expectancy ) , within the wooded forest area using a previously produced forest mask : Langner et al . ( 2022 , doi.org/10.3220/DATA20221205151218 ) . The reference data for model training and map validation is based on the third German National Forest Inventory which was recorded in 2011/2012 ( https://www.bundeswaldinventur.de/en/third-national-forest-inventory , last accessed : 02 . Nov. 2022 ) . To exclude inventory plots where tree species composition might have changed in between 2012 and 2018 , we used the forest disturbance maps by Senf et al . ( 2020 , doi.org/10.1038/s41893-020-00609-y ) . The map accuracies were estimated based on a stratified estimator according to Stehman et al . ( 2014 , doi.org/10.1080/01431161.2014.930207 ) . Map accuracies were estimated for pure species stands in a first step and for the entire map , including mixed species stands , in a second step . The overall accuracy of pure - species stands is 87.07 ± 0.3 % and 75.53 ± 0.07 % for the entire map , including mixed - species stands . For further details please see the scientific publication : Blickensdörfer , L. , Oehmichen , K. , Pflugmacher , D. , Kleinschmit , B. , Hostert , P. , 2024 . National tree species mapping using Sentinel-1/2 time series and German National Forest Inventory data . Remote Sensing of Environment . 304 , 114069 . DOI : 10.1016 / j.rse.2024.114069 . EPSG:3035