## 2 Materials and Methods
### Data  
In order to evaluate the potential of the Sentinel-2 data in an optimal scenario, several scenes were selected according to the criteria of minimum cloud cover for several stages of the vegetation period. Furthermore, data was selected for a year that was close to the data of the collection of the inventory data. Specifications of the datasets are given in Table 1. Data was downloaded from the Copernicus  
Figure 2: Map showing the two project regions (Ebersberger Forest and Freisinger forest). The true color image is from the Sentinel-2 scene in September after atmospheric correction. The areas used to generate the training and testing points during the classification process are also shown. Note that not all areas used in the first step delineating coniferous vs. broadleaved trees could be used on a species basis in the next step. Thus, the bigger symbols for “Deciduous Forest” beneath single tree species.  
Open Access Hub. Three different dates over the year were chosen for multitemporal analysis. The product level of all files is L1C, i.e., calibration of data to top of atmosphere (ToA) reflectance.  
Sentinel-2 has 13 bands with three different spatial resolutions and different bandwidth (Figure 3). There are four bands with a 10 m resolution, six bands with a 20 m resolution and three bands with 60 m resolution. It has a swath of 290 km and allows for an ideal revisit time of five days at the equator.  
For training and validation, we used inventory data and orthophotos provided by the Bayerischen Staatsforsten (acquired during a regular forest inventory by the Bavarian State Forest Enterprise). The inventory data contains actual data from 2014 to 2016 with information about the percentage of tree species. The circular plot size is 500 m\({}^{2}\) (radius: 12.62 m) in the Freisinger forest and 400 m\({}^{2}\) (radius = 11.28 m) in the Ebersberger forest. In total, eight different tree groups are included in the inventory data (Table 2). Spruce is the most abundant coniferous tree type with only minor Pine, Larch or Fir trees. Oak and Beech dominate the broad-leaved trees and all other minor trees are summarized within a mixed class. We filtered all data to obtain only pure inventory circles that contain only one species in order to derive the training and testing data for the classification workflow (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Date** & **Aquisition Time** & **Cloud Coverage \%** & **Product Level** \\ \hline
22 May 2016 & 18:24:38 & 28.7 & 1C \\
09 August 2016 & 05:07:27 & 4.5 & 1C \\
29 September 2016 EF * & 18:51:41 & 0.0 & 1C \\
29 September 2016 FF * & 18:19:08 & 0.0 & 1C \\ \hline \end{tabular}
* EF/FF: Ebersberger Forest, Freisinger Forest.  
\end{table}
Table 1: Specifications of Sentinel-2 Images for the Ebersberger and Freisinger Forest that were used in this project.  
Figure 3: Spatial and Spectral resolution of Sentinel-2 (modified from [PERSON] et al. [15]).  
In addition to the inventory data, orthophotos from the Bavarian Administration of Surveying with a 20-cm spatial resolution were used for visual validation of the training and testing samples. Two types of orthophotos were used, RGB, as well as color infrared (CIR) orthophotos. This guarantees the quality of the input data which is crucially important for the performance of machine-learning algorithms