## 3 Materials and Methods
### Classification Workflows  
The presence of tree cover and the distribution of the forest types at a national level were predicted through three different classification workflows. From the training dataset, Level 0 of Table 2 was used in order to predict the binary presence or absence of tree cover, while Level 1 was used to predict the different levels of canopy cover. For closed canopy cover, we distinguished between tree cover from evergreen and deciduous forests (classes 101 and 102 from Level 3).  
The classification was done based on the 3 \(\times\) 3 pixels central plot from the 5 \(\times\) 5 pixels that were interpreted for each plot. The 9 annotated pixels from the central plot were used into two different thematic mapping approaches: the first approach, called \"pixel\" in  
Figure 5: The 50 m by 50 m square plot interpreted in Collect Earth and the interpretation rules. Maps data: Google, © 2021 Maxar Technologies.  
Figure 7, classified the pixels into classes according to their intrinsic spectral values, while the second approach, called \"window\" in Figure 7, used a contextual spectral approach to classify the pixels while also considering their neighboring pixels.  
Figure 6: Spatial distribution and number of samples of the reference dataset. Maps data: Google, © 2021 Maxar Technologies.  
Figure 7: “pixel” and “window” classification workflows.  
The binary tree cover-no tree cover discrimination was trained with the \"pixel\" approach only; that is, the 9 pixels were considered as 9 independent training samples. For the classification of the canopy cover classes, both approaches were tested. The \"window\" approach, where the 9 pixels were considered as a single training sample, was specifically used to include the class Tree Cover 1-10%, expressing the presence of one or several single trees on the interpreted plot. Indeed, in a window of 3 \(\times\) 3 pixels, the presence of single trees would not give a homogenous signal for each of the single pixels of the pixel model. The use of the window method allowed for compensation for the possible geo-localization gap between a single tree that was interpreted on a VHR images from Google Earth and the Sentinel layers.  
For both the binary and the canopy cover predictions, two different machine-learning classifiers were used with the S1 and S2 layers; the random forest (RF) classifier and the extreme tree classifier (ETC). The linear regression CV (LRCV) and support vector machine (SVM) classification algorithms were also tested for the \"pixel\" and the \"window\" approaches, respectively, but were then discarded due to the poor results that were obtained.  
The 6 classification models were trained with the spectral and backscatter information from the 9 layers of S1 and S2 of Table 1 and the labels from the 3060 plots of the training dataset.  
The hyper-parameters of each classification model were optimized with a grid-search approach. The classification was done on the JRC Big Data Analytics platform (BDAP) [56], a versatile multi-petabyte-scale platform from the European Commission's Joint Research Centre. The classification algorithms of the scikit-learn Python project [57] were used.