## 3 Results  
### End-of-Season Classification  
The first experiment examined the effect of temporal resolution on the classification performance of all models. All models achieved the highest F1-scores on the daily temporal resolution for the coarse-label classification, with a median F1-score of 86.5% for the RF model, 86.5% for the TSF model, and 85.9% for the CNN model (Figure 7). Every model type reached the lowest F1-score on the monthly time resolution, and here, the median values for the F1-scores were 85.1% for RF, 81.8% for TSF, and 83.6% for CNN. Further, each model type has a different rate in the decline of the F1-scores, where the TSF model has the largest decline in performance, while RF has the lowest decline.  
In part A of the second experiment, we then looked at the influence of biophysical variables on the classification performance. At both daily and dekadal temporal resolutions, the dataset, including the biophysical variables (fAPAR and FCover), attained higher F1-scores for every model type compared to the NDVI-only dataset (Figure 8A).  
Part B of this second experiment looked at the classification performance of the models on the time series, purely derived from Sentinel-2 data. Three sets of input features were compared at a dekadal temporal resolution: NDVI, the raw bands, and normalized bands. When using NDVI as an input feature, the median F1-scores were in the range of 85.1-87.5% (Figure 8B). However, the CNN resulted in a larger range of F1-scores (83.5-86.0%) compared to the forest-based models. This is especially the case for the F1-scores with raw bands as an input dataset, where a wide range of values was obtained (F1-scores 82.0-87.9%). However, the CNN model showed less variability in model performance  
Figure 8: Violin plots for the F1-scores of the second experiment given for three temporal resolutions and three different models (Random Forest or RF, Time Series Forest or TSF, and One-Dimensional Convolutional Neural Network or 1D-CNN). **(A)** Part A of the second experiment and (B) part B of the second experiment. Within the violin plots, boxplots are depicted in black.  
Figure 7: Violin plots for the F1-scores of the first experiment given for three temporal resolutions and three different models (Random Forest or RF, Time Series Forest or TSF, and a One-Dimensional Convolutional Neural Network or 1D-CNN). Within the violin plots, the boxplots are depicted in black.  
when the Sentinel-2 bands were normalized (F1-scores 88.5-89.5%). In contrast, the TSF model resulted in a constant performance with an F1-score of around 85.3-85.8%. The median F1-score for the RF model showed a decrease from 87.5% to 84.2% when using the raw bands of Sentinel-2 compared to that using NDVI only. When normalizing the bands of Sentinel-2, performance increases again to a median F1-score of 88.8%.  
Looking more closely at the fine-label classification of the three models, it was shown that all models confused the three catch crop groups (leafy, grass-like, and mixture) with each other (Figure 9). The leafy group was mostly confused with the mixtures, and the mixtures were mostly confused with the grass group. There were also some misclassifications between catch crops and winter cereals, mostly between the grass-like and mixture groups. Small groups like legumes, other winter crops, and the no-crop group mostly ended up in the three biggest groups: the grass group, mixtures, and winter cereals.