## 3 Results
### Early-Season Classification  
From the first two experiments, the input feature set that achieved the highest overall F1-scores was selected, which was the daily NDVI, fAPAR, and FCover time series. The third experiment focused on assessing the early-season performance of the models and their temporal transferability using this 'best' input feature combination. When training and evaluating the same years, all models showed an increase in F1-scores when including extra time steps throughout the first three months (September, October, November) (Figure 10). However, adding extra time steps only marginally improves model performance from December onwards, as indicated by a stagnation in the F1-scores. The inclusion of March and April into the time series again resulted in an increase in F1-scores.  
When evaluating the early-season performance of the models separately, the forest-based models (RF and TSF) attain a median F1-score surpassing 85% in November, whereas the CNN model only reaches this threshold in March (Figure 10). By April, all models reach their highest median F1-score. However, the CNN model resulted in lower and more variable F1-scores, particularly earlier in the season.  
Figure 9: Confusion matrices for the fine-label, end-of-season classification for three different models: (**A**) Random Forest (RF), (**B**) Time Series Forest (TSF), and (**C**) One-Dimensional Convolutional Neural Network (1D-CNN). The blue frames indicate the coarse-label classification groups.  
When applying the trained models to the time series of a target (winter) year to assess transferability, the increase in F1-score with time steps remained for the forest-based models (Figure 10). In contrast, the CNN model showed a very unstable performance that lacked this general upward trend and produced a larger range of F1-scores compared to the other two models. All models reached maximum model accuracy in April with median F1-scores of 88.8% for RF, 88.3% for TSF, and 81.5% for the CNN model. Therefore, the forest-based models reached very similar median F1-scores when training and evaluating in the same years in April (RF 88.7% and TSF 88.9%), while the CNN model reached a lower median F1-score in comparison (87.9%).  
Revisiting the early-season performance of each model, TSF achieves a median F1-score of 85% in December, followed by RF in February, whereas the CNN model fails to reach a median F1-score of 85%. Compared to the same year training and testing, the models attain this threshold later in the season during the separate validation year. Maximum F1 bscores were again all reached in April. The CNN model reached a score of 83%, while RF and TSF reached F1-scores of 89% and 88%, respectively. These classification results were illustrated on a map using the RF model (Figure 11).  
Figure 10: F1-scores for the third experiment, which was completed on daily time series with features NDVI-fAPAR-FCover for three models (RF, TSF, and 1D-CNN). (**A**) Results when trained and tested on the same years; (**B**) results when the models are applied on a target year. Month on the \(x\)-axis shows the end point of the time series: e.g., Dec represents a time series starting in August and ending in December. Shadowing indicates the standard deviation on the results from the different model runs. Note that the results for RF and TSF largely overlap in panel (**A**).  
Figure 11: Maps to illustrate the results of the Random Forest (RF) model on time series until April. Binary (**A**) ground truth and (**B**) classification results, fine-label (**C**) ground truth and (**D**) classification results. (**E**) Location of maps (**A–D**) in the study area. Land use: Department of Environment and Spatial Development (www.vlaanderen.be/datavindplaats (accessed on 22 November 2024)).