## 4 Discussion
### Early-Season Classification Performance for Separate Target Year  
The third experiment showed the trends in classification accuracy throughout the winter season. The F1-scores increased until November, then stagnated, and increased again throughout March and April. Overall, the models improved as the time series lengthened, which is an expected result and consistent with previous research [14]. The stagnation could potentially be explained by the limited number of cloud-free images that are available in these parts of the season, which could restrict the early-season classification for winter crops compared to that of main crops. Peak F1-scores were reached by the end of April, which is likely due to the distinct temporal profile of winter cereals when April is included (Figure 6). Because winter cereals, unlike catch crops, remain on the field after winter, their time series show a continuous increase in the vegetation index values around March and April, which contrasts with the downward trend of catch crops over the same period (Figure 6). This discriminating characteristic is probably also the reason that the fine-label classification of winter cereals performed relatively well compared to other classes (Figure 9). When comparing the models for their early-season classification capabilities on the validation year, we see that the forest-based models reach the threshold F1-score of 85% later in the season than for the years included in the training. A slight drop in performance on a separate target year was expected, as temporal (and spatial) transferability is generally a challenge in crop classification, even when multiple years have been used for training [28]. This is due to different weather conditions between years, which affect crop development and result in different temporal profiles for the same crop [28; 29]. Reference [19] even reports that the variation in spectral profiles between years is the main factor in reducing the accuracy of the early-season mapping of main crops. Interannual differences could, therefore, offer an explanation for the slightly lower accuracies of the forest-based models earlier in the season (Appendix C). The differing weather conditions during the summer and autumn months every year impact the timing of growth stages of the catch crops, resulting in variations in crop development earlier in the season. For example, a large part of the training data come from the year 2021 (winter year 2021-2022), with an exceptionally wet summer and a rather dry autumn, while 2022 (winter year 2022-2023) had an exceptionally dry summer followed by a warm autumn, which is likely to have affected the growth stages of the catch crops differently compared to the previous year [64; 65]. However, interannual differences do not entirely explain the lack of generalization to the target year for the 1D-CNN model; its results most likely point to an overfitting problem. Although various regularization methods, such as dropout and batch normalization, were used to prevent overfitting, the model type itself also plays an important role: given the large number of parameters that need to be fitted for a deep learning model such as a CNN, the limited number of samples is again the most likely explanation. Even though the potential of deep learning approaches, and more specifically, 1D-CNN, have been reported for early-season mapping [19], our application does not seem to benefit from deep learning models over machine learning models. RF is the better option for early-season classification, given our limited dataset. This issue of temporal model generalizability, particularly in the context of early-season mapping, has gained attention in recent crop mapping research [28; 29; 30]. These studies have focused on early-season time series classification with transformer-based models, which represent the current state-of-the-art in deep learning.