## 4 Discussion  
### Model Comparison  
Our results show marked differences in the performance of three tested classification models (RF, TSF, and 1D-CNN). While the RF algorithm demonstrated its versatility by delivering consistent results on different datasets, the TSF model was more dependent on the input dataset than RF, and the 1D-CNN model often resulted in lower accuracies than the other models and displayed a high degree of variability between model runs. Given that RF has been used in a wide range of remote sensing classification tasks, as reviewed by [20], our finding that it performs robustly across datasets is consistent with previous research. In contrast, the results of our time series-specific algorithms differ from previous research. For example, in their study comparing time series classification algorithms on univariate time series, TSF outperformed both baseline classifiers, with RF showing similar performance to the baseline classifiers [56]. However, this study did not include remotely sensed time series, and their datasets mostly contain time series with <1000 training instances that are longer than 50 time steps. In contrast, our datasets contain >5900 training instances with 9, 27, or 273 time steps, depending on the temporal resolution (respectively monthly, dekadal, and daily), which may also explain the different results.  
Further, in time series-based crop classification, several studies have demonstrated enhanced classification results with the use of a deep learning classifier in comparison with shallow machine learning techniques. For example, refs. [15; 17] reported improved crop classification results using 1D-CNNs compared to RFs. However, in our application, 1D-CNNs often did not outperform a baseline RF, with the exception of the normalized dekadal band dataset in experiment 2B. A very likely explanation for these contrasting results is the limited number of training samples in our field-visit and object-based approach, resulting in approximately 6000 training samples over multiple years and ranging from 325 to 2359 samples per class (Figure 4B). A recent study on time series-based crop classification using 1D-CNNs used 4000 training samples per class, a number chosen after an analysis of the amount of training data and the mapping accuracy [57]. These results suggest that 28,000 training samples, spread evenly across classes, would be required for deep learning to be effective in our catch crop classification. Often, many more training samples are used. References [15; 17] used labels available from a single-year crop survey in combination with a pixel-based approach, resulting in approximately 1 million and 2 million training samples, respectively, with samples per class ranging from 9000 to 292,000. Due to its data limitation, the catch-crop classification could not fully benefit from the advantages that deep learning models provide. Therefore, the large number of labeled data needed for deep learning models proved to be a limitation for improved catch crop-type classification with these models. Switching to a pixel-wise classification would be a quick win for creating more training data without increasing the field efforts. Other options for handling the large data requirement of deep learning models are data augmentation and transfer learning; the latter will be discussed below.