## 1 Introduction
Time Series Forest (TSF) is an example of such an RF where temporal features--mean, standard deviation, and slope--are calculated over predefined (randomly chosen) temporal intervals, and these deduced features are subsequently used for classification [22]. The model, therefore, incorporates crop phenology by explicitly using the temporal dimension. Over the last few years, deep learning algorithms have also appeared in crop monitoring research [15; 23]. Different kinds of deep learning architectures have been compared for the task of crop classification, such as RNNs, MS-ResNet, and TempCNN [23]. Notably, TempCNN was introduced by [17], exploring the use of One-Dimensional CNNs (1D-CNN) for time series-based crop type classification, a lightweight model compared to other deep learning architectures. The use of deep neural networks comes with two major challenges: (1) the expertise required for optimizing both the model architecture and the hyperparameter values, shifting the difficulty from feature engineering to architecture engineering, and (2) the large number of parameters that have to be estimated, which makes deep learning methods prone to overfitting and thus reliant on a large set of labeled training data [17]. Even though deep learning models have shown promising results in main crop type mapping, catch crop monitoring has predominantly relied on machine learning models to date [3; 24; 25]. However, for catch crop mapping, the use of a Multilayer Perceptron network (MLP) has demonstrated improvements over the use of machine learning methods [26]. To our knowledge, this study is one of the few to compare traditional machine learning with deep learning methods for the end-of-season classification of catch crops while using optical time series input data. In contrast with traditional machine learning algorithms, deep learning models can learn feature representations from raw input (time series) without feature engineering [15; 23; 27], but in real-world applications with limited training samples, optimizing the input dataset in both the time and spectral dimensions may still be necessary [14]. Moreover, deep learning models have shown promising results in early-season classification [19], which is useful when applying the models in the operational scenario of guiding in-season fieldwork to areas where the catch crop presence is most uncertain. In this application, the models also need to be generalizable in time, i.e., transferable between years. However, as most crop classification studies use training and testing data from the same year, the generalizability of models across years has not been well studied [28]. The temporal generalization of models, also named temporal transferability, is a crucial aspect of early-season classification in an operational setting when only training labels from previous years are available [29]. Specifically in the context of early-season mapping, this topic has received increasing attention recently [19; 28; 29; 30; 31]. The main objective of this paper is to test the potential of different machine learning and deep learning methods that differ in the way they handle the temporal dimension of the data for classifying catch crops from a time series of medium-resolution satellite images. We will also assess the impact of (1) the temporal resolution of the input time series and (2) the spectral resolution of the input features in order to further optimize the performance of the classification models. In the second objective, the optimized models are applied in a real-world scenario, where we aim to test the potential of these models for early-season catch crop classification. In this case, the target year is different from the training year(s), so this is an assessment of temporal model transferability. These research objectives give rise to four research questions:  
1. What is the (relative) accuracy of different machine learning and deep learning classifiers for winter crop classification from optical satellite time series? 2.