## 1 Introduction  
When mineral nitrogen remains in the soil after harvest of the main crop, nitrate leaching can lead to elevated nitrate levels in surface and groundwater, adversely affecting the environment and human health [1]. For this reason, the Nitrates Directive in the European Union aims to reduce water pollution from agricultural sources (91/676/EEC) and thereby requires Member States to monitor their water quality and implement good agricultural practices. An example of such practice is the cultivation of catch crops, which are also supported by the Common Agricultural Policy (CAP) in the context of soil health and are often referred to as cover crops in this context [2]. Catch crops are grown from late summer to early spring, bridging the gap between main cropping seasons [3], with the goal of reducing nitrate leaching. Replacing fallow with catch crops reduces nitrate leaching through multiple mechanisms, including active uptake of both nitrogen and water [4, 5]. Their effectiveness is influenced by factors such as (i) biomass, (ii) species, and (iii) sowing and termination dates, with a recent meta-analysis showing an average reduction in nitrate leaching by 68% [4, 5]. Monitoring the adoption of catch crops by farmers currently relies on on-site field visits by authorities [3]. Field visits are, however, very labor- and time-consuming, and therefore, only a small fraction of all fields can be visited. To get a more complete overview of the extent to which catch crops are adopted across a region, satellite remote sensing can be used to map catch crop presence and prioritize field visits to areas of higher uncertainty [3]. Satellite remote sensing offers large-scale coverage and high spatio-temporal resolution and has already been successfully used in crop monitoring and policy support for the CAP [6]. Catch crop monitoring with remote sensing has not yet been researched extensively, but the field has recently been growing due to the mainstreaming of these cropping practices combined with the recent rise in the availability of satellite data with sufficiently high spatial, spectral, and temporal resolution [7]. As outlined by [8], past studies have focused on three main aspects of catch crops: (a) their presence [3], (b) their emergence and termination date [9], and (c) their biomass [8; 10; 11]. These studies applied a wide range of methods, including phenology through optical satellite time series, to monitor catch crops [3; 12; 13]. The use of phenological profiles can help differentiate catch crops from other crops and vegetation because the spectral differences between vegetation types may be small at any single moment in time, while differences in timing may be more pronounced [14]. Especially for crop type classification, the different growth patterns and differences in the timing of the growth stages can be used as discriminating features [15; 16]. Distinguishing between catch crop types can help estimate their effect on N-leaching, as different types have different nitrogen uptake capacities. Both the type of catch crop and its biomass accumulation influence the amount of nitrogen retained, making catch crop type an important variable to consider when modeling N-uptake by catch crops across a region [11]. Simultaneously, with the increased focus on time series data, data-driven machine learning models have become prominent in (catch) crop monitoring [3; 17; 18]. For example, for the classification of catch crops, ref. [3] used satellite time series and Random Forest (RF), a model that is also popular in main crop classification [18; 19]. Some advantages of RFs are their easy-to-tune parameters and their robustness to overfitting [17; 20; 21]. However, RFs do not explicitly take into account the temporal dependencies between the observations, as they handle each time step as an independent variable. This drawback has been overcome by the introduction of a new branch of RFs specifically designed for time series classification and prediction.