## 1 Introduction  
With an average net annual productivity of 161 gC year\({}^{-1}\) m\({}^{-2}\)[1], grasslands emerge as crucial focal points in climate change mitigation efforts [2]. On the European stage, this significance is recognized as a pivotal driver in the implementation of greening policies [3, 4]. Beyond environmental considerations, the economic facet is underscored by the integration of grass into cow diets, leading to a reduction in feed ration costs [5]. The frequency of moving events, the timing of their occurrence, and the number of times they are executed can significantly influence forage yield and quality [6; 7]. Additionally, moving practices impact grassland biodiversity, affecting fauna heterogeneity, with potential consequences for arthropods [8] and specific cases such as butterflies, where it hinders the destruction of larvae and eggs [9]. Regarding the flora of grasslands and pastures, managing well the moving events can promote plant species diversity [10], though other factors like hydrological considerations, as mentioned in [11], must be considered. Whether pertaining to cattle diets or biodiversity implications, awareness of moving events can inform better management practices. From a societal perspective, grass plays a crucial role in shaping public perceptions of farming practices [3; 12]. Furthermore, the inclusion of grass in the diet of dairy cows triggers alterations in milk fatty acid composition, such as an increase in C18:3, contributing to enhanced organoleptic and nutritional qualities attributed to pasture grazing or fresh grass intake [13; 14]. To align with contemporary consumer expectations, dairy companies have established protected designations of origin mandating specific grazing parameters, either in terms of time periods or available areas. Despite the existence of these specifications, there is a notable absence of factual verification regarding adherence to these conditions. As a response to this challenge, the Chronopature tool was recently developed with the objective of automatically quantifying the number of days a herd spends grazing, utilizing GPS collars [15]. Unfortunately, the widespread deployment of such equipment encounters hindrances, as it entails a considerable financial investment. An alternative proposition by [16] suggests detecting a grass-based diet through milk mid-infrared spectral data, although its relevance is confined to dairy herds. Consequently, this has led to the exploration of moving event detection through satellite data in various studies. Sentinel-1 (S1) [17] and Sentinel-2 (S2) data [18; 19] are frequently deliberated upon, sometimes supplemented by Landsat 8 [20], and occasionally integrated with weather time series [21]. Various algorithms, including deep learning with neural networks [20; 22], random forest, and support vector machine [21], have been tested. Key constraints of this approach highlighted in the literature include optical data availability, as seen in [18], where missing data were supplemented using S1 data. In other studies [20], availability and performance were only monitored over available data periods. A second constraint is the lack of comprehensive reference data [18], requiring grazing/management calendars. Both [17; 23] underscore this challenge. However, various satellite-based proxies, such as the normalized difference vegetation index and the enhanced vegetation index values [21; 23], and biomass assessments, like the leaf area index [23; 24], have been employed for moving event detection. Extending the range of proxies to grass height, as explored in [25; 26], or even compressed sward height (CSH), as in [27], offers the opportunity to gather more reference data with reduced time and effort. CSH, measured under a rising plate meter, is more reproducible than classical grass height assessment and is often considered a reliable proxy for biomass [28].