## 3 Results  
As was explained in the introduction, training models to precisely detect mowing events over the year requires extensive knowledge of the mowing dates. This proves to be a challenge to acquire on a large scale. Therefore, this study introduced an alternative approach using CSH predicted from S1, S2, and meteorological data. Employing a multi  
Figure 3: Walloon region of Belgium and parcels position. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Temporality** & **Category** & **2018** & **2019** & **2020** & **2021** \\ \hline \multirow{4}{*}{With a precise date} & N events & 12 & 11 & 44 & 0 \\  & N farms & 2 & 4 & 5 & 0 \\  & N parcels & 12 & 11 & 11 & 0 \\  & N refusal mowing 1 & 0 & 0 & 8 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of the validation set. year perspective spanning from 2018 to 2021, the study focused on entire grazing seasons, detecting distinctive patterns through features derived from the predicted CSH to categorize parcels as mown or not mown. Three feature datasets--week_std, week_fft, and yearstats--were tested, along with two UACs, namely, k-means and IF. Applying the threshold based on standard practices in grazing management in southern Belgium, IF divided 80% of the samples into a normal population (referred to as Group 0) and 20% into an abnormal population (referred to as Group 1). Mown parcels are anticipated to exhibit concurrently higher values for MEAN_YEAR and SD_YEAR. Some descriptive statistics of these traits for each of the two groups identified by IF are presented in Table 4. Across all tested datasets, MEAN_YEAR and SD_YEAR were consistently higher for Group 1, indicating that this group likely comprises the mown parcels. The k-means algorithm identified five clusters, designated as clusters 0 to 4 in Table 5. However, the assignment of cluster labels may vary across datasets, as it is influenced by the distinct starting points and differences in observations and features inherent to k-means. Consequently, the first step is to highlight the clusters associated with mowing events for each dataset. Following a methodology akin to the one utilized for IF, data labeling was performed by interpreting the descriptive statistics of MEAN_YEAR and SD_YEAR. This analytical process allowed the identification of 1 to 3 clusters associated with mown events for each dataset, as delineated in Table 5. By positing the working hypothesis that a parcel tagged as mown in each DAS for the same year is more likely to be a mown parcel, we could establish an indirect reference dataset. Conversely, a similar rationale was applied for the detection of grazed parcels. Table 6 provides the count of common parcels identified as mown or grazed based on the studied year and the UAC used. We were thus able to consider the number of parcels flagged as mown by the three datasets. For each of them, across the 4 years, the mean variation of the percentage of tagged mown parcels ranged from 0.39 to 0.40 for k-means, and from 0.19 to 0.23 for IF, as presented in Table 7.  
Assuming the correctness of the parcel labels, we constructed a comprehensive reference dataset that encompassed all parcels labeled for each studied year. Leveraging this new dataset enabled the application of supervised learning, which was executed using PLS-DA. The prediction performance, obtained through a 10-fold cross-validation, is presented in Table 8 for each feature matrix. The discrimination between mowing and grazing events proved to be excellent, with observed accuracies surpassing 93% for all models. Models utilizing datasets generated from the k-means segregation technique exhibited higher accuracy compared to those using the IF method.