## 4 Discussion  
As highlighted in the introduction, there is a growing interest in understanding grazing and mowing management practices, particularly to assess the potential environmental impacts [6, 8, 11]. This could be addressed with the creation of a grazing calendar, but its updating proves to be time-consuming, limiting its adoption among farmers [20]. Another possible solution is the utilization of GPS collars [15], but despite their effectiveness, they face restrictions due to the significant financial investment involved. An alternative that holds appeal for farmers is associated with the use of readily available spatial data, especially through projects like Copernicus [46]. However, the varying availability of optical data and the lack of comprehensive reference data pose constraints on the development of models [18, 20] for detecting mowing events. The innovative aspect of this study lies in the application of unsupervised learning based on CSH predicted from S1, S2, and meteorological data. This approach aims to address the challenge of the scarcity of reference data, providing a novel solution for mowing event detection. The detection of mowing clusters was possible with both UACs, but only 1858 parcels/year were consistently labeled as mown by these two techniques, representing 11.56% in the k-means dataset and 6.76% in the IF dataset. This difference could be explained by the fact that for the IF algorithm, the isolation of parcels considered mown was based on a fixed threshold (i.e., 20%). To solve this issue, we considered examining the percentage of parcels flagged as mown by the three datasets, as shown in Table 7. The results showed mean variation from 0.39 to 0.40 for k-means and from 0.19 to 0.23 for IF across the 4 years. The percentage observed for k-means is therefore more in line with the Walloon statistics [50]. This previous step, which allowed labeling of parcels that seem to be affected by mowing events, can be used to train a PLS-DA with the aim of automatically detecting mowing events based on a large dataset. Indeed, the dataset size was 42,784 for k-means and 277,547 for IF. This has never been used in past studies related to the detection of the moving effect. Indeed, they used between 236 and 1200 reference records [17, 18, 20, 21]. The size of our training set, reachable with the unsupervised learning method we presented, is interesting to improve the robustness of a model [52]. The PLS-DA models showed a good ability to detect seemingly mowing events, with a calibration accuracy ranging from 0.94 to 0.99. The ability of the k-means output to build the PLS-DA models was also better. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Total** **Parcels**} & \multicolumn{5}{c}{**Percentage of Parcels Flagged as Mown**} \\ \cline{3-7}  & & **week\_fft** & **week\_fft** & **week\_fft** & **week\_fft** & **week\_fft** & **week\_fft** \\  & & **(0.50)** & **(0.45)** & **(0.40)** & **(0.35)** & **(0.30)** \\ \hline
2018 & 86,218 & 42 & 53 & 65 & 77 & 88 \\
2019 & 86,617 & 41 & 48 & 57 & 66 & 78 \\
2020 & 91,224 & 57 & 66 & 75 & 84 & 91 \\
2021 & 136,892 & 21 & 27 & 36 & 47 & 65 \\ Total & 400,915 & 40 & 49 & 58 & 68 & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Percentage of parcels labeled as mown in the entire dataset using the PLS-DA based on the week\_fft dataset with the k-means approach for several thresholds of mown probability (in parentheses). To check the ability to detect confirmed moving parcels, a validation was conducted using a limited dataset (N = 68). The IF method employed in the study exhibited lower accuracy, reaching a maximum detection rate of 40%, as shown in Table 8. Two main hypotheses can be proposed to explain this result: either the model was trained with a fixed threshold that does not match enough with the actual number of moving events, or the validation data had patterns that did not align well with the eventual patterns found by the IF.