## 4 Discussion
Intense precipitation events can lead to a change in the soil surface roughness [46; 47] that can be misinterpreted as a soil tillage operation [23]. Thus, precipitation data could be added to the input data to try to distinguish the effect of precipitation events. However, weather data frequently have a very coarse spatial resolution, e.g., the ECMWF spatial resolution is 14 km [48], which can make it difficult to detect the effects of intense precipitation in small-sized parcels like those in Portugal. There are other events, like animal soil trampling in pastures and other grazed parcels, that can also produce similar changes to soil tillage operations [49]. The grazing events are significantly more difficult to identify and predict since they change significantly between regions, farms, and years. For example, in a wet winter and spring, plant growth is higher and more feed is available so animals can graze for longer periods, but when winter and spring are drier, the grazing season is significantly shorter. The SMOTE has been shown to improve classifier performance by providing a more comprehensive distribution of the minority class, which leads to better model generalization compared to using only undersampling. Due to the limited size of our dataset, we opted not to use undersampling or alternative oversampling techniques. Reducing the number of majority class samples through undersampling would risk discarding valuable information, limiting the model's generalizability. Likewise, more oversampling techniques could lead to overfitting, especially with such a small dataset. The SMOTE, however, generates synthetic samples by interpolating between existing samples rather than duplicating data points, making it a more suitable choice. This approach enriches the minority class while preserving the dataset's diversity, ultimately enhancing the model's ability to learn the patterns associated with soil tillage operations [50]. Importantly, this technique was applied exclusively to the training set (as explained in Section 2.3 for more details on the dataset partition between the training and test) and not to the test set. This approach ensures the model's evaluation is based on real data, preserving the authenticity of performance assessments [51]. In Portugal, the sizes of the parcels vary greatly, with a considerable number of parcels being small or very small (less than 0.1 ha). In these cases, the spatial resolution of Sentinel-2 is too coarse for the reliable discrimination of tillage practices. To overcome this limitation, high-resolution satellite imagery such as Pleiades or GEOSat, which offer sub-meter spatial resolution, can be used as a viable alternative [52]. Nonetheless, acquiring such data entails substantial costs, which hinders their application in large areas like mainland Portugal. For the monitoring of commitments in the scope of the CAP, the cost of monitoring with high-spatial resolution data could be a hinderance. Furthermore, orthophoto maps derived from aerial photographs can also provide a higher spatial resolution [53], which is essential for accurately identifying permanent pastures in very small parcels [54]. However, orthophoto maps do not provide the temporal resolution required to track interannual patterns because they are typically available no more than once a year. In contrast, satellites orbit over the same region several times over the course of a year. Another limitation is the presence of data gaps in Sentinel-2 caused by cloud cover, which can affect the continuity of monitoring. To address these gaps, a potential solution could be the integration of Sentinel data with other sources, such as ground-based sensors, drones, or high-frequency satellite constellations that offer improved temporal coverage. Combining Sentinel-1 and Sentinel-2 data with these additional sources could enhance the robustness of tillage detection by mitigating cloud-related gaps and refining the spatial and temporal resolution.