## 4 Discussion  
In this paper, we used remotely sensed data from two Sentinel missions (Sentinel-1 and Sentinel-2) to detect soil tillage practices. Three different approaches were employed: one based on rules and the other two based on machine learning. In the machine learning approaches, one approach used the original dataset, which is highly imbalanced, while the other employed a SMOTE approach to increase the representation of soil tillage operations in the training set. The results obtained highlight the challenge of detecting these operations regardless of the approach used. However, the XGB-SMOTE approach showed the most promising results, achieving a recall of 67% and an AUC-ROC of 74%. Here, we aimed to detect only soil tillage operations. However, in the parcels used, other agricultural operations also occurred, including activities such as tree pruning (in permanent crops) and agro-chemical applications like fertilization and pesticide treatments. These operations not only alter the soil structure but also impact satellite data observations, introducing additional variability that complicates the identification of tillage practices. Furthermore, in most parcels, the frequency of these other operations is 5 to 10 times higher than the number of soil tillage operations. This fact introduces a difficulty in detecting soil tillage operations because even during operations such as pruning or harvesting, which do not aim to produce an effect on the soil, when tractors move they produce soil compaction [13], which can also be \"observed\" in the radar time series. Another difficulty in the detection of soil tillage operations is the use of agri-forestry systems, where the operation, at the parcel level, might not produce a \"visible\" discontinuity in the satellite time series because most of the area might be covered by the tree crown [17]. Considering that permanent crops occupied about 70% of the used parcels, this might also justify the difficulty in the detection of soil tillage operations. In other land cover classes such as temporary crops, detecting these operations can be easier than in permanent crops, but they represent less than 15% of the parcels. Detecting soil tillage operations in permanent pastures can be more difficult as in permanent crops or easier as in temporary crops, as it depends on the tree cover of the parcels. If a parcel has no or a low tree cover density, the operation can be more easily detected by remote sensing data, but if the tree cover density is high, the operation will be difficult to detect. In Portugal, most of the permanent pastures are in agri-forestry systems, where trees are present, thus the effect of the tree cover density must always be considered [39; 40; 41]. One possibility to overcome or reduce the tree cover effect is the use of a pixel-based approach instead of a parcel-based  
Figure 4: Area under the receiver operating characteristic curve for the XGB-SMOTE approach. approach (used in this paper). In the pixel-based approach, in the parcels with trees only the \"tree-free\" pixels would be used, and so the effect of soil tillage would be more easily detected by remote sensing [23]. Nevertheless, in terms of computational resources, a pixel-based approach requires significantly more resources than a parcel-based approach. In this work, we intended to use a rules-based approach that was grounded on prior research and was therefore based only on the VH, NDVI, and CR. To enable a fair comparison to the machine learning approaches, we used the same data. Now, since we found that machine learning outperforms the rules-based approach, future models can additionally use the other Sentinel-2 bands (and vegetation indices). For example, soil properties are usually more related to the NIR spectral range [42; 43; 44; 45], which can be added to machine learning models. In order to improve the detection of soil tillage operations, more data could be used to distinguish between operations and natural events that produce a significant change in the soil.