## 3 Results
### Soil Tillage Detection Results  
The performance metrics (recall, precision, F1 and F2 score, and AUC-ROC) on the test set of all approaches are presented in Table 1. Regarding the rules-based approach, the values that maximized the soil tillage detection were the VHthreshold: 0.58 dB; the NDVI threshold: 0.49; and the CR threshold: \(-12.4\) dB. [PERSON] et al. (2019) employed a VH threshold of 2.5 dB, an NDVI threshold of 0.3, and a CR threshold of \(-8\) dB. Regarding the lower obtained VHthreshold, this suggests a higher sensitivity to VH values for detecting soil tillage operations, i.e., in absolute values, a lower change in the VH time series. Regarding  
Figure 3: Example of the VH, CR, and NDVI time series for one of the parcels with a soil tillage operation. the NDVIthreshold, here, a higher value was detected, and this indicates that the detected soil tillage does not produce a severe discontinuity in the NDVI time series. Regarding the CRthreshold, a higher value (in absolute terms) was obtained, which suggests that a more severe discontinuity in the CR time series is required to identify the soil tillage operations. Using Bayesian optimization with 100 iterations, we identified the optimal hyperparameters for the XGBoost model. The optimal number of estimators was set to one, suggesting that the model could effectively converge with a minimal number of boosting rounds. The learning rate was determined to be 0.6, providing a balance that allowed the model to learn efficiently without overfitting. The maximum depth of the trees was set to nine, which allowed the model to capture sufficient complexity without becoming overly intricate. For regularization, the L1 and L2 terms were optimized to 5.1 and 4.7, respectively, helping control model complexity and reduce the risk of overfitting, thus enhancing the model's generalizability. Comparing the performance of the different approaches, both ML approaches using XGB outperformed the rules-based approach in most of the performance metrics used. Furthermore, the XGB-SMOTE approach showed the best performance of the two ML methods (Table 1). For instance, the recall of XGB-SMOTE was 21% higher than the recall of the rules-based approach (67% versus 46%) and 13% higher than XGB with an imbalanced dataset (67% versus 54%). A recall of 67% indicates that the XGB-SMOTE model successfully detects 67% of the actual soil tillage operations in the dataset, which is critical for applications where missing a tillage event may have significant implications, such as in agricultural policy compliance or environmental monitoring. However, it also implies that 33% of true tillage events go undetected, which may limit the model's effectiveness in situations requiring near-complete detection. As expected, since our dataset was highly imbalanced (with only 4% of the data points having identified soil tillage), the precision for all three methods is very low, ranging between 6% (for the rules-based approach) and 8% (for the XGB-SMOTE approach). In practical terms, this low precision means the model generates a high number of false positives, which could impact real-world applications by overestimating the number of tillage events. In terms of F1 and F2 scores, both metrics have very low values due to the low precision (Table 1). Even the F2 score, which gives more weight to recall at the expense of precision, remains low. Nevertheless, the XGB-SMOTE approach achieves the highest performance for both metrics, with F1 and F2 scores of 14% and 27%, respectively, indicating that it achieves the best balance among the tested methods. Figure 4 presents the AUC-ROC for the XGB-SMOTE approach, which achieved the highest performance with an AUC-ROC of 74%. Considering our highly imbalanced dataset, the classification threshold can have a significant impact on the performance metrics.