## 4 Results and Discussion
### Most Suitable Classification Method
Conversely, if a large volume of training data is available, more complex models such as XGB or MLP could potentially deliver superior performance, capturing subtle patterns and relationships in the data that RF might miss. The overall accuracy increased from CS1 to CS2 and CS3 using multitemporal PS imagery. In this context, since vegetation indices are influenced by many factors other than vegetation (e.g., soil moisture, atmospheric conditions, and shadows), MT PS data produced an improvement to capture changes in vegetation cover and reduced the impact of non-vegetation factors. Vegetation indices derived from the MT data are related to the biological behavior of the different species during the year, i.e., the phenological pattern associated with each species [73]. Similar to this research, MLP outperformed other ML classifiers using MT PS imagery [74], but with additional GLCM features, XGB performed the best. In the study from [PERSON] et al. [51], XGB was found to outperform RF and SVM tested in three different study areas, mainly in larger sample sizes. Although the F1 score is mostly used to measure a model's performance where the classes are imbalanced, both F1 and FoM were calculated to assess the ability of differentiation between different allergenic tree species. From CS1 to CS3, both measures increase according to the temporal information and ancillary data added, and in CS3, the XGB classifier obtained the highest F1 and FoM values for each class, of which plane tree has the most accurately identified positive cases. Furthermore, along with the above-mentioned accuracy measures, [PERSON] and [PERSON] [75] suggest reporting UA and PA, as well as their complementary measures (i.e., commission errors or false positives, and omission errors or false negatives). Therefore, from this point on, the best results from each classification scenario (i.e., RF from CS1, MLP from CS2, and XGB from CS3) are analyzed more in detail and their visualizations are shown (Table 5, Figures 2 and 3). The lowest UA and PA values across all three classification scenarios were for other tree classes, which highlights the difficulty distinguishing the primary chosen allergenic tree species from the rest. Most misclassification occurs with the birch plant and ash. In CS1, the birch plant had the highest UA and PA values, and in CS2 and CS3, the plane trees were detected with the lowest number of false positives and false negatives. Overall, results obtained in the classification scenario three are encouraging since most of the studies focused on allergenic pollen vegetation mapping on a large scale [10; 11]. If similar research (i.e., tree species mapping in urban areas) are taken into comparison, [PERSON] et al. [76] achieved OA for each tree species ranging from 51% to 70% using integrated airborne hyperspectral and LiDAR remote sensing data. [PERSON] et al. [77] used WorldView-2 satellite data for three types of tree species and applied maximum likelihood classification (MLC) and SVM classifier. The accuracy for MLC and SVM was 62.07% and 71.53%, respectively,and misclassification was a common error due to the spectral similarity between tree species classes. As such, our research extensively leveraged the potential of the vegetation indices and texture features in CS2 and CS3, respectively, culminating in an overall accuracy value of 73.13%. However, we acknowledge that the aggregation of multiple tree species into a single class named 'other' could have potentially influenced our reported accuracy and presents a limitation to the precision with which we can distinguish individual allergenic tree species, especially since similar research, e.g., [PERSON] et al. [78] and [PERSON] et al. [79] used 15 and 17 classes in their studies, respectively. Former research achieved an OA of 90.79%, whereas the latter achieved an OA of 81%. Beyond the scope of allergenic tree species detection, the utilization of multitemporal PS imagery opens up novel possibilities for vegetation phenology monitoring.