## 5 Discussion  
Although all indices used in this study are closely related to the chlorophyll content of plants, their performance in crop field detection was found to vary slightly. This situation may be due to the different characteristics of each of these indices, the short time series used in this study, and the local settings. Surprisingly, the accuracy of the classification carried out for all investigated vegetation indices was not the highest as presented by  
Figure 8: Proportions of different crops grown in the investigated agricultural study areas based on MCARI classification (**a**) and for ground truth (**b**). Figure 9: Feature importance for each classification experiment. First part represents single index classification, second part represents multi index classification while third part represents PCA-based classification. Various color bars portrays specific acquisition dates in each of the classification experiments. Black color represent that this image is not within the top 10 important variables. other authors in literature ([PERSON] et al., (2020) [5], [PERSON] et al., (2014) [17]). In some cases, crop type mapping based on each single vegetation index achieved higher accuracy than the integrated use of all indexes (MCARI, NDI45, and WDVI). Furthermore, [PERSON] et al., (2020) [16] also reported that the application of additional indices did not increase classification accuracy. Figure 10 presents a graphical representation for the best and the worst results of the spectral-based classification and a comparison with the ground truth. As can be observed, mostly potato was classified as sunflower in the process with all indexes (the worst results). As can be seen, MCARI results corresponded relatively accurately with the ground truth. Both classification results provided noisy information in the northern part of the investigated area. This could have been caused by small agricultural fields in this area being problematic in correctly classifying the crop types. In Figures 11 and 12, an NDVI and an MCARI time series is presented for the various crop types in the investigated study area, respectively. As can be observed, the time series of the NDVI was similar for maize and beetroot and for sunflower and potato, making them challenging to distinguish. This similar time series for these species is the answer for the false positives achieved in various classification tests as presented in Figures 8 and 10. In addition to the similar NDVI time series responses of these species, the harvesting time of them is similar, which was also confirmed in Figure 2; the harvest (circles) and sowing periods of these two plants are comparable. This may present challenges in distinguishing between these two species. In contrast, another situation is presented by the MCARI. Maize and beetroot clearly have varying time series; this enables the more accurate detection of crop types. Unfortunately, we did not have access to cloud-free images in October; therefore, it was impossible to observe the spectral behavior of beetroot, maize, and sunflower. Figure 11: Time series of the NDVI for different types of crops, calculated as average values of all fields with harvesting dates marked with circles. Figure 10: Classification with the best and the worst spectral indices and comparison with the ground truth. Another aspect worth investigating in the future is related to the investigated time period, which should be strictly adjusted to the phenological development of the plants. In this study, we captured images from May to September, which is not adequate to observe the development of all species. For example, wheat and canola were very well-developed plants in May, characterized by an NDVI value of 0.9. Therefore, to achieve better crop mapping results, it is worth covering the time period in which each of the plant species is sown and harvested. Therefore, a wider time series (preferably one year) would help differentiate between various crop species.