## 4 Discussion  
### Classification Performance and Uncertainties  
The proposed approach demonstrated that the highest value of the fraction images on the annual composite allowed the discrimination of LULC classes based on their highest peaks during the year, while standard deviation emphasized the seasonality of these classes, improving the separability of LULC classes with similar spectral responses. However, some misclassifications remained, for example, the confusion between pasturelands and croplands that has already been reported by several authors [5; 6; 17]. The managed pastures promote vegetation growth similar to that of the croplands. Such homogeneous vegetation covering confuses the classifier, hence reducing the accuracy of this class [5; 6]. For other natural classes from the Cerrado biome, such as the dry and humid savannas reported here, the misinterpretation may be even worse. According to the Cerrado vegetation classification system proposed by [45], there are at least seven different phytophysiognnies in the Cerrado biome, which differ in terms of vegetation cover, the amount of biomass, and proportion of trees, shrubs, and grasses. In addition, the changes in LULC classes during the observed year can produce error in the classification, confusing the classifier when assigning the last change as a correct class. For instance, the conversion of forest in pasture could classify this class as forest due to the highest vegetation fraction in that year. Compared with the validation sample design applied in the MapBiomas project, we noticed that cropland, forestland, water bodies, and pastureland presented similar user and producer accuracies (92-100%) (Table 4). However, dry and humid savannas presented high levels of mismatches (53-64%), probably due to the legend definitions. The overall accuracy classification performance for Mato Grosso state was similar to that reported by the MapBiomas project, which was 90% in 2015 [16]. The overall accuracy in terms of biome was 95% for the Amazonia, 83% for the Cerrado, and 81% for the Pantanal, demonstrating again the greater difficulty in mapping savanna formations ([[https://mapbiomas.org/en/estatistica-de-acuracia?cama_set_language=en](https://mapbiomas.org/en/estatistica-de-acuracia?cama_set_language=en)]([https://mapbiomas.org/en/estatistica-de-acuracia?cama_set_language=en](https://mapbiomas.org/en/estatistica-de-acuracia?cama_set_language=en))). The total area classification performance agreed with that of the previous study [16] for most classes (Table 5). One of the main differences between those mappings was that the extent of cropland estimates was 32%. Here, it is important to note that the MapBiomas maps do not differentiate between annual and perennial crops. Perennial crops are classified as forestlands in our classification. Our approach did not discriminate among perennial and semiperennial crops and annual crops because of the low seasonality of the perennial and semiperennial crops. If the semiperennial crops (sugarcane, for example) were excluded from the area comparison based on the MapBiomas project, the difference would be 10% higher. Furthermore, both initiatives have the limitation of not classifying more than one annual harvest cycle (typically, double cropping), a common practice in Mato Grosso state [34; 57]. Other studies showed larger cropland estimates than those in our method: 98,488 km\({}^{2}\)[58] and 98,723 km\({}^{2}\)[59], in which double-cropped areas were counted twice [59]. To overcome this limitation, one possibility is the use of a crop calendar (wet-dry seasons), as proposed by [60], including the short