## Materials and Methods
### Sampling Design and Classification  
To ensure the representativeness of sampling, a set of 622 samples (348 in the Amazonia biome, 234 in the Cerrado, and 40 in the Pantanal) was randomly distributed over the study area by visual inspection (Figure 5). Figure 5: Location of 622 training and validation samples in the study area. In the upper right, the RGB (Red-Green-Blue) color composite of median PROBA-V data: Red, NIR, and blue of an agricultural sample, with respective conceptual behavior of the ancillary data from MODIS NDVI and the two seasons of the Landsat-8 OLI (RGB:654) in the bottom right. In total, we selected 151 (84 for validation sampling) samples of croplands; 128 (56) forestland samples; 100 (36, 37, and 36) pastureland, humid, and dry savanna samples; and 43 (14) water body samples. The number of samples for water bodies was low because they occupy a relatively small area in Mato Grosso state. We collected samples only from perennial large rivers, streams, and lakes. For the dry savanna, the small samples were due to confusion with pastures, especially because that class comprises several other classes with a gradient of shrubs and grasslands. Training samples were chosen empirically by observing the sample distribution, accuracy assessment, and classification performance. On the other hand, the number of validation samples was calculated based on the statistical approach proposed by [47], which consists of generating an adequate probability dataset among stratified random sampling. The sample size for validation was based on the extension area of each stratum and their expected accuracy to ensure sufficient size to produce sufficiently precise estimates. As there was no reference map with the same LULC classes for the study area, a previous classification map was developed using two-thirds of total training samples and one-third for testing (Figure 5) to obtain the expected overall accuracies by each LULC. Thus, the set parameters were 2% of the standard error of expected overall accuracy; 90% of the expected user's accuracy of the high-confidence accuracies for forestlands, croplands, and water bodies; and 70% of the expected user's accuracy to the low-confidence accuracies for humid savanna, pastureland, and dry savanna. Since some classes are identified more easily than others, this measure will influence the overall size of the samples. Finally, the mosaics were processed by the random forest classifier in the Google Earth Engine platform. Random forest is a supervised classifier where each tree handles a random selection from the training data by creating a set of a multiple decision trees using bootstrap [48]. The random replacement of trees and samples makes the classifier robust for dealing with a low number of training samples [49]. At each bootstrap replacement, a new tree node is built by gaining information from the comparison of the multiple attributes (spectral bands). This procedure is repeated for the other nodes of the tree, which grow without pruning. Then, \(k\) number of trees is generated, forming the (random) forest [48]. The parameter \(k\) was set to 30, which is the maximum number of trees to be created. The accuracy was evaluated by the confusion matrix and by the comparison of results obtained by the MapBiomas project (Table 1). The same validation dataset was used to assess the agreement with MapBiomas project classes according to [50; 51]. As MapBiomas raster-based LULC products are available at 30-m spatial resolution, the data were degraded to 100-m spatial resolution by using the most likely class in the pixel (mode), enabling intercomparison between datasets ([[https://code.earthengine.google.com/?accept_repo=users/mapbiomas/user-toolkit](https://code.earthengine.google.com/?accept_repo=users/mapbiomas/user-toolkit)]([https://code.earthengine.google.com/?accept_repo=users/mapbiomas/user-toolkit](https://code.earthengine.google.com/?accept_repo=users/mapbiomas/user-toolkit))). The legend consistency between this study and the MapBiomas project is shown in Table 1.