## Materials and Methods
### Data Processing  
The main steps of the methodological approach applied in this study and conducted in the Google Earth Engine platform [41] are shown in Figure 2. First, we imported the PROBA-V 100-m scenes from 2015 in the Google Earth Engine platform and clipped to the Mato Grosso state boundary [42]. The following bands were considered: Blue, red, NIR, SWIR, and SM. The annual mosaic of cloud-free images from 2015 was generated by a script for masking pixels whose values stated as cloud or cirrus in the SM band were excluded [40]; thus, only pixels with good radiometric quality, that is, classified in the SM band as 0 (clear), were selected.  
Next, we selected homogeneous areas of bare soils, large croplands, and clear water bodies in the PROBA-V annual mosaic to obtain the pure endmembers of soil, vegetation, and shade, respectively. Then, the soil, vegetation, and shade fraction images were obtained by selecting endmembers by visual inspection of the median values of the blue, red, NIR, and SWIR bands. The representative reflectance values of each endmember in the blue, red, NIR, and SWIR spectral regions for Mato Grosso state are shown in Figure 3.  
The LSMM assumes that pixel values are a linear combination of reflectance values from a certain number of elements (endmembers). The fraction image is obtained for each endmember representing the proportion of the corresponding endmember within a pixel by minimizing the sum of squares of errors in the original data [27]. The proportion of each element is constrained to the range from 0% (absence) to 100% (maximum presence) at each cell resolution. The LSMM can be represented by Equation (1):  
\[R_{i}=\sum_{j=1}^{m}f_{i}r_{i,j}+\varepsilon_{i}\ \ (i=1,\ \ldots,\ p) \tag{1}\]  
where:  
\(R_{i}\) = the spectral reflectance in the \(i\)th spectral band;  
\(r_{ij}\) = the spectral reflectance of the \(j\)th component in the \(i\)th spectral band (endmember);  
\(f_{i}\) = the proportion of the \(j\)th component within the pixel;\(i=1,p\) (number of spectral bands used);  
\(j=1,\)\(m\) (number of considered components); and  
\(\varepsilon_{i}=\) the residual for the \(i\)th spectral band.  
Figure 3: Endmembers collected from median reflectance values of Project for On-Board Autonomy–Vegetation (PROBA-V) annual composites (**A**) and their corresponding samples of “Crop”, “Bare soil”, and “Clear water” in the municipalities of Sapezal, Sorriso, and Chapada dos Guimaraes (Mato Grosso state), respectively (**B**).  
Figure 2: Processing steps of the land use and land cover classification of Mato Grosso state. NIR, near infrared; SWIR, shortwave infrared; LULC, land use and land cover; OLI, Operational Land Imager; MODIS, Moderate Resolution Imaging Spectroradiometer; NDVI, normalized difference vegetation index; and MT, Mato Grosso.  
The set of PROBA-V images obtained in 2015 was reduced to three maximum and three standard deviation fraction images of vegetation, soil, and shade. This approach ensured that the highest fraction value was selected on the composites within the year [30]. Therefore, the information of one year of PROBA-V images (in theory, 73 cloud-free images) was condensed in six bands. The novel idea is that the highest vegetation fraction value refers to the most likely class, and the standard deviation of its fractions represents the seasonality of that class throughout the year. The standard deviation of the fraction images is calculated as the deviation from the average value of each fraction image in the annual mosaic composites.