## 4 Discussion
### Perspective  
The method presented encouraging results for Mato Grosso state, but it may face some difficulty in other regions or in discriminating more detailed LULC classes. For example, the Cerrado biome is characterized by landscape patterns varying from open grassland to dense canopies; therefore, obtaining accurate LULC classification in this biome may be slightly challenging [65]. In this study, we grouped Cerrado physiognonies into dry savanna and humid savanna to produce LULC classes  
Figure 9: (**A**) Spatial distribution of cloud-free and cloud shadow-free pixels in an annual PROBA-V 100-m composite. (**B**) Histogram of the frequencies of cloud-free and cloud shadow-free pixels in Mato Grosso state. Vertical, dashed lines represent a hypothetical maximum number of pixels in the Landsat-8 OLI and Sentinel-2 collection. (**C**) Enlargement of a cropland-dominated region located in the Campo Novo do Parccis municipality. In the upper right, the red-NIR-SWIR color composite of PROBA-V. In the center right, the spatial distribution of data availability of a year of PROBA-V images and Sentinel-2 (bottom right).  
with good performance. However, if there is a necessity to discriminate among the seven types of physiognnies (two grassland classes, three shrubland classes, and two forestland classes) according to the Cerrado vegetation classification system proposed by [45] or to precisely discriminate between natural grasslands and planted pasturelands, the accuracy of the method may decrease substantially because of the similarities in the spectral responses.  
The use of spectral mixture models has been reported to estimate woody and herbaceous cover using the photosynthetic vegetation (PV) fraction in addition to the nonphotosynthetic vegetation (NPV) fraction [66; 67], which can include materials such as dry organic matter, litter, wood, and dry logs. According to [67], using only the vegetation fraction can overestimate seasonal vegetation, and the use of NPV can be useful to analyze the dynamics, function, and structure of vegetation. The use of four components (PV, NPV, shade, and soil) to generate land cover classifications in forested areas with high biomass, such as the Amazon rainforest, has been considered [68]. Recent research has focused on collecting photosynthetic and nonphotosynthetic endmembers of vegetation to enable the discrimination of dry and wet vegetation in environments with strong seasonality during the year [69]. However, few studies have investigated the applicability of these four components to quantify and monitor changes in seasonal vegetation and with low levels of biomass, as is the case for the Cerrado biome.  
Currently, there are some innovative methods and image synergisms that have been tested to improve the discrimination of land cover classes. The use of L-band synthetic aperture radar (SAR) images provided a significant improvement in the discrimination of woody canopy cover from African dry savanna [70]. This radar-based approach may improve the classification of humid savannas since SAR data are sensitive to moist vegetation. The use of the tasseled cap transformation of the Landsat and Sentinel-2 time series to discriminate the Cerrado physiognnies presented an overall accuracy of 63% [43]. Previous studies also showed that Bayesian unsupervised classification and multiple platforms/sensors improved LULC discrimination [71]. Harmonic models applied to time series have been used to classify forest types [72], facilitating the overview of phenological cycles that could be used to improve interclass separation.  
For future investigations, we suggest evaluating the feasibility of combining the proposed approach with other methods that rely on multiple sensors and different image processing techniques to derive accurate LULC products in complex ecosystems.