## 3 Results
### Discriminative Ability of the Spectral and Spatial Attributes  
Given the significant reflectance differences observed between class pairs during the transition from the rainy to the dry season, as detailed in the preceding section on Mahalanobis distance results, our exploratory analysis investigated the discriminative ability of the spectral and spatial attributes. We focused the RF classification on data acquired by SuperDove over the five sites on 2 June 2022 (DOY 153). During this date, when the SMA was applied to pasture plots across all sites representing the five classes of pasture degradation, the technique automatically detected three endmembers. These endmembers consisted of two pixels of green vegetation (GV1 and GV2) and one pixel of bare soil (S). Along with a synthetic shade spectrum, these were considered to compose a four-endmember model. Upon locating these pixels in the images, we observed that the first endmember originated from an area of non-degraded pasture (green line in Figure 7), while the other two were retrieved from areas of severe agronomic (blue line) and severe biological (red line) degradation, respectively. When compared to areas of SAD and SBD, areas of NDP exhibited better-defined chlorophyll absorption bands around 660 nm and higher reflectance in the NIR interval due to leaf scattering (Figure 7). NDP displayed a more homogeneous vegetation cover, in contrast to SAD, characterized by a heterogeneous mixture of pasture leaves and invasive plant species, accounting for the observed differences in Figure 7. The endmember reflectance spectrum of SBD (red line in Figure 7) closely resembled that of bare soil, lacking any spectral features of green leaves. due to the significant proportions of soil fractions sensed by SuperDove over biologically degraded plots (Figure 8b). Both the SMA fractions and the vegetation indices exhibited close relationships with each other. For example, regions with spectral mixtures featuring high abundance values of GV1 (red color in the SMA middle panel of Figure 9) also demonstrated elevated NDVI values (sea green color in the NDVI right panel of Figure 9). Conversely, pasture plots characterized by a high abundance of soil (blue color in the SMA middle panel of Figure 9) displayed low NDVI values (orange color in the NDVI right panel of Figure 9). Consequently, GV1 showed a positive relationship with NDVI, while the soil fraction exhibited an inverse correlation with this index. In the SMA color composite, less degraded pastures  
Figure 8: Scatterplots illustrating the relationships between (**a**) NDVI and GRND and (**b**) EVI and REND for three field-sampled classes of pasture degradation: Non-degraded pasture (NDP) and pastures with severe agronomic (SAD) and biological (SBD) degradation. Figure 7: Endmember reflectance spectra derived from Sequential Maximum Angle Convex Cone (SMACC) for SuperDove data acquired on 2 June (DOY 153) over areas exhibiting varying degrees of pasture degradation across the five studied sites in central Brazil. were distinctly delineated by shades of green vegetation, whereas severe degradation, particularly of a biological nature, was more prominently detected by the blue color in the soil fraction image. Figure 9: False color composites (SuperDove bands 8, 7, and 6 in RGB) illustrating examples of the five classes of pasture degradation (NDP, LID, MID, SAD, and SBD) are presented on the left side of the figure. In the middle panel, color composites of green vegetation (GV1), GV2, and soil (S) fraction images in RGB are displayed. Lastly, NDVI images are presented on the right side. Finally, the analysis of GLCM texture metrics for the NIR band of SuperDove revealed that this type of attribute could contribute to differentiating pasture degradation. For instance, examination of the two most common GLCM metrics showed a decrease in texture mean (Figure 10a) and variance (Figure 10b) over plots of severely degraded pastures (SAD and SBD).