## 4 Discussion
In alignment with our findings, EVI was deemed the most effective vegetation index for distinguishing non-degraded pastures from those with invasive plants or palatable species (indicative of agronomic degradation) by [PERSON] et al. [52]. Due to its strong dependence on NIR reflectance responses, EVI exhibits increased sensitivity to changes in vegetation structure and in the amounts of dry leaves or NPV over the pasture surfaces [39]. The second most influential vegetation index in our study was NDVI, which was also employed in trend analysis by [PERSON] et al. [11] for mapping degraded pastures  
Figure 16: Variations in F1-score and Overall Accuracy (OA) resulting from the Random Forest (RF) classification of five classes of pasture degradation (NDP, LID, MID, SAD, and SBD) using reflectance data of 10 bands (10-m and 20-m spatial resolution) from the Multispectral Instrument (MSI)/Sentinel-2 (400–2500 nm) and eight bands from the SuperDove (400–900 nm). Images from both instruments were acquired in approximately coincident dates (2 and 4 June 2022). The reported results in blue (SuperDove) and red (MSI/Sentinel-2) colors refer to the validation dataset. in the Cerrado biome using MODIS data. Lastly, GRND is likely to have captured changes in foliage within degraded and non-degraded areas in response to increasing water stress observed towards the dry season during the RF classification. Endmember fractions derived from sub-pixel analysis of SuperDove data significantly improved the separability of severely degraded areas when compared to those with low-to-moderate intensity degradation. In the RF classification, compared to other fractions, the soil fraction exhibited the highest Mean Decrease in Accuracy value. This outcome was somewhat anticipated, given that soil cover was identified as one of the most crucial criteria for assessing degradation in the field [40]. High soil fractions were observed over degraded pastures, particularly in areas affected by biological processes of degradation. In contrast to endmember fractions, most GLCM image texture metrics displayed limited importance in the classification. When compared to other classes, pastures with severe agronomic and biological degradation (SAD and SBD) exhibited lower values for NIR-band texture mean and variance, suggesting more homogeneous surfaces in these biologically and agronomically degraded areas. However, it is noteworthy that image texture metrics, when considered individually, demonstrated the poorest classification performance with an overall accuracy of 0.34, compared to the value of 0.66 obtained using only band reflectance. In this analysis, the entire set of SuperDove bands was not used to calculate the texture metrics; instead, the focus was on the NIR band. In this spectral interval, the energy incident over the pastures has higher transmittance through leaves and greater variability than that generated in the visible bands. In a study by [PERSON] et al. [53], GLCM texture metrics derived from the entire set of PlanetScope bands have been successfully used for detecting double cropping areas in the Cerrado. Our study has certain limitations that deserve consideration in future investigations. Notably, despite the PlanetScope SuperDove constellation's capability to acquire daily observations, the quality of the time series varied across the five studied sites due to cloud cover, resulting in gaps in the frequency of image acquisition, particularly in the first (January and February) and last (November and December) two months of the year. As a practical consequence of the cloud cover-related problem, we encountered challenges in interpolating the time series over each studied site, filling the gaps, and retrieving phenological metrics that could potentially contribute to the classification of degraded pastures.