## 4 Discussion
Depending on the frequency of cattle presence and grazing conditions, weeds may vary in incidence and height. Because of the different sensitivities to precipitation or water stress, the reflectance of pasture species and weeds can differ seasonally to the satellites in the transitional periods between rainy and dry seasons, facilitating their discrimination. Conversely, in biological degradation, a significant decline in aboveground forage biomass is typically observed, often revealing patches of degraded exposed soils sensed by satellites across pasturelands. Due to the marked decrease in vegetation productivity, pastures exhibiting signs of severe biological degradation displayed greater sensitivity to water stress compared to non-degraded pastures, manifesting earlier reflectance changes in the visible and near-infrared bands (VNIR) than other classes. Similarly, earlier reflectance modifications in SuperDove spectra were noted at the onset of the rainy season over severely degraded areas. In ecosystems like the Cerrado, characterized by a prolonged dry season and a naturally less vigorous vegetation, due to soil and climate conditions, biological degradation is the predominant form [5]. Various pasture management strategies and techniques exist to recover or renew degraded pastures in the Brazilian savanna, with costs increasing in direct proportion to degradation levels [5; 9; 10; 11]. In our study, this observation validates the approach of incorporating varying levels of degradation into our data analysis strategy. During the dry season, distinguishing biologically degraded from non-degraded pastures is complicated by the presence of NPV or dry leaves covering the surfaces [49]. In non-degraded pastures, the relative proportions of NPV to GV observed by the SuperDove increase from the rainy to the dry season. Given the spectral similarity between NPV and soils in the VNIR interval [44], discriminating between NDP and SBD becomes more challenging in the dry season than in other periods of the seasonal cycle due to the increased amounts of NPV observed over non-degraded areas. In this context, incorporating Shortwave Infrared (SWIR) spectral bands into the analysis, particularly narrow bands within the 2100-2400 nm range from hyperspectral instruments, could enhance class separability between soil and NPV during the dry season. In terms of spectral resolution, the SuperDove (PSB.SD) demonstrated significant improvements in band configuration compared to the other two generations of Cube  
Figure 14: 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 the combined sets of attributes. Results are presented for two dates representing the transition from the rainy to the dry season (DOY 153 in June; blue color results) and the middle of the dry season (DOY 227 in August; red color results). The reported results refer to the validation dataset. Sats, which used to acquire images in four multispectral bands (PS2 and PS2.SD). Within the set of eight SuperDove bands, those situated in the red, red-edge, and NIR spectral intervals were identified by the RF algorithm as the most important for discriminating pasture degradation. Pastures with severe biological degradation exhibited distinct rates of change in the reflectance of these bands with increasing soil water stress, particularly when contrasted with other classes. As commonly observed in savanna grassland areas, a reduction in chlorophyll content and an increase in NPV generally lead to an increase in red reflectance and a decrease in NIR reflectance as the dry season progresses [28; 50]. During the transition from the rainy to the dry season, non-degraded pastures display more green leaves and a well-defined 660-nm chlorophyll absorption band in SuperDove spectra, when compared to other pasture degradation classes (Figure 7).