## 4 Discussion
In comparison to biologically degraded areas having more exposed soils, the SuperDove spectra of non-degraded pastures also exhibited higher reflectance in the NIR range. This change can be attributed to the increase in leaf scattering (plant structure), resulting from gains in leaf area index (LAI) and aboveground biomass observed in NPD areas. In agronomical degradation, plant structure can vary based on the quantity, type, and height of palatable weeds for cattle, influencing NIR reflectance through changes in surface roughness detected by the satellite. The spectral effects of NPV, soils, and leaf tissue water content primarily manifest in the SWIR, which is not covered by the SuperDove bands. An illustration of this phenomenon is presented using Operational Land Imager (OLI)/Landsat-8 reflectance spectra for a period corresponding to the transition from the rainy to dry season in May (Figure 15a). In the OLI SWIR bands located close to 1650 nm and 2200 nm, the reflectance of SBD areas exceeds that observed in NDP and SAD areas. This difference is attributed to the reduced water content in leaf tissues and the increased presence of NPV and exposed soils in biologically degraded pastures viewed by the Landsat instrument. Figure 15: Average reflectance spectra from OLI/Landsat-8 data acquired over field-surveyed plots representing non-degraded pastures and pastures experiencing severe agronomical or biological degradation. The results are presented for various dates during the year 2021, specifically during (**a**) the transition from the rainy to the dry season, (**b**) the middle of the dry season, and (**c**) after the occurrence of the first rainfall events in the new seasonal cycle in October. Upon preliminary examination of OLI/Landsat-8 spectra, the potential discriminatory power of the SWIR spectral region (around 1650 nm and 2200 nm) is evident (Figure 15). In a study focused on mapping pasture degradation in the Atlantic Forest biome, [PERSON] et al. [51] observed gains in classification accuracy upon incorporating SWIR bands into the data analysis. Inspection of Figure 15 also confirmed challenges in separability between degraded and non-degraded pastures, previously observed with the SuperDove instrument during the dry season. For instance, compared to the middle of the dry season (DOY 209 in Figure 15b), the discrimination of non-degraded pastures (NDP) from pastures with severe biological or agronomic degradations (SAD and SBD) is more feasible during the transition from the rainy to the dry season (DOY 145 in Figure 15a) or toward the new cycle of the rainy season in October (DOY 289 in Figure 15c). Comparison of preliminary RF classification results for the five classes of degraded pastures (NDP, LID, MID, SAD, and SBD) utilizing reflectance data of 10 bands (10-m and 20-m spatial resolution) from the MSI/Sentinel-2 (400-2500 nm) and eight bands from the SuperDove (400-900 nm), both acquired between 2 and 4 June 2022, demonstrated enhanced overall classification accuracy with Sentinel-2 data (0.79 versus 0.66 in Figure 16). Consistent with the spectral patterns depicted in Figure 15 using Landsat data, these enhancements are likely to arise from the integration of SWIR data into the classification methodology, leading to the higher F1-scores observed for most classes in Sentinel-2 data compared to SuperDove observations (Figure 16). Hence, additional research is necessary to precisely determine the impact of SWIR bands on the classification efficacy of pasture degradation. The acquisition of data in eight bands by the SuperDove enabled the determination of five vegetation indices (EVI, GRND, MPRI, NDVI, and REND), each having different biophysical sensitivities. In the RF classification, the three most important vegetation indices were identified as EVI, NDVI, and GRND, with each accounting for at least 10% of importance.