## 5 Conclusions  
In alignment with our specific objectives, the following conclusions were drawn from the study:  
(i) Enhanced discrimination between the five classes of pasture degradation in the Cerrado was observed during the rainy season compared to the dry season, particularly in the transition from the rainy to the dry season and the onset of a new seasonal rainy cycle in October. Challenges arise in the dry season due to the increased presence of non-photosynthetic vegetation in leaves and surfaces, posing difficulties in distinguishing non-degraded pastures from areas with biological degradation characterized by high soil exposure. (ii) The classification using all attributes yielded an Overall Accuracy (OA) of 0.69, outperforming the use of individual datasets of reflectance (OA = 0.66), SMA (OA = 0.52), vegetation indices (OA = 0.38), and GLCM texture metrics (OA = 0.34). While the classification performance using VIs alone was not the most remarkable, the combined set of variables, including EVI, NDVI, and GRND, exhibited the highest importance in terms of Mean Decrease in Accuracy. Despite achieving accuracy with the combined set of variables and five classes (OA = 0.69), there were notable omissions and commissions, particularly in the intermediate classes of degradation (LID and MID). (iii) The second classification scenario, focusing on severe degradation classes (SAD and SBD) and non-degraded pasture (NDP), demonstrated an increase in OA from 0.69 (five classes) to 0.88 (three classes). This result underscores the challenges associated with discriminating intermediate stages of degradation, a difficulty that is sometimes encountered even in the field. Preliminary analysis of MSI/Sentinel-2 data and OLI/Landsat-8 reflectance spectra indicates the potential discriminatory power of SWIR bands to separate the classes of pasture degradation. An initial evaluation of RF classification outcomes for the five classes of pasture degradation using only reflectance data from MSI/Sentinel-2 (400-2500 nm) and SuperDove (400-900 nm), to ensure fair comparison of results, showed a significantly improved overall accuracy (0.79 versus 0.66) with Sentinel-2 data. This gain in OA is probably due to the insertion of SWIR Sentinel-2 spectral bands in the analysis. Further studies are necessary to confirm this evidence, as the SWIR bands are not covered by the PlanetScope SuperDove satellite constellation. Additionally, further investigations are needed to evaluate the potential discriminatory power of land surface phenology metrics to differentiate these classes. Conceptualization and data analyses, A.G.P.S., L.S.G. and L.G.F.J. ; writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript. This study was partially funded by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001, and by the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) (grant number 307792/2021-8). The shapefiles for the different sites are publicly available at the Laboratorio de Processamento de Imagens e Geoprocessamento (LAPIG) from the Universidade Federal de Goias (UFG) at the Pasture Atlas website ([[https://atlasdasapastagens.ufg.br/map](https://atlasdasapastagens.ufg.br/map)]([https://atlasdasapastagens.ufg.br/map](https://atlasdasapastagens.ufg.br/map)), accessed on 15 May 2023). This resource includes not only the field points used in this study, but also additional data and maps. **Acknowledgments:** We thank (c2022 Planet Labs Inc. for providing the images made available by the NICFI (Norway's International Climate and Forest Initiative). Comments by the anonymous reviewers and Editor are highly appreciated. **Conflicts of Interest:** The authors declare no conflicts of interest. * [PERSON] et al. (2011) [PERSON]; [PERSON]; [PERSON]; [PERSON] Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models. _ISPRS J. Photogramm.