## 4 Discussion  
This inaugural study utilizing high spatial and temporal resolution data from the eight-band PlanetScope SuperDove satellite constellation considered two distinct classification scenarios of pasture degradation within the Brazilian savanna ecosystem. The first scenario, a more intricate one, encompasses five classes of pasture degradation (NDP, LID, MID, SAD, and SBD). Meanwhile, the second scenario involves a more streamlined classification, considering only three classes of degradation (NDP, SAD, and SBD). By incorporating a pre-existing pastureland mask and delving into the discrimination of five pasture degradation classes (scenario 1), we have elevated the analysis to a higher level of complexity. The inclusion of low- to moderate-intensity classes of degradation (LID and MID) in the analysis is particularly noteworthy due to the comparatively lower costs associated with the recovery or renewal of these areas compared to severely degraded areas. Notably, the Brazilian government, with the aim of rehabilitating 15 million hectares of degraded pastures by 2030, is supporting such efforts through subsidized credits to facilitate management and fertilization actions [13]. Due to the reduction in the number of classes in the second classification scenario and the apparent simplification of the analysis, the overall accuracy increased from 0.69 (scenario 1) to 0.88 (scenario 2) when employing the combined sets of spectral and spatial attributes. When compared to non-degraded or severely degraded pastures, classes with low-to-moderate-intensity degradation (LID and MID) exhibited greater variability in the field, contributing to misclassifications. In these instances, both agronomic and biological degradation processes were detected in pasture plots with MID, leading to variable proportions of green vegetation (invasive plants) and exposed soils for the satellite constellation. Consequently, this variability introduced spectral changes in the attributes measured by the SuperDove, making MID the class with the lowest Precision (0.58) in the RF classification. Another contribution of this study, stemming from the PlanetScope SuperDove satellite constellation's ability to generate densely spaced time series, lies in the identification of optimal periods for remote sensing discrimination of pasture degradation. In comparison to the dry season, more favorable scenarios for discriminating pasture degradation were observed during the rainy season or in transitional periods between both seasons. For example, in the middle of the dry season, classification accuracy metrics were comparatively lower (OA = 0.20 for DOY 227 in Figure 14) than the values observed in the transition from the rainy to the dry season (OA = 0.69 for DOY 153 in Figure 14). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Reference** & & \\ \hline  & **NDP** & **SAD** & **SBD** \\ \hline NDP & 160 & 20 & 0 \\ SAD & 11 & 138 & 31 \\ SBD & 0 & 2 & 178 \\ \hline F1-score & 0.91 & 0.81 & 0.91 \\ Recall & 0.89 & 0.77 & 0.99 \\ Precision & 0.93 & 0.89 & 0.91 \\ \hline Overall Accuracy (AO) & 0.88 & & \\ Kappa & 0.81 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix resulting from the classification using the combination of spectral and spatial attributes in discriminating three degradation classes: non-degraded pasture (NDP) and pastures with severe agronomic (SAD) and biological (SBD) degradations. Our study, therefore, has identified suitable periods for data acquisition, notably the transition from the rainy to the dry season (May to June) and the beginning of a new seasonal cycle in October after the initial rainfall events. Compared to the peak of the rainy season (January-February), these transitional periods offer better opportunities for acquiring cloud-free data over the studied sites. In agronomic degradation, shifts in botanical composition lead to decreased forage productivity and increased weed infestation [5].