## 2 Methodology
### Determination of SuperDove Attributes  
After identifying the most suitable period for discriminating between the five classes of pasture degradation, we calculated some spectral and spatial attributes from SuperDove data (Table 2). Given the SuperDove band configuration outlined in Table 1, various vegetation indices can be computed. Following the approach proposed by [PERSON] et al. [39], we calculated five vegetation indices for subsequent examination of their efficacy in discriminating pasture degradation classes: EVI, GRND, MPRI, NDVI, and REND. Their equations and references are shown in Table 3.  
Figure 3: Frequency of cloud-free images captured by the SuperDove satellite constellation in 2022 for each of the five selected sites targeted for analysis. The dry season period is indicated for reference. The selection of these five vegetation indices is grounded in their biophysical significance. For example, as discussed by [PERSON] et al. (2019) and [PERSON] et al. (2019), EVI is particularly sensitive to vegetation structure. It addresses limitations of NDVI related to signal saturation over dense vegetation or the influence of soil background over sparse vegetation ([PERSON], 2017; [PERSON] et al., 2019; [PERSON] et al., 2019). Conversely, GRND is potentially responsive to leaf flushing ([PERSON] et al., 2019), capturing seasonal changes in new foliage. Following [PERSON] et al. (2019), the equation adopted in Table 3 for calculating MPRI is a variant of the original formulation proposed by [PERSON] et al. (2019), designed to transform negative values of this index into positive ones. MPRI is potentially sensitive to alterations in carotenoid pigments in live foliage, serving as a proxy for photosynthetic light use efficiency or vegetation productivity and stress ([PERSON] et al., 2019). Lastly, REND explores the red-edge interval, depicting the spectral transition from chlorophyll absorption to leaf-canopy scattering ([PERSON] et al., 2019; [PERSON] et al., 2019). Endmember fractions were considered in the current study because most areas of biologically degraded pastures have soil exposure to the satellites. In this study, the SMA was preceded by employing the Sequential Maximum Angle Convex Cone (SMACC) technique to automatically identify spectral endmembers for constructing the model. The methodology described by [PERSON] et al. (2019) uses residual minimization (convex cone model) to find pixels with contrasting brightness in the scene, which are defined a priori as candidate endmembers. The algorithm is implemented in the Environment for Visualizing Images (ENVI) software (L3 Harris Geospatial Solutions, Inc., Manila, Philippines, 2022, version 5.6.3). SMACC was applied to the surface reflectance of the eight SuperDove bands of the field-visited plots, with the maximum number of endmembers set to four for the sake of model simplicity. The unmixing Root Mean Squared Error Tolerance was set to 0.01, and endmember spectra were constrained to positive values at any wavelength. To merge candidate endmembers with reflectance spectra of similar shape, the Spectral Angle Mapper (SAM) technique was integrated into the SMACC algorithm. A threshold angle of 0.10 radians was selected for this purpose. Using the SMACC-derived endmember spectra, selected over plots of NDP, SAD and SBD, and a synthetic shade spectrum ([PERSON], 2017), \begin{table}
\begin{tabular}{c c} \hline \hline
**SuperDove Attribute** & **Metric** \\ \hline \multirow{4}{*}{Spectral} & I. Surface reflectance of the eight SuperDove bands \\ \cline{2-3}  & II. Five vegetation indices (EVI, GRND, MPRI, NDVI and REND) \\ \cline{2-3}  & III. Four endmember fractions from SMA \\ \cline{2-3}  & (NDP, SAD, SBD, and shade) \\ \hline \multirow{2}{*}{Spatial} & Eight GLCM texture metrics calculated from the NIR band \\  & (texture mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) \\ \hline \hline \end{tabular}
\end{table}