## 1 Introduction
In reality, various classes of pasture degradation are often overlooked in most studies, including low-intensity degradation (LID), moderate-intensity degradation (MID), severe agronomic degradation (SAD), and severe biological degradation (SBD). Mapping these distinct classes in the Cerrado environment, along with non-degraded pastures (NDP), proves to be crucial yet challenging when employing remote sensing techniques. Detailed mapping of degraded pastures is vital for understanding their nuanced impacts, influencing the costs associated with their recovery, and defining strategies necessary for restoration. Regrettably, existing literature lacks comprehensive studies testing the discrimination of different levels of pasture degradation and examining how these areas respond spectrally and seasonally to the satellites. The precise identification of pastures at various stages of degradation becomes instrumental in supporting public policies aimed at their renewal or recovery, catering to both livestock intensification and the generation of surplus low-cost open land for alternative uses [26]. In this context, the recent introduction of the PlanetScope satellite constellation by Planet Labs PBC. holds significant promise for distinguishing non-degraded pastures from areas exhibiting varying levels of degradation. The third generation of CubeSats, known as SuperDove, has been consistently capturing daily data since 2020, utilizing eight spectral bands positioned between 400 and 900 nm (including blue I and II, green I and II, red I and II, red-edge, and near-infrared (NIR)). Offering a spatial resolution of 3.7 m and a radiometric resolution of 12 bits, SuperDove's dense time series of observations have the potential to reveal nuanced differences in reflectance corresponding to the condition and degradation stage of pastures. Despite this considerable potential, the application of SuperDove data in studies of pasture degradation in Brazil remains unexplored. To address the complexity of spectral discrimination among various levels of pasture degradation, diverse metrics can be extracted from SuperDove data and subsequently subjected to machine learning classification. Among the various classification techniques employed for mapping savannas in Brazil, Random Forest (RF) has consistently demonstrated adequate performance compared to other classifiers [27; 28; 29; 30]. Even when compared with deep learning approaches, RF has showed slightly lower F1-scores, as demonstrated with validation data by [PERSON] et al. [31] in a study to separate pasture from non-pasture areas in the Cerrado. Therefore, RF is a suitable technique for handling a large number of metrics, as it operates as an ensemble of decision trees, with the final classification being determined by the most voted class [32; 33]. Examples of metrics that can be used as input data for RF classification include the reflectance values from the eight bands of SuperDove, a suite of derived vegetation indices, endmember fractions obtained through Spectral Mixture Analysis (SMA), and image texture attributes. Using the SuperDove data, the suite of derived vegetation indices encompasses the Enhanced Vegetation Index (EVI) [34], the Green-Red Normalized Difference (GRND) [35], the Modified Photochemical Reflectance Index (MPRI) as a variant of the original formulation [36], the Normalized Difference Vegetation Index (NDVI) [37], and the Red-Edge Normalized Difference (REND) [38]. Some of these vegetation indices are sensitive to vegetation structure, while others can effectively monitor changes in new foliage and pigments within pastures [39]. Considering the significance of soil cover in assessing degradation in the field [40], approaches utilizing SMA can become valuable. Particularly, data associated with endmember-soil fractions in areas marked by severe biological degradation (SBD) with comparatively high soil exposure can be instrumental. These endmember fractions can, therefore, be considered as input data for a given classification model.