## 5 Discussion  
The classification of land cover in the ecologically diverse CAT presents significant challenges due to the complex interplay of vegetation types, canopy structures, and anthropogenic disturbances. Traditional approaches based on single-source remote sensing data have proven inadequate in capturing the subtle biophysical variations and patchy distributions of land cover, especially in mixed forest-savanna mosaics. In this study, we address this limitation by producing the first high-resolution land cover map of the CAT using a deep learning framework that fuses PlanetScope optical imagery, Sentinel-2 multispectral data, and Sentinel-1 SAR data, which integrative approach offers an integrative solution for mapping transitional ecosystems. Our results demonstrate that fused datasets significantly outperform single-source models, particularly in distinguishing complex and often-confused vegetation classes such as Amazon forest, Cerradao, and Savanna. Notably, the ability to spatially isolate Cerradao--an understudied and ecologically sensitive vegetation type--marks a key advancement over previous classification efforts. The resulting map not only achieves high accuracy (\(F1=0.85\)) but also reveals pronounced spatial heterogeneity and fragmentation patterns across the CAT. While the Amazon forest dominates the northern portion of the study area with large, contiguous patches, the Savanna and Cerradao formations appear more fragmented and spatially dispersed, with Cerradao accounting for only 8.4% of the total area. These landscape configurations reflect the long-term impacts of agricultural expansion, particularly in the Cerrado, where ecological connectivity is increasingly disrupted and regional carbon stocks are degraded [89; 90; 91]. We found significant differences in classification accuracy depending on the combinations of imagery used to map the CAT (Table 6). Whether from fused optical data, PlanetScope, or Sentinel-2, models that relied solely on optical imagery performed particularly poorly for distinguishing Cerradao (Figure 6b,c). Cerradao, as a transitional vegetation type, possesses characteristics of both forest and open savanna, and thus exhibits a complex structure whose spectral features closely resemble those of dense forest and partially open savanna [22]. Consequently, optical data alone may fail to capture these structural nuances. In addition, Cerradao often spans narrow transition zones between dry forest and Savanna, sometimes only a few kilometers wide [21; 31]. Such a limited distribution often leads to Cerradao intermixing with forest and savanna, creating a highly patchy pattern that further complicates vegetation classification. However, we observed that using both multispectral Sentinel-2 and Sentinel-1 SAR data improved classification accuracy (Table 6; Figure 6b,c). The integration of optical and SAR imagery offers a powerful and complementary approach for monitoring heterogeneous landscapes such as those found in the Brazilian Cerrado, located within the southern CAT. Optical sensors are highly sensitive to the biochemical and biophysical properties of vegetation, capturing information related to canopy greenness, pigment content, and phenological dynamics [92; 93; 94]. In contrast, Sentinel-1's C-band SAR, with its sensitivity to surface geometry and vegetation structure (e.g., roughness, dielectric properties, and canopy architecture), provides valuable insights into vertical and structural changes that are often obscured by cloud cover [95; 96]. This complementary nature is particularly advantageous in the Cerrado, a biome characterized by a complex mosaic of vegetation \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Area (km2)** & **Proportion of Study Area (\%)** \\ \hline Water & 75.1 & 0.2 \\ \hline Other & 391.5 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._types and marked seasonality, where optical observations may be limited during the wet season due to persistent cloud cover.