## 5 Discussion
Multi-sensor fusion approaches have shown promise in improving land cover classification, detecting subtle vegetation changes, and enhancing the accuracy of degradation and vegetation loss monitoring [97; 98; 99]. Therefore, leveraging both spectral and structural information through the fusion of Sentinel-2, Planet, and Sentinel-1 data can significantly strengthen ecosystem monitoring and inform policy interventions in this increasingly threatened savanna biome. Furthermore, our results indicate that models using PlanetScope data achieved better classification accuracy for mapping Savanna than other optical-only data models (Table 6). The CAT region contains expansive pastures that tend to regenerate when grazing ceases or when management intensity is reduced. These regenerating pastures often contain abundant herbaceous plants and shrubs, closely resembling open savanna formations [100]. Thus, in the model using only Sentinel-2, regenerating pastures are frequently confused with sparse Cerrado, leading to inaccuracies in identifying pasture boundaries (Figure 6a). By contrast, PlanetScope's higher spatial resolution better captures the traces of human activities and management in agricultural areas [101], such as shrubs, herbaceous plants, bare soil, fences, or animal tracks. Moreover, while regenerating pastures and savannas exhibit similar spectral properties in optical data, their surface structure differs substantially. Pastures often feature more homogeneous vegetation composition, likely possessing less variable surface roughness than Savanna. In terms of surface moisture, pastures also tend to have lower evapotranspiration than the Savanna [102]. Hence, Sentinel-1 SAR data, which is sensitive to both structural and moisture characteristics, may compensate for the limitations of optical imagery, improving classification performance (Table 6). With regard to agriculture classes, we detected several areas of extremely high reflectance in agricultural lands within the study area in both PlanetScope and Sentinel-2 images (Figure 6d), which may have arisen from exposed soil during cultivation, fallow periods, or from irrigation that increases reflectance in certain spectral bands [103; 104; 105]. High reflectance frequently leads to confusion between agricultural lands and built-up areas or roads [106], and PlanetScope's high spatial resolution demonstrates a strong capacity for discriminating among these types (Figure 6d). This can also be attributed to PlanetScope's ability to capture fine-scale agricultural surface features [44], including subtle internal field structures, crop planting patterns, and mixed patches of bare soil and vegetation. Furthermore, the Fusion + Sentinel-1 dataset performed well in the \"other\" category (Table 6), which encompasses isolated buildings, urban areas, and paved roads. Compared with natural vegetation, roads and buildings typically exhibit distinct color and textural characteristics [107]. Nonetheless, narrow roads, particularly those surrounded by vegetation, may be obscured by tree canopies and shadows, thereby influencing the physical attributes of vegetation [108]. The combination of high-resolution multispectral optical data with SAR fully harnesses the advantages of both. Optical data provides detailed spectral information, whereas SAR data captures morphological features. High-resolution optical data more effectively detects linear and continuous roads [109], while SAR data excels at identifying geometric properties such as flatness and width [110], thereby enhancing overall classification performance. High-resolution fused remote sensing imagery demonstrates superior performance in complex tropical ecosystems, capturing fine-scale variations in canopy height and coverage more effectively [41; 44; 87]. Our study confirms this advantage within the CAT region. The classification accuracy of fused datasets not only outperforms single-source data overall (Table 5) but also excels in identifying specific vegetation types like Cerradao (Table 6).