Therefore, more effective classification methods are urgently needed, especially as recent economic and political pressures threaten to revise official land classifications in Mato Grosso, potentially reclassifying forested areas and opening them to legal deforestation [35; 36]. For instance, Bill No. 337/2022 proposes changes to the classification criteria for areas defined as Legal Reserves, potentially reducing forest protection in the Cerradoo by allowing for the reclassification of native vegetation as non-forest. Similarly, the Supplementary Bill to PLC No. 18/2024 seeks to alter environmental zoning frameworks, aiming to expand agricultural and infrastructure development in previously protected zones. These legislative proposals could significantly weaken environmental safeguards and accelerate land-use change in ecologically sensitive regions such as the Cerrado-Amazon transition zone [37; 38]. Improved mapping not only reveals the CAT ecosystem spatial structure but also supports the formulation of informed conservation and management strategies. Previous studies that have attempted to map the CAT region have predominantly used medium-resolution (30 m) remotely sensed imagery [29; 34; 39; 40]. However, the region's heterogeneous species composition and dynamic ecological processes pose significant challenges for accurately identifying dominant vegetation types using such data. This is particularly true for Cerradao, which has largely disappeared across much of its former extent, now persisting only as scattered and disconnected fragments [1]. In other tropical areas of Brazil, vegetation mapping efforts are increasingly turning to high spatial resolution optical imagery, which offers more detailed surface characterization and enhances the ability to distinguish between vegetation types [41; 42; 43; 44]. Despite its great potential, the use of high-resolution data in the CAT region has so far been largely limited to mapping deforestation and agricultural crops [45; 46; 47]. While high spatial resolution imagery offers advantages, for accurate vegetation detection and monitoring, especially in areas with highly heterogeneous vegetation communities, spectral characteristics, as well as the spatial resolution of satellite imagery, are important [48]. To address these challenges, researchers have explored data fusion techniques, combining high-resolution imagery with multispectral data to improve classification outcomes [49]. Beyond optical data, Sentinel-1 synthetic aperture radar (SAR), operating in the C-band with a wavelength of approximately 5.6 cm, plays a pivotal role in monitoring tropical ecosystems such as the Cerrado (Brazilian savanna). Its cloud-penetrating capability and sensitivity to vegetation structure make it especially valuable in regions with persistent cloud cover. Sentinel-1 data have been widely applied in near-real-time, large-scale monitoring of forest degradation and deforestation across tropical environments [50]. Furthermore, polarimetric SAR measurements have demonstrated sensitivity to variations in vegetation cover and structure, reinforcing their utility for assessing heterogeneous landscapes like the Cerrado and adjacent tropical forest systems [51]. Integrating radar and optical datasets has shown significant improvements in distinguishing vegetation types in ecologically complex regions such as the Amazon and Cerrado [52; 39; 53]. This multi-source synergy presents new opportunities to overcome the mapping challenges in the CAT region. Remote sensing classification accuracy is influenced not only by data characteristics and site-specific factors but also by the choice of classification methods. Deep learning techniques--particularly U-net--are emerging as powerful tools for tackling the classification of heterogeneous and complex landscapes [54; 55; 56]. U-net algorithms excel at capturing intricate spatial patterns, even when training samples are limited, which is especially beneficial given the high costs and logistical difficulties of field data collection [57; 58].