Wildfires can cause significant damage to the environment, ecosystems, and social economics. The timely and accurate identification of wildfire areas is critically important for post-fire assessment and emergency response. Using Synthetic Aperture Radar (SAR) to generate optical-style images for wildfire detection enables reliable imagery under cloudy conditions. Traditional Generative Adversarial Network based methods are prone to mode collapse. In this study, we propose a Spectral-Feature-Guided Controllable Diffusion (SCDiff) model, which consists of two key components: (1) a spectral feature guidance stage, where the Normalized Burn Ratio is introduced as a spatial attention mechanism to guide translation and enforce spectral consistency in burned areas; and (2) a hierarchical structure guidance stage, where a hierarchical fusion strategy combines global low-resolution context with local high-resolution details to alleviate patch-based artifacts. SCDiff employs a resolution-agnostic design, enabling high-quality reconstruction at the full-image level across large-scale wildfire scenes, ensuring structural continuity and spatial consistency. Experiments on 58 major wildfires in Canada demonstrate that SCDiff generates more detailed and realistic fire-disturbed areas from Sentinel-1 SAR to Sentinel-2 multi-spectral images, outperforming the baselines in both spectral similarity and downstream wildfire detection.