To address the issue of image distortion and reduced usability caused by thin cloud removal in optical remote sensing images, this study proposes a novel thin cloud removal method: SFGAN, that integrates slow feature analysis (SFA) with generative adversarial networks (GANs), aiming to enhance image quality and provide reliable data support for forestry remote sensing analysis.【Method】First, a slow-varying feature module is designed to calculate cloud reflectance and high-dimensional feature slowness. The slow-varying feature vectors are concatenated with random initial vectors as the generator input, improving cloud feature recognition. Second, cloud reflectance is utilized as a discriminative constraint factor to iteratively optimize the discriminator, thereby generating high-quality cloud-free images through adversarial training.【Result】Experiments on public datasets RICE1 and PRSC demonstrate that the SFGAN outperforms existing methods in both quantitative metrics (e.g., PSNR=33.740 7 and SSIM=0.958 2 on RICE1,PSNR=24.341 3 and SSIM=0.879 2 on PRSC) and visual assessments. Validation using Landsat 8 imagery shows SFGAN achieves superior cloud removal effects in both real and simulated cloud scenarios, with a processing time of 0.98 seconds per image.【Conclusion】The SFGAN framework effectively mitigates thin cloud interference in forestry optical remote sensing images by synergizing SFA and GANs, significantly improving data usability and analytical accuracy at the source level.