Smoke, a byproduct of forest and grassland combustion, holds the key to precise and rapid identification-an essential breakthrough in early wildfire detection, critical for forest and grassland fire monitoring and early warning. To address the scarcity of middle-high-resolution satellite datasets for forest and grassland fire smoke, and the associated challenges in identifying smoke, the CAF_SmokeSEG dataset was constructed for smoke segmentation. The dataset was created based on GF-6 WFV smoke images of forest and grassland fire globally from 2019 to 2022. Then, an optimized segmentation algorithm, GFUNet, was proposed based on the UNet framework. Through comprehensive analysis, including method comparison, module ablation, band combination, and data transferability experiments, this study revealed that GF-6 WFV data effectively represent information related to forest and grassland fire smoke. The CAF_SmokeSEG dataset was found to be valuable for pixel-level smoke segmentation tasks. GFUNet exhibited robust smoke feature learning capability and segmentation stability. It demonstrated clear smoke area delineation, significantly outperforming UNet and other optimized methods, with an F1-Score and Jaccard coefficient of 85.50% and 75.76%, respectively. Additionally, augmenting the common spectral bands with additional bands improved the smoke segmentation accuracy, particularly shorter-wavelength bands like the coastal blue band, outperforming longer-wavelength bands such as the red-edge band. GFUNet was trained on the combination of red, green, blue, and NIR bands from common multispectral sensors. The method showed promising transferability and enabled the segmentation of smoke areas in GF-1 WFV and HJ-2A/B CCD images with comparable spatial resolution and similar bands. The integration of high spatiotemporal multispectral data like GF-6 WFV with the advanced information extraction capabilities of deep learning algorithms effectively meets the practical needs for pixel-level identification of smoke areas in forest and grassland fire scenarios. It shows promise in improving and optimizing existing forest and grassland fire monitoring systems, providing valuable decision-making support for fire monitoring and early warning systems.