Moso bamboo forests (MBFs) are unique subtropical ecosystems characterized by distinct leaf phenology, bamboo shoots, rapid growth, and carbon sequestration capability. Leaf area index (LAI) is an essential metric for evaluating the productivity and ecological quality of MBFs. However, accurate and large-scale methods for remote-sensing-based LAI monitoring during the winter growth stage remain underdeveloped. This study introduces a novel method integrating hyperspectral indices from Zhuhai-1 Orbit Hyperspectral Satellites (OHS) imagery with the particle swarm optimization-support vector machine (PSO-SVM) coupling model to estimate LAI in winter MBFs. Five traditional vegetation indices (VI_Rs) and their red-edge variants (VI_REs) were optimized to build empirical models. Machine learning algorithms, including SVM, Random Forest, extreme gradient boosting, and partial linear regression, were also applied. The PSO-SVM model, integrating three VI_Rs and three VI_REs, achieved the highest accuracy (R² = 0.721, RMSE = 0.490), outperforming traditional approaches. LAI was strongly correlated with indices, such as NDVI_R, RVI_R, EVI_RE, and SAVI_R (R > 0.77). LAI values of MBFs primarily ranged from 2.1 to 5.5 during winter, with values exceeding 4.5 indicating high winter bamboo shoot harvesting. These findings demonstrate the potential of OHS data to improve LAI retrieval models for large-scale LAI mapping, offering new insights into MBFs monitoring and contributing to sustainable forest management practices.