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　(VIRs)　and　their　red-edge　variants　(VIREs)　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　VIRs　and　three　VIREs,　achieved　the　highest　accuracy　(R2　=　0.721,　RMSE　=　0.490),　outperforming　traditional　approaches.　LAI　was　strongly　correlated　with　indices,　such　as　NDVIR,　RVIR,　EVIRE,　and　SAVIR　(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.　©　2025　by　the　authors.