Leaf　area　index　(LAI)　and　chlorophyll　content　are　crucial　variables　in　photosynthesis,　respiration,　and　transpiration,　playing　a　vital　role　in　monitoring　vegetation　stress,　estimating　productivity,　and　evaluating　carbon　cycling　processes.　Currently,　physical　models　are　widely　adopted　for　estimating　LAI　and　canopy　chlorophyll　content　(CCC).　However,　the　main　challenges　of　physical　model-based　methods　for　estimating　LAI　and　CCC　are　the　high　computational　cost　and　the　fact　that　different　combinations　of　canopy　variables　result　in　similar　spectral　reflectance　for　local　minima.　To　address　this　limitation,　a　hybrid　model　was　proposed　to　invert　the　LAI　and　CCC　in　Moso　bamboo　(Phyllostachys　pubescens)　forests.　This　approach　utilized　the　PROSAIL　canopy　radiation　transfer　model,　established　look-up　table　(LUT)　for　LAI　and　CCC,　and　employed　the　Stacking　ensemble　learning　framework.　Compared　to　the　PROSAIL　LUT　method,　the　hybrid　model　demonstrated　higher　performance　in　predicting　LAI　and　CCC　by　incorporating　the　strengths　of　different　models　within　the　hybrid　framework.　The　R2　values　between　predicted　and　measured　values　were　improved　by　3.28%　and　7.15%,　while　the　RMSE　values　were　reduced　by　19.71%　and　16.14%,　respectively.　Moreover,　the　hybrid　model　based　on　Stacking　ensemble　learning　achieved　an　86%　reduction　in　running　time.　Therefore,　the　hybrid　model,　which　integrates　the　PROSAIL　model　with　the　Stacking　ensemble　learning　framework,　offers　a　more　efficient　and　accurate　approach　for　remotely　estimating　the　LAI　and　CCC　in　Moso　bamboo　forests.　The　high　efficiency　of　this　method　makes　it　promising　and　suitable　for　application　to　other　types　of　vegetation.　　©　2008-2012　IEEE.