High　spectral　analysis　technology　has　the　advantages　of　fast,　accurate　and　nondestructive,　and　is　widely　used　in　the　field　of　leaf　nitrogen　analysis.　In　order　to　explore　the　optimal　inversion　model　for　monitoring　the　nitrogen　content　of　Phyllostachys　pubescens.　The　collected　Phyllostachys　pubescens　sample　data　were　divided　into　modeling　set　and　validation　set　based　on　SPXY　(Sample　Set　Partitioning　based　on　Joint　X-Y　Distance　Sampling)　method　and　Random　method,　respectively.　The　SPA　(Successive　Projections　Algorithm)　was　used　to　extract　the　characteristic　wavelengths　of　the　original　and　transform　spectra.　And　the　vegetation　index　and　red　edge　parameters　with　high　correlation　with　the　nitrogen　content　of　Phyllostachys　edulis　were　selected.　Then　the　PLSR　estimation　model　based　on　the　nitrogen　content　of　Phyllostachys　edulis　was　established.　The　results　showed　that　compared　with　the　random　sample　partition　method,　the　SPXY　sample　partition　method　increased　the　estimation　accuracy　R2by　0.13　on　average,　reduced　RMSE　by　0.50　on　average,　and　increased　RPD　by　0.58.　The　PLSR　estimation　model　of　CR-FDR　established　had　the　highest　fitting　accuracy　of　N　content　in　Phyllostachys　pubescens,　R2was　0.85,　RMSE　was　1.32,　RPD　was　2.42.　The　inversion　model　combined　with　UAV　hyperspectral　monitoring　data　can　better　reflect　the　spatial　difference　of　Phyllostachys　pubescens　nitrogen　content.　©　2021　SPIE.　All　rights　reserved.