Leaf　water　content　(LWC)　is　very　important　in　the　growth　of　vegetation.　LWC　and　leaf　spectra　change　when　the　leaves　are　under　pest　stress;　exploring　the　change　mechanism　between　LWC,　leaf　spectra,　and　pest　stress　can　lay　the　foundation　for　pest　detection.　In　this　study,　we　measured　the　LWC　and　leaf　spectra　of　moso　bamboo　leaves　under　different　damage　levels,　used　the　Pearson-Lasso　method　to　screen　the　features,　and　established　a　multiple　linear　regression　(MLR)　and　random　forest　regression　(RFR)　model　to　estimate　the　LWC.　We　analyzed　the　relationship　between　LWC　and　spectral　features　of　moso　bamboo　leaves　under　Pantana　phyllostachysae　Chao　(PPC)　stress　and　their　changes.　The　results　showed　that:　(1)　the　LWC　showed　a　decreasing　trend　as　the　pest　level　increased.　(2)　The　spectra　changed　substantially　when　the　leaves　were　under　pest　stress.　(3)　The　number　and　significance　of　response　features　associated　with　the　LWC　were　diverse　under　different　damage　levels.　(4)　The　estimation　of　LWC　under　different　damage　levels　differed　significantly.　LWC,　leaf　spectra,　response　features,　and　the　model　estimation　effect　were　diverse　under　different　damage　levels.　The　correlation　between　LWC　and　features　was　higher　for　healthy　leaves　than　for　damaged　and　off-year　leaves.　The　two　models　were　more　effective　in　estimating　the　LWC　of　healthy　leaves　but　less　effective　for　damaged　and　off-year　leaves.　This　study　provides　theoretical　support　for　the　prediction　of　PPC　stress　and　lays　the　foundation　for　remote　sensing　monitoring.