Pantana　phyllostachysae　Chao　(PPC)　is　one　of　the　deadliest　defoliators　of　Moso　bamboo.　Accurately　locating　and　evaluating　PPC　damage　is　essential　for　the　management　of　bamboo　forests.　Moso　bamboo　has　a　unique　biennial　growth　cycle,　consisting　of　the　on-year　period　(bamboo　shoots　are　incubated　and　then　produced)　and　the　off-year　period　(old　leaves　are　dropped　and　then　new　leaves　are　grown,　and　no　bamboo　shoots　are　produced　in　the　coming　year).　The　similar　physiological　characteristics　of　off-year　bamboo　and　damaged　on-year　bamboo　create　difficulties　in　monitoring　PPC　damage　using　remote　sensing　data.　In　this　study,　we　synergistically　used　Sentinel-1,　Sentinel-2,　and　field　inventory　data　to　construct　machine　learning　(extreme　gradient　boosting,　XGBoost)　models　monitoring　PPC　damage.　The　results　show　that　the　single-time　observation　feature-based　model　(using　images　from　October)　outperformed　the　double-time　observation　feature-based　model　(using　the　differences　between　remote　sensing　signals　from　October　and　February　or　April)　due　to　the　interference　from　other　disturbance　agents　(e.g.,　logging　and　weeding).　The　overall　accuracy　(OA)　values　of　the　single-time　observation　feature-based　model　were　at　least　3%　and　10%　higher　than　those　for　double-time　observation　feature-based　models　for　on-　and　off-year　samples,　respectively.　With　the　consideration　of　the　on-　and　off-year　phenological　differences,　OA　was　improved　by　over　4%.　The　model　without　differentiation　of　the　phenological　difference　tended　to　underestimate　the　damaged　area　of　on-year　bamboo　and　overestimate　that　of　off-year　bamboo.　We　also　found　that　the　responses　of　optical　and　SAR　(synthetic　aperture　radar)　features　to　PPC　damage　were　different.　The　optical　features　increased　or　decreased　with　increasing　damage　severity.　SAR　features　decreased　significantly　at　the　initial　stage　of　PPC　damage　and　then　changed　marginally　with　the　increase　in　damage　severity.　The　addition　of　SAR　features　to　optical　features　improved　the　model　performance,　mainly　for　healthy　and　mildly　damaged　samples.　The　methodology　developed　in　this　study　provides　technical　and　theoretical　support　for　the　pest　monitoring　of　bamboo　forests　using　remote　sensing　data.　©　2022　by　the　authors.