Objective　The　XGBoost　algorithm　was　applied　to　establish　a　remote　sensing　factor-stock　volume　model　containing　forest　age,　to　evaluate　the　effectiveness　of　combining　the　remote　sensing　estimated　forest　age　factor　with　the　remote　sensing　factor　to　improve　the　accuracy　of　forest　volume　estimation,　and　to　provide　a　new　idea　and　method　to　achieve　efficient,　fast　and　accurate　forest　volume　estimation　on　a　large　scale.MethodTaking　Jiangle　County,　Fujian　Province　as　a　case,　firstly,　based　on　the　time-series　Landsat　images　from　1987—2016,　combined　with　the　measured　stock　volume　data　of　subcompartment　of　forest　resource　inventory　and　planning,　the　LandTrendr　forest　disturbance　and　restoration　monitoring　algorithm　was　used　to　monitor　the　annual　stand　turnover　disturbance　and　estimate　the　forest　age　in　the　disturbance　area;　Second,　based　on　the　GF-1　image　spectral,　texture,　and　topography　features,　the　recursive　feature　elimination　random　forest　algorithm　(RFE-RF)　to　estimate　the　forest　age　in　the　non-disturbed　area;　Finally,　the　GF-1　image　spectral　and　texture　factors　were　combined　with　the　forest　age　factor　by　the　extreme　gradient　boosting　algorithm　(XGBoost)　to　estimate　the　forest　stock　of　the　study　area.　The　accuracy　of　forest　stock　estimation　with　and　without　the　forest　age　factor　was　compared　to　further　verify　the　importance　of　remote　sensing　forest　age　factor　to　improve　the　accuracy　of　forest　stock　estimation.ResultThe　error　of　forest　age　obtained　by　using　LandTrendr　algorithm　in　the　forest　disturbance　area　was　only　1-2　years,　and　the　accuracy　of　forest　age　estimation　was　significantly　better　than　that　of　the　traditional　estimation　of　forest　age　using　remote　sensing　factors　(error　of　4-12　years).　When　only　conventional　remote　sensing　factors　were　used　to　estimate　the　volume,　the　model　R2　of　XGBoost　was　0.59　and　the　average　RMSE　was　30.72　m3·hm−2,the　rRMSE　was　16.46%;　after　adding　the　forest　age　factor,　the　model　R2　increased　to　0.73,　the　average　RMSE　decreased　to　23.73　m3·hm−2the　rRMSE　was　13.26%and　the　average　overall　accuracy　of　the　stock　volume　estimation　improved　by　about　10.4%　to　84.4%.ConclusionThe　accuracy　of　the　XGBoost　algorithm　combined　with　the　forest　age　parameter　for　estimating　the　stock　volume　is　close　to　the　requirements　of　the　relevant　regulations　of　forest　resources　survey,　which　can　provide　important　technical　support　for　the　rapid　survey　and　assessment　of　forest　resources　on　a　large　scale.　©　2023　Chinese　Society　of　Forestry.　All　rights　reserved.