Vegetation　biomass　is　a　key　biophysical　parameter　for　many　ecological　and　environmental　models.　The　accurate　estimation　of　biomass　is　essential　for　improving　the　accuracy　and　applicability　of　these　models.　Light　Detection　and　Ranging　(LiDAR)　data　have　been　extensively　used　to　estimate　forest　biomass.　Recently,　there　has　been　an　increasing　interest　in　fusing　LiDAR　with　other　data　sources　for　directly　measuring　or　estimating　vegetation　characteristics.　In　this　study,　the　potential　of　fused　LiDAR　and　hyperspectral　data　for　biomass　estimation　was　tested　in　the　middle　Heihe　River　Basin,　northwest　China.　A　series　of　LiDAR　and　hyperspectral　metrics　were　calculated　to　obtain　the　optimal　biomass　estimation　model.　To　assess　the　prediction　ability　of　the　fused　data,　single　and　fused　LiDAR　and　hyperspectral　metrics　were　regressed　against　field-observed　belowground　biomass　(BGB),　aboveground　biomass　(AGB)　and　total　forest　biomass　(TB).　The　partial　least　squares　(PLS)　regression　method　was　used　to　reduce　the　multicollinearity　problem　associated　with　the　input　metrics.　It　was　found　that　the　estimation　accuracy　of　forest　biomass　was　affected　by　LiDAR　plot　size,　and　the　optimal　plot　size　in　this　study　had　a　radius　of　22　m.　The　results　showed　that　LiDAR　data　alone　could　estimate　biomass　with　a　relative　high　accuracy,　and　hyperspectral　data　had　lower　prediction　ability　for　forest　biomass　estimation　than　LiDAR　data.　The　best　estimation　model　was　using　a　fusion　of　LiDAR　and　hyperspectral　metrics　(R2　=　0.785,　0.893　and　0.882　for　BGB,　AGB　and　TB,　respectively,　with　p　2　by　5.8%,　2.2%　and　2.6%,　decreased　AIC　value　by　1.9%,　1.1%　and　1.2%,　and　reduced　RMSE　by　8.6%,　7.9%　and　8.3%　for　BGB,　AGB　and　TB,　respectively.　These　results　demonstrated　that　biomass　accuracies　could　be　improved　by　the　use　of　fused　LiDAR　and　hyperspectral　data,　although　the　improvement　was　slight　when　compared　with　LiDAR　data　alone.　This　slight　improvement　could　be　attributed　to　the　complementary　information　contained　in　LiDAR　and　hyperspectral　data.　In　conclusion,　fusion　of　LiDAR　and　other　remotely　sensed　data　has　great　potential　for　improving　biomass　estimation　accuracy.　©　2016　Elsevier　Ltd