Forest　is　one　of　the　vital　renewable　resources　for　sustainable　development　of　renewable　resources;　it　plays　an　important　role　in　global　climate　change,　water　and　soil　conservation,　and　carbon　cycle　in　terrestrial　ecosystem.　Forest　biomass,　therefore,　is　now　attracting　attention　worldwide　from　both　scholars　and　policy　makers.　Using　Landsat8　OLI　images　and　296　survey　samples　in　Fujian　Province,　we　found　that　the　leaf　biomass　is　negatively　correlated　with　the　reflectance　of　near　infrared　wave　band　and　the　slope　of　near　infrared　and　red　band.　Therefore,　the　slope　of　the　near　infrared　and　red　band　reflectance　can　be　used　as　an　effective　indicator　for　describing　the　differences　in　leaf　biomass　of　forest.　Currently,　empirical　models　are　mainly　used　to　estimate　forest　biomass　such　as　the　vegetation-index　models　based　on　multiple　spectra　remote　sensing　imageries　and　backscattering-coefficient　inversion　models　based　on　active　microwave　remote　sensing　imageries.　However,　most　of　these　empirical　models　lack　physical　mechanism.　Here,　we　established　a　spectral-slope　based　model　building　on　the　spectral　characteristic　of　multiple　spectra　remote　sensing　of　forest.　We　firstly　used　the　pixel　unmixing　model　to　select　the　pure　vegetation　pixels　from　the　images　and　calculated　the　slope　(Slope(red　infraled))　of　the　reflectivity　of　red　band　and　near　infrared　band　from　image　spectral　curve　characteristics　based　on　the　pure　vegetation　pixels　of　forest　communities.　We　then　set　up　the　leaf　biomass　(LB)　reversion　models　based　on　the　relationship　between　the　spectral　slope　and　the　leaf　biomass　by　using　the　linear　regression　method　to　estimate　the　leaf　biomass　of　coniferous　forest,　broad-leaved　forest,　and　mixed　forest　in　Fujian　Province.　We　finally　verified　the　results　by　using　the　in　situ　biomass　data.　The　spectral-slope-based　estimation　algorithms　for　retrieving　the　leaf　biomass　of　coniferous　forest,　broad-leaved　forest,　and　mixed　forest　are　LB　conifer　=　59.358-38.948×Slope(　red,　infarred　)　(R　2　=　70.55%),　LB　=　28.622-12.527　Slope(　red,　infarred　)(R　2　=　68.89%),　and　LB=　23.281-10.952　Slope(　red　irfraled　)(R　2　-51.75%),　respectively,　which　indicated　that　our　method　is　feasible　and　effective.　The　relationships　between　the　leaf　and　above-ground　biomass　(AB)　of　coniferous　forest,　broad-leaved　forest,　and　mixed　forest　are　AB　conifer　-　12.079×LB　conifer　-17.61　(R2　=　77.10%),　AB　broad　=　23.634×LB　borad　-34.124　(R　2　=　88.86%),　and　AB　mixed　,　=　14.582　×LB　mixed　-　10.789　(R　2　=　86.78%),　respectively.　Our　results　showed　that　our　method　is　suitable　for　the　leaf　biomass　estimation　with　high　correlation　coefficient　in　Fujian　Province.　We　also　used　296　sample　survey　data　to　model　the　leaf　biomass　and　the　above-ground　biomass　including　vegetation　stem,　branch,　and　leaf.　The　root　mean　square　errors　(RMSEs)　of　leaf　biomass　estimation　model　for　coniferous　forest,　broad-leaved　forest,　and　mixed　forest　are　29.2467　t/hm　2　(R　2　=　66.64%),　14.0258　t/hm　2　(R　2　=　61.13%),　and　10.1788　t/hm　2　(R　2　=　55.43%),　respectively.　The　RMSEs　of　above-ground　biomass　estimation　model　for　coniferous　forest,　broad-leaved　forest,　and　mixed　forest　are　49.8315　t/hm　2　(R　2　=　54.65%),　45.1820　t/hm　2　(R　2　=49.01%),　and　41.5131　t/hm　2　(R　2　=　38.79%),　respectively.　©　2017,　Ecological　Society　of　China.　All　rights　reserved.