【Objective】　Aiming　at　the　over-fitting　problem　caused　by　information　redundancy　from　multi-source　remote　sensing　data　and　their　derived　high-dimensional　features,　this　study　is　to　effectively　pre-select　the　optimal　feature　combination　to　optimize　the　k-nearest　neighbor　(k-NN)　for　regional　forest　above-ground　biomass　(AGB)　estimation.【Method】　This　study　proposed　a　fast　iterative　features　selection　method　for　k-NN　method　(KNN-FIFS).　This　method　iteratively　pre-select　the　optimal　features　which　determined　by　the　minimum　root　mean　square　error　(RMSE)　between　the　measured　forest　AGB　values　and　the　k-NN　estimates　based　on　the　leave-one-out　(LOO)　cross-validation.　Based　on　KNN-FIFS,　multi-source　data,　including　Landsat-8　OLI　and　its　vegetation　indices,　texture　metrics,　topographic　factors,　HV　polarization　of　P-band　synthetic　aperture　radar　(SAR)　data,　and　forest　inventory　data　(PHV),　were　used　to　estimate　forest　AGB　over　Daxing＇an　Mountain　Genhe　forest　reserve　located　in　Inner　Mongolia.　Afterwards,　the　model　behaviors　between　KNN-FIFS　and　stepwise　multiple　linear　regression　(SMLR)　method　were　compared.【Result】　For　KNN-FIFS　method,　the　best　configuration　was　that　one　with　k　of　3,　the　remotely　sensed　features　using　PHV,　second　moment　of　1st　and　2nd　short-wave　infrared　bands　(S6,S7),　homogeneity　of　1st　short-wave　infrared　band　(H6),　correlation　of　coastal　aerosol　(Cr1),　correlation　of　the　near　infrared　(Cr5),　dissimilarity　of　coastal　aerosol　(D1)　and　the　enhanced　vegetation　index　(EVI).　This　configuration　generated　the　most　accurate　estimates　with　R2=0.77　and　RMSE=22.74　t•hm-2,which　performed　much　better　than　SMLR　with　R2=0.53　and　RMSE=32.37　t•hm-2.【Conclusion】　KNN-FIFS　is　a　more　suitable　method　for　forest　AGB　estimation　than　SMLR.　KNN-FIFS　can　efficiently　select　the　optimal　feature　combination　to　estimate　regional　forest　AGB　by　use　of　multi-source　remote　sensing　data　with　high-dimensional　information.　©　2018,　Editorial　Department　of　Scientia　Silvae　Sinicae.　All　right　reserved.