Recently,　multi-source　remote　sensing　data　and　their　derived　features　such　as　vegetation　indices,　texture　metrics　have　been　frequently　applied　to　quantitatively　estimate　forest　above-ground　biomass　(AGB).　However,　it　is　still　challenging　to　efficiently　select　the　optimal　features　for　modeling　the　forest　AGB.　In　this　study,　a　fast,　efficient　and　automatic　method　has　been　proposed,　called　as　k-nearest　neighbor　with　fast　iterative　features　selection　(KNN-FIFS).　This　method　iteratively　pre-select　the　optimal　features　which　determined　by　the　minimum　root　mean　square　error　(RMSE)　between　the　forest　field　data　and　the　k-nearest　neighbor　(k-NN)　estimates　based　on　the　leave-one-out　(LOO)　cross-validation.　By　use　of　KNN-FIFS　and　multi-source　data,　including　Landsat-8　OLI　(operational　land　imager)　and　its　vegetation　indices,　texture　metrics,　HV　polarization　of　P-band　Synthetic　Aperture　Radar　(SAR)　data　(HV　P),　and　forest　inventory　data,　were　applied　to　estimate　forest　AGB　over　Genhe　forest　reserve　located　in　Inner　Mongolia,　China.　Afterwards,　the　model　behaviors　between　KNN-FIFS　and　stepwise　multiple　linear　regression　(SMLR)　methods　were　compared,　which　showed　that　the　KNN-FIFS　method　(R-2=0.77　and　RMSE　=　22.74　t.ha(-1))　was　superior　to　the　SMLR　method　(R-2=0.53　and　RMSE　=　32.37　t.ha(-1)).