Forest　canopy　height　is　a　basic　metric　characterizing　forest　growth　and　carbon　sink　capacity.　Based　on　full-polarized　Advanced　Land　Observing　Satellite/Phased　Array　type　L-band　Synthetic　Aperture　Radar　(ALOS/PALSAR)　data,　this　study　used　Polarimetric　Interferometric　Synthetic　Aperture　Radar　(PolInSAR)　technology　to　estimate　forest　canopy　height.　In　total　the　four　methods　of　differential　DEM　(digital　elevation　model)　algorithm,　coherent　amplitude　algorithm,　coherent　phase-amplitude　algorithm　and　three-stage　random　volume　over　ground　algorithm　(RVoG_3)　were　proposed　to　obtain　canopy　height　and　their　accuracy　was　compared　in　consideration　of　the　impacts　of　coherence　coefficient　and　range　slope　levels.　The　influence　of　the　statistical　window　size　on　the　coherence　coefficient　was　analyzed　to　improve　the　estimation　accuracy.　On　the　basis　of　traditional　algorithms,　time　decoherence　was　performed　on　ALOS/PALSAR　data　by　introducing　the　change　rate　of　Landsat　NDVI　(Normalized　Difference　Vegetation　Index).　The　slope　in　range　direction　was　calculated　based　on　SRTM　(Shuttle　Radar　Topography　Mission)　DEM　data　and　then　introduced　into　the　s-RVoG　(sloped-Random　Volume　over　Ground)　model　to　optimize　the　canopy　height　estimation　model　and　improve　the　accuracy.　The　results　indicated　that　the　differential　DEM　algorithm　underestimated　the　canopy　height　significantly,　while　the　coherent　amplitude　algorithm　overestimated　the　canopy　height.　After　removing　the　systematic　coherence,　the　overestimation　of　the　RVoG_3　model　was　restrained,　and　the　absolute　error　decreased　from　23.68　m　to　4.86　m.　With　further　time　decoherence,　the　determination　coefficient　increased　to　0.2439.　With　the　introduction　of　range　slope,　the　s-RVoG　model　shows　improvement　compared　to　the　RVoG　model.　Our　results　will　provide　a　reference　for　the　appropriate　algorithm　selection　and　optimization　for　forest　canopy　height　estimation　using　full-polarized　L-band　synthetic　aperture　radar　(SAR)　data　for　forest　ecosystem　monitoring　and　management.