global　oceanic　and　coastal　waters,　a　multilayer　back　propagation　neural　network　(MBPNN)　is　developed　to　retrieve　the　diffuse　attenuation　coefficient　for　the　downwelling　spectral　irradiance　at　the　wavelength　490　nm　(K-d(490)).　The　applicability　of　Lee＇s　quasi-analytical　algorithm-based　semi-analytical　model,　Wang＇s　switching　model,　Chen＇s　semi-analytical　model,　jamet＇s　neural　network　model,　and　the　MBPNN　model　is　evaluated　using　the　NASA　bio-optical　marine　algorithm　dataset　(NOMAD)　and　the　Eastern　China　Seas　dataset.　Based　on　the　comparison　of　Kd(490)　predicted　by　these　five　models,　with　field　measurements　taken　in　global　oceanic　and　coastal　waters,　it　is　found　that　the　MBPNN　model　provides　a　stronger　performance　than　the　Lee,　Wang,　Chen,　and　Jamet＇s　models.　The　atmospheric　effects　on　the　MODIS　data　are　eliminated　using　near-infrared　band-based　and　shortwave　infrared　band-based　combined　models,　and　the　K-d(490)　is　quantified　from　the　MODIS　data　after　atmospheric　correction　using　the　MBPNN　model.　The　study　results　indicate　that　the　MBPNN　model　produces　similar　to　28%　uncertainty　in　estimating　K-d(490)　from　the　MODIS　data.　Finally,　an　exemplification　of　the　applicability　of　the　model　to　the　coastal　regions　in　the　Eastern　China　Seas　is　carried　out.　Our　results　suggest　that　the　K-d(490)　shows　a　large　variation　in　the　Eastern　China　Seas,　ranging　from　0.02　to　4.0　m(-1),　with　an　average　value　of　-0.17　m(-1).