The　objectives　of　this　study　are　to　evaluate　the　applicability　of　three　existing　retrieval　models　of　total　suspended　matter　(TSM)　in　the　Bohai,　Yellow,　and　East　China　Seas　(BYES),　and　to　propose　a　multilayer　back　propagation　neural　network　(MBPNN)　model.　Based　on　the　comparison　of　TSM　predicted　by　these　models　with　in　situ　measurements　taken　in　the　BYES,　it　was　found　that　the　MBPNN　model　produces　a　superior　performance　to　the　three　existing　models　selected.　Near-infrared　band-based　and　shortwave　infrared　band-based　combined　model　was　used　to　remove　the　atmospheric　effects　from　the　moderate　resolution　imaging　spectroradiometer　(MODIS)　data,　and　the　TSM　concentration　was　quantified　from　the　MODIS　data　after　atmospheric　correction　using　the　MBPNN　model.　It　was　found　that　the　MBPNN　model　produces　41.4%　uncertainty　in　deriving　TSM　concentration　from　the　MODIS　data.　The　MBPNN　model-based　climatological　seasonal　mean　TSM　concentration　indicated　that　the　highest　TSM　concentrations　were　found　around　the　river　estuaries,　while　the　lowest　values　were　found　in　the　far　offshore　regions　of　the　BYES.　The　MBPNN　model-based　monthly　mean　TSM　concentration　revealed　that　the　highest　TSM　concentrations　occurred　in　February,　and　the　lowest　in　July.　The　diffuse　attenuation　coefficient　at　490　am　(K-d(490))　depended　heavily　on　TSM　concentration　in　the　BYES.　The　significant　relationship　(R-2　=　0.72,　p　＜　0.05)　between　K-d(490)　and　TSM　concentration　indicated　that　the　BYES　waters　are　TSM　dominated　types.　(C)　2015　Elsevier　Ltd.　All　rights　reserved.