Accurate　estimation　of　ocean＇s　interior　salinity　information　based　on　surface　remote　sensing　data　is　quite　significant　for　understanding　complex　dynamic　processes　in　the　ocean.　This　study　adopts　two　kinds　of　ensemble　learning　algorithm,　Random　Forest　(RF)　and　Gradient　Boosting　Decision　Tree　(GBDT)　to　estimate　the　subsurface　salinity　anomaly　(SSA)　in　the　upper　2000　m　of　the　global　ocean　from　multisource　satellite-based　sea　surface　parameters.　The　model　performance　is　measured　by　R-square　(R2)　and　normalized　root-mean-square　error　(NRMSE).　The　results　indicate　the　RF　and　GBDT　models　are　both　well　suitable　for　retrieving　SSA　in　the　global　ocean＇s　interior　and　RF　model　outperforms　GBDT　model;　the　models　accuracy　generally　decreased　with　the　depth　below　500　m.　This　study　is　helpful　in　understanding　subsurface　and　deeper　ocean　environment　response　to　recent　global　warming.　©　2019　IEEE.