Subsurface　thermal　structure　of　the　global　ocean　is　a　key　factor　that　reflects　the　impact　of　global　climate　variability　and　change.　Accurately　determining　and　describing　the　global　subsurface　and　deeper　ocean　thermal　structure　from　satellite　measurements　are　becoming　even　more　important　for　understanding　the　ocean　interior　anomaly　and　dynamic　processes　during　recent　global　warming　and　hiatus.　The　extent　to　which　such　surface　remote　sensing　observations　can　be　used　to　develop　information　about　the　global　ocean　interior　is　essential　but　challenging.　This　work　proposes　a　Support　Vector　Regression　(SVR)　method,　a　popular　machine　learning　method　for　data　regression　used　to　estimate　Subsurface　Temperature　Anomaly　(STA)　in　the　global　ocean.　The　SVR　model　can　well　estimate　the　global　STA　upper　1000　m　through　a　suite　of　satellite　remote　sensing　observations　of　sea　surface　parameters　[including　Sea　Surface　Height　Anomaly　(SSHA),　Sea　Surface　Temperature　Anomaly　(SSTA),　Sea　Surface　Salinity　Anomaly　(SSSA),　and　Sea　Surface　Wind　Anomaly　(SSWA)]　with　in　situ　Argo　data　for　training　and　testing　at　different　depth　levels.　In　this　study,　we　employed　the　Mean　Squared　Error　(MSE)　and　squared　correlation　coefficient　(R　2　)　to　assess　the　performance　of　SVR　on　STA　estimation.　Results　from　the　SVR　model　were　validated　to　test　the　accuracy　and　reliability　using　the　worldwide　Argo　STA　data　(upper　1000　m　depth).　The　average　MSE　and　R　2　of　the　15　levels　are　0.0090/0.0086/0.0087　and　0.443/0.457/0.485　for　two　attributes　(SSHA,　SSTA)/three　attributes　(SSHA,　SSTA,　SSSA)/four　attributes　(SSHA,　SSTA,　SSSA,　SSWA)　SVR,　respectively.　The　estimation　accuracy　was　improved　by　including　SSSA　and　SSWA　for　SVR　input　(MSE　decreased　by　0.4%/0.3%　and　R　2　increased　by　1.4%/4.2%　on　average).　The　estimation　accuracy　gradually　decreased　with　the　increase　in　depth　from　500　m.　With　the　increase　in　depth,　the　absolute　value　of　STA　became　smaller,　i.e.,　it　became　more　indistinctive　in　the　spatial　heterogeneity.　The　STA　became　less　intensive　in　the　deeper　ocean　due　to　the　water　stratification　and　stability.　Results　showed　that　SSSA　and　SSWA,　in　addition　to　SSTA　and　SSHA,　are　useful　parameters　that　can　help　estimate　the　subsurface　thermal　structure　and　improve　the　STA　estimation　accuracy.　Moreover,　an　obvious　advantage　for　SVR　is　the　absence　of　limitation　on　the　input　of　sea　surface　parameters.　Therefore,　we　can　figure　out　more　potential　and　useful　sea　surface　parameters　from　satellite　remote　sensing　as　input　attributes　to　further　improve　the　STA　sensing　accuracy　from　SVR　machine　learning.　This　study　provides　a　helpful　technique　for　studying　thermal　variability　in　the　ocean　interior,　which　has　played　an　important　role　in　recent　global　warming　and　hiatus　from　satellite　observations　over　global　scale.　©　2017,　Science　Press.　All　right　reserved.