Accurately　retrieving　and　describing　subsurface　temperature　on　a　large　scale　can　provide　valuable　information　that　can　be　used　for　subsurface　dynamic　and　variability　studies.　This　study　develops　a　new　satellite-based　geographically　weighted　regression　(GWR)　model　to　estimate　a　subsurface　temperature　anomaly　(STA)　in　the　upper　2,000m　of　the　Indian　Ocean　by　combining　satellite　observations　(sea　surface　height,　sea　surface　temperature,　sea　surface　salinity,　and　sea　surface　wind)　and　Argo　in　situ　data　(STA).　This　model　improves　the　estimation　accuracy　by　considering　the　significant　spatial　nonstationarity　feature　between　the　surface　and　subsurface　parameters　in　the　ocean.　The　performance　of　the　GWR　model　is　measured　by　using　Akaike　Information　Criterion　combined　with　root-mean-square　error　and　R-2.　The　results　showed　that　the　proposed　GWR　model　can　easily　retrieve　the　STA　and　outperform　the　ordinary　least　squares　model.　The　GWR　model　can　also　explain　the　contribution　from　each　variable　via　a　local　regression　coefficient　distribution.　The　sea　surface　height　from　altimetry　is　the　most　significant　variable　for　GWR　estimation.　This　study　demonstrates　the　great　potential　and　advantage　of　the　GWR　model　for　large-scale　subsurface　modeling　and　information　retrieving.　Thus,　we　have　developed　a　novel　approach　for　investigating　subsurface　thermal　anomaly　and　variability　from　satellite　observations.　Plain　Language　Summary　Detecting　the　subsurface　temperature　structure　is　becoming　even　more　important　since　recent　evidences　suggest　widespread　warming　in　the　ocean＇s　interior　as　a　response　to　the　global　climate　variability　and　change.　This　study　proposes　a　new　satellite-based　geographically　weighted　regression　(GWR)　model　to　retrieve　subsurface　temperature　anomaly　in　the　upper　2,000m　of　the　Indian　Ocean　by　combining　satellite　observations　and　Argo　float　data.　This　new　model　improves　the　detection　accuracy　by　considering　the　significant　spatial　nonstationarity　feature　in　the　ocean.　The　results　showed　that　our　proposed　GWR　model　can　easily　retrieve　the　subsurface　temperature　anomaly　and　outperform　the　ordinary　least　squares　model.　The　GWR　model　can　also　explain　the　contribution　from　each　independent　variable.　The　sea　surface　height　from　altimetry　is　the　most　important　variable　for　GWR　modeling.　This　study　demonstrates　the　great　potential　and　advantage　of　the　GWR　model　for　large-scale　subsurface　modeling　and　information　detection　and　can　provide　a　useful　approach　for　developing　subsurface　and　deeper　ocean　remote　sensing　technique　and　investigating　subsurface　thermal　variability　from　satellite　observations.