Subsurface　density　(SD)　is　a　crucial　dynamic　environment　parameter　reflecting　a　3-D　ocean　process　and　stratification,　with　significant　implications　for　the　physical,　chemical,　and　biological　processes　of　the　ocean　environment.　Thus,　accurate　SD　retrieval　is　essential　for　studying　dynamic　processes　in　the　ocean　interior.　However,　complete　spatiotemporally　accurate　SD　retrieval　remains　a　challenge　in　terms　of　the　equation　of　state　and　physical　methods.　This　study　proposes　a　novel　multiscale　mixed　residual　transformer　(MMRT)　neural　network　method　to　compensate　for　the　inadequacy　of　the　existing　methods　in　dealing　with　spatiotemporal　nonlinear　processes　and　dependence.　Considering　the　spatial　correlation　and　temporal　dependence　of　dynamic　processes　within　the　ocean,　the　MMRT　addresses　temporal　dependence　by　fully　using　the　transformer＇s　processing　of　time-series　data　and　spatial　correlation　by　compensating　for　deficiencies　in　spatial　feature　information　through　multiscale　mixed　residuals.　The　MMRT　model　was　compared　with　the　existing　random　forest　(RF)　and　recurrent　neural　network　(RNN)　methods.　The　MMRT　model　achieves　the　best　accuracy　with　an　average　determination　coefficient　(R2)　of　0.988　and　an　average　root　mean　square　error　(RMSE)　of　0.050　kg/m3　for　all　layers.　The　MMRT　model　not　only　outperforms　the　RF　and　RNN　methods　regarding　reliability　and　generalization　ability　when　estimating　global　ocean　SD　from　remote　sensing　data　but　also　has　a　more　interpretable　encoding　process.　The　MMRT　model　offers　a　new　method　for　directly　estimating　SD　using　multisource　satellite　observations,　providing　significant　technical　support　for　future　remote　sensing　super-resolution　and　prediction　of　subsurface　parameters.　　©　1980-2012　IEEE.