Retrieving　information　concerning　the　interior　of　the　ocean　using　satellite　remote　sensing　data　has　a　major　impact　on　studies　of　ocean　dynamic　and　climate　changes;　however,　the　lack　of　information　within　the　ocean　limits　such　studies　about　the　global　ocean.　In　this　paper,　an　artificial　neural　network,　combined　with　satellite　data　and　gridded　Argo　product,　is　used　to　estimate　the　ocean　heat　content　(OHC)　anomalies　over　four　different　depths　down　to　2000　m　covering　the　near-global　ocean,　excluding　the　polar　regions.　Our　method　allows　for　the　temporal　hindcast　of　the　OHC　to　other　periods　beyond　the　2005-2018　training　period.　By　applying　an　ensemble　technique,　the　hindcasting　uncertainty　could　also　be　estimated　by　using　different　9-year　periods　for　training　and　then　calculating　the　standard　deviation　across　six　ensemble　members.　This　new　OHC　product　is　called　the　Ocean　Projection　and　Extension　neural　Network　(OPEN)　product.　The　accuracy　of　the　product　is　accessed　using　the　coefficient　of　determination　(R2)　and　the　relative　root-mean-square　error　(RRMSE).　The　feature　combinations　and　network　architecture　are　optimized　via　a　series　of　experiments.　Overall,　intercomparison　with　several　routinely　analyzed　OHC　products　shows　that　the　OPEN　OHC　has　an　R2　larger　than　0.95　and　an　RRMSE　of　©　2020　by　the　authors.