Satellite-derived oceanic data are frequently affected by cloud cover, resulting in spatiotemporal gaps. The Multi-DINEOF method is widely used to reconstruct multiple oceanic variables. However, Multi-DINEOF essentially remains a matrix-based DINEOF approach and does not fully leverage the correlations among multiple variables. To address this limitation, this study proposes the T-DINEOF model, aiming to improve the accuracy of reconstructing multiple oceanic variables simultaneously. When applied to sea surface temperature (SST), sea surface chlorophyll-a (SCHL), and sea surface wind (SSW) collectively, T-DINEOF reduces root mean square error (RMSE) by 12.9 %, mean absolute error (MAE) by 13.8 %, and mean absolute percentage error (MAPE) by 11.9 % compared to Multi-DINEOF. For each individual oceanic variable, T-DINEOF outperforms both Multi-DINEOF and the original DINEOF methods, reducing RMSE by 9.0 and 14.7 %, MAE by 10.5 and 14.6 %, and MAPE by 13.7 and 13.4 % for SST; reducing RMSE by 9.3 and 11.8 %, MAE by 9.9 and 13.4 %, and MAPE by 8.3 and 11.8 % for SCHL; and reducing RMSE by 16.6 and 3.7 %, MAE by 16.8 and 3.5 %, and MAPE by 16.4 and 3.1 % for SSW. Additionally, T-DINEOF proves effective in regions with a high proportion of missing data and in cases of low data correlation.