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A T-DINEOF model for multiple oceanic variables reconstruction
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DOI：10.5194/os-22-2101-2026.png)
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Abstract
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Abstract. 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.
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