A T-DINEOF model for multiple oceanic variables reconstruction

Ping, Bo; Yang, Ruiting; Meng, Yunshan; Su, Fenzhen; Xue, Cunjin

doi:10.5194/egusphere-2026-1164

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https://doi.org/10.5194/egusphere-2026-1164

Preprints

09 Mar 2026

| 09 Mar 2026

Status: this preprint is open for discussion and under review for Ocean Science (OS).

A T-DINEOF model for multiple oceanic variables reconstruction

Bo Ping, Ruiting Yang, Yunshan Meng, Fenzhen Su, and Cunjin Xue

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.

Received: 02 Mar 2026 – Discussion started: 09 Mar 2026

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Bo Ping, Ruiting Yang, Yunshan Meng, Fenzhen Su, and Cunjin Xue

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RC1:
'Comment on egusphere-2026-1164', Anonymous Referee #1, 23 Mar 2026 reply
This manuscript presents a novel extension of the DINEOF method by incorporating tensor decomposition (T-SVD) to reconstruct multiple oceanic variables simultaneously. The proposed T-DINEOF model addresses a genuine limitation of the existing Multi-DINEOF approach, which essentially remains a matrix-based method that cannot fully exploit correlations across variables. The paper is well-structured, and the results demonstrate clear improvements across multiple accuracy metrics. I recommend making some revisions before acceptance.

Specific comments:
Page 1 Line 38: “satellite-derived missing data reconstruction is receiving increasing attention" The phrasing can be improved. Suggest: "reconstruction of missing satellite-derived data”

Page 4 Lines 21-23: Please also consider citing a paper with the detailed descriptions of current MODIS SST retrieval algorithm, e.g.: Jia, C., & Minnett, P. J. (2020). High latitude sea surface temperatures derived from MODIS infrared measurements. Remote Sensing of Environment, 251, 112094.

Page 5 Line 12: Firstly, since the resolution of MODIS data (4 km) is higher than that of AMSR2 (25 km), “downscaled” should be used instead of “upscaled” if the MODIS data were adjusted to the resolution of AMSR2 data. Otherwise, “upscaled” is correct. Secondly, have the authors considered the impact of this spatial aggregation as using nearest-neighbor interpolation may introduce artifacts? Does that mean reconstructed datasets for all variables have the same spatial resolution? If so, can the authors make any comments on that considering the resolution of reconstructed MODIS data is lower in order to accommodate the AMSR2 data? A paragraph in the discussion section should be helpful.

According to Table 1, the minimum SST in Subregion 3 is -1.81 ºC. Usually, a temperature threshold is applied to distinguish between ice (typically lower than -1.8°C) and open water. Even though this study excludes high latitude regions, but a sea ice mask is still necessary for SST data but it seems not mentioned in the text.

Page 6 Line 8: In Southern Hemisphere, the summer/winter months are opposite to the Northern Hemisphere. Please revise the descriptions, not only in this sentence, but also in Line 15 and Line 16 (Subregions 1 and 3 have the same seasonal pattern, peaking in the winter).

Page 12 Line 20: Please also highlight the red ellipse in Fig. 7b as it states “both methods tend to underestimate high-value pixels”.

Page 13: For Fig. 7, the color bar is not consistent with the density color shown in the plots. Same in Fig. 13.

For Figs. 9-11: Please do not consider the missing pixels as zero here because variables like SST could be 0 ºC causing unnecessary confusion (even though it is not the case for the northern Pacific in April), also because the difference between the reconstructed and original data is meaningless at those missing pixels. So, please set them as NaN and use another color for the missing pixels (e.g., gray) in the maps.

Page 16 Line 8: Black ellipses are not found in Fig. 11d.

For the figure captions of Figs. 9-11, please add the information of the time of the map.

Page 20: For the temporal characteristics of the reconstruction accuracy, it might be useful to demonstrate the overall monthly RMSE variations during the experimental period to better reveal potential seasonal patterns.

Page 20 Lines 21-23: The monthly RMSE time series show interesting patterns, particularly the periodic behavior in subregion 1 for SCHL. The explanation linking this to homogeneity is plausible but could be strengthened with quantitative correlation analysis between RMSE and standard deviation.

Page 22 Line 16: The authors acknowledge that T-DINEOF requires longer computation times, but no quantitative comparison is provided. Given that computational cost is a practical concern for operational applications, an added table comparing runtime for Single-DINEOF, Multi-DINEOF, and T-DINEOF across the three subregions is helpful.

Page 22 Lines 23-24: “if the source datasets contain systematic biases, such errors may be propagated”. This should be true for any reconstruction method. Please consider adding that tensor methods might be more susceptible to bias propagation because errors in one variable could affect others through the coupled decomposition.

Page 25 Lines 1-2: Is the statement that “the optimal configuration for reconstruction is the SST-SCHL-SSW order” made barely based on the three input orders presented here or all the possible six input orders? If it is the former case, it is inappropriate using “optimal”. It should be always cautious using “optimal”.

Reply
Citation: https://doi.org/10.5194/egusphere-2026-1164-RC1
- AC2:
  'Reply on RC1', Bo Ping, 24 Mar 2026 reply
  
  We sincerely thank Reviewer 1 for the insightful comments on this manuscript. Our detailed responses to the reviewer’s comments have been compiled and provided in the Supplementary file.
  
  Reply
  
  Citation: https://doi.org/10.5194/egusphere-2026-1164-AC2
  - RC2: 'Reply on AC2', Anonymous Referee #1, 25 Mar 2026 reply
    
    Thank you for the quick response from the authors. The authors have well-addressed most of my questions and comments, except for Comment 11. My initial expression might lead to misunderstanding, and what I meant is to examine the total monthly variation (Jan-Dec, not the monthly time series) of RMSE for each variable in each subregion (not over the entire study region) to better demonstrate the inherent seasonal patterns, rather than just some fluctuations during the experimental period.
    
    Reply
    
    Citation: https://doi.org/10.5194/egusphere-2026-1164-RC2
    
    AC4: 'Reply on RC2', Bo Ping, 25 Mar 2026 reply
    
    According to the reviewer’s comments, we have recalculated and updated the monthly RMSE distributions of SST, SCHL, and SSW for the three subregions. The new results have replaced Fig. S3 in the Supplementary File, and the corresponding descriptions in the manuscript have been revised accordingly. Please refer to the attachment for details.
    
    Reply
    
    Citation: https://doi.org/10.5194/egusphere-2026-1164-AC4
  - RC3: 'Reply on AC2', Anonymous Referee #1, 25 Mar 2026 reply
    
    Thank you for the quick response from the authors. The authors have well-addressed most of my questions and comments, except for Comment 11. My initial expression might lead to misunderstanding, and what I meant is to examine the total monthly variation (Jan-Dec, not the monthly time series) of RMSE for each variable in each subregion (not over the entire study region) to better demonstrate the inherent seasonal patterns, rather than just some fluctuations during the experimental period.
    
    Reply
    
    Citation: https://doi.org/10.5194/egusphere-2026-1164-RC3
    
    AC3: 'Reply on RC3', Bo Ping, 25 Mar 2026 reply
    
    According to the reviewer’s comments, we have recalculated and updated the monthly RMSE distributions of SST, SCHL, and SSW for the three subregions. The new results have replaced Fig. S3 in the Supplementary File, and the corresponding descriptions in the manuscript have been revised accordingly. Please refer to the attachment for details.
    
    Reply
    
    Citation: https://doi.org/10.5194/egusphere-2026-1164-AC3
AC1: 'Comment on egusphere-2026-1164', Bo Ping, 24 Mar 2026 reply

We sincerely thank Reviewer 1 for the insightful comments on this manuscript. Our detailed responses to the reviewer’s comments have been compiled and provided in the Supplementary file.

Reply

Citation: https://doi.org/10.5194/egusphere-2026-1164-AC1

Bo Ping, Ruiting Yang, Yunshan Meng, Fenzhen Su, and Cunjin Xue

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Bo Ping, Ruiting Yang, Yunshan Meng, Fenzhen Su, and Cunjin Xue

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Short summary

Satellite observations are often incomplete due to cloud cover, resulting in missing ocean data. To address this, we developed T-DINEOF, a reconstruction method that simultaneously estimates sea surface temperature, chlorophyll concentration, and wind conditions by learning relationships among variables. Results show that T-DINEOF improves reconstruction accuracy, especially in regions with sparse data or weak correlations, providing more reliable ocean information for environmental monitoring.


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