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:

1. 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”
2. 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.
3. 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.
4. 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.
5. 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).
6. Page 12 Line 20: Please also highlight the red ellipse in Fig. 7b as it states “both methods tend to underestimate high-value pixels”.
7. Page 13: For Fig. 7, the color bar is not consistent with the density color shown in the plots. Same in Fig. 13.
8. 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.
9. Page 16 Line 8: Black ellipses are not found in Fig. 11d.
10. For the figure captions of Figs. 9-11, please add the information of the time of the map.
11. 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.
12. 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.
13. 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.
14. 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.
15. 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”.

This manuscript presents a novel T-DINEOF model for simultaneous reconstruction of multiple oceanic variables (SST, SCHL, and SSW). The core contribution lies in replacing the matrix-based framework of Multi-DINEOF with a third-order tensor structure using T-SVD decomposition, which better preserves the intrinsic correlations among spatial, temporal, and variable dimensions. The experimental results demonstrate that T-DINEOF achieves superior reconstruction accuracy compared to both Multi-DINEOF and single-variable DINEOF, particularly in regions with high proportions of missing data. The manuscript is well-structured and the methodology is technically sound. However, I have several concerns that should be addressed in a revised version.

1. Figure 7 combines data from three variables (SST, SCHL, and SSW) with different physical units into a single scatter density plot without axis labels or units. The same issue applies to Figure 13. Please either (a) plot each variable separately with proper physical units (°C for SST, mg/m³ for SCHL, and m/s for SSW) on the axes, or (b) if maintaining the combined plot format, clearly state in the figure caption that values are shown in original physical units after inverse normalization, and list the respective units for each variable.
2. The longitude and latitude labels in Figures 9–11 appear compressed or distorted. Please check.
3. The validation is performed only against the original satellite-derived data. Given that the original data themselves contain uncertainties and gaps, I suggest validating the reconstructed results against other high-quality monthly products (e.g., reanalysis data) to more rigorously assess the absolute accuracy of T-DINEOF.
4. To better compare the detail-preserving capabilities of DINEOF, Multi-DINEOF, and T-DINEOF, I suggest adding local standard deviation maps or gradient maps.
5. In Figures 7 and 13, a small number of points show underestimation relative to the 1:1 line. What causes this? Please analyze whether these underestimated points correspond to specific geographic regions (e.g., coastal upwelling zones, frontal regions), specific seasons (e.g., summer stratification periods), or specific variables (e.g., high chlorophyll values). This would help clarify the physical mechanisms behind the reconstruction bias.
6. Figure 15 shows obvious periodic fluctuations in SST RMSE over time, particularly in subregion 1. Is this due to lower data availability in summer?
7. In Figure 18, the three colored lines (blue, red, grey) are difficult to distinguish. Please revise the figure using more distinct colors, line styles, or symbols to ensure clear visual separation of the three cross-validation proportions.
8. On page 10 (line 1), the manuscript states that 3% of existing pixels are selected as cross-validation pixels, while page 23 tests three proportions (3%, 10%, and 20%). Please clarify the rationale for selecting 3% in Section 3.2 (Methodology) rather than deferring this justification to the Discussion section. Readers should understand the basis for this choice when first encountering the method, not later in the paper.

Dear Authors,

Please find attached my review report. The report contains my detailed comments and recommendations regarding the manuscript.