Dear Editor,

I have carefully reviewed the manuscript entitled Gap-filling processes on GOCI-derived daily sea surface salinity product for Changjiang diluted water front in the East China Sea. In this paper, the authors employ decision trees to derive a sea surface salinity (SSS) dataset for the Yellow Sea and East China Sea. Specifically, the input data comprises sea surface salinity (SSS) from three data products: GLORYS12, SMAP, and HYCOM, accompanied by other data such as sea surface temperature (SST), sea surface height (SSH), velocity, and wind stress curl. Notably, the "ground-truth" data used by the authors is the GOCI-derived SSS data from Kim et al. (2021), which was derived from SMAP data. The methods employed are three decision tree-based algorithms: fine trees, boosted trees, and bagged trees. The authors first compare the three SSS data products with the GOCI-derived SSS, then test different input combinations to determine the optimal inputs and algorithms. They demonstrate that the model with SMAP as input yields the highest accuracy, and the bagged trees perform best. However, models using the other two data products with bagged trees also produce satisfactory results. Comparisons between the SSS derived from the different models and SSS observations all yield coefficients of determination (R²) greater than 0.6. Ultimately, the authors decide to utilize the models with SMAP and CMEMS GLORYS data inputs as the "final results" for further analysis. In the last section, the authors use the SSS derived from Model 1 (SMAP input) and Model 2 (GLORYS input) to describe the evolution of the Changjiang Diluted Water in the East China Sea during the period of 2015-2019.

Despite the technical proficiency exhibited in applying fine trees, boosted trees, and bagged trees algorithms, and the methodological rigor in comparing and selecting data inputs and algorithms, I have significant reservations that compel me to recommend rejection of the manuscript for the following reasons:

• Ground-Truth Data Appropriateness: The designation of GOCI-derived SSS as "ground-truth" is fundamentally flawed. "ground-truth" refers to direct, in-situ measurements used to validate remote sensing products. GOCI-derived SSS from Kim et al. (2021), being a remotely sensed product itself, cannot serve as ground-truth, undermining the study's validation framework.
• Circular Logic in Methodology and Results: The manuscript's conclusion that SMAP SSS closely aligns with GOCI-derived SSS is tautological. Given that GOCI-derived SSS from Kim et al. (2021) was established using SMAP SSS data as "ground truth," it is logically unsound to use it as a benchmark for validation, leading to an inherent circularity in the comparative analysis. 
• Presentation and Organization. Despite acknowledging the challenges faced by non-native English speakers, the manuscript's organization and language clarity fall below acceptable scientific standards. The presentation and organization of results, particularly in the manuscript's final section, are areas of concern. The narrative primarily enumerates outcomes from various models without effectively synthesizing these findings into coherent conclusions. This approach leaves readers struggling to discern the central thesis and implications of the research. It is critical for the authors to convincingly demonstrate the superiority and applicability of these products for future research with clear evidence. The manuscript currently falls short in this regard, relying on the reader’s inference rather than providing direct, substantiated arguments.
  
  Additionally, it is particularly troublesome that crucial terms are consistently misspelled (e.g., "fine trees" as "find trees", even in the abstract), indicating a lack of thorough proofreading. While I respect the authors' efforts, the manuscript's frequent errors, especially in critical sections like the abstract, suggest a lack of thorough preparation. This oversight implies a disregard for the peer review process and makes me feel very disappointed.
• Journal Scope Alignment. ESSD is a journal dedicated to "the publication of articles on original research data (sets), furthering the reuse of high-quality data of benefit to Earth system sciences". The primary emphasis of this manuscript on methodological comparison overshadows the utility and novelty of the data product itself, rendering it better suited for specialized remote sensing journals, such as IEEE Transactions on Geoscience and Remote Sensing, Remote Sensing, or Remote Sensing of Environment. Even for these journals, the first subsection comparing different data products would be more appropriate as supplementary information. In addition, the manuscript claims to present two final SSS products, one from SMAP (Model 1) and the other from GLORYS (Model 2). However, I checked the final output data via the link provided for data download (on ESSD website, the link in the manuscript is non-functional), only one SSS product is accessible. This discrepancy between the stated contributions and the actual accessible data undermines the research's completeness and poses significant concerns regarding data accessibility and transparency.

In summary, while the topic under investigation holds potential for advancing regional oceanographic research, the aforementioned concerns regarding methodology validation, manuscript quality, and alignment with journal scope are too substantive to overlook. A thorough revision addressing these fundamental issues is essential before reconsideration.

Despite the concerns highlighted, the achievement of an R^2 greater than 0.6 across various models is noteworthy and demonstrates the potential value of this study. From the perspective of a possible data user, I offer several suggestions aimed at enhancing the work's reliability and utility. These opinions are primarily based on two considerations: as a data user, how can I trust that this dataset is reliable? What details do I need from the author to utilize the data effectively? I highly recommend considering these improvements and look forward to the potential resubmission of this work.

General suggestions:

1. Experiment Design and Data Utilization: In the development of remote sensing data products, two primary methodologies are commonly employed: (a) When observational samples (Y_obs) are scarce, an algorithm is designed to estimate values (Y_est) independently of these observations. The estimated values are then compared with the observed values (Y_obs) to evaluate accuracy. (b) With a large dataset of observations (Y_obs), the data is divided into two distinct subsets (Y1_obs and Y2_obs), collected at different times or during different cruises. The model is initially trained and tested with Y1_obs, followed by an independent evaluation using Y2_obs. Both strategies offer a convincing foundation for trusting the derived estimates (Y_est) in scenarios where direct observations are unavailable.
   
   The authors opted for the latter strategy. However, the use of GOCI-derived SSS as "ground-truth" or observational data is problematic. I recommend that the authors consider the first approach in writing the manuscript, which does not require that the GOCI-derived SSS are “true values” or not. The significant contribution of this work should be the development of an algorithm capable of producing a SSS data product without relying on direct observations, validated independently through comparisons with NIFS data and (h–n) I-ORS data to ensure the product's reliability.

1. Data Product Accessibility: provide a final data product. It is crucial to provide a clear and easily accessible final data product, instead of multiple choice from multiple models. Enhancing the accessibility of the derived SSS product would significantly improve the manuscript's utility to both readers and potential data users, making it a more practical resource in the field.
2. Uncertainty Analysis. Offering an explicit estimation of the uncertainties associated with the final data product is essential for end-users. The authors should estimate uncertainties following methodologies from prior studies such as Wang et al., (2014) or Landschützer et al., (2014), thereby enhancing the data's reliability and user trust.
3. Revisiting the Comparison Framework. The comparison between SMAP and GOCI-derived SSS may be more effectively presented after the final data product has been robustly defined and derived. A subsequent comparison of this final product with existing data products (SMAP, GLORYS, HYCOM) against independent observational data (from NIFS and I-ORS) would more convincingly demonstrate its advantages, establishing it as the current "best solution".
4. Refinement of the Last Subsection. Simplify the discussion by focusing on the derived product's improved spatial or temporal resolution and its implications for studying environmental phenomena at new scales. Illustrating specific examples or case studies where the enhanced resolution provides novel insights would underscore the significance of this work in advancing our understanding of Earth system processes. Focus on showcasing advancements that were previously unattainable due to limitations in temporal and spatial precision. Make sure to clearly state these novel contributions in the opening or concluding sentence of the paragraph, ensuring that the reader immediately grasps the significance of the work's higher accuracy in both dimensions.

Ref:

Landschützer, P., Gruber, N., Bakker, D. C. E., & Schuster, U. (2014). Recent variability of the global ocean carbon sink. Global Biogeochemical Cycles, 28(9), 927–949. https://doi.org/10.1002/2014GB004853

Wang, G., Dai, M., Shen, S. S. P., Bai, Y., & Xu, Y. (2014). Quantifying uncertainty sources in the gridded data of sea surface CO 2 partial pressure. Journal of Geophysical Research: Oceans, 119(8), 5181–5189. https://doi.org/10.1002/2013JC009577

Review of Gap-filling processes on GOCI-derived daily sea surface salinity product for Changjiang diluted water front in the East China Sea

by

Jisun Shin, Dae-Won Kim, So-Hyun Kim, Gi Seop Lee, Boo-Keun Kim, Young-Heon Jo

The paper presents a method to generate daily gap-filled sea surface salinity fields using as input lower resolution passive microwave data taking into account the correlation between ocean color and salinity fields and exploiting machine learning. The study focuses on the short term evolution of the Changjiang diluted water  (CDW) front. It is essentially ok for publication after the recommendation below are followed.

My first recommendation is to modify the title that in its current form suggests that there are gap-filling processes at work in the East China Sea while I believe the authors should use instead of “Gap-filling processes on GOCI-derived…”  the following: “Gap-filling  techniques applied to GOCI-derived…”

It would important if the difference in methodology betweenKim, D. W., Kim, S. H., and Jo, Y. H.: Machine Learning to Identify Three Types of Oceanic Fronts Associated with the

Changjiang Diluted Water in the East China Sea between 1997 and 2021, Remote Sens., 14(15), 3574, doi:10.3390/rs14153574,

2022b and the current paper were clearly explained to show the reader the novelty of the current approach.

After the  first definition of GOCI (Line 15) do not spell it out in the text and figure legends.

The following figures needs to be regenerated  with increased resolution:

2, 3, 4, 5, 7 and 8

Detailed suggested changes to improve readability and correct apparent mistakes:

Line 13 Replace “high” with “higher”

Line 16 Replace “season  from with “seasons of”

Line 17 Replace “Copernicus Marine Environment Monitoring Service” with “Copernicus Marine Service”

Line 30 Replace “horizontal” with “zonal”

Line 37 remove “—the salinity of the ocean at its surface—“

Line 28 replace “dataset” with “products”

Line 69   Replace “Copernicus Marine Environment Monitoring Service” with “Copernicus Marine Service”

LIne 130 replace and track with “along track”

Line 130-131 replace “using a reduced-order Kalman filter, and track altimeter data, satellite SST and in situ TS profiles

were jointly assimilated” with “using a reduced-order Kalman filter, along  track altimeter data, satellite SST, and in situ TS profiles.”

Line 184 Replace “horizontal” with “zonal”

Line 431   Replace “patter” with “patterns”

Line 518 & 519  It seems from the manuscript  that “>31 psu” (at line 518) should be swapped with  “<31 psu” (at line 519)

Line 519 What is meant by “most oceanic environment”? “Typical” perhaps?

In Table 4: Model 1 (SMAP)  with bagged trees reports the best RMSE 1.17 psu  with 1.36 MSE however, we have either 1.36 MSE with  1.17 RMSE or 1.35 MSE with 1.16 RMSE. So a rounding off error was made. Since this model was selected as the best, from validation results,  it is a relevant issue.