Effect of the One-to-Many Relationship between the Depth and Spectral Profile on Shallow Water Depth Inversion Based on Sentinel-2 Data

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Attached 

Comments for author File: Comments.pdf

Author Response

Dear Reviewer,

Thank you for your careful review. We are deeply impressed by your thorough examination, insightful comments and reflective thoughts, from which we have greatly benefited and drawn considerable inspiration.

I have carefully reviewed and revised our manuscript accordingly, and we hope that it now meets with your approval. It is important to note that because our revisions were made in Word, there are differences in line and page numbers compared to the PDF version you have seen; therefore, we have not used these markers in our detailed responses to your queries. We apologize for any inconvenience this may have caused.

Nevertheless, your thorough review has greatly improved the quality of our paper. We are very grateful for your valuable suggestions.

We have read the comments carefully and made corrections accordingly. Revised portions are marked in the latest revised manuscript by using the “Track Changes” function with balloons option of “Show All Revisions Inline”. Note that the page and line numbers in the responses are listed under the “Track Changes” model of the latest revised manuscript.

Followings are the responses to the reviewer’s comments one by one.

1. what about deep water??? does bathymetry is not necessary for deep one? revise this section.

Response:

We apologize for the misunderstanding caused by the incomplete presentation, this paper focuses only on depth inversion of shallow water bathymetry and it doesn’t mean that the deep-water bathymetry is not crucial, we have added the restriction "around islands and reefs as well as in nearshore areas" for shallow water bathymetry.

2. max depth ????

Response:

We appreciate your feedback, but we must admit that we are having difficulty fully understanding the intent behind their comment. The comment provided seems rather concise, and we would greatly benefit from some additional elaboration or clarification from you. If you could kindly provide more context or specific examples to help us better comprehend their perspective, we would be grateful. Our aim is to address all concerns raised effectively, and further explanation from you would assist us in doing so appropriately.

Let's respond to this comment as we understand it, the maximum depth for accurate retrieval of water properties from nearshore and reef-surrounding waters using optical multispectral satellite data is generally limited to 20 meters or so, it is mainly affected by the transparency of the water body, and this is demonstrated in almost all optical satellite inversion bathymetry literature. Above 20m water depth, the accuracy of the inversion will decrease more, so this paper concentrates on water depths of 20m and shallower.

3. why???? compare with above 2 models

Response:

We appreciate your comment regarding the need to compare statistical models with the other two types of depth inversion models mentioned (theoretical analytical models, semi-theoretical and semi-empirical models).

Statistical models are widely used for depth inversion from passive optical multispectral images due to their relative simplicity and ability to account for local variations in water properties and bottom types. While theoretical analytical models rely on accurate knowledge of inherent optical properties and semi-theoretical and semi-empirical models require site-specific calibration, statistical models like the Stumpf dual-band logarithmic ratio algorithm can be applied more broadly with minimal calibration data. However, we acknowledge that each model type has its own advantages and limitations, and a comprehensive comparison of their performance under different environmental conditions and data availability scenarios would be valuable. In the revised manuscript, we provide a more balanced discussion of the strengths and weaknesses of each model type, highlighting the trade-offs between accuracy, generalizability, and data requirements.

4. Reference

Response:

We thank you for requesting a reference to support the statement about ICESat-2, We acknowledge that this statement requires further substantiation. Since we don't use the ICESat-2 data in this paper, and on further consideration, we think about the sentence may not be essential to the overall clarity and flow of the manuscript, and have therefore decided to remove the sentence from the revised version of the manuscript. We appreciate your attention to detail, which has helped us to streamline the content and improve the overall quality of the paper.

5. any justification why you were selected this water body for you case study? low quality water bodies, lagoon

Response:

We appreciate your valuable comments and suggestions. These feedbacks help us further clarify the rationale behind the selection of the study areas and the focus of our paper.

We would like to explain that although Kaneohe Bay is influenced by human activities to some extent, the water bodies in these regions generally maintain good transparency, and the coral reef ecosystems are well developed. More importantly, previous studies have conducted passive optical remote sensing bathymetry retrieval in these areas, providing useful references for our research. Therefore, these regions remain reasonable choices for our study.

Here are some articles related to water quality, or optical satellite bathymetry concerning Kaneohe Bay in Oahu Island:

1. Fusion of hyperspectral and bathymetric laser data in Kaneohe Bay, Hawaii. (https://www.spiedigitallibrary.org/conference-proceedings-of-spie/5093/1/Fusion-of-hyperspectral-and-bathymetric-laser-data-in-Kaneohe-Bay/10.1117/12.488438.short)
2. Confidence levels, sensitivity, and the role of bathymetry in coral reef remote sensing. (https://www.mdpi.com/2072-4292/12/3/496)
3. High-coverage satellite-based coastal bathymetry through a fusion of physical and learning methods. (https://www.mdpi.com/2072-4292/11/4/376)
4. Influence of atmospheric and sea-surface corrections on retrieval of bottom depth and reflectance using a semi-analytical model: a case study in Kaneohe Bay, Hawaii. (https://opg.optica.org/abstract.cfm?uri=ao-47-28-F1).

 

6. Country

Response:

We thank you for your bringing to our attention the need to specify the countries to which our study areas belong. We agree that including this information will provide better context for our readers and will enhance the understanding of the regional relevance of our research.

Accordingly, we have updated the manuscript to clearly state that Kaneohe Bay is located in the United States. We appreciate this valuable suggestion and have made the necessary amendments to the text for clarity and completeness. We trust that this addition will improve the manuscript and are grateful for your guidance in this matter.

7. any comparison with water quality parameters? both water bodies are affected by inputs from land-based rivers and human discharge.

Response:

We sincerely thank you again for your valuable comments, which will help us further optimize and improve our manuscript. We recognize that our manuscript may have inadvertently drawn attention to water quality issues. Therefore, in order to maintain the focus of the paper and to avoid unnecessary misunderstandings, we have removed descriptions of water quality characteristics that are not central to the main objectives of our study. This revision helps to focus the reader's attention on the core aspects of our research. We hope that the changes we have made address your concerns and clarify the intent of the paper. We sincerely appreciate your suggestions.

8. why did you select this dates? good to include in above section/ paragraph.

Response:

Thank you for your valuable suggestion. We agree that including the dates in the paragraph would enhance the manuscript.

The dates selected for satellite image acquisition were chosen with great care. The criteria guiding our decision included:

1. Minimal Cloud Cover: To ensure the highest quality of data, we targeted dates with the least cloud cover, which is critical for obtaining unobstructed and clear satellite imagery of the study area.
2. Reduced Solar Flare Activity: Recognizing the potential for sun glint to introduce noise into the data. This consideration helps to ensure that the images have the least amount of radiometric distortion, enhancing the reliability of our remote sensing analysis.

We have revised the following sentences to include the dates:

“The satellite images (Figure 1 & Figure 2) used in this study used are listed in Table 1, with the Sentinel-2A image overpassed on 29 January 2022 at 14:57 (UTC) and the Sentinel-2B image on 1 December 2020 at 12:23 (UTC). The images of Sentinel-2A and Sentinel-2B were downloaded from the European Space Agency (ESA), and are Level 2A product which have been atmospheric corrected.”

We are grateful for the constructive feedback and hope that the revisions address your suggestion adequately.

9. the images are with Spatial resolution of 10. The average water level of your study water body is 8. so who can you correct this issues? it is good if you add any measured data for your result calibration/ validation

Response:

Thank you for your raising concerns regarding the spatial resolution of the satellite imagery. We value your diligence and the opportunity to clarify this aspect of our study.

According to the official specifications in technical documentation from ESA, the Sentinel-2 Multi-Spectral Instrument (MSI) has a spatial resolution of 60 meters for the band B1.

The spatial resolution mentioned in the manuscript refers to the ground sampling distance or pixel size of the satellite imagery, which is a characteristic of the sensor's spatial resolution. This spatial resolution is independent of the water depth in the study area, which averages 8 meters. The spatial resolution of the imagery determines the level of detail and the smallest features that can be resolved in the satellite data. In contrast, the average water depth of the study area is a physical characteristic of the bathymetry itself, and it does not directly impact or relate to the spatial resolution of the satellite imagery.

Thanks again for your insightful comments, which will help us improve the clarity and robustness of our manuscript.

10. is it measured or estimated depth? good to calibrate your results with such data.

Response:

We appreciate your attention to detail and the opportunity to clarify this potential misunderstanding for the measured depth of Figure 3(a) and Figure 4(a). We apologize if our description has caused any confusion regarding the figures.

We believe that the words "bathymetric data" are causing the misunderstanding, so "bathymetric data" will be replaced with "measured depth" in section "(2) Measured depth", which describes the sources of the measured depth in detail.

We appreciate your insightful comments, which will help us improve the clarity and robustness of our manuscript.

11. symbols existing in side the map???? h1 V1???? what does it mean? and also others the maps text are not visible to read

Response:

Thank you for your pointing out the lack of clarity in the figure(s) mentioned in comment.

We acknowledge that the figures in question need better clarity and more explicit representation of the information being conveyed.

In the revised version of the manuscript, we provide detailed explanations in the figure captions as the following sentences:

“Figure 7. Structural schematic diagram of the mixture density network. The subscript number of the characters ‘h’ represents the serial number of hidden layer which ranges from 1 to 5, and the subscript number i of v_i, ,  and  respectively represents the i-th node, weight coefficient, mean and variance, where the subscript i ranges from 1 to 100.”

We appreciate the feedback, as it helps us identify areas for improvement and enhance the overall quality of our manuscript.

12. the figures (8, 9, 10 and 11) are too poor. how can we read it???

Response:

Thank you again for your pointing out the lack of explanation in the figures mentioned in comment. We acknowledge that the figures in question need better clarity and more explicit representation of the information being conveyed.

In the revised version of the manuscript, we provide detailed explanations in the figure captions as the following sentences:

“The bule marker represents two-band only for Stumpf model, the red markers represent three-band (equation (5), or equation (7) which model’s name with subscription “hood”) the yellow ones represent four-band (equation (6), or equation (8) which model’s name with subscription “hood”).  To avoid overlap, three-band marker and four-band marker are respectively located on each side of gridlines responding to label of model’s name. The representation of in next Figures 9, 10 and 11 are similar.”

Furthermore, the following sentences are added to the begin of section 4.1 better clarity of error bar:

 “Since each random sampling results in a training dataset with a different spatial distribution of random points, error bar is used to illustrate the variation in RMSE caused by ten times random samplings, representing the uncertainty due to distinct spatial distributions of training datasets.”

It is hoped that these additional supplementary will clearly convey the findings of this paper to you and other readers

We appreciate the feedback, as it helps us identify areas for improvement and enhance the overall quality of our manuscript.

13. the results are good but the image are poor.

Response:

Thank you again for your pointing out the lack explanation for figures mentioned in comment. We acknowledge that the figures in question need better detailed and more explicit representation of the information being conveyed.

In the revised version of the manuscript, we provide detailed explanations in the figure captions as the following sentences:

“Inversion depth imagery (a) and the pixels with only  (b) for Oahu Island. Only 0 ~ 20m water depth is shown, others depth is left blank for (a). To show more clearly where the pixels with larger errors are located, only  is plotted, and others are blanked in (b). The representation in the next Figure 13 is similar.”

We hope these supplementary can clearly represent the results in Figures.

We appreciate the feedback, as it helps us identify areas for improvement and enhance the overall quality of our manuscript.

14. this result must be validated !!! Otherwise how can we accept it?

Response:

We thank you for your valuable comments on the validation of our results. We acknowledge the critical nature of thorough validation and the need to communicate it appropriately.

In addition to the figures and comparative analysis with actual measured data described in our manuscript, we have taken careful steps to validate our computational results, including error calculations designed to confirm the accuracy of the inversion depth relative to the measured depth.

Attached supplementary with revised manuscript to this correspondence is a comprehensive presentation of all inversion depth results derived from MDN_hood models of ten times used in the paper.

I must also note that due to the significant computational demands of running the models on a personal computer, and the pressing time constraints of revising the manuscript, this current approach represents the extent of validation we can provide at this time. However, we very much appreciate your suggestion to include more examples. We agree that this is an excellent suggestion and something that we intend to pursue in future work.

If it is indeed further examples that you are seeking, we appreciate this guidance and will endeavor to include additional validation examples in subsequent research, which we believe will enhance the applicability and robustness of the study.

Thank you again for your feedback, which helps us to strengthen our work, and we remain open to any further suggestions for improvement that you may have.

15. the difference is too clear at the figure 17 a and b. this needs a detail discussion why this comes

Response:

Thank you for your insightful comment regarding Figure 17. We appreciate you drawing our attention to the clear difference observed between Figure 17a and Figure 17b.

You raise an important point, and we acknowledge the need for a more detailed discussion to explain the reasons behind this significant difference.

Figures 3 and 7 demonstrate that the water depth distribution is non-Gaussian, while the water depth inversion model is a mixture of probabilistic densities. Interestingly, the errors in water depth inversion adhere to a Gaussian distribution. Investigating the reasons or mechanisms behind this discrepancy requires extensive data from numerous research areas and goes beyond the scope of this paper. We believe that one-to-many relationship is main reason, the evidences come from the histogram about depth of pixels with the SSPBDD in Figure 5 and 6. As for the significant differences between the two graphs in Figure 7, we believe that they may result from varying proportions of SSPBDD and differences in the number of pixel points. For Buck Island, the percentage of the SSPBDD pixels (56.0%) is slightly greater than that of the non-SSPBDD pixels, number of the SSPBDD pixels is near to that of the non-SSPBDD pixels, so their shapes of histogram are similar, and it looks like reasonable that the histogram of SSPBDD pixels is higher than that of non-SSPBDD. For Oahu Island, although the percentage of the SSPBDD pixels is only 26.3% as shown in Table 2, But the number of pixels with inversion errors in |∆D|≥1 m is greater than number of the non-SSPBDD pixels in Figure 17(a) , which indicates that the contribution of the SSPBDD pixels to the inversion errors is significant when |∆D|≥1 m, so the SSPBDD pixels account for a majority of the inversion errors. The main reason maybe come from the histogram about depth of pixels with the SSPBDD in Figure 5 and 6.

We value your feedback, as it highlights the importance of thoroughly analyzing and interpreting our results, particularly when significant differences are observed.

Thank you again for your valuable suggestion. We appreciate the opportunity to clarify and expand upon our findings, ensuring a comprehensive and robust presentation of our research.

 

 

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript focus on the phenomenon of same spectral profile but different depths. It employs the Stumpf model and three machine learning models, and analyses the performance of these models based on the spatial distribution of the training dataset and the input information composition. Here are the following issues that need to be addressed.

1.        There is no need to mention ICESat-2 in the introduction since it is not used here.

2.        Line 31: The title of Section 1 should be “Introduction”.

3.        Section 2.3 analyzed the SSPBDD phenomenon, and should not be included in the “Study area and data”. Content related to data analysis should be placed in the following sections.

4.        The manuscript is essentially a comparison of several models. Why is MDN model presented in a separate section.

5.        Figures 8-11 compare the error bars with different combinations for only SSPBDD and only non-SSPBDD. As long as the amount of training data is sufficient, machine learning models are intelligent enough to ignore the influence of SSPBDD. Why not show the error bars with band combinations for both SSPBDD and non-SSPBDD.

6.        The format of the image name is not consistent, such as the font of Figure 1. Besides, the font of line 248 is incorrect. For range indicating, there are “200-300” and “0~20”.

7.        The numbering of the figures is discontinuous. Figures 12 and 13 are missing.

8.        There is an issue with the spacing in the sentence, such as the space between “percentage” and “of” in line 189.

9.        Please renumber all the equations.

10.     Page 19: Some texts do not need to be bolded, while others only have the first letter of the word bolded.

11.     The reference format is not uniform. For example, some journals are abbreviated, some are full names. And the names of authors should be in the same format. Now there are several forms such as “Ma, Y.”, “Y Wang” and “Benqing Chen”.

Author Response

Dear Reviewer,

Thank you for your careful review. We are deeply impressed by your thorough examination, insightful comments and reflective thoughts, from which we have greatly benefited and drawn considerable inspiration.

I have carefully reviewed and revised our manuscript accordingly, and we hope that it now meets with your approval. It is important to note that because our revisions were made in Word, there are differences in line and page numbers compared to the PDF version you have seen; therefore we have not used these markers in our detailed responses to your queries. We apologise for any inconvenience this may have caused.

Nevertheless, your thorough review has greatly improved the quality of our paper. We are very grateful for your valuable suggestions.

We have read the comments carefully and made corrections accordingly. Revised portions are marked in the latest revised manuscript by using the “Track Changes” function with balloons option of “Show All Revisions Inline”. Note that the page and line numbers in the responses are listed under the “Track Changes” model of the latest revised manuscript.

Followings are the responses to the reviewer’s comments one by one.

1. There is no need to mention ICESat-2 in the introduction since it is not used here.

Response:

We appreciate your suggestion to remove the mention of ICESat-2 from the introduction, as it is not utilized in our research. We concur that its inclusion may lead to confusion, given that our study does not involve data from this source.

Accordingly, we have revised the manuscript to omit any references to ICESat-2 in the introduction. This edit simplifies the content and keeps the focus directly on the methods and data that are relevant to our study.

Thank you for your constructive comment, which has helped us improve the clarity of our manuscript.

2. Line 31: The title of Section 1 should be “Introduction”.

Response:

Thank you for your pointing out the inadvertent error regarding the title of Section 1 in our manuscript.

We have taken your suggestion into account and have corrected the title of Section 1 to "Introduction". This change ensures consistency and adheres to the standard formatting for scientific manuscripts.

We appreciate your vigilance in reviewing our manuscript and thank you for assisting us in enhancing its accuracy and presentation.

3. Section 2.3 analyzed the SSPBDD phenomenon, and should not be included in the “Study area and data”. Content related to data analysis should be placed in the following sections.

Response:

We agree with your insightful suggestion that the analysis of the SSPBDD phenomenon should not be included in the "Study area and data" section but rather be placed in a subsequent section more appropriate for data analysis discussions.

In response to your guidance, we have restructured the manuscript to relocate the analysis previously in Section 2.3. The content is now appropriately positioned in a later section, ensuring that each segment of our paper is aligned with its most relevant headings and reflects a logical progression of the study.

We believe that these changes enhance the clarity and flow of the manuscript. We would like to thank you for your help in refining the structure of our article.

4. The manuscript is essentially a comparison of several models. Why is MDN model presented in a separate section.

Response:

We appreciate your astute observation regarding the structure of our manuscript and the presentation of the MDN model.

In light of the suggestion, we have revised the manuscript to integrate the details of the MDN model into Section 3.3 Depth Inversion Models. This adjustment allows for a more cohesive comparison of the models and aligns with the overall structure and focus of the manuscript. Now the structure of Section 3 is as following:

3.1 Same Spectral Profile but Different Depth (SSPBDD)

3.2 Parameters

3.2.1 Depth Invariant Index (DII)

3.2.2 Spatial Neighbourhood Parameters

3.3 Depth Inversion Models

3.3.1 Stumpf Model

3.3.2 Random Forest (RF) model

3.3.3 Support Vector Machine (SVM) model

3.3.4 Mixture Density Network (MDN) model

3.4 Training and Evaluation of the Models

Thank you for your guidance on improving the manuscript's organization, which we believe will provide readers with a clearer and more direct understanding of the comparative nature of our study.

5. Figures 8-11 compare the error bars with different combinations for only SSPBDD and only non-SSPBDD. As long as the amount of training data is sufficient, machine learning models are intelligent enough to ignore the influence of SSPBDD. Why not show the error bars with band combinations for both SSPBDD and non-SSPBDD.

Response:

Thank you for your insightful suggestion regarding the presentation of error bars for Figures 8-11 in our manuscript.

The development of current artificial intelligence models is revolutionary; as long as there are sufficient training samples, they seem almost omnipotent, we argue that for machine learning models, the issue is not whether the number of training datasets is large enough, but whether the training datasets fully cover all features.

Your comment regarding the inclusion of error bars for band combinations that include both SSPBDD and non-SSPBDD scenarios is indeed thoughtful. The intention behind the original figures was to specifically demonstrate the performance of the model under different, isolated conditions to highlight its robustness.

However, we recognize the value of your recommendation to illustrate a comprehensive comparison that includes both conditions. We agree that this would provide a more complete view of the model's predictive capabilities. To address this, we have modified the figures to include error bars representing band combinations for both SSPBDD and non-SSPBDD. We believe this will provide a more complete understanding of the model's behaviors under different scenarios and enhance the manuscript's contribution to the field.

We are grateful for your comments, which have led to a useful refinement of our work.

6. The format of the image name is not consistent, such as the font of Figure 1. Besides, the font of line 248 is incorrect. For range indicating, there are “200-300” and “0~20”.

Response:

Thank you for your attention to detail regarding the formatting inconsistencies in our manuscript.

We have carefully addressed your concerns by reformatting the names and styles of Figures 1 and 2 to ensure consistency in font and presentation across all figures within the paper.

Additionally, we acknowledge the confusion caused by the use of the term “range” to denote the concept of "极差" (maximum difference in statistics) in our earlier submission. To provide clarity and eliminate any possible misunderstandings, we have replaced the term with "maximum difference" throughout the manuscript.

We hope that these changes meet your approval and improve the readability of the paper. Thank you for guiding us to refine our manuscript further.

7. The numbering of the figures is discontinuous. Figures 12 and 13 are missing.

Response:

Thank you for bringing our attention to the discrepancy in the figure numbering sequence in our manuscript. You are correct that Figures 12 and 13 were missing due to an oversight with the figure numbering refresh process.

We have now corrected this issue by updating the manuscript so that all figures are numbered consecutively and appropriately reflect the order in which they appear in the text.

We appreciate your detailed review and assistance in rectifying this error, ensuring the manuscript meets the necessary standards of presentation.

8. There is an issue with the spacing in the sentence, such as the space between “percentage” and “of” in line 189.

Response:

We are grateful for your observation concerning the spacing issues within our manuscript, specifically the one mentioned on line 189. We sincerely apologize for these errors, which unfortunately detracted from the manuscript's professionalism.

We have meticulously gone through the document and corrected all spacing inconsistencies to ensure that the text adheres to proper formatting standards. Steps have also been taken to prevent such oversights in future submissions.

Thank you for bringing this to our attention, thus enabling us to enhance the quality of our work.

9. Please renumber all the equations.

Response:

Thank you for your highlighting the need to renumber the equations in our manuscript. We understand that consistent numbering is pivotal for clear and easy reference throughout the paper.

We have since carefully renumbered all equations to ensure accuracy and ease of reading. We apologize for this oversight and any inconvenience it may have caused.

The your attention to detail is much appreciated, and we are grateful for your help in improving our manuscript.

10. Page 19: Some texts do not need to be bolded, while others only have the first letter of the word bolded.

Response:

We appreciate your feedback regarding the formatting inconsistencies identified on page 19 of our manuscript. Ensuring that the text is presented clearly and correctly is important to us.

Upon review, we have adjusted the text to correct the bolding inconsistencies. We have ensured that only the necessary sections of the text are bolded for emphasis and that the formatting is consistent throughout the document.

Thank you for your drawing our attention to this matter, and we hope the revisions now reflect a more polished and professional manuscript.

11. The reference format is not uniform. For example, some journals are abbreviated, some are full names. And the names of authors should be in the same format. Now there are several forms such as “Ma, Y.”, “Y Wang” and “Benqing Chen”.

Response:

Thank you for pointing out the inconsistencies in our manuscript's reference formatting. We understand the importance of maintaining uniformity in citations to ensure clarity and professionalism.

Following your recommendation, we have thoroughly reviewed and revised the references section to standardize the format. Journal names are now consistently abbreviated or spelled out according to the guideline of the journal, and the author names are uniformly presented in a single, consistent format throughout.

We appreciate your attention to detail, and the guidance has significantly contributed to improving the manuscript's quality.

 

Reviewer 3 Report

Comments and Suggestions for Authors

This paper researches the influence of the same spectral profile but different depths (SSPBDD) phenomenon in shallow water depth inversion based on Sentinel-2 data. Through increasing the visible spectral information and the spatial neighbourhood information in the input layer of machine learning models, the inversion accuracy and stability of the models can be improved. However, the paper needs careful revisions. Below please find some key aspects that require attention.

1. Some aspects of the paper are inappropriate. For instance, equation numbers are not properly matched. Additionally, in line 264, the phrase “Stumpf [34] proposed” is unclear, as Stumpf refers to a method, and there is no relevant literature cited where Stumpf is first introduced. Furthermore, ICESat-2 is mentioned without a detailed explanation.

2. It is recommended to include a bathymetric map obtained from different inversion methods, along with the corresponding error map, to better illustrate the performance differences among various inversion methods.

3. In the “Introduction”, it is stated that “ICESat-2 can provide more accurate bathymetry data as a training set.” Given this capability of ICESat-2, clarify the significance of the main research content of this paper, which focuses on “Shallow Water Depth Inversion Based on Sentinel-2 Data.”

 

4. This paper lacks sufficient innovation and primarily compares several methods of water depth inversion and analyzes the results. Consider enhancing the innovative aspects of the research.

Comments on the Quality of English Language

No comments

Author Response

Dear Reviewer,

Thank you for your careful review. We are deeply impressed by your thorough examination, insightful comments and reflective thoughts, from which we have greatly benefited and drawn considerable inspiration.

I have carefully reviewed and revised our manuscript accordingly, and we hope that it now meets with your approval. It is important to note that because our revisions were made in Word, there are differences in line and page numbers compared to the PDF version you have seen; therefore we have not used these markers in our detailed responses to your queries. We apologise for any inconvenience this may have caused.

Nevertheless, your thorough review has greatly improved the quality of our paper. We are very grateful for your valuable suggestions.

We have read the comments carefully and made corrections accordingly. Revised portions are marked in the latest revised manuscript by using the “Track Changes” function with balloons option of “Show All Revisions Inline”. Note that the page and line numbers in the responses are listed under the “Track Changes” model of the latest revised manuscript.

Followings are the responses to the reviewer’s comments one by one.

1. Some aspects of the paper are inappropriate. For instance, equation numbers are not properly matched. Additionally, in line 264, the phrase “Stumpf [34] proposed” is unclear, as Stumpf refers to a method, and there is no relevant literature cited where Stumpf is first introduced. Furthermore, ICESat-2 is mentioned without a detailed explanation.

Response:

Thank you very much for your constructive feedback on our manuscript. We have taken your comments seriously and have made the following revisions to enhance the clarity and accuracy of our paper:

1. Equation Numbering: We have rechecked the entire manuscript and corrected the equation numbers to ensure they match appropriately with the references made in the text.
2. Clarification of Methods (Referring to Stumpf): We have edited line 264 to clarify that "Stumpf" is indeed a method. We have now properly introduced the Stumpf method alongside the citation of the original literature at its first mention earlier in the manuscript for clarity and ease of reference.
3. ICESat-2: According to other reviewer’s suggestion, we have removed the mention of ICESat-2 from the introduction and revised the manuscript to omit any references to ICESat-2 in the introduction as it is not utilized in our research. We concur that its inclusion may lead to confusion, given that our study does not involve data from this source.

We appreciate your thorough review and believe that these revisions have significantly improved the manuscript’s content and readability.

2. It is recommended to include a bathymetric map obtained from different inversion methods, along with the corresponding error map, to better illustrate the performance differences among various inversion methods.

Response:

We appreciate your valuable suggestion to include bathymetric maps obtained from various inversion methods along with their corresponding error maps. Your insight into the importance of direct visual comparisons to demonstrate performance differences is well-received.

However, as you have rightly mentioned, adding multiple comprehensive maps to the main body of our article would significantly increase its length, possibly detracting from the conciseness expected in a journal publication.

To address this, we propose the following solution: we will prepare an extensive set of supplementary materials, including complete bathymetric and error maps for all methods evaluated. This comprehensive materials are available as an supplementary, thus ensuring that the interested audience can delve into the detailed comparisons without overloading the main paper.

We hope that this approach addresses your concerns and provides a balanced solution that maintains the manuscript's focus while also making the data fully accessible to our readers.

Thank you once again for your constructive feedback, which has undoubtedly contributed to the robustness of our research presentation.

3. In the “Introduction”, it is stated that “ICESat-2 can provide more accurate bathymetry data as a training set.” Given this capability of ICESat-2, clarify the significance of the main research content of this paper, which focuses on “Shallow Water Depth Inversion Based on Sentinel-2 Data.”

Response:

Thank you for allowing us the opportunity to clarify the significance and relevance of our main research content concerning "Shallow Water Depth Inversion Based on Sentinel-2 Data".

Our research highlights the potential of Sentinel-2 data for shallow water depth inversion as a complementary solution, offering wider coverage and greater revisit frequency, which is essential for dynamic coastal environments. The main thrust of the paper should be to demonstrate how Sentinel-2 data can be effectively utilized for bathymetric mapping in shallow waters, enabling more frequent monitoring and updating.

We sincerely apologize for any confusion caused by our improper description to you and the other reviewer. Since ICESat-2 data was not utilized in our study, we believe that removing the relevant descriptions of ICESat-2 from our manuscript is a prudent decision that will allow us to focus more clearly on the core objectives of our paper.

Thank you again for your insightful comment, which has prompted us to more explicitly particulate the rationale behind our work.

4. This paper lacks sufficient innovation and primarily compares several methods of water depth inversion and analyzes the results. Consider enhancing the innovative aspects of the research.

Response:

We highly appreciate your feedback concerning the innovative aspects of our research. We have taken your comments into thoughtful consideration and would like to outline the unique contributions that our paper makes to the field of water depth inversion:

1. This paper conducts a statistical analysis of the SSPBDD phenomenon and suggests through our analysis that this phenomenon is likely widespread in multispectral satellite remote sensing imagery and has a certain level of impact on the accuracy of water depth inversion.
2. The influence of SSPBDD primarily relates to the one-to-many relationship between water depth and spectra. Our research finds that machine learning models based on probability density functions can be suitably applied to model the one-to-many relationships, thereby improving the results of water depth inversion.
3. Our study also finds that not only SSPBDD affects the accuracy of water depth inversion, but the spatial distribution of training data also plays a role.

Based on these results, we are not aware of any similar studies. We believe that these findings are useful for improving the accuracy of water depth inversion from multispectral satellite remote sensing and can also be useful for other related research.

We are committed to further highlighting these innovative elements and will revise our manuscript to ensure that these points are more prominent and obvious to our readers.

If there are additional areas of innovation that could be expanded upon in our manuscript, we would welcome your suggestions and look forward to any additional insights you can provide.

Thank you for guiding us to raise the level of originality and innovation in our work.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

There is no more comments and suggestions.

Author Response

Dear Reviewer,

     Thank you for your second review of our manuscript. We appreciate your efforts in evaluating our work and providing valuable feedback.

     We are pleased to note that you have no further comments or suggestions regarding our manuscript. This indicates that we have satisfactorily addressed all the concerns raised during the initial review stage.

     We are grateful for your constructive comments, which have helped us improve the quality and clarity of our manuscript.

    Thank you again for your time and consideration.

Best regards!

Reviewer 3 Report

Comments and Suggestions for Authors

I have no further comments.

Author Response

Dear Reviewer,

   Thank you for taking the time to review our manuscript again.

   We are pleased to note that you have no further comments on our revised manuscript. This indicates that we have adequately addressed the concerns raised in the previous round of review and made the necessary improvements to the manuscript.

   We greatly appreciate your constructive comments and suggestions throughout the review process. Your insights have been instrumental in enhancing the quality and clarity of our paper.

   Thank you again for your efforts and contributions to improving our manuscript. We look forward to the next steps in the publication process.

Best regards!