Multi-Feature Fusion and Cloud Restoration-Based Approach for Remote Sensing Extraction of Lake and Reservoir Water Bodies in Bijie City

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

What is the main question addressed by the research? The authors attempt to explain the water masses of lakes and reservoirs using clouds through remote sensing software.

Do you consider the topic original or relevant to the field? Does it address a specific gap in the field? Please also explain why this is/is not the case. The method is novel and useful, although it requires additional interpretation software that is not easily available. The availability of these tools would help disseminate the research further.

What does it add to the subject area compared with other published material? The research should be repeated using easily accessible software or the software used should be made freely available.

What specific improvements should the authors consider regarding the methodology? The methodology requires relevant mathematical or computational expertise.

Are the conclusions consistent with the evidence and arguments presented and do they address the main question posed? Please also explain why this is/is not the case. The conclusions are supported by the research provided.

Are the references appropriate? The references are appropriate, but they do not appear to meet the journal's standards.

Any additional comments on the tables and figures:

Line 126: Indicate on a map the geology of the area (rock type, etc.), as well as the distribution of the water bodies (lakes, rivers, etc.) considered. Explain whether the water bodies are all surface water or whether aquifers or karst areas with water appear. 

Line 191: Explain what L1C is.

Author Response

Comment 1：What is the main question addressed by the research? The authors attempt to explain the water masses of lakes and reservoirs using clouds through remote sensing software.

Response 1: Thank you for your comment. We fully agree with this comment. The main research question addressed in this study is to elucidate an effective method for interpreting the water masses of lakes and reservoirs via cloud data, with the support of remote sensing software. Your comment, which reflects the understanding that we attempted to explain the water masses of lakes and reservoirs through remote sensing software, is fully consistent with the core focus of our study.

 

Comment 2：Do you consider the topic original or relevant to the field? Does it address a specific gap in the field? Please also explain why this is/is not the case. The method is novel and useful, although it requires additional interpretation software that is not easily available. The availability of these tools would help disseminate the research further.

Response 2： Thank you for your comment. We agree with this comment. Therefore, we have supplemented the description of a free and easily accessible alternative interpretation software in the "Methodology" section of the revised manuscript, and attached its official download link to address the issue of software availability and promote the dissemination of this research. This revision can be found in the revised manuscript on Section 2.2.2."[To improve the accessibility of the research method, we supplement QGIS (a free and open-source geographic information system software) as an alternative to the previously mentioned interpretation software. Its official download link is: https://www.qgis.org/en/site/forusers/download.html. Users can obtain this software free of charge through the link, which effectively solves the problem of difficult access to interpretation tools and provides convenience for other researchers to replicate and apply this research method.]".

Comment 3：What does it add to the subject area compared with other published material? The research should be repeated using easily accessible software or the software used should be made freely available.

Response 3：Thank you for your comment. We agree with this comment. Therefore, we have supplemented the description of a free and easily accessible alternative interpretation software in the "Methodology" section of the revised manuscript, and attached its official download link to address the issue of software availability and promote the dissemination of this research. This revision can be found in the revised manuscript on Section 2.2.2."[To improve the accessibility of the research method, we supplement QGIS (a free and open-source geographic information system software) as an alternative to the previously mentioned interpretation software. Its official download link is: https://www.qgis.org/en/site/forusers/download.html. Users can obtain this software free of charge through the link, which effectively solves the problem of difficult access to interpretation tools and provides convenience for other researchers to replicate and apply this research method.]". 

Comment 4：What specific improvements should the authors consider regarding the methodology? The methodology requires relevant mathematical or computational expertise.

Response 4：Thank you for your comment. We fully agree with this comment. Specifically, our methodology does require a certain level of mathematical proficiency—including basic numerical calculation and computational logic—to ensure the accuracy of key steps such as parameter calculation and result validation. For researchers who need guidance on applying this methodology due to this requirement, we are willing to provide targeted guidance to them following the acceptance of this article.

Comment 5：Are the conclusions consistent with the evidence and arguments presented and do they address the main question posed? Please also explain why this is/is not the case. The conclusions are supported by the research provided.

Response 5：Thank you for your comment.

Comment 6：Are the references appropriate? The references are appropriate, but they do not appear to meet the journal's standards.

Response 6：Thank you for your comment. We agree with this comment. Therefore, we have fully reorganized the format of all references in the manuscript, strictly following the latest reference formatting standards specified in the journal’s "Author Guidelines"—including adjusting details such as author name abbreviations, publication year placement, journal title italicization, and DOI formatting to ensure full compliance with the journal’s requirements. This revision can be found in the "References" section of the revised manuscript, covering all entries from Reference 1 to Reference 18.

Comment 7：Line 126: Indicate on a map the geology of the area (rock type, etc.), as well as the distribution of the water bodies (lakes, rivers, etc.) considered. Explain whether the water bodies are all surface water or whether aquifers or karst areas with water appear.

Response 7：Thank you for your comment. Thank you for your valuable comment. In our study, we focus on lakes and rivers—both of which are explicitly surface water. No aquifers or water-bearing karst areas are involved in our research scope, which further clarifies the type of water bodies we analyzed.

Comment 7：Line 191: Explain what L1C is.

Response 7： Thank you for your comment. Sentinel-2 Level-1C (L1C) is a pre-processed product that provides Top-of-Atmosphere (TOA) reflectance after radiometric calibration and orthorectification using a Digital Elevation Model (DEM) for geometric accuracy. It includes 13 multispectral bands with resolutions of 10m, 20m, and 60m, formatted as 100 km² tiles in UTM/WGS84 projection. Unlike Level-2A data, L1C does not undergo atmospheric correction, requiring users to apply tools like Sen2Cor for further processing if surface reflectance (BOA) is needed.

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript addresses a highly relevant issue in environmental remote sensing, proposing a robust methodology for water body extraction using multi-feature fusion and cloud restoration on Sentinel-2 imagery via GEE. The study's focus on karst landscapes and high extraction accuracy is commendable. However, several aspects of the manuscript would benefit from greater methodological clarity, more rigorous statistical validation, and stronger integration with international research contexts. The following detailed comments are organized by section and line references to assist the authors in enhancing the manuscript’s scientific rigor.

Abstract

[L27–L29]: The problem framing is clear but could be strengthened by referencing current global benchmarks in water body extraction accuracy.
[L38–L41]: The phrase "non-water targets" lacks precision. Replace with "false positives due to topographic and anthropogenic features" and quantify performance improvement.

Introduction

[L85–L89]: The classification of methods (object-oriented, deep learning, index-based) is useful but lacks citation density. Provide at least one more recent citation per method, especially for deep learning (post-2020).

Methods

[Table 1]: The indices used are well selected. However, the paper should clarify if index thresholds are static or adaptive. Indicate whether thresholds (e.g., for NDVI > 0.2) are fixed or derived from training data.

Results 

[Table 3]: The evaluation is robust, but the low sample size for large lakes (n=6) may weaken statistical generalizability. Improvement suggested: Increase sample size or provide justification for sample representativeness.

[Figure 5 and Table 4]: The comparison is well visualized but should include statistical significance (e.g., p-values or confidence intervals).

Discussion and Conclusion

[L476–L479]: The claimed universality is again not sufficiently validated. Include a limitations paragraph clarifying the scope of generalization and need for cross-regional trials.

Author Response

Abstract

Comment 1：[L27–L29]: The problem framing is clear but could be strengthened by referencing current global benchmarks in water body extraction accuracy.

Response 1：Response 1: Thank you for pointing this out. We agree with this comment. Therefore, we have supplemented the problem framing with accuracy data of well-recognized global water body datasets to enhance the context of the research gap. This change can be found in the Abstract section, Lines 27–29 of the revised manuscript.[updated text in the manuscript]:Current lake and reservoir water body extraction algorithms are confronted with two critical challenges: 1) design dependency on specific geographical features, leading to constrained cross-regional adaptability (e.g., the JRC Global Water Body Dataset [8] achieves ~90% overall accuracy globally, while the ESA WorldCover 2020 [10] reaches ~92% for water body classification, both showing degraded performance in complex karst terrains); 2) information loss due to cloud occlusion, compromising dynamic monitoring accuracy. To address these limitations, this study presents a multi-feature fusion and multi-level hierarchical extraction algorithm for lake and reservoir water bodies, leveraging the Google Earth Engine (GEE) cloud platform and Sentinel-2 multispectral imagery in the karst landscape of Bijie City.

Comment 2：[L38–L41]: The phrase "non-water targets" lacks precision. Replace with "false positives due to topographic and anthropogenic features" and quantify performance improvement.

Response 2：Response 2: Thank you for pointing this out. We agree with this comment. Therefore, we have refined the imprecise term and added quantitative data on performance improvement to make the results more concrete. This change can be found in the Abstract section, Lines 38–41 of the revised manuscript.[updated text in the manuscript]:Experimental validation using high-resolution reference data demonstrates that the algorithm achieves an overall extraction accuracy exceeding 96% in Bijie City, effectively suppressing dark object interference (e.g., false positives due to topographic and anthropogenic features) while preserving water body boundary integrity. Compared with single-index methods (e.g., MNDWI), this method reduces false positive rates caused by building shadows and terrain shadows by 15%–20%, and improves the IoU (Intersection over Union) by 6%–13% in typical karst sub-regions.

 

Introduction

Comment 3：[L85–L89]: The classification of methods (object-oriented, deep learning, index-based) is useful but lacks citation density. Provide at least one more recent citation per method, especially for deep learning (post-2020).

Response 3：Thank you for pointing this out. We agree with this comment. Therefore, we have added one recent (post-2020) citation for each of the three method categories, and supplemented the corresponding explanatory content to enrich the literature context. This change can be found in the Introduction section, Lines 85–L89 of the revised manuscript.[updated text in the manuscript]:Current water body extraction methods based on optical remote sensing imagery primarily include object-oriented methods [2–3, 19], deep learning methods [3, 20], and band combination methods [4, 21]. Object-oriented methods are susceptible to segmentation thresholds and classification criteria, making them highly empirical [19] (Su et al., 2022, who optimized segmentation parameters for karst water bodies using multi-scale object partitioning). Deep learning methods require substantial sample data, with recent advances focusing on small-sample adaptation (e.g., Liang et al., 2021 [20], who proposed a transfer learning framework for water body extraction in data-scarce karst areas). In contrast, the water body index method, as a type of band combination method, extracts water body areas by constructing ratios between bands, offering the advantages of simplicity, high extraction accuracy, and speed [21] (Wang et al., 2023, who developed a multi-temporal index to enhance water-body/non-water-body discrimination in seasonal karst regions). It has been successfully applied in the extraction and dynamic monitoring of various surface water bodies, including lakes, reservoirs, urban landscape water bodies, and rivers [5–6].

Newly added citations:[19] Su, L. F.; Li, Z. X.; Zhang, H. Y. Optimization of object-oriented water body extraction parameters for karst plateau lakes based on Sentinel-2 imagery. Journal of Remote Sensing, 2022, 26(5): 1089–1102.[20] Liang, Z. Y.; Wang, J. X.; Chen, Y. Transfer learning-based water body extraction from Sentinel-2 imagery in karst areas with limited samples. Remote Sensing of Environment, 2021, 267: 112789.[21] Wang, Z. F.; Liu, J. G.; He, Y. Multi-temporal modified water index for surface water mapping in karst regions with seasonal dynamics. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 199: 245–260.

 

Methods

Comment 4：[Table 1]: The indices used are well selected. However, the paper should clarify if index thresholds are static or adaptive. Indicate whether thresholds (e.g., for NDVI > 0.2) are fixed or derived from training data.

Response 4： Thank you for pointing this out. We agree with this comment. Therefore, we have added a new column "Threshold Determination Method" to Table 1 and supplemented explanatory notes to clarify whether each index’s threshold is static/adaptive and its derivation source. This change can be found in the Methods section, Table 1 of the revised manuscript.[updated text in the manuscript (revised Table 1)].

 

Results

Comment 5：[Table 3]: The evaluation is robust, but the low sample size for large lakes (n=6) may weaken statistical generalizability. Improvement suggested: Increase sample size or provide justification for sample representativeness.

Response 5：Thank you for pointing this out. We agree with this comment. Therefore, we have increased the sample size of large lakes/reservoirs from n=6 to n=10, and supplemented explanations of sample spatial representativeness and cross-validation results to enhance statistical rigor. This change can be found in the Results section, Table 3 of the revised manuscript.[updated text in the manuscript (revised Table 3)].

Comment 6：[Figure 5 and Table 4]: The comparison is well visualized but should include statistical significance (e.g., p-values or confidence intervals).

Response 6：Thank you for pointing this out. We agree with this comment. Therefore, we have added a "Statistical Significance" column to Table 4 (including p-values and t-test results) and revised the caption of Figure 5 to clarify the statistical significance of accuracy differences. These changes can be found in the Results section, Table 4 and Figure 5 caption of the revised manuscript.[updated text in the manuscript (revised Table 4)].

 

Discussion and Conclusion

Comment 7：[L476–L479]: The claimed universality is again not sufficiently validated. Include a limitations paragraph clarifying the scope of generalization and need for cross-regional trials.

Response 7：Thank you for pointing this out. We agree with this comment. Therefore, we have added a new "Limitations and Future Directions" paragraph to clarify the current scope of generalization of the method, existing limitations in non-karst regions, and plans for future cross-regional validation. This change can be found in the Discussion and Conclusion section, newly added "5. Limitations and Future Directions" paragraph of the revised manuscript. [updated text in the manuscript (newly added paragraph)].

Reviewer 3 Report

Comments and Suggestions for Authors

Title: Multi-Feature Fusion and Cloud Restoration-Based Approach for Remote Sensing Extraction of Lake and Reservoir Water Bodies in Bijie City

Objective: The study presents a multi-feature fusion and multi-level hierarchical extraction algorithm for lake and reservoir water bodies, leveraging the Google Earth Engine (GEE) cloud platform and Sentinel multispectral imagery in the karst landscape of Bijie City.

 

Observations:

Lines 64 to 69: This information needs to cite authors as a reference.

Lines 79 to 84: This information needs to cite authors as a reference.

Lines 103 to 104: Make adjustments to the writing.

Line 106: Suppress - (Section 3.6).

Figure 1, Figure 8: Includes geographical details such as coordinates.

Figure 7: The figure resolution can be adjusted.

2.2 Data: Make adjustments to the writing. The authors must develop a text. It should be more integrated.

Figure 3. Distribution of water and non-water bands and index values: Adjust the Y axis.

Lines 285 to 290 (… sample points): It is methodology.

The choices made when processing the images included selecting the period with the lowest cloud cover (4.2 Comparison of Extraction Effects of Different Algorithms). Does this mean that the driest period was used? (4.4 Comparison of Different Products). Does water coverage, therefore, reflect the smallest flooded area? (Table 4)

Lines 381 to 395: There is a lot of information. The authors could explain the seasonal effect of rain and the limitations of satellite resolution more directly.

4.5. Changes in the spatial distribution of lakes and reservoirs: Suggestion (4.5.1 and 4.5.2).

Table 6. Lake and Reservoir Water Body Areas in Bijie City from 2017 to 2021: Does the region's land use or land cover dynamics have any impact on the area?

Add a discussion topic that highlights the methodology evaluation and its applicability to water resources planning and protection actions.

Author Response

Comment 1：Lines 64 to 69: This information needs to cite authors as a reference.

Response 1：Thank you for your comment. We have added references to the corresponding locations.

Comment 2：Lines 79 to 84: This information needs to cite authors as a reference.

Response 2：Thank you for your comment. We have added references to the corresponding locations.

Comment 3：Lines 103 to 104: Make adjustments to the writing.

Response 3：Thank you for your comment. We have made improvements to Lines 103 to 104.

Comment 4：Line 106: Suppress - (Section 3.6).

Response 4：Thank you for your comment. The excess parts in Line 106 have been removed.

Comment 5：Figure 1, Figure 8: Includes geographical details such as coordinates.

Response 5：Thank you for your comment. We have added geographical details such as Figure 1 and its coordinates.

Comment 6：Figure 7: The figure resolution can be adjusted.

Response 6：Thank you for your comment. We have increased the resolution of Figure 7 to 500dpi.

Comment 7：2.2 Data: Make adjustments to the writing. The authors must develop a text. It should be more integrated.

Response 7：Thank you for your comment. We have made significant revisions in section 2.2 Data.

Comment 8：Figure 3. Distribution of water and non-water bands and index values: Adjust the Y axis.

Response 8：Thank you for your comment. The y-axis of Figure 3 has been adjusted.

Comment 9：Lines 285 to 290 (… sample points): It is methodology.

Response 9：Thank you for your comment. We have moved Lines 285 to 290 to Section 3.8 of the methodology and provided a detailed description.

Comment 10：The choices made when processing the images included selecting the period with the lowest cloud cover (4.2 Comparison of Extraction Effects of Different Algorithms). Does this mean that the driest period was used? (4.4 Comparison of Different Products). Does water coverage, therefore, reflect the smallest flooded area? (Table 4)

Response 10：Thank you for your valuable comment regarding the potential associations between the selection of low-cloud-cover periods, drought conditions, and the representation of water body areas. We greatly appreciate this opportunity to clarify these key methodological considerations in detail. First, regarding the relationship between cloud cover selection and drought periods: The selection of periods with cloud cover < 30% for image compositing in this study is guided by the core objective of "minimizing the interference of cloud occlusion on water body information," rather than being linked to "drought conditions." Notably, the selected time frame encompasses all seasonal phases from 2017 to 2021, including the spring flood period in April, the late rainy season in July, and the water storage period in October. For instance, July 2021 had a cloud cover of 25%—a period that corresponds to the late rainy season (not a drought period)—which directly confirms that cloud cover thresholds are determined independently of aridity, and solely for ensuring the quality of spectral information in water body extraction. Second, regarding the correlation between water body area and seasonal hydrological dynamics: In Table 4 ("Comparison of Water Body Areas Extracted by Different Products") of the manuscript, the area of large water bodies extracted by our method in July (4.52 km²) is smaller than the area of large water bodies recorded in WorldCover’s October data (4.76 km²). This discrepancy stems from "seasonal hydrological variations"—specifically, reservoir water levels decline in July due to flood discharge, while water levels rise in October during the intentional water storage period. Importantly, this difference does not reflect a "minimum submerged area." Given that our study synthesizes monthly datasets from 2017 to 2021 (rather than relying on single-time-point data), the final extraction results represent the "typical distribution of water bodies" under normal hydrological conditions, rather than extreme area values associated with any single seasonal or climatic anomaly. This design is consistent with the study’s goal of large-scale long-term dynamic monitoring of lakes and reservoirs in Bijie’s karst region.

Comment 11：Lines 381 to 395: There is a lot of information. The authors could explain the seasonal effect of rain and the limitations of satellite resolution more directly.

Response 11：Thank you for your comment. The content of "Analysis of Differences between Different Products" in this section has been streamlined, focusing directly on the two core factors of "seasonal effects of rainfall" and "satellite resolution limitations".

Comment 12：4.5. Changes in the spatial distribution of lakes and reservoirs: Suggestion (4.5.1 and 4.5.2).

Response 12：Thank you for your comment. We will change the title of 4.5. to Changes in the spatial distribution of lakes and reservoirs. And merge sections 4.5.1 and 4.5.2 into one part.

Comment 13：Table 6. Lake and Reservoir Water Body Areas in Bijie City from 2017 to 2021: Does the region's land use or land cover dynamics have any impact on the area?

Response 13：Thank you for your comment. We have added the impact of land use/cover dynamics in the analysis section of Table 6 (Area Changes from 2017 to 2021).

Comment 14：Add a discussion topic that highlights the methodology evaluation and its applicability to water resources planning and protection actions.

Response 14：Thank you for your comment. We have added a discussion topic in section 4.6, emphasizing the evaluation of methods and their applicability to water resource planning and conservation actions.

Reviewer 4 Report

Comments and Suggestions for Authors

Peer Review

The study of Xue et al. entitled “Multi-Feature Fusion and Cloud Restoration-Based Approachfor Remote Sensing Extraction of Lake and Reservoir Water Bodies in Bijie City” is a very nice work , with significant contribution to large-scale dynamic monitoring of lakes and reservoirs based on remote sensing. The article is well written, and the results follow a logical flow. Some minor comments are listed below:

• Line 56-57: please specify  and explain based on scientific literature  what you mean with the terms “evolving climate patterns” which patterns are you referring to and how they evolve? 
• Line 57-59: references for these lines are needed
• Line 64- 69 & 79-84: These  parts  (1-3 & 4-5) need to be bibliographically justified: 1) Traditional approaches for investigating lake and reservoir dynamics are typically  constrained to small spatial scales, failing to enable long-term continuous monitoring. 2)This limitation results in delays in integrating lake/reservoir change information and re gional interconnections, thereby undermining decision-making efficiency based on water body variations. 3)Additionally, estimations of storage changes remain heavily dependent on field observations, which are time-consuming, labor-intensive, and ill-suited for scaling to meet large-scale monitoring requirements &  4)Lake and reservoir areas and water level elevations, as key parameters for calculating storage changes, have been widely incorporated into research on continuous lake and reservoir change monitoring.  5)By combining water body surface areas with water level information, automatic and precise extraction of multi-temporal lake and reservoir boundaries from remote sensing imagery can further monitor changes in lake and reservoir surface areas and storage volumes.
• Line 96-98: “Sentinel-2 imagery, with its superior spatial resolution and shorter revisit cycle, has become the preferred data source for scholars worldwide.” Why it has superior resolution , based on what evidence you justify this? & Please replace the word scholars with scientists.
• Line 99: please remove the : after the word persist and start the new sentence (“Limited…’’) with something like “The first one is Limited ….”
• Line 114-121: please strengthen the contribution and novelty of your work in the paragraph. Also, say emphasize why your study is more advanced and contributing than other similar studies
• Line 147 & line 184 : figure 1 & figure 2: need a better quality figure with bigger fonts
• Line 227: figure 3: English letters should be used
• Line 251: remove the “ symbol
• Line 252: need to write a source for equation 1
• Line 258: figure 4: need a bigger figure and bigger fonts
• Line 274: need to write a source for equation 2
• Line 397: figure 7: need a better resolution picture and bigger

 

Author Response

Comment 1：Line 56-57: please specify  and explain based on scientific literature  what you mean with the terms “evolving climate patterns” which patterns are you referring to and how they evolve?

Response 1：Thank you for your comment. We clarify the specific manifestations of climate model evolution (changes in precipitation patterns, extreme events, warming and evaporation) in Lines 56-57, and cite the research of Tong et al. (2023) and Wan et al. (2025) as scientific basis, in line with the content of the literature list.

Comment 2：references for these lines are needed

Response 2：Thank you for your comment. We have added references to the literature in lines 57-59.

Comment 3：Line 64- 69 & 79-84: These  parts  (1-3 & 4-5) need to be bibliographically justified: 1) Traditional approaches for investigating lake and reservoir dynamics are typically  constrained to small spatial scales, failing to enable long-term continuous monitoring. 2)This limitation results in delays in integrating lake/reservoir change information and re gional interconnections, thereby undermining decision-making efficiency based on water body variations. 3)Additionally, estimations of storage changes remain heavily dependent on field observations, which are time-consuming, labor-intensive, and ill-suited for scaling to meet large-scale monitoring requirements &  4)Lake and reservoir areas and water level elevations, as key parameters for calculating storage changes, have been widely incorporated into research on continuous lake and reservoir change monitoring.  5)By combining water body surface areas with water level information, automatic and precise extraction of multi-temporal lake and reservoir boundaries from remote sensing imagery can further monitor changes in lake and reservoir surface areas and storage volumes.

Response 3：Thank you for your comment. The references have been cited in the corresponding locations.

Comment 4：Line 96-98: “Sentinel-2 imagery, with its superior spatial resolution and shorter revisit cycle, has become the preferred data source for scholars worldwide.” Why it has superior resolution , based on what evidence you justify this? & Please replace the word scholars with scientists.

Response 4：Thank you for your comment. We clearly stated the resolution advantage (10m vs Landsat 30m) and revisit period (5 days) in the article, cited corresponding references as evidence, and replaced "scholars" with "scientists".

Comment 5：Line 99: please remove the : after the word persist and start the new sentence (“Limited…’’) with something like “The first one is Limited ….”

Response 5：Thank you for your comment. We have removed the ':' after 'persist' in the article and added 'The first one is' as the beginning of the sentence to make the logic more coherent.

Comment 6：Line 114-121: please strengthen the contribution and novelty of your work in the paragraph. Also, say emphasize why your study is more advanced and contributing than other similar studies

Response 6：Thank you for your comment. In my article, we clarify the concept of "innovation" (multi feature fusion+cloud restoration for karst), compare the shortcomings of single index/deep learning methods, highlight the advantages of relevant research, and strengthen research contributions.

Comment 7：Line 147 & line 184 : figure 1 & figure 2: need a better quality figure with bigger fonts.

Response 7：Thank you for your valuable comment. We have made targeted optimizations regarding the suggestions for Figures 1 and 2: we have adjusted the font size in both images to improve readability, and exported and saved the modified Figures 1 and 2 at a resolution of 500 dpi.

Comment 8：Line 227: figure 3: English letters should be used.

Response 8：Thank you for your comment. We have replaced all the letters with English.

Comment 9：Line 251: remove the “ symbol. “

Response 9：Thank you for your comment. We will remove the symbol “ corresponding to the position.

Comment 10：Line 252: need to write a source for equation 1.

Response 10：Thank you for your comment. We have marked the references for equation 1.

Comment 11：Line 258: figure 4: need a bigger figure and bigger fonts.

Response 11：Thank you for your comment. We have made adjustments to the font of Figure 4.

Comment 12：Line 274: need to write a source for equation 2

Response 12：Thank you for your comment. We have marked the references for equation 2.

Comment 13：Line 397: figure 7: need a better resolution picture and bigger.

Response 13：Thank you for your comment. We have adjusted the resolution and size of Figure 7.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have worked hard to improve the text and the contributions to their work. A better paper has been achieved, and the work can be accepted in its current form.

Reviewer 3 Report

Comments and Suggestions for Authors

Adjust the end of line 488.
The authors provided the necessary justifications and adjustments.