Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear authors:

I have reviewed the work “Application of MSI and SAR Sensors for Monitoring Algal Blooms: A Review”. Your work is very interesting and on a topic that is currently gaining prominence. My recommendation is publishable with minor changes that I detail below:

Lines 46-67:  It is important to remember that harmful algal blooms (HABs) include cyanobacteria, which are not algae, and this should be clarified. In lines 60–62, cyanobacteria and their toxins are mentioned, and the beginning of the paragraph could be understood as suggesting that cyanobacteria are algal blooms.

Lines 99-125: This entire section with the objective-by-objective description is too extensive; it should be summarized by generalizing the proposed objectives into shorter sentences.

Lines 126-131: It would have been interesting to include other search engines beyond Google Scholar. However, I understand that it is one of the most widely used. It is necessary to clarify the period considered—was it from 2015 to 2024 or 2025? Please specify. Also, clarify the languages considered: were only papers in English included, or also in Spanish, Portuguese, etc.?

Figure 1 and Figure 2: The data could be arranged from the highest to the lowest number of research papers so that the bars are displayed in decreasing order in the graph.

Table 1: It is not clear what is meant by ‘No Data’; this should be clarified somewhere in the text. How were the studies subdivided into the SAR Data, PolSAR Data, and MSI Data columns? Were the papers reviewed in detail, or were these keywords included in the search to perform the subdivision?

Lines 207-216: Provide a deeper discussion of the limitations of PolSAR.

Lines 219-220: Replace ‘multispectral imagery’ with MSI.

Line 257: Which limitations are being referred to?

Line 297: Replace ‘multispectral imagery’ with MSI.

Line 310: Replace ‘multispectral imagery’ with MSI.

Section 3.2.2 “Machine Learning”: This section should be summarized and structured a bit better; there are concepts that are repeated. Perhaps a table could be added as a synthesis, including the algorithms, applications, advantages, and disadvantages.

Section 4.: Case Studies and Applications: I am not sure whether this section is necessary or contributes to the paper.

Section Conclusion: This section could also be summarized a bit; the conclusions should be more specific.

Author Response

Reviewer 1
 Dear authors: I have reviewed the work “Application of MSI and SAR Sensors for Monitoring Algal Blooms:
 A Review”. Your work is very interesting and on a topic that is currently gaining prominence. My recom
mendation is publishable with minor changes that I detail below:
 (Point 1) Lines 46-67: It is important to remember that harmful algal blooms (HABs) include cyanobac
teria, which are not algae, and this should be clarified. In lines 60–62, cyanobacteria and their toxins are
 mentioned, and the beginning of the paragraph could be understood as suggesting that cyanobacteria are algal
 blooms.
 Response:
 We appreciate the reviewer pointing this out. In the revised manuscript, it is changed as per the sugges
tion.
 (Point 2) Lines 99-125: This entire section with the objective-by-objective description is too extensive;
 it should be summarized by generalizing the proposed objectives into shorter sentences.
 Response:
 The objective by objective is now shorten in the revised manuscript.
 (Point 3) Lines 126-131: It would have been interesting to include other search engines beyond Google
 Scholar. However, I understand that it is one of the most widely used. It is necessary to clarify the period
 considered—was it from 2015 to 2024 or 2025? Please specify. Also, clarify the languages considered: were
 only papers in English included, or also in Spanish, Portuguese, etc.?
 Response:
 This review is primarily based on two widely recognized research platforms, Google Scholar and IEEE
 Xplore, as they provide comprehensive coverage of peer-reviewed literature across a wide range of disci
plines, including remote sensing, environmental monitoring, and geoscience. Google Scholar is particularly
 valuable for its broad indexing of journals, conference proceedings, and grey literature, while IEEE Xplore
 offers access to high-quality technical papers and conference articles, especially in engineering and remote
 sensing domains. Together, these platforms ensured a balanced and representative literature base for the
 study.
 1
The literature survey covers publications from 2015 to 2024, reflecting the period relevant to the study
 at the time of writing this review. Also, only English-language sources were included, as publications in
 other languages (for example, Spanish and Portuguese) were beyond the authors’ proficiency.
 (Point 4) Figure 1 and Figure 2: The data could be arranged from the highest to the lowest number of
 research papers so that the bars are displayed in decreasing order in the graph.
 Response:
 As per the reviewer’s suggestion, authors’ have updated the Figure 1 and Figure 2 in the revised
 manuscript.
 (Point 5) Table 1: It is not clear what is meant by ‘No Data’; this should be clarified somewhere in the
 text. How were the studies subdivided into the SAR Data, PolSAR Data, and MSI Data columns? Were the
 papers reviewed in detail, or were these keywords included in the search to perform the subdivision?
 Response:
 In Table 1, the term ‘No Data’ refers to studies that discussed harmful algal blooms (HABs) without incor
porating remote sensing datasets such as MSI or SAR. As per the reviewer’s suggestion, the clarification
 has been added in the text at the point where Table 1 is cited.
 The subdivision into SAR Data, PolSAR Data, and MSI Data columns was carried out based on the
 presence of these specific keywords during the literature search. These terms were used as filters in the
 search engines rather than through a detailed manual classification of each study.
 (Point 6) Lines 207-216: Provide a deeper discussion of the limitations of PolSAR.
 Response:
 PolSAR data can be used for the algal bloom detection, but it suffers with certain limitations that reduce
 its reliability compared to optical sensors. Studies show that blooms often appear as dark features in
 SAR imagery due to dampening of capillary waves; however, similar signatures can also be caused by
 low wind zones or oil films, leading to ambiguity in detection [1]. The calm surface of water reflects very
 little radar energy, which makes PolSAR analysis more difficult. Since algal blooms create only slight
 variations in surface texture, the scattering information is limited and standard decomposition methods
 struggle to capture them effectively [2]. In addition, SAR is unable to detect blooms beneath the water
 surface and its performance is strongly influenced by wind conditions, revisit frequency, and calibration
 accuracy, which restrict its use mainly to visible surface scums [3]. Although new techniques such as deep
 learning and multi-sensor data fusion are being explored to improve detection, they are computationally
 intensive and rely heavily on large, high-quality training datasets [4]. As a result, PolSAR is best seen
 as a supporting tool—particularly valuable when clouds or rain obscure optical sensors—rather than a
 standalone solution for reliable algal bloom monitoring.
 1. Wang, Y.; Gao, L.; Liu, S.; Yang, X.; Li, J. Discrimination of Algal-Bloom Using Spaceborne SAR
 Observations. Remote Sensing 2018, 10, 767. https://doi.org/10.3390/rs10050767.
 2.
 Li, A.; Shi, Z.; Wang, G.; et al. On the Capacity of Sentinel-1 Synthetic Aperture
 Radar in Detecting Floating Macroalgae. Science of the Total Environment 2022, 833, 155155.
 https://doi.org/10.1016/j.scitotenv.2022.155155.
 3. Khan, R.M.; Salehi, B.; Mahdianpari, M.; Mohammadimanesh, F.; Mountrakis, G.; Quackenbush,
 L.J. A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing
 Perspective. Remote Sensing 2021, 13, https://doi.org/10.3390/rs13214347.
 2
4. Phetanan, K., Kwon, D. H., Lee, J., Jeong, H., Nam, G., Hwang, E., ... Cho, K. H. (2025). SAR
 remote sensing for monitoring harmful algal blooms using deep learning models. GIScience & Remote
 Sensing, 62(1). https://doi.org/10.1080/15481603.2025.2524202
 Authors’ have included this discussion on the limitations of PolSAR along with references in the revised
 manuscript on Page 9 of the manuscript.
 (Point 7) Lines 219-220; Line 297; Line 310: Replace ‘multispectral imagery’ with MSI.
 Response:
 The authors apologize for the oversight in not using abbreviations consistently. In the revised manuscript,
 ‘multispectral imagery’ has been replaced with its abbreviation MSI throughout the text.
 (Point 8) Line 257: Which limitations are being referred to?
 Response:
 The limitations are added as few lines in red color.
 (Point 9) Section 3.2.2: “Machine Learning”: This section should be summarized and structured a bit
 better; there are concepts that are repeated. Perhaps a table could be added as a synthesis, including the
 algorithms, applications, advantages, and disadvantages.
 Response:
 The section 3.2.2 is rewritten and summarized in the tabular form in Table 5.
 (Point 10) Section 4: Case Studies and Applications: I am not sure whether this section is necessary or
 contributes to the paper.
 Response:
 We thank the reviewer for the comment on Section 4: Case Studies and Applications. This section
 provides essential context by demonstrating how MSI- and SAR-based techniques have been applied to
 real-world algal bloom monitoring across diverse regions. It links the discussed methods to practical
 applications, highlights operational challenges and successes, and enhances the review’s relevance for
 researchers, policymakers, and practitioners. For these reasons, we consider it important to retain Section
 4 in the manuscript.
 (Point 11) Section Conclusion: This section could also be summarized a bit; the conclusions should be
 more specific.
 Response:
 As per the reviewer’s suggestion, conclusion has been rewritten to make it more summarized and specific

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript under consideration is devoted to review of use of Multispectral Imagery and Synthetic Aperture Radar, with polarimetric one, in the monitoring of harmful algal blooms and other kinds of aquatic pollution.

The authors write that the eutrophication is a natural process. It is completely true sentention. So, there is hardly understandable - why massive algal blooms and toxic blooms are related by authors with anthropogenic activity. The reviewer demands to stress the natural character of algal blooms. They are just natural events, though they can be related with anthropogenic pressure in the form of nutrients enrichment and so on.

The reviewer is to stress the oversaturation of the text of the manuscript with abbreviations. There are multiple abbreviations even in the abstract (lines 6, 7, 8, 10, 13) and keywords (l.18) as well as in the title . The reviewer reminds to authors the necessity to avoid the use of abbreviations in these parts of the paper as well as the possibility to insert the list of abbreviations somewhere between the abstract and the introduction. It will improve the understandability of the text by the readers, the most of them are not specialists in the authors field and are not used for these abbreviations. The reviewer is a bit confused with the references in the manuscript. When he sees [?] or [??] instead of normal reference it is a kind of problem, is not it? Reviewer understand that this is some mistake of technical origion, but…

Nevertheless, the work presented is a good analytic review of the problem, illustrated at one hand by extensive statistical analysis of bibliography data bases and at other hand by excellent case studies.

The general conclusion of authors: “Inter-disciplinary collaboration and working together with space agencies, environmental organizations, and local communities play a crucial role in turning satellite data into practical solutions that help protect and manage our water ecosystems to be more

Sustainable” is proven by the work they’ve fulfilled and the reviewer completely agrees with it.

The review is very useful for the people working in this field as well as for specialists working in the interrelated areas. This work is to be published after proposed revisions listed in the current review report.

Author Response

Reviewer 2
 The manuscript under consideration is devoted to review of use of Multispectral Imagery and Synthetic
 Aperture Radar, with polarimetric one, in the monitoring of harmful algal blooms and other kinds of aquatic
 pollution.
 (Point 1) The authors write that the eutrophication is a natural process. It is completely true sententious.
 So, there is hardly understandable- why massive algal blooms and toxic blooms are related by authors with
 anthropogenic activity. The reviewer demands to stress the natural character of algal blooms. They are just
 natural events, though they can be related with anthropogenic pressure in the form of nutrients enrichment
 and so on.
 Response:
 Authors thank the reviewer for pointing that out. We really appreciate the insight. Reviewer is right:
 eutrophication is a natural process, and algal blooms, including harmful ones, can happen without human
 interference. In the revised article, it is clarified that while these blooms are mostly natural, activities
 like wastewater discharge, agricultural runoff, and changes in land use can make them more frequent and
 intense.
 (Point 2) The reviewer is to stress the oversaturation of the text of the manuscript with abbreviations.
 There are multiple abbreviations even in the abstract (lines 6, 7, 8, 10, 13) and keywords as well as in
 the title. The reviewer reminds the authors the necessity to avoid the use of abbreviations in these parts
 of the paper as well as the possibility to insert the list of abbreviations somewhere between the abstract and
 the introduction. It will improve the understandability of the text by the readers, the most of them are not
 specialists in the authors field and are not used for these abbreviations. The reviewer is a bit confused with
 the references in the manuscript. When he sees [?] or [??] instead of normal reference it is a kind of problem,
 is not it? Reviewer understand that this is some mistake of technical origion, but...
 Response:
 Abbreviations have been removed from the title and keyword sections. In the abstract, both the abbre
viations and their full forms are provided. The issue of missing references and citations was due to a
 conversion glitch in the MDPI submission portal, which we noticed only later.
 (Point 3) Nevertheless, the work presented is a good analytic review of the problem, illustrated at one hand
 by extensive statistical analysis of bibliography data bases and at other hand by excellent case studies.
 Response:
 Thank you for the kind words; we greatly appreciate them. In this work, we aimed to approach the topic
 from both perspectives—conducting a rigorous statistical analysis of the literature while also incorporating
 real-world case studies to demonstrate practical applications. We are glad to hear that this balance is
 evident in the manuscript.
 (Point 4) The general conclusion of authors: “Inter-disciplinary collaboration and working together with
 space agencies, environmental organizations, and local communities play a crucial role in turning satellite
 data into practical solutions that help protect and manage our water ecosystems to be more Sustainable” is
 proven by the work they’ve fulfilled and the reviewer completely agrees with it.
 4
Response:
 Thank you for your thoughtful feedback. We are pleased that the emphasis we placed on collaboration is
 evident. One of our main goals was to illustrate how cooperation among space agencies, environmental
 organizations, and local communities can translate satellite data into practical solutions. It is encouraging
 to know that you recognize and support this aspect of our work.
 (Point 5) The review is very useful for the people working in this field as well as for specialists working
 in the interrelated areas. This work is to be published after proposed revisions listed in the current review
 report.
 Response:
 We are pleased that the review is proving useful not only to researchers in this field but also to those
 in related areas, reflecting the broader relevance we aimed to achieve. All proposed revisions have been
 carefully addressed to strengthen the manuscript for publication

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript titled " Application of MSI and SAR Sensors for Monitoring Algal

Blooms: A Review" may be accepted pending Major revisions.

 

 

 

Introduction

 

General Comment

 

References: The manuscript currently does not include any in-text references or a reference list at the end. This is a major issue, and the authors must add citations throughout the body text and provide a properly formatted bibliography.

 

Abstract

 

The abstract is well written and provides a good overview of the study. No major changes are required here.

 

Introduction

 

The Introduction should be expanded to include more details about remote sensing applications and techniques that can improve the analysis of algal flora and water quality. This will provide a stronger scientific background and emphasize the relevance of the study.

 

Technical Clarifications Needed

 

Sensors on water surface: The authors should explain more about the specific sensors or instruments that can be deployed directly on or near the water surface (e.g., in situ spectroradiometers, fluorometers, Environmental Sample Processors). Such descriptions would improve the reader’s understanding of validation and data integration.

 

Spatial errors in satellite data: The manuscript must include an explanation of how spatial errors or uncertainties from the satellite-derived data were evaluated. For example, what was the pixel resolution used, and how was georeferencing accuracy ensured? Were there any correction methods applied?

 

Effect of water surface waves: The authors should clarify how their analysis accounts for or corrects the influence of wind-driven surface waves, which can distort spectral signals and radar backscatter. Techniques such as image filtering, atmospheric correction models, or data fusion should be described.

 

NDWI method: It is strongly recommended that the authors incorporate and discuss the Normalized Difference Water Index (NDWI). This index is widely used for enhancing the detection of water bodies and can support more accurate differentiation of algal blooms from surrounding land or turbid waters.

 

 

 

In reflection of all these points, I recommend that the authors consult and cite at least one relevant study that presents comparable methods and techniques, as this would strengthen the methodological framework of the paper.

 

 

The recommended reference is

 

- Valjarević, A. GIS-Based Methods for Identifying River Networks Types and Changing River Basins. Water Resour Manage 38, 5323–5341 (2024). https://doi.org/10.1007/s11269-024-03916-7.

 

he authors should include a table that organizes and divides the topic by continents.

Advances of this paper

Integration of MSI and SAR Sensors

The paper highlights the complementary strengths of MSI (spectral discrimination) and SAR (all-weather, day–night surface feature detection). Their integration provides robust and resilient bloom mapping strategies.

 

Development of Spectral Indices

Advances in indices such as NDVI, NDCI, FAI, AFAI, and ABDI have improved the ability to delineate and quantify bloom events, especially in optically complex waters.

 

Machine Learning and AI Applications

The review demonstrates how SVM, Random Forests, LSTM networks, and deep learning architectures significantly enhance bloom detection, prediction, and classification accuracy.

 

Bio-optical Algorithms and Hybrid Models

Semi-analytical models like the Quasi-Analytical Algorithm (QAA), when coupled with machine learning, have improved biomass estimation accuracy and adaptability across diverse aquatic systems.

 

Case Studies Demonstrating Real-world Applications

Practical applications in Lake Erie (USA), Vembanad Lake (India), and Korean coastal waters show operational early warning systems and demonstrate the societal value of these technologies.

 

Emergence of New Sensor Technologies

Upcoming hyperspectral missions (e.g., EnMAP, PRISMA, Landsat Next) promise higher spectral, spatial, and temporal resolutions, enabling more precise detection of harmful algal blooms.

 

Policy and Decision-support Integration

The review emphasizes the translation of remote sensing advances into policy frameworks and early-warning systems (e.g., NOAA’s ESP, GOCI-based programs), aligning scientific progress with environmental management.

 

 

Limitations of this paper can be addressed

 

Atmospheric and Water Column Interference

MSI retrievals are affected by aerosols, clouds, and water turbidity, leading to distorted reflectance values and potential false positives.

 

Ambiguities in SAR Detection

SAR signatures of algal slicks can be confused with oil spills, floating debris, aquaculture activity, or calm wind zones, which reduces specificity.

 

Wind Dependence in SAR Observations

Calm winds mimic bloom signatures, while strong winds can obscure bloom-induced features, creating detection challenges.

 

Validation Constraints

Ground truth data (chlorophyll-a, phycocyanin, turbidity) are often limited in space and time, especially in developing regions. This weakens the calibration and generalizability of satellite-based algorithms.

 

Temporal Mismatches Between In-situ and Satellite Data

Rapidly changing aquatic conditions can create discrepancies when comparing satellite overpasses with field samples, reducing validation accuracy.

 

Results

Tell me the which one month is with the most generalized data, explain better.

Specific comment

The authors need to add statistical software or method that used within this research!!!

 

 

 

 

Overall Recommendations

The paper can be accepted after a Major revision to address the specified points.

Reviewer# 2

Comments for author File: Comments.pdf

Author Response

Reviewer 3
 The manuscript titled ”Application of MSI and SAR Sensors for Monitoring Algal Blooms: A Review” may
 be accepted pending Major revisions.
 (Point 1) General Comment: References: The manuscript currently does not include any in-text refer
ences or a reference list at the end. This is a major issue, and the authors must add citations throughout
 the body text and provide a properly formatted bibliography.
 Response:
 We apologize for this inconvenience. The references were included and cited in the original manuscript.
 However, the issue of missing references and citations was due to a conversion glitch in the MDPI sub
mission portal, which we noticed only later.
 In the revised version, the authors have corrected this issue to ensure it does not recur.
 (Point 2) Abstract: The abstract is well written and provides a good overview of the study. No major
 changes are required here.
 Response:
 Authors thank the reviewer for the appreciation.
 (Point 3) Introduction: The Introduction should be expanded to include more details about remote sensing
 applications and techniques that can improve the analysis of algal flora and water quality. This will provide
 a stronger scientific background and emphasize the relevance of the study.
 Response:
 Aparagraph in the article has been added to discuss remote sensing applications and techniques that can
 improve the analysis of algal flora and water quality.
 (Point 4) Technical Clarifications Needed: Sensors on water surface: The authors should explain more
 about the specific sensors or instruments that can be deployed directly on or near the water surface (e.g., in
 situ spectroradiometer, fluorometer, Environmental Sample Processors). Such descriptions would improve
 the reader’s understanding of validation and data integration.
 Response:
 For more clarification, a new subsection has been added in the article as 2.4 : In Situ Sensors for Validation
 and Data Integration.
 (Point 5) Spatial errors in satellite data: The manuscript must include an explanation of how spatial
 errors or uncertainties from the satellite-derived data were evaluated. For example, what was the pixel
 resolution used, and how was georeferencing accuracy ensured? Were there any correction methods applied?
 6
Response:
 As per the reviewer’s suggestion, an explanation is added in Section 3.4: Spatial Resolution, Geo
referencing Accuracy, and Error Mitigation in the revised manuscript.
 (Point 6) Effect of water surface waves: The authors should clarify how their analysis accounts for or
 corrects the influence of wind-driven surface waves, which can distort spectral signals and radar backscatter.
 Techniques such as image filtering, atmospheric correction models, or data fusion should be described.
 Response:
 A detail discussion is added in the subsection 3.4.1: Correction for Wind-Driven Surface Waves
 and Glint Effects.
 (Point 7) NDWI method: It is strongly recommended that the authors incorporate and discuss the
 Normalized Difference Water Index (NDWI). This index is widely used for enhancing the detection of water
 bodies and can support more accurate differentiation of algal blooms from surrounding land or turbid waters.
 Response:
 Authors have incorporated the discussed the NDWI in subsection 3.4.2: Incorporation of NDWI
 for Water Body Enhancement
 (Point 5,6,7) In reflection of all these points, I recommend that the authors consult and cite at least one
 relevant study that presents comparable methods and techniques, as this would strengthen the methodological
 framework of the paper.
 The recommended reference is,
 Valjarevi´c, A. GIS-Based Methods for Identifying River Networks Types and Changing River Basins. Water
 Resour Manage 38, 5323–5341 (2024). https://doi.org/10.1007/s11269-024-03916-7.
 The authors should include a table that organizes and divides the topic by continents.
 Response:
 The suggested reference is cited by including a subsubsection 3.4 in the revised version of the manuscript.
 Authors have also included a Table 7. Summary of reviewed HAB monitoring studies organized
 by continent, on page 17 of the manuscript to organize and divide the topic by continents.
 (Point 8) Advances of this paper
 Integration of MSI and SAR Sensors: The paper highlights the complementary strengths of MSI (spec
tral discrimination) and SAR (all-weather, day–night surface feature detection). Their integration provides
 robust and resilient bloom mapping strategies.
 Development of Spectral Indices: Advances in indices such as NDVI, NDCI, FAI, AFAI, and ABDI
 have improved the ability to delineate and quantify bloom events, especially in optically complex waters.
 Machine Learning and AI Applications: The review demonstrates how SVM, Random Forests, LSTM
 networks, and deep learning architectures significantly enhance bloom detection, prediction, and classification
 accuracy.
 Bio-optical Algorithms and Hybrid Models: Semi-analytical models like the Quasi-Analytical Algorithm
 (QAA), when coupled with machine learning, have improved biomass estimation accuracy and adaptability
 across diverse aquatic systems.
 7
Emergence of New Sensor Technologies: Upcoming hyperspectral missions (e.g., EnMAP, PRISMA,
 Landsat Next) promise higher spectral, spatial, and temporal resolutions, enabling more precise detection of
 harmful algal blooms.
 Policy and Decision-support Integration: The review emphasizes the translation of remote sensing
 advances into policy frameworks and early-warning systems (e.g., NOAA’s ESP, GOCI-based programs),
 aligning scientific progress with environmental management.
 Response:
 Authors thank the reviewer for the deep review and highlighting the key points of each section, and for
 the appreciation and motivation.
 (Point 9) Limitations of this paper can be addressed
 Response:
 The challenges and limitations have been thoroughly discussed in Section 5, on Page 18 of the revised
 manuscript.
 (Point 10) Results: Tell me which one month is with the most generalized data, explain better.
 Response:
 We thank the reviewer for this comment. Since this work is a review article, the data presented were
 synthesized from multiple published studies conducted across different regions and time frames. Therefore,
 there is no single “one month” that represents the most generalized data within our work. Instead, the
 review highlights seasonal patterns of algal blooms reported in the literature, which vary with geographic
 location and environmental conditions.
 (Point 11) Specific Comments: The authors need to add statistical software or methods that are used
 within this research.
 Response:
 We thank the reviewer for this comment. As this work is a review article, no original statistical analyses
 were performed by the authors. Instead, we systematically synthesized and critically evaluated findings
 from published studies.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript titled Application of Multi-Spectral Imagery and Synthetic Aperture

Radar Sensors for Monitoring Algal Blooms: A Review is suitable for acceptance in its current form

The paper now is total suitable for acceptance

The section Abstract now is much better written and now is acceptable

The reason because I accepted this paper in this stage

1. Integration of MSI and SAR for HAB Monitoring

 

The paper demonstrates how combining Multispectral Imagery (MSI) and Synthetic Aperture Radar (SAR) provides a complementary and more reliable approach to detect and monitor harmful algal blooms, overcoming the limitations of single-sensor methods

 

 

 

2. Use of Advanced Spectral Indices

 

It highlights progress in spectral indices such as NDCI, FAI, AFAI, NDMAI, and others, which significantly improve the ability to detect algal blooms in optically complex waters and under varied environmental conditions

 

 

 

3. Application of Machine Learning and AI

 

The review presents the growing use of machine learning and deep learning models (e.g., SVM, Random Forest, LSTM, CNNs) for more accurate bloom detection, classification, and forecasting—showing how AI enhances predictive power compared to traditional threshold methods

 

 

 

4. Demonstration of Data Fusion Approaches

 

It emphasizes data fusion techniques (optical–radar integration, SAR–MSI fusion, machine learning hybrids) that improve detection accuracy even under cloud cover or turbid water conditions—critical for operational early-warning systems

 

 

5. Rich Case Study Applications

 

The article synthesizes practical applications from Lake Erie (USA/Canada), Vembanad Lake (India), Baltic Sea (Europe), Korean coastal waters, and Lake Victoria (Africa), illustrating the real-world utility of these methods for different geographic contexts

 

 

 

6. Advancement in Validation and Ground Truthing

 

It integrates in situ instruments (spectroradiometers, fluorometers, Environmental Sample Processors, multiparameter sondes) into workflows for calibration and validation, strengthening the reliččability of satellite-based monitoring

 

7. Forward-looking Perspectives

 

The paper provides a clear vision of future progress, discussing hyperspectral sensors (EnMAP, PRISMA), AI-driven analytics, and cloud-based platforms (Google Earth Engine) as emerging technologies that will transform algal bloom monitoring into near real-time operational systems

 

 

 

The number of references now are sufficient.

 

I have accepted the manuscript in full

Sincerely,

Reviewer#4

 

 

Comments for author File: Comments.docx