Development and Calibration of Sentinel-2 Spectral Indices for Water Quality Parameter Estimation in Alqueva Reservoir, Southern Portugal

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This study developed localized Sentinel-2 models to estimate water quality parameters (Chl-a, TSS, SDD) in Portugal's Alqueva Reservoir. By integrating a decade of field data and comparing atmospheric correction methods, C2RCC-COMPLEX was identified as optimal. The resulting empirical models showed strong correlations and were used to generate maps that effectively captured significant spatial and seasonal variations, such as higher pollution during drought and improved conditions after winter.

 

Comments:

1. While the study successfully develops empirical models for Chl‑a, TSS, and SDD using nine years of in‑situ data from Alqueva Reservoir, the limited number of sampling sites may constrain the models’ transferability to other Mediterranean or coastal water bodies with different optical properties. It is recommended that the “Discussion” or “Conclusions” section include a dedicated paragraph evaluating the feasibility of applying these Sentinel‑2 band‑based algorithms (e.g., B7, B6, B8A) to other reservoirs under similar climatic‑hydrological regimes, along with potential adaptation strategies.

 

2. The comparison of atmospheric correction methods focuses on the C2RCC family, with C2RCC‑COMPLEX selected as optimal. However, the “Limitations” section notes that sunglint and adjacency effects were not explicitly addressed. To improve the robustness and accuracy of water‑quality retrievals, the manuscript could benefit from a more diverse and forward‑looking evaluation of atmospheric processors.

 

3. The use of a ±3‑day temporal window and a 60 m spatial resolution may introduce uncertainties during rapid algal blooms or in narrow riverine zones. The authors should further discuss in the “Discussion” how these choices affect result uncertainty. Additionally, incorporating higher‑temporal‑resolution sensors like Sentinel‑3 OLCI, or exploring data‑fusion approaches, could be suggested as a feasible way to enhance dynamic monitoring capabilities.

 

4. Although long‑term in‑situ data (2014–2022) support the model calibration, the temporal distribution of field sampling (e.g., seasonal and inter‑annual coverage) is not clearly described. This omission hinders a rigorous distinction between seasonal patterns and extreme event‑driven variations (e.g., drought). Providing a sampling timeline or frequency table would strengthen the seasonal analysis and ensure the models are not biased toward specific hydrological conditions.

 

Recommendation:

Major Revision. The manuscript has a solid foundation and addresses a relevant application. However, the major comments listed above, particularly concerning the interpretation of model performance and sample size limitations need to be adequately addressed to ensure the scientific validity and impact of the study.

Author Response

REVIEWER 1

Comments and Suggestions for Authors

This study developed localized Sentinel-2 models to estimate water quality parameters (Chl-a, TSS, SDD) in Portugal's Alqueva Reservoir. By integrating a decade of field data and comparing atmospheric correction methods, C2RCC-COMPLEX was identified as optimal. The resulting empirical models showed strong correlations and were used to generate maps that effectively captured significant spatial and seasonal variations, such as higher pollution during drought and improved conditions after winter.

Comments:

1. While the study successfully develops empirical models for Chl‑a, TSS, and SDD using nine years of in‑situ data from Alqueva Reservoir, the limited number of sampling sites may constrain the models’ transferability to other Mediterranean or coastal water bodies with different optical properties. It is recommended that the “Discussion” or “Conclusions” section include a dedicated paragraph evaluating the feasibility of applying these Sentinel‑2 band‑based algorithms (e.g., B7, B6, B8A) to other reservoirs under similar climatic‑hydrological regimes, along with potential adaptation strategies.

We thank the reviewer for this relevant comment. We agree that the transferability of empirical, site-specific bio-optical models to other water bodies with different optical properties is inherently limited. Mediterranean reservoirs, such as Alqueva, are optically complex systems whose behavior is controlled by multiple interacting variables (e.g., phytoplankton composition, suspended inorganic matter, colored dissolved organic matter, hydrodynamics, and seasonal stratification). Consequently, direct extrapolation of empirical equations to other lakes or reservoirs is generally not recommended.

In this context, the main contribution of this study is not the universal applicability of the derived equations, but rather the transferability of the methodological framework, including variable selection, Sentinel-2 band combinations, and model development strategy. These algorithms can be adapted to other reservoirs with similar climatic-hydrological regimes through local recalibration using in situ measurements.

Regarding the number of sampling stations, their spatial distribution was constrained by the availability of long-term in situ data, which were collected and provided by Empresa de Desenvolvimento e Infra-Estruturas do Alqueva (EDIA) (see line 128). Despite this limitation, the dataset spans nine years and captures a wide range of environmental and trophic conditions, thereby strengthening the robustness of the empirical relationships.

Validation and recalibration with local in situ data remain essential steps when applying the proposed methodology to other water bodies.

We have added two paragraphs in discussion, lines 529-543:

The transferability of empirical bio-optical models across different aquatic systems remains a recognized challenge, particularly in optically complex Mediterranean reservoirs. The equations derived in this study are site-specific and should not be directly extrapolated to other lakes or reservoirs with distinct optical characteristics. However, the methodological approach—based on long-term in situ data, systematic evaluation of Sentinel-2 band combinations (e.g., B6, B7, B8A), and robust regression techniques—is transferable. When applied to other reservoirs under comparable climatic-hydrological conditions, the proposed framework can be effectively adapted through local validation and recalibration using in situ observations.

Inland waters are highly complex systems whose behavior depends on multiple variables; therefore, empirical algorithms, although robust, are often limited to the specific regions and datasets from which they are derived [47]. Consequently, while the methodological framework of this work may be transferable to other water bodies, the algorithms may not always perform with the same accuracy. For application elsewhere, algorithms should be validated with in situ data and recalibrated if necessary to maintain accuracy.

 

1. The comparison of atmospheric correction methods focuses on the C2RCC family, with C2RCC‑COMPLEX selected as optimal. However, the “Limitations” section notes that sunglint and adjacency effects were not explicitly addressed. To improve the robustness and accuracy of water‑quality retrievals, the manuscript could benefit from a more diverse and forward‑looking evaluation of atmospheric processors.

Thanks for the comment. These three versions have been selected by ESA for inclusion in the Sentinel-2 water correction framework. They are novel, freely available, and trained using a wide range of cases and environmental conditions. Therefore, evaluating their applicability for operational use by water resource managers is of particular interest.

1. The use of a ±3‑day temporal window and a 60 m spatial resolution may introduce uncertainties during rapid algal blooms or in narrow riverine zones. The authors should further discuss in the “Discussion” how these choices affect result uncertainty. Additionally, incorporating higher‑temporal‑resolution sensors like Sentinel‑3 OLCI, or exploring data‑fusion approaches, could be suggested as a feasible way to enhance dynamic monitoring capabilities.

In a recent publication by Schröder et al. (2024), entitled Exploring Spatial Aggregations and Temporal Windows for Water Quality Match-Up Analysis Using Sentinel-2 MSI and Sentinel-3 OLCI Data, the authors reach the following affirmations: To investigate appropriate spatial aggregations and time windows for validation (the match-up approach), we performed a statistical comparison of different spatial aggregations (1 pixel; 3 × 3, 5 × 5, and 15 × 15 macropixels; and averaging over the whole waterbody) and time windows (same day, ±1 day, and ±5 days). The results show that waterbody-wide values achieved similar accuracies and biases compared with the macropixel variants, despite the large differences in spatial aggregation and spatial variability. An expansion of the temporal window to up to ±5 days did not impair the agreement between the in situ and remote sensing data for most target variables and sensor–processor combinations, while resulting in a marked rise in the number of matches.

For these reasons, we have modified the paragraph in lines 497-506 and added the reference [23]:

Despite using a 3-day matching window and 60 m resolution, this does not necessarily result in temporal mismatches or large differences due to spatial aggregation. A recent study by Schröder et al. [23], Exploring spatial aggregations and temporal windows for water quality match-up analysis using Sentinel-2 MSI and Sentinel-3 OLCI data, they demonstrated that varying spatial aggregations and temporal windows yields similar accuracies and biases. Moreover, although the 60 m resolution may overlook fine-scale variability in narrow inflows, C2RCC processors produces images with a salt-and-pepper effect, and using a 60 m resolution is preferable because it integrates larger areas and averages reflectance values. Furthermore, since the study area is a very large reservoir, substantial changes over short distances are not expected.

Also, your latest suggestion is also included in lines 515-518: Future research, in great body waters could integrate sensors with higher temporal resolution (such as Sentinel-3 OLCI) to enhance dynamic monitoring capabilities through improved temporal coverage and also employ machine learning techniques to address non-linear optical interactions.

1. Although long‑term in‑situ data (2014–2022) support the model calibration, the temporal distribution of field sampling (e.g., seasonal and inter‑annual coverage) is not clearly described. This omission hinders a rigorous distinction between seasonal patterns and extreme event‑driven variations (e.g., drought). Providing a sampling timeline or frequency table would strengthen the seasonal analysis and ensure the models are not biased toward specific hydrological conditions.

 

Thanks for your comment. We add a Table and an introductory text at the end of part 2.1. (lines 158-169): These field measurements were temporally matched with satellite observations, retaining only samples acquired within a ±3-day window of a cloud- and shadow-free Sentinel-2 overpass. This procedure resulted in 133 spatiotemporally coincident observations. The seasonal and spatial distribution of samples is summarized in Table 1. The greater availability of cloud-free imagery and more favorable field conditions during summer led to a higher number of observations in that season. Owing to occasional operational constraints, the final number of validated coincident measurements differed among biophysical variables, with 133 samples for chlorophyll-a, 81 for total suspended solids, and 88 for Secchi disk depth, all of which were used for the calibration and validation of the satellite-derived products.

Table 1. Number of valid data points (N) per season and sampling location.

SEASON	N		LOCATION	N
Spring	16		A1: Lucefécit	34
Summer	85		A2: Mourão	36
Autumn	9		A3: Alcarrache	26
Winter	23		A4: Montante	37

 

Recommendation:

Major Revision. The manuscript has a solid foundation and addresses a relevant application. However, the major comments listed above, particularly concerning the interpretation of model performance and sample size limitations need to be adequately addressed to ensure the scientific validity and impact of the study.

Thank you very much for your comments and suggestions, which have enabled us to improve the quality of the paper.

Reviewer 2 Report

Comments and Suggestions for Authors

This study is based on years of in-situ observation data and Sentinel-2 remote sensing imagery, and it developed an empirical inversion model for water quality parameters applicable to the Alqueva Reservoir. It systematically evaluated the suitability of different C2RCC atmospheric correction schemes. The research is well-structured, supported by solid data, and employs methods with strong replicability and value for management applications. Overall, there are no apparent fundamental errors, and the conclusions are generally credible.

 Suggestion:

1. In terms of data and experimental design, it is recommended to clearly state the sample size for each model in the Results or Discussion sections, rather than only mentioning it sporadically in the main text. Additionally, explanations should be provided for the significant differences in the matched sample sizes of Chl-a, TSS, and SDD.
2. In this study, the C2X scheme showed the lowest RMSE for Chl-a inversion, while the C2RCC-COMPLEX scheme exhibited the best performance for TSS and SDD inversion, so the C2RCC-COMPLEX scheme was selected. It is recommended to explain why different atmospheric correction schemes were used for different water quality parameters.
3. The empirical model proposed in this paper is constructed based on long-term in situ observation data from the Alqueva Reservoir, with good regional representativeness. Meanwhile, the method framework is described as "transferable", but its scope of application needs to be further clarified. It is recommended to further distinguish between the transferability of the model parameters themselves and the generalizability of the overall research framework.
4. The research results indicate that single-band empirical models based on the red-edge and near-infrared bands exhibit relatively good statistical performance for retrieving Chla, TSS, and SDD in the Alqueva Reservoir. However, considering the optical complexity of this water body, it is recommended to further supplement the physical explanation for the applicability of single-band models in the discussion section, as well as the reason why multi-band indices did not demonstrate a significant advantage over single-band models.
5. It is recommended to appropriately strengthen the summarizing elaboration in the Conclusion section to further enhance the overall coherence of the paper.

Author Response

REVIEWER 2

Comments and Suggestions for Authors

This study is based on years of in-situ observation data and Sentinel-2 remote sensing imagery, and it developed an empirical inversion model for water quality parameters applicable to the Alqueva Reservoir. It systematically evaluated the suitability of different C2RCC atmospheric correction schemes. The research is well-structured, supported by solid data, and employs methods with strong replicability and value for management applications. Overall, there are no apparent fundamental errors, and the conclusions are generally credible.

 Suggestion:

1. In terms of data and experimental design, it is recommended to clearly state the sample size for each model in the Results or Discussion sections, rather than only mentioning it sporadically in the main text. Additionally, explanations should be provided for the significant differences in the matched sample sizes of Chl-a, TSS, and SDD.

Thanks for your comment. We add a Table and an introductory text at the end of part 2.1. (lines 158-169):

These field measurements were temporally matched with satellite observations, retaining only samples acquired within a ±3-day window of a cloud- and shadow-free Sen-tinel-2 overpass. This procedure resulted in 133 spatiotemporally coincident observations. The seasonal and spatial distribution of samples is summarized in Table 1. The greater availability of cloud-free imagery and more favorable field conditions during summer led to a higher number of observations in that season. Owing to occasional operational constraints, the final number of validated coincident measurements differed among biophysical variables, with 133 samples for chlorophyll-a, 81 for total suspended solids, and 88 for Secchi disk depth, all of which were used for the calibration and validation of the satellite-derived products.

Table 1. Number of valid data points (N) per season and sampling location.

SEASON	N		LOCATION	N
Spring	16		A1: Lucefécit	34
Summer	85		A2: Mourão	36
Autumn	9		A3: Alcarrache	26
Winter	23		A4: Montante	37

 

 

 

1. In this study, the C2X scheme showed the lowest RMSE for Chl-a inversion, while the C2RCC-COMPLEX scheme exhibited the best performance for TSS and SDD inversion, so the C2RCC-COMPLEX scheme was selected. It is recommended to explain why different atmospheric correction schemes were used for different water quality parameters.

We explained this in Results, lines 330-334:

Although three atmospheric correction processors were used, the inherent optical properties training ranges of C2RCC are low and do not cover the full range of in situ data obtained in the Alqueva reservoir. Therefore, among the C2X and C2X-C processors, the statistics are more favorable for C2X-C.

1. The empirical model proposed in this paper is constructed based on long-term in situ observation data from the Alqueva Reservoir, with good regional representativeness. Meanwhile, the method framework is described as "transferable", but its scope of application needs to be further clarified. It is recommended to further distinguish between the transferability of the model parameters themselves and the generalizability of the overall research framework.

Thanks for the comment. We add a paragraph in the discussion, lines 535-550:

The transferability of empirical bio-optical models across different aquatic systems remains a recognized challenge, particularly in optically complex Mediterranean reservoirs. The equations derived in this study are site-specific and should not be directly extrapolated to other lakes or reservoirs with distinct optical characteristics. However, the methodological approach—based on long-term in situ data, systematic evaluation of Sentinel-2 band combinations (e.g., B6, B7, B8A), and robust regression techniques—is transferable. When applied to other reservoirs under comparable climatic-hydrological conditions, the proposed framework can be effectively adapted through local validation and recalibration using in situ observations.

Inland waters are highly complex systems whose behavior depends on multiple variables; therefore, empirical algorithms, although robust, are often limited to the specific regions and datasets from which they are derived (Matthews, 2011). Consequently, while the methodological framework of this work may be transferable to other water bodies, the algorithms may not always perform with the same accuracy. For application elsewhere, algorithms should be validated with in situ data and recalibrated if necessary to maintain accuracy.

 

 

1. The research results indicate that single-band empirical models based on the red-edge and near-infrared bands exhibit relatively good statistical performance for retrieving Chla, TSS, and SDD in the Alqueva Reservoir. However, considering the optical complexity of this water body, it is recommended to further supplement the physical explanation for the applicability of single-band models in the discussion section, as well as the reason why multi-band indices did not demonstrate a significant advantage over single-band models.

Thank you for this valuable comment. The selection of single-band empirical models for Chla, TSS, and SDD was primarily driven by the methodological goal of minimizing estimation error while seeking the most parsimonious solution. The fact that more complex multi-band indices (like SR or NDI) did not offer a significant statistical advantage suggests that the added complexity did not effectively compensate for or separate the co-varying optical signals under the conditions captured in our field campaign. Regarding the transferability of the method and equations, we have added two paragraphs in the discussion, lines 535-550.

 

1. It is recommended to appropriately strengthen the summarizing elaboration in the Conclusion section to further enhance the overall coherence of the paper.

 

Based on your comments, the conclusions section has been summarized, highlighting the most important conclusions.

Thank you very much for your comments and suggestions, which have enabled us to improve the quality of the paper.

Reviewer 3 Report

Comments and Suggestions for Authors

The study integrates a decade of in situ data (2014–2022) with Sentinel-2 imagery to develop localized models for Chl-a, TSS, and SDD in the Alqueva Reservoir. It evaluates three atmospheric correction algorithms, identifying C2RCC-COMPLEX as the optimal one. The research delivers a transferable, cost-effective framework for monitoring eutrophication and sediment dynamics in Mediterranean reservoirs, effectively resolving spatiotemporal variability in optically complex inland waters. While innovative, the manuscript necessitates thorough and comprehensive revisions prior to acceptance.

1. The abstract should more explicitly emphasize the models’ stability across varying hydrological conditions. This will enable readers to quickly grasp the study’s key findings and significance without sifting through excessive methodological details.
2. The introduction should address gaps related to the optical complexity of Mediterranean reservoirs, while strengthening the logical link between these research deficiencies and the study’s core objectives.
3. Provide more detailed background information on the hydrological and optical characteristics of the study area. Explain the specific drivers of optical differences among distinct sampling sites to furnish sufficient justification for the rationality of localized model calibration.
4. Explicitly clarify the potential impacts of selecting a 60 m spatial resolution on model accuracy, ensuring transparency regarding the trade-offs associated with this methodological choice.
5. Elaborate on the rationale for selecting specific spectral indices and regression models within the ARTMO software, providing substantive reasoning for prioritizing these combinations over alternative options.
6. Incorporate an analysis of differences in model performance across individual sampling sites to offer a more nuanced understanding of the models’ applicability under heterogeneous environmental conditions.
7. Add comparative analyses of the same seasons across multiple years to reinforce the demonstration of climate-driven impacts on water quality changes, enhancing the robustness of the study’s temporal insights.
8. Reduce redundant descriptions that overlap with the results section. Instead, focus on expanding horizontal comparisons between the current models and those reported in similar studies to contextualize the research’s academic contributions.
9. Specifically elaborate on the operational procedures and implementation steps for applying the model in reservoir management, outlining clear, actionable pathways to translate the research outcomes into practical application scenarios.
10. Streamline the summary content in the conclusion to prominently highlight the study’s core innovations and unresolved issues.

Author Response

REVIEWER 3

Comments and Suggestions for Authors

The study integrates a decade of in situ data (2014–2022) with Sentinel-2 imagery to develop localized models for Chl-a, TSS, and SDD in the Alqueva Reservoir. It evaluates three atmospheric correction algorithms, identifying C2RCC-COMPLEX as the optimal one. The research delivers a transferable, cost-effective framework for monitoring eutrophication and sediment dynamics in Mediterranean reservoirs, effectively resolving spatiotemporal variability in optically complex inland waters. While innovative, the manuscript necessitates thorough and comprehensive revisions prior to acceptance.

1. The abstract should more explicitly emphasize the models’ stability across varying hydrological conditions. This will enable readers to quickly grasp the study’s key findings and significance without sifting through excessive methodological details.

Thank you for your comment. We add the range data used in the algorithm retrieval of each variable. Regarding the transferability of the method and equations, we have added two paragraphs in the discussion, lines 535-550.

1. The introduction should address gaps related to the optical complexity of Mediterranean reservoirs, while strengthening the logical link between these research deficiencies and the study’s core objectives.

Following your comment, clarifications on this issue have been added in lines 99-103, in the Introduction seccion. Also, information about other studies of the Alqueva reservoir and their relationship to this work has been added (lines 70-89).

1. Provide more detailed background information on the hydrological and optical characteristics of the study area. Explain the specific drivers of optical differences among distinct sampling sites to furnish sufficient justification for the rationality of localized model calibration.

The limnological differences among the sampling sites, and their consequent effects on optical properties, are described in lines 138-143, where the following paragraph has also been added: The distribution of sampling sites along the entire longitudinal axis of such an elongated reservoir provides observations spanning a wide range of the studied variables. This longitudinal gradient reflects progressive changes in water properties: inflowing waters in the upstream (riverine) zone typically exhibit higher suspended matter and nutrient concentrations, which gradually decrease toward the dam area.

1. Explicitly clarify the potential impacts of selecting a 60 m spatial resolution on model accuracy, ensuring transparency regarding the trade-offs associated with this methodological choice.

Thank you for this valuable comment. In a recent publication by Schröder et al. (2024), entitled Exploring Spatial Aggregations and Temporal Windows for Water Quality Match-Up Analysis Using Sentinel-2 MSI and Sentinel-3 OLCI Data, the authors reach the following affirmations: To investigate appropriate spatial aggregations and time windows for validation (the match-up approach), we performed a statistical comparison of different spatial aggregations (1 pixel; 3 × 3, 5 × 5, and 15 × 15 macropixels; and averaging over the whole waterbody) and time windows (same day, ±1 day, and ±5 days). The results show that waterbody-wide values achieved similar accuracies and biases compared with the macropixel variants, despite the large differences in spatial aggregation and spatial variability. An expansion of the temporal window to up to ±5 days did not impair the agreement between the in situ and remote sensing data for most target variables and sensor–processor combinations, while resulting in a marked rise in the number of matches.

For these reasons, we have modified the paragraph in lines 503-512 and added the reference [23]: Despite using a 3-day matching window and 60 m resolution, this does not necessarily result in temporal mismatches or large differences due to spatial aggregation. A recent study by Schröder et al. [23], exploring spatial aggregations and temporal windows for water quality match-up analysis using Sentinel-2 MSI and Sentinel-3 OLCI data, they demonstrated that varying spatial aggregations and temporal windows yields similar accuracies and biases. Moreover, although the 60 m resolution may overlook fine-scale variability in narrow inflows, C2RCC processors produce images with a salt-and-pepper effect, and using a 60 m resolution is preferable because it integrates larger areas and averages reflectance values. Furthermore, since the study area is a very large reservoir, substantial changes over short distances are not expected.

 

1. Elaborate on the rationale for selecting specific spectral indices and regression models within the ARTMO software, providing substantive reasoning for prioritizing these combinations over alternative options.

Thank you for your comment. The explanation is in lines 296:

“These indices were selected due to their widespread use in inland water remote sensing and their proven sensitivity to optically active constituents such as Chl-a and TSS in optically complex Case-2 waters [28,29]. And they also provide the smallest error and make physical sense.”

1. Incorporate an analysis of differences in model performance across individual sampling sites to offer a more nuanced understanding of the models’ applicability under heterogeneous environmental conditions.

Thanks for this appreciation, we have added a table with the RMSE at each sampling point and a comment in this respect. Lines 400-408 and Table 8: To understand the model applicability under the heterogeneity conditions of the Alqueva reservoir, the RMSE for each sampling site was calculated (Table 8). The highest RMSE for Chl-a were reached in sites A1 and A3, for TSS was A1 and A4 and for SDD was A3 and A4. All of them are the sites with higher values in the respective variables.

Table 8. RMSE values among in situ water quality parameters at each of the sampling sites. Values calculated using equations 1, 2, and 3.

RMSE	Chl-a (mg/m3)	TSS (g/m3)	SDD (m)
A1: Lucefécit	11.0	2.4	0.8
A2: Mourão	4.7	0.8	0.6
A3: Alcarrache	11.7	1.0	0.9
A4: Montante	4.0	3.8	1.0

 

1. Add comparative analyses of the same seasons across multiple years to reinforce the demonstration of climate-driven impacts on water quality changes, enhancing the robustness of the study’s temporal insights.

We appreciate the reviewer’s suggestion. The primary objective of this article is to develop and validate a robust methodological framework for satellite-based water quality estimation and to assess its applicability under representative conditions. Conducting detailed multitemporal analyses based on this methodology would shift the focus toward a more limnological investigation, rather than the development and evaluation of estimation algorithms, which is the core aim of the present study. Nevertheless, we agree that the proposed methodology provides a solid basis for future multitemporal and process-oriented analyses, and this potential has now been acknowledged in the Discussion/Conclusions section (lines 535-555, 565-566).

1. Reduce redundant descriptions that overlap with the results section. Instead, focus on expanding horizontal comparisons between the current models and those reported in similar studies to contextualize the research’s academic contributions.

Thank you for your comment. In the introduction, we have added other important remote sensing studies on Alqueva (lines 70-89) and added the references 7, 8, 12, 13 y 23.

1. Specifically elaborate on the operational procedures and implementation steps for applying the model in reservoir management, outlining clear, actionable pathways to translate the research outcomes into practical application scenarios.

We add a paragraph in the discussion, in lines 551-555: The operational procedures for applying the methodological framework in Alqueva reservoir follow these steps: Level-1C images are downloaded and opened in SNAP; the C2X-C processor is executed; and Equations 1, 2, and 3 are applied. Pixels influenced by coastal proximity and clouds and shadows, and sunglint are excluded to minimize potential sources of uncertainty and ensure the robustness of the results.

Regarding the transferability of the method and equations, we have added two paragraphs in the discussion, lines 535-550.

 

 

1. Streamline the summary content in the conclusion to prominently highlight the study’s core innovations and unresolved issues.

Based on your comments, the conclusions section has been summarized to highlight the most important points.

Thank you very much for your comments and suggestions, which have enabled us to improve the quality of the paper.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thank the author for their revisions.

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have modified the manuscript according to the reviewer's comments and cleared up all my questions. I do not have further questions or comments. I think the manuscript can be accepted for publication now.