Temporal Analysis of Land Surface Temperature Variability and Urban Climate Dynamics: A Remote Sensing Use Case in Benguerir City, Morocco

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This study systematically analyzes the spatiotemporal dynamics and driving factors of land surface temperature (LST) and urban heat island (UHI) effects in Benguerir, Morocco, over a 30-year period (1994–2024) using multi-source remote sensing data and ground-based sensor networks. The research design is rigorous, the methodology is scientifically sound, and the data are comprehensive, providing valuable insights into the interaction between urbanization and climate in a semi-arid medium-sized city.  But the paper still requires further optimization in certain areas. Below are the detailed review comments:

1.Data Limitations: The calibration details of the Sniffer Bike sensors (e.g., model, error range) and the spatiotemporal matching method with satellite data are not specified and should be supplemented.

2.Model Validation: The residual diagnostics (e.g., normality, heteroscedasticity) of the multiple linear regression model are only briefly mentioned. More detailed statistical test results (e.g., Q-Q plots, Breusch-Pagan test) should be provided.

3.Thermal Comfort Indicators: In the calculation of UTCI and PMV, wind speed is assumed to be a fixed value (1.5 m/s), which may overlook local wind field variations. The impact of this assumption on the results should be discussed.

4.Insufficient Analysis of Driving Mechanisms: The study does not thoroughly explore other potential influencing factors (e.g., population density, energy consumption, urban geometry) contributing to UHI effects. Some references maybe useful ，Assessing urban population exposure risk to extreme heat: Patterns, trends, and implications for climate resilience in China；Strengthening of surface urban heat island effect driven primarily by urban size under rapid urbanization: Empirical evidence from China

5.Lack of Comparative Studies: A comparison with LST change rates in other global semi-arid cities (e.g., Cairo, Egypt; Riyadh, Saudi Arabia) would enhance the generalizability of the conclusions.

6.Generalized Policy Recommendations: The proposed "green infrastructure" strategies should be tailored to Benguerir’s actual conditions (e.g., water resource constraints) with specific implementation plans. Some references suggested，The cooling capacity of urban vegetation and its driving force under extreme hot weather: A comparative study between dry-hot and humid-hot cities；Impact of vegetation coverage and configuration on urban temperatures: a comparative study of 31 provincial capital cities in China

Author Response

Response to Reviewer 1 Comments

This study systematically analyzes the spatiotemporal dynamics and driving factors of land surface temperature (LST) and urban heat island (UHI) effects in Benguerir, Morocco, over a 30-year period (1994–2024) using multi-source remote sensing data and ground-based sensor networks. The research design is rigorous, the methodology is scientifically sound, and the data are comprehensive, providing valuable insights into the interaction between urbanization and climate in a semi-arid, medium-sized city. But the paper still requires further optimization in certain areas. Below are the detailed review comments:

Response: Thank you very much for taking the time to review this manuscript. Please find the detailed response below and the corresponding revisions and corrections highlighted in track changes in the resubmitted files.

Point-by-point response to comments and suggestions 

Point 1: Data Limitations: The calibration details of the Sniffer Bike sensors (e.g., model, error range) and the spatiotemporal matching method with satellite data are not specified and should be supplemented.

Response 1: We thank the reviewer for this insightful comment and for highlighting the need for greater methodological detail. We agree that these specifications are essential for the transparency and reproducibility of our work. We have revised the manuscript to include a comprehensive description of the sensor calibration and the spatiotemporal matching protocol used.

1. Sensor Calibration Details: Our Sniffer Bike network's mobile sensor platform was outfitted with an Air Pollution Monitoring System (APMS_R2) sensor to record ambient air temperature and relative humidity. The calibration procedure addressed potential sensor bias and maintained data quality across the network with accuracy. A linear regression model was created to compare the sensor data with the reference data, and correction factors were derived using the Benguerir Weather Station datasets. These correction factors were subsequently applied to the entire dataset, and this process is elaborated in the manuscript.
2. Spatiotemporal Matching Method with Satellite Data: We clarified the approach for aligning ground-based Sniffer Bike data with satellite-derived Land Surface Temperature (LST) data from Landsat. We also explicitly state that we are comparing the near-surface air temperature from the Sniffer bike with satellite-derived surface temperature (LST).

This detailed methodology has been incorporated into the revised manuscript in Section 2.2, "Data and Methodology Adopted," in lines 196-221.

 We believe that these additions comprehensively address the reviewer's concerns, and we thank you for assisting us in enhancing the clarity and rigor of our manuscript.

Point 2: Model Validation: The residual diagnostics (e.g., normality, heteroscedasticity) of the multiple linear regression model are only briefly mentioned. More detailed statistical test results (e.g., Q-Q plots, Breusch-Pagan test) should be provided.

Response 2: Thank you so much; we appreciate your insightful feedback. In response, we have calculated the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) under clear-sky conditions to validate our findings in lines 208-211. Additionally, (Lines 420-431) incorporated statistical test results and utilized Q-Q plots tests.

Point 3: Thermal Comfort Indicators: In the calculation of UTCI and PMV, wind speed is assumed to be a fixed value (1.5 m/s), which may overlook local wind field variations. The impact of this assumption on the results should be discussed.

Response 3: Thank you for your comments and concerns. Generally, the use of a fixed wind speed of 1.5 m/s in calculating the Universal Thermal Climate Index (UTCI) and Predicted Mean Vote (PMV) simplifies the process but may overlook local wind field variations, potentially impacting the results. This approach was chosen based on standard comfort assessment practices to ensure consistency across different urban areas, making it easier to compare locations and simplifying the computational process. However, it is important to acknowledge that assuming a constant wind speed might lead to an overestimation of thermal comfort in areas with higher wind speeds, an underestimation in areas with lower wind speeds, and reduced sensitivity to microclimatic variations within urban environments. To address this limitation, future research could incorporate more local wind field data or use computational fluid dynamics (CFD) simulations to estimate site-specific wind speeds, thereby improving the accuracy of thermal comfort predictions. Additionally, a sensitivity analysis could be performed by varying wind speeds within a realistic range (e.g., 0.5 m/s to 3 m/s) to assess the impact of this assumption on UTCI and PMV values. These enhancements would offer a more comprehensive understanding of thermal comfort indicators while acknowledging the limitations of the current methodology. (Section 3.8 and in Line 672-686)

The Discussion section is thoroughly revised considering your valuable suggestion in the revised manuscript. Hopefully you will find it justified.

Point 4: Insufficient Analysis of Driving Mechanisms: The study does not thoroughly explore other potential influencing factors (e.g., population density, energy consumption, urban geometry) contributing to UHI effects. Some references may be useful. Assessing urban population exposure risk to extreme heat: Patterns, trends, and implications for climate resilience in China；Strengthening of surface urban heat island effect driven primarily by urban size under rapid urbanization: Empirical evidence from China

Response 4: We express our sincere gratitude for the reviewer's insightful comments regarding the necessity for a more comprehensive examination of the mechanisms underlying urban heat island (UHI) phenomena. In response, we intend to augment our discussion by incorporating critical elements informed by insights from the recommended references. While our current data primarily focuses on thermal-environment relationships, we will specifically address these additional influencing factors in our limitations in the discussion section and propose them as essential areas for future research. This approach aims to facilitate a more thorough understanding of UHI formation mechanisms in urban contexts.

We agree and have taken the reviewer’s comment into full consideration in the revised manuscript accordingly.

Point 5: Lack of Comparative Studies: A comparison with LST change rates in other global semi-arid cities (e.g., Cairo, Egypt; Riyadh, Saudi Arabia) would enhance the generalizability of the conclusions.

Response 5: Thank you for your suggestion. A comparison with LST change rates in other global semi-arid cities, such as Cairo, Egypt, and Riyadh, Saudi Arabia, has been incorporated into the Discussion section (Lines 743-752). This section has been significantly revised to enhance clarity for the reader, in accordance with the reviewer's advice.

Point 6: Generalized Policy Recommendations: The proposed "green infrastructure" strategies should be tailored to Benguerir’s actual conditions (e.g., water resource constraints) with specific implementation plans. Some references suggested, The cooling capacity of urban vegetation and its driving force under extreme hot weather: A comparative study between dry-hot and humid-hot cities；Impact of vegetation coverage and configuration on urban temperatures: a comparative study of 31 provincial capital cities in China.

Response 6: Thank you for highlighting this! We found your comments extremely helpful and have been revised in the manuscript accordingly. In the conclusion section, we have added policy recommendations as follows:

       Adapting to Benguerir's arid climate requires the incorporation of drought-resistant plant species, water-efficient irrigation techniques, rainwater harvesting systems, and greywater recycling methods. The strategic implementation of permeable surfaces and shade structures is proposed to further mitigate the UHI effect and create more thermally comfortable outdoor environments. This research significantly contributes to the understanding of urban thermal dynamics in semi-arid regions and underscores the urgent need for climate-resilient urban planning in the context of rapid urbanization and climate change. (in lines 827-834)

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Comments and Suggestions for Authors

1. Language and Style (Lines 14–34; 36–92; 312–335)

The manuscript is overall understandable, but the English should be improved in several sections to better communicate the findings. Some phrases are too long or awkwardly written, and few sentences lacks academic tone.

• For instance, in lines 23–24: “very hot zones expanding from 80.3% to 90.4%” could be rephrased as “The proportion of areas classified as 'very hot' increased from 80.3% to 90.4%.”
• In lines 315–322, the description of PMV method is not always clear and would benefit from more consistent phrasing.

Suggestion: We recommend revision by fluent English speaker or professional editor to increase clarity and impact of the text.

 

2. Study Area Justification (Lines 66–71; 120–134)

The reasons for selecting Benguerir as the case study is valid, but could be explained with more depth. At the moment, it's not fully clear why this city is especially relevant to wider urban climate debates.

Suggestion: Please expand the rationale behind the choice of Benguerir. Is it typical of other secondary cities in North Africa? Is there special policy relevance?

 

3. Method Description and Technical Detail (Lines 165–180; 262–311)

While the LST estimation process is described in detail, it could be improved by adding few more references and adjusting the structure.

• Line 296: The emissivity formula Ε= 0.004 ? PV+ 0.986 is used without citation.
• Some equations (e.g., Eq. 11) are overly complex for general readers and could be placed in supplementary material instead.

 LST= ( ( BT ) ) 譒 n(Ε)                    (11)

 Lλ= ML譗 cal +AL                    (11)

Suggestion: Keep main methodological steps in the text, but consider moving parts of equations to supplement. Make sure all formulas are referenced and notation is consistent.

 

4. Thermal Comfort Indices (Lines 313–335; 530–555)

The use of PMV and UTCI is innovative, especially with Sniffer Bike data. However, PMV is typically used for indoor settings, and it is not fully explained how assumptions were adapted to outdoor urban environment.

Suggestion: Acknowledge the limitation of using PMV outdoors, and briefly justify the validity of using UTCI-PMV combination in a semi-arid urban context.

 

5. Figures and Data Presentation (e.g., Figures 3–12; Lines 358–560)

Some of the figures are informative but quite difficult to read due to low resolution or small labels.

• Figure 3 (Line 358): LULC map could be clearer with better contrast and higher DPI.
• Figures 9–10: Consider combining correlation graphs or simplifying layout to avoid redundancy.

Suggestion: Improve visual quality of all figures and harmonize color schemes where possible. This will help the readers understand your results more easily.

 

6. Discussion of Global Comparisons (Lines 561–609)

The international references (e.g., Bangalore, Doha, Cairo) are helpful, but comparisons are sometimes not fully contextualized. Different cities may have different climate and urban morphology.

Suggestion: When referring to other case studies, briefly mention what makes them similar or different from Benguerir. This will help reader to follow your argument better.

 

7. Statistical Analysis (Lines 336–349; 488–528)

The regression model is valuable, but could be improved by including more diagnostic details.

• For example, confidence intervals of regression coefficients are missing.
• It is also not specified how many samples were used in each year.

Suggestion: Please include confidence intervals or standard errors for your regression results, and mention the number of observations used.

 

The manuscript presents an important contribution to the field of urban climate and land surface temperature analysis, with strong use of remote sensing and mobile sensors. The results are robust and relevant, especially for fast-growing cities in arid regions.

However, minor revisions are needed in terms of English language, figures and explanation of some methodological choices. Once revised, the manuscript will be suitable for publication in Sustainability.

Comments on the Quality of English Language

The English could be improved to more clearly express the research.
The manuscript is mostly understandable, but the English should be improved in several places to enhance clarity, flow, and academic tone.

• The writing sometimes sounds like it was directly translated, which leads to some long or repetitive sentences and slightly awkward phrasing.
• There are few expressions that are not entirely clear or accurate, for example:
  • "prompting the need for innovative urban planning to address these challenges” – it’s not obvious what “these challenges” exactly refers to.
  • "This temperature variation can lead to higher energy use for cooling, worsen air pollution, and adversely affect human health." – the sentence could be structured better to improve the flow.
• Also, in several places words are repeated or too vague, such as:
  • “hydrological areas” – it’s not very clear what this means in the context.

Example phrases that should be revised:

• “The LST continued to rise from 2014 to 2024. The lowest minimum and highest maximum temperatures increased during this period.”
  → A more natural version could be: “LST kept increasing from 2014 to 2024, with both minimum and maximum values going up.”
• “very hot zones expanding from 80.3% to 90.4%”
  → It would sound better as: “The proportion of ‘very hot’ areas rose from 80.3% to 90.4%.”

Author Response

Response to Reviewer 2 Comments

Point 1: Language and Style (Lines 14–34; 36–92; 312–335)

The manuscript is overall understandable, but the English should be improved in several sections to better communicate the findings. Some phrases are too long or awkwardly written, and few sentences lacks academic tone.

• For instance, in lines 23–24: “very hot zones expanding from 80.3% to 90.4%” could be rephrased as “The proportion of areas classified as 'very hot' increased from 80.3% to 90.4%.”
• In lines 315–322, the description of PMV method is not always clear and would benefit from more consistent phrasing.

Suggestion: We recommend revision by fluent English speaker or professional editor to increase clarity and impact of the text.

Response 1: We appreciate your suggestions regarding the clarity and tone of the manuscript. We have thoroughly considered the reviewer's comments, and the language has been meticulously revised as recommended to enhance overall readability, with particular attention to sentence structure, conciseness, and academic tone.

• Line 23-24 has been rephrased to “very hot zones expanding from 80.3% to 90.4%” into “The proportion of areas classified as 'very hot' increased from 80.3% to 90.4%.” (in Lines 23-25)
• Line 315-322 the description of PMV method has been revised accordingly in “section 2.8 Surface Temperature variation and Thermal comfort in Benguerir” (in Lines 337-390)

We have improved the structure of all sentences and updated the manuscript to increase the clarity and impact of the text. Finally, the English language was edited to improve the quality of the article. Hopefully now you will find it rational.

Point 2: Study Area Justification (Lines 66–71; 120–134)

The reasons for selecting Benguerir as the case study is valid, but could be explained with more depth. At the moment, it's not fully clear why this city is especially relevant to wider urban climate debates.

Suggestion: Please expand the rationale behind the choice of Benguerir. Is it typical of other secondary cities in North Africa? Is there special policy relevance?

Response 2: Thank you for pointing this out! We found your comments extremely helpful and have revised accordingly in the manuscript as follow:

Benguerir was selected for this study due to its status as a rapidly expanding city in North Africa, exemplifying significant urban climate challenges like those encountered by other urban areas in the region. The city's ongoing growth, driven by industrial development and regional planning initiatives, renders it an ideal case for examining the intersection of rapid urbanization and climate vulnerabilities. Located in a semi-arid region, Benguerir is increasingly experiencing extreme heat, with a marked rise in 'very hot' areas, highlighting the urgent necessity for climate adaptation strategies. As a typical semi-arid city, Benguerir provides valuable insights into localized urban heat island effects and thermal comfort issues that are frequently overlooked in studies concentrating solely on major cities. Furthermore, Benguerir holds significant policy interest due to its strategic role in regional development plans, encompassing industrial zones and infrastructure investments. Its evolving urban environment offers an opportunity to assess the effectiveness of climate resilience measures in similar North African contexts. (in Line 163-176).

This Study area section has improved and has also updated with addition of recent publications in the revised manuscript accordingly.

Point 3: Method Description and Technical Detail (Lines 165–180; 262–311)

While the LST estimation process is described in detail, it could be improved by adding few more references and adjusting the structure.

• Line 296: The emissivity formula Ε= 0.004 ? PV+ 0.986 is used without citation.
• Some equations (e.g., Eq. 11) are overly complex for general readers and could be placed in supplementary material instead.

 LST= ( ( BT ) ) 譒 n(Ε)                    (11)

 Lλ= ML譗 cal +AL                    (11)

Suggestion: Keep main methodological steps in the text, but consider moving parts of equations to supplement. Make sure all formulas are referenced and notation is consistent.

Response 3: Thank you so much for your comments. We agree that enhancing the clarity of the methodology and incorporating additional references can improve the precision and robustness of our description.

In our updated manuscript, we have elaborated on the LST estimation process, incorporating additional references to pivotal studies that form the basis of the methodology. Furthermore, we have reorganized the structure to enhance readability and logical coherence, ensuring that each step of the process is clearly explained and well-supported by existing literature. This comprehensive approach not only enhances the transparency of our methods but also aligns with best practices in remote sensing and surface temperature analysis, making our findings more comparable and replicable for future research. (Section 2.2 and Section 2.3)

We value your suggestion, which has contributed to improving the clarity and depth of our methodological section.

The emissivity formula (E) has been cited in the revised manuscript accordingly, in Line 337-338 as "Eq.10."

 

Equation 11 has been revised and corrected in the manuscript to enhance comprehension. Hopefully you will find it logical

 

Eq. 7 The top-of-atmosphere radiance (TOA = λL) has been enhanced and corrected in the revised manuscript accordingly.

 

We have thoroughly reviewed the manuscript to ensure that all formulas related to Land Surface Temperature (LST estimation), such as Lλ (top-of-atmosphere radiance), Brightness Temperature (BT), PV (Proportion of vegetation), and E (Land surface Emissivity), are accurately corrected within the revised manuscript accordingly.

Furthermore, we have ensured that the notation for each parameter remains consistent throughout the manuscript, adhering to the established norms in the literature. These changes are intended to enhance the clarity and precision of our methodological explanations, thereby promoting a better understanding and reproducibility of our research.

Point 4: Thermal Comfort Indices (Lines 313–335; 530–555)

The use of PMV and UTCI is innovative, especially with Sniffer Bike data. However, PMV is typically used for indoor settings, and it is not fully explained how assumptions were adapted to outdoor urban environment.

Suggestion: Acknowledge the limitation of using PMV outdoors, and briefly justify the validity of using UTCI-PMV combination in a semi-arid urban context.

Response 4: Thank you so much for your precious comments and constructive feedback. You raise an important methodological point that deserves clarification.

We acknowledge that PMV was originally developed for indoor thermal comfort assessment and its application to outdoor environments requires careful consideration of the underlying assumptions. In our study, we adapted PMV for outdoor use by incorporating several key modifications: adjusting the metabolic rate assumptions for outdoor activities, accounting for solar radiation effects through mean radiant temperature calculations.

The PMV-UTCI combination approach is particularly relevant for our semi-arid urban context because it allows us to capture both the physiological thermal sensation (PMV) and the bio-meteorological stress conditions (UTCI) that pedestrians and cyclists experience. While UTCI inherently accounts for outdoor conditions, the parallel PMV analysis provides insight into subjective thermal comfort that can be directly related to human behavior patterns observed through our Sniffer Bike data.

We found your comments extremely helpful and have been thoroughly revised considering your valuable suggestion in the revised manuscript in section (3.8. Thermal Comfort analysis of Benguerir City (1994-2024)). Hopefully you will find it justified.

Point 5: Figures and Data Presentation (e.g., Figures 3–12; Lines 358–560)

Some of the figures are informative but quite difficult to read due to low resolution or small labels.

• Figure 3 (Line 358): LULC map could be clearer with better contrast and higher DPI.
• Figures 9–10: Consider combining correlation graphs or simplifying layout to avoid redundancy.

Suggestion: Improve visual quality of all figures and harmonize color schemes where possible. This will help the readers understand your results more easily.

Response 5: Thank you for these valuable suggestions regarding the visual presentation of our figures. We agree that clear, high-quality figures are essential for effective communication of our results.

We have addressed each of your specific concerns as follows:

1. Figure 3 (LULC map): the map resolution has been enhanced to a minimum of 300 DPI and has improved the color contrast between different land use categories in the revised manuscript for better representation.
2. Figures 9-10 (Correlation graphs): We appreciate this suggestion, and these figures have been restructured and corrected in the revised version.

Now all figures have significantly corrected and improved the accessibility and interpretability of our results for readers. Hopefully, you will find it logical.

Point 6: Discussion of Global Comparisons (Lines 561–609)

The international references (e.g., Bangalore, Doha, Cairo) are helpful, but comparisons are sometimes not fully contextualized. Different cities may have different climate and urban morphology.

Suggestion: When referring to other case studies, briefly mention what makes them similar or different from Benguerir. This will help reader to follow your argument better.

Response 6: Thank you so much for your precious comments. The Discussion section is thoroughly revised considering your valuable suggestion in the revised manuscript. We also updated literature reviews with addition of recent publications with the same urban morphology and climate.

Hopefully you will find it justified.

Point 7: Statistical Analysis (Lines 336–349; 488–528)

The regression model is valuable, but could be improved by including more diagnostic details.

• For example, confidence intervals of regression coefficients are missing.
• It is also not specified how many samples were used in each year.

Suggestion: Please include confidence intervals or standard errors for your regression results, and mention the number of observations used.

The manuscript presents an important contribution to the field of urban climate and land surface temperature analysis, with strong use of remote sensing and mobile sensors. The results are robust and relevant, especially for fast-growing cities in arid regions.

However, minor revisions are needed in terms of English language, figures and explanation of some methodological choices. Once revised, the manuscript will be suitable for publication in Sustainability.

Response 7: Thank you so much for your comments. We agree with your suggestion that including additional diagnostic details would strengthen our analysis.

In response, the statistical analysis has been updated in revised manuscript in section “2.9 Multi-Linear regression approach “

• Include confidence intervals for the regression coefficients have been included to better reflect the precision of our estimates and specified the number of samples (observations) used each year in our analysis.

Thank you again for your constructive suggestions. We are pleased that you find our work to be a valuable contribution to the field.

The whole minor issues you mentioned have been significantly revised to make it clearer for the reader in the revised manuscript thoroughly according to the reviewer’s advice. Finally, the English language has edited to improve the article. Hopefully now you will find it rational.

Point 8: Comments on the Quality of English Language

The English could be improved to more clearly express the research. The manuscript is mostly understandable, but the English should be improved in several places to enhance clarity, flow, and academic tone.

• The writing sometimes sounds like it was directly translated, which leads to some long or repetitive sentences and slightly awkward phrasing.
• There are few expressions that are not entirely clear or accurate, for example:
• "prompting the need for innovative urban planning to address these challenges” – it’s not obvious what “these challenges” exactly refers to.
• "This temperature variation can lead to higher energy use for cooling, worsen air pollution, and adversely affect human health." – the sentence could be structured better to improve the flow.
• Also, in several places words are repeated or too vague, such as:
• “hydrological areas” – it’s not very clear what this means in the context.

Example phrases that should be revised:

• “The LST continued to rise from 2014 to 2024. The lowest minimum and highest maximum temperatures increased during this period.”
  → A more natural version could be: “LST kept increasing from 2014 to 2024, with both minimum and maximum values going up.”
• “very hot zones expanding from 80.3% to 90.4%”
  → It would sound better as: “The proportion of ‘very hot’ areas rose from 80.3% to 90.4%.

Response 8: Thank you so much for your suggestion on the manuscript's clarity. We acknowledge the importance of precise and polished language to effectively communicate our research. We have thoroughly revised the manuscript to improve the overall clarity, flow, and academic tone, ensuring that our explanations are more accessible and comprehensible.

We appreciate your constructive comments and are committed to further improving the manuscript thoroughly according to the reviewer’s advice.

 

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Generally, I am inclined to require the authors to substantially revise this manuscript due to its problematic design and some misleading terminology. My major concerns were listed as follows,

1. The literature review overall covers the impact of urbanization on UHI effects. However, it could be further improved by incorporating more recent studies on urban heat island mitigation strategies. Besides, what are the major research gaps among the existing studies in this domain?
2. Why selected the Benguerir city in Morocco as a case? Is it a representative case in the semi-arid and arid environment?
3. According to Table 1, the multiple-source datum used in this study have different spatiotemporal resolutions. In particular, three Landsat images dated in three days are inadequately represent the LST dynamics. Besides, the temporal span between Landsat images,Climatic data,and Power Climate data are quite different. Then, how can they be used together to analyze the possible cause-effect relationships between land use/land cover change and UHI effect?
4. There are two many errors in Equations 1,7,10,and 11. Besides, in line 304, the value of 1.438 × 10 2 m is completely wrong.
5. According to Figure 3, only three land use types were recognized. Then, result for land use components shown in Table 3 should sum to 100%.
6. In first column of Table 6, since the mean LST of each land use type is over 30 celsius degree, then how can the mean LST of all land use be 21.00 celsius degree? Similar errors exist in this table.
7. Table 7overall exhibits the variations in relative UHI intensity rather than UHI.
8. Visual quality of Figure 10 must be improved. It is unclear. Besides, the scattering plot seems not good even if some scattering plots with small p-value.
9. The discussion section is too weak to highlight the innovation and major limitations of this study. The assumed relationship between LULC change and UHI effect must consider the impact of global climate change.  

Author Response

Response to Reviewer 3 Comments

Dear reviewer,

Thank you very much for your valuable suggestions and comments on our manuscript. Those comments are of great assistance to me for improving and revising our manuscript. We have studied comments carefully and have made corrections in line and sections with the suggestions made by you. Revised portions are marked in red in the paper. The main corrections in the paper and the responses to the reviewer’s comments and remarks are as follows.

“Any model Generally, I am inclined to require the authors to substantially revise this manuscript due to its problematic design and some misleading terminology. My major concerns were listed as follows,”

Point 1: The literature review overall covers the impact of urbanization on UHI effects. However, it could be further improved by incorporating more recent studies on urban heat island mitigation strategies. Besides, what are the major research gaps among the existing studies in this domain?

Response 1: Thank you for your insightful feedback. We appreciate your suggestion to include more recent studies on urban heat island (UHI) mitigation strategies. In the revised version, we have incorporated several recent publications that discuss innovative approaches such as green infrastructure, cool roofs, and urban planning policies aimed at reducing UHI effects. These additions provide a more comprehensive overview of current mitigation efforts. “Section 1 Introduction”

Regarding the identification of major research gaps in this domain, our review indicates that while significant progress has been made, certain areas require further investigation. For instance, there is a need for more longitudinal studies assessing the long-term effectiveness of various mitigation strategies. Additionally, more research is needed to understand the socio-economic barriers to implementing these strategies across different urban contexts. Lastly, integrating high-resolution spatial data and modeling techniques could enhance the accuracy of UHI predictions and mitigation planning.

We believe these improvements will strengthen the review's scope and provide valuable insights into future research directions. We are grateful to the reviewer for these kind observations.

Point 2: Why was the city of Benguerir in Morocco selected as a case? Is it a representative case in the semi-arid and arid environment?

Response 2: Thank you for pointing this out! We found your comments extremely helpful.

       Benguerir was selected as a case study due to its unique characteristics as a growing urban center situated within a semi-arid environment, which makes it a relevant site for examining UHI effects in such climate zones. Additionally, Benguerir has undergone significant urban development recently, providing valuable insights into the impacts of urbanization in semi-arid regions. While it may not represent all cities within semi-arid and arid environments, its specific climatic, geographic, and urban characteristics make it a pertinent and illustrative case for this study.

Furthermore, the findings from Benguerir can contribute to understanding UHI dynamics in similar environments, and the methodologies employed can be adapted for other comparable settings.

Thank you again for highlighting this important aspect.

Point 3: According to Table 1, the multiple-source datum used in this study have different spatiotemporal resolutions. In particular, three Landsat images dated in three days are inadequately represent the LST dynamics. Besides, the temporal span between Landsat images,Climatic data,and Power Climate data are quite different. Then, how can they be used together to analyze the possible cause-effect relationships between land use/land cover change and the UHI effect?

Response 3: Thank you for your valuable comment. We agree with your suggestion, and we would like to clarify that the multiple-source data used in this study, including Landsat 5 and Landsat 8 images, have the same spatiotemporal resolution of 30 meters. The primary difference lies in the climate data, which we collected using our own sniffer bike sensor. This climate dataset is directly comparable to the spatial data in terms of resolution, allowing for more consistent analysis.

Regarding the temporal aspect, although the Landsat images are taken on three different years, they are selected to represent typical conditions within the study period. Climatic data, collected concurrently with these images, provides continuous temporal coverage, enabling us to examine potential cause-effect relationships between land use/land cover change and UHI effects. While there is a difference in the temporal span, the integrated approach allows us to draw relevant inferences about the local land-atmosphere interactions.

We appreciate your feedback and hope this clarification addresses your concern.

Point 4: There are two many errors in Equations 1,7,10,and 11. Besides, in line 304, the value of 1.438 × 10 2 m is completely wrong.

Response 4: Thank you for bringing these errors to our attention. We sincerely apologize for the mistakes in Equations 1, 7, 10, and 11, as well as the incorrect value stated in line 304.

We have thoroughly reviewed and corrected all the mentioned equations to ensure their accuracy and clarity in section “2.7 Land Surface Temperature (LST) Estimation”. Additionally, the value in line 304 has been updated to the correct measurement in the revised manuscript. We appreciate your careful review, which helps us improve the quality of our work.

Point 5: According to Figure 3, only three land use types were recognized. Then, result for land use components shown in Table 3 should sum to 100%.

Response 5: Thank you very much for your constructive comment. In the revised version, both Figure 3 and Table 3 have been corrected to ensure accurate representation and consistency, with the land use components appropriately summing to 100%.

Point 6: In first column of Table 6, since the mean LST of each land use type is over 30 celsius degree, then how can the mean LST of all land use be 21.00 celsius degree? Similar errors exist in this table.

Response 6: Thank you for pointing this out! We found your comments extremely helpful and have revised Table 6 accordingly.

Point 7: Table 7 overall exhibits the variations in relative UHI intensity rather than UHI.

Response 7: Thank you very much for your valuable suggestion. However, Table 7 represents the distribution of UHI across different land use and land cover types based on the mean, minimum, and maximum UHI values calculated over the period from 1994 to 2024. Table 7 has been thoroughly revised considering your valuable suggestion. Hopefully, you will find it justified

Point 8: Visual quality of Figure 10 must be improved. It is unclear. Besides, the scattering plot seems not good even if some scattering plots with small p-value.

Response 8: Thank you so much for your precious suggestion. (Figure 10 becomes Figure 11) has been revised to enhance clarity and visual appeal. Additionally, we have re-evaluated the scatter plot; although some scatter plots have small p-values, the overall presentation has been improved to better illustrate relationships. We appreciate your constructive comments and believe the updated figure provides a clearer representation.

Point 9: The discussion section is too weak to highlight the innovation and major limitations of this study. The assumed relationship between LULC change and UHI effect must consider the impact of global climate change.

Response 9: Thank you so much for your precious comments. The Discussion section is thoroughly revised considering your valuable suggestion in the revised manuscript.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

The authors' effort in enhancing the quality of this manuscript is highly appreciated. They really tried to correct the errors and improve the scientific soundness of their work. However, as I mentioned, only three Landsat images dated on three dates are far from represent the LST dynamics over the past three decades. Nor can the LST variations be simply ascribed to human activities. I suggest the authors to add more Landsat images to support their conclusion.

Author Response

Response to Reviewer 3 Comments

Dear reviewer,

Thank you very much for your valuable suggestions and comments on our manuscript. Your feedback has been very helpful in guiding our revisions. We have carefully reviewed your comments and made corrections in the relevant lines and sections in accordance with your suggestions. The revised parts are marked in red in the paper. The main corrections and our responses to the reviewer’s comments are outlined below.

Point 1: The authors' effort in enhancing the quality of this manuscript is highly appreciated. They really tried to correct the errors and improve the scientific soundness of their work. However, as I mentioned, only three Landsat images dated on three dates are far from represent the LST dynamics over the past three decades. Nor can the LST variations be simply ascribed to human activities. I suggest the authors to add more Landsat images to support their conclusion.

Response 1: Thank you for your constructive feedback and for acknowledging our efforts to improve the manuscript. We have addressed your concerns as follows:

• We have incorporated additional Landsat images across multiple time points throughout the study period, including seasonal variations, to better represent LST dynamics over the past three decades, “in Table 1, section 2.2 Data and methodology adopted.” Lines 200-201.
• We have included meteorological data and climate variables to distinguish between natural climate variability and anthropogenic influences on LST variations, providing a more balanced assessment of contributing factors, “Section 3.3 in Line 52.”
• We have implemented Q-Q plots testing to validate our statistical assumptions and ensure robust analysis of the temporal LST patterns, “Figure 10 in Line 656-658.”
• We have revised the discussion to offer a more comprehensive analysis of our findings. Additional insights have been incorporated regarding the implications of our results for future research in the field.

All maps and tables in the manuscript have been revised to improve clarity, with Landsat data updated for the 30-year period (1994-2024). The revised maps and tables now provide a more comprehensive visualization of land use changes and land surface temperature (LST) over this three-decade span. This extended timeframe allows for a more robust analysis of long-term trends and patterns in LST dynamics.

These revisions provide a more comprehensive and scientifically sound analysis that better supports our conclusions regarding LST dynamics and their underlying drivers.

 

 

Author Response File: Author Response.pdf

Round 3

Reviewer 3 Report

Comments and Suggestions for Authors

After carefully reading this latest version, I am pleasured to find the authors presented a high-quality manuscript adter major revision. Therefore, I recommend it for publication in its present form.