Identifying the Effects of Vegetation on Urban Surface Temperatures Based on Urban–Rural Local Climate Zones in a Subtropical Metropolis

Round 1

Reviewer 1 Report

Dear Authors,

thanks for the work done, it results clear and complete.

You will not find any comments by my side, but I wold like to suggest you to review the english.  

Author Response

Dear Authors,

thanks for the work done, it results clear and complete. You will not find any comments by my side, but I would like to suggest you to review the english. 

         Response: Thanks for your recognition of our work. The article was edited by MDPI Services, and we hope it could meet your requirements.

        The certificate also can be found in the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

General Comments:

The paper presents a comprehensive analysis of the relationship between urban thermal environment and vegetation using LCZ classification in the context of the Guangzhou-Foshan metropolis. Overall, the study provides valuable insights into the cooling effect of vegetation and its potential for mitigating the urban heat island (UHI) effect. However, there are several areas that require attention and improvement. Detailed comments are provided below:

Abstract:

1. The abstract should provide a concise summary of the main objectives, methods, and key findings of the study. Currently, it is too brief and does not adequately reflect the content of the paper. Please revise and provide more specific information.

Introduction:

2. The introduction should provide a clearer rationale for the study. Why is it important to explore the relationship between vegetation and the urban thermal environment in the Guangzhou-Foshan metropolis specifically? Please provide a more detailed justification.

3. The research questions or objectives of the study should be explicitly stated.

Methods:

4. The paper briefly mentions the use of MODIS data for LST and NDVI calculations. However, details about the specific MODIS products used, their spatial resolution, and the processing methods need to be provided. This information is crucial for reproducibility.

5. The classification process of LCZs should be described in more detail. How were the LCZs identified and classified? Were there any challenges or limitations in the classification process? Discuss these aspects in greater detail.

6. The statistical methods used to analyze the data should be clearly explained. Specify the statistical tests or techniques employed for assessing the correlations between variables.

Results:

7. The results section presents a substantial amount of data and figures. However, the interpretation of the results is somewhat limited.

Discussion:

8. The discussion section should provide a comprehensive analysis and interpretation of the results. Connect the findings with the stated objectives of the study and explain how they contribute to the existing knowledge on the topic.

Conclusion:

9. The conclusion should provide a concise summary of the main findings and their implications. Currently, it is too brief and does not sufficiently address the research objectives and outcomes. Consider revising and expanding the conclusion to provide a more comprehensive summary of the study's contributions.

Language and Structure:

10. The paper needs significant improvements in language and structure. There are several grammatical errors, awkward sentence structures, and unclear phrases throughout the manuscript. Proofread the entire paper carefully to improve clarity and readability.

The paper needs significant improvements in language and structure. There are several grammatical errors, awkward sentence structures, and unclear phrases throughout the manuscript. Proofread the entire paper carefully to improve clarity and readability.

Author Response

We have also submitted the reply and certificate (the last page of the document） in the form of attachment, hoping to provide you with convenience.

General Comments:

The paper presents a comprehensive analysis of the relationship between urban thermal environment and vegetation using LCZ classification in the context of the Guangzhou-Foshan metropolis. Overall, the study provides valuable insights into the cooling effect of vegetation and its potential for mitigating the urban heat island (UHI) effect. However, there are several areas that require attention and improvement. Detailed comments are provided below:

 Response: Thanks for your recognition of our work. The revised version has been organized concisely following comments from Reviewer #2, as well as Reviewer #1 and Reviewer #3.

According to the recommendations of Reviewer #2, we have made specific elaborations in the abstract section and significant revisions in the introduction, methodology, and results sections to make them more detailed and explicit. The discussion section has been reorganized to establish a stronger connection with the results and highlights the emphasis on the novel findings of this study. Additionally, the conclusion section has undergone significant modifications to provide a more comprehensive and detailed summary of the research contributions.

Abstract:

1. The abstract should provide a concise summary of the main objectives, methods, and key findings of the study. Currently, it is too brief and does not adequately reflect the content of the paper. Please revise and provide more specific information.

 Response: Thank you for your suggestion. We have elaborated on the abstract section regarding the main objectives (lines 19–22), methods (lines 22–26), and key findings (lines 26–41), providing a more comprehensive and accurate reflection of the content presented in this article.

The revised abstract is:

“Many studies have observed the crucial role of vegetated local climate zones (LCZs) in mitigating the surface urban heat island (SUHI) effect. However, research analyzing the spatial variations in land surface temperatures (LSTs) in metropolis based on an urban–rural LCZ scheme and exploring the cooling effects of different vegetation types is still lacking. This study focuses on the Guangzhou–Foshan metropolis and aims to elucidate the spatial variations in LST in subtropical cities and the regulating effect of vegetation on LSTs. Additionally, this study analyzes the relationship between this effect and different vegetation types. We used observational data (NDVI, LST) from MODIS products for the years 2000, 2009, and 2019, as well as LCZ maps, urban–rural gradient data, and land use and land cover (LULC) maps. Spatial (urban–rural), seasonal, and diurnal comparative analyses were conducted using average processing, logarithmic regression, Pearson partial correlation, and comparison analysis. The results showed that LST values for built LCZs were generally higher than those of land-cover LCZs, showing a positive correlation with building density and height. Specifically, LCZ 1 exhibited weaker warming abilities compared with LCZ 2, while LCZ 8 and LCZ 10 displayed significantly pronounced warming phenomena. The LST decreased logarithmically across the urban–rural gradients, with a rapid decrease initially, followed by a flattening trend. The LST in the near-gradient urban area was significantly higher than in the farther-gradient rural area. Regarding vegetated LCZs (A-D), the NDVI showed a more significant negative correlation with the LST during the daytime and a positive correlation during the nighttime. The cooling effect of vegetated LCZs was evident, with an average cooling amplitude of 1.92°C over three years. The cooling effect of dense evergreen broadleaf forests (LCZ A1) was most remarkable, with an LST lower than the average surface temperature by 0.94°C. The cooling effectiveness of vegetation was influenced by the season and time of day, with better cooling effects observed during the summer and daytime compared with other seasons and nighttime. In conclusion, urban LSTs are closely associated with LCZ types, urban–rural gradients, NDVIs, and vegetation types. The cooling ability of vegetation exhibited seasonal and diurnal variations, with a special emphasis on the cooling effect of dense evergreen broadleaf forests. Our findings offer valuable insights and can guide urban ecological construction and management by comprehensively assessing the impact of vegetation on urban surface temperatures.”

Introduction:

2. The introduction should provide a clearer rationale for the study. Why is it important to explore the relationship between vegetation and the urban thermal environment in the Guangzhou-Foshan metropolis specifically? Please provide a more detailed justification.

Response: Thank you for your suggestion. We have included a paragraph in the introduction section explaining the reasons for choosing the Guangzhou-Foshan metropolis as the study area (lines 161–174).

“The Guangzhou–Foshan metropolis was selected as the research area for this study. Firstly, because of substantial development, the built areas of Guangzhou and Foshan have become interconnected and integrated, making it essential to study them as a unified entity in line with the local development goal of "Guangzhou–Foshan urban integration development" [33–35]. Secondly, the Guangzhou–Foshan metropolis represents a typical subtropical metropolis characterized by high temperatures throughout the year. The results obtained from this region can reflect distinctive features unique to subtropical cities, setting it apart from other latitudinal zones. Thirdly, being part of the GBA, one of the three major city clusters with the highest levels of development in China, the selection of this highly developed study area holds significant guiding significance for the future development and construction of other cities [35]. Finally, with ongoing urbanization, the SUHI phenomenon in the Guangzhou–Foshan metropolis is becoming increasingly prominent [35–37]. Researching this area will be beneficial for local urban management and planning efforts.”

3. The research questions or objectives of the study should be explicitly stated.

Response: Thank you for your suggestion. In the final paragraph of the introduction section, we have reorganized the content related to the research questions and objectives (lines 179–195). The revised version provides a more explicit and comprehensive elucidation of the research questions and objectives:

“This study used MODIS data from the years 2000, 2019, and 2020 (including LSTs and NDVIs) to conduct a novel remote sensing experiment on the Guangzhou–Foshan metropolis based on the urban–rural LCZ classification scheme. The primary purpose of this research is to explore the spatial variations (LCZ types and urban–rural gradients) and temporal (seasonal and diurnal) in the LST and its relationship with the NDVI. Additionally, by integrating the GLC_FCS30 dataset, a more refined LCZ-LC vegetation classification framework was established to investigate the differential regulating capacity of different vegetation types on the LST. The specific objectives of this re-search can be summarized as follows: firstly, to analyze the spatial variations (LCZ types and urban–rural gradients) and temporal (seasonal and diurnal) characteristics of the LST in the study area; secondly, to examine the relationships between daytime and nighttime LST and seasonal NDVI, revealing the temporal variations in the LST-NDVI relationship in the subtropical metropolis; thirdly, to assess the regulating effects of different vegetation cover types on the LST in the subtropical metropolitan area based on the LCZ-LC classification. By achieving these objectives, our study aims to gain a deeper understanding of urban heat environment issues in the Guangzhou–Foshan metropolis and shed light on the crucial role of vegetation in regulating urban LSTs.”

Methods:

4. The paper briefly mentions the use of MODIS data for LST and NDVI calculations. However, details about the specific MODIS products used, their spatial resolution, and the processing methods need to be provided. This information is crucial for reproducibility.

Response: Thank you for your suggestion. In the revised version, we have provided more detailed explanations on how to obtain LST and NDVI data using MODIS products in sections 2.3 and 2.4 of the Methods. This includes specifying the full names, versions, and spatiotemporal resolutions of the two MODIS products utilized. Additionally, we have provided more elaborate descriptions of how to process the data using the Google Earth Engine (GEE) platform.

2.3. Land surface temperature (lines 242–262)

“This study utilized the MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid (MOD11A1 V6.1) product to acquire LST data for the Guangzhou–Foshan metropolis. The data processing and analysis were conducted using the Google Earth Engine (GEE) platform. The MOD11A1 dataset is structured in segments and presented in a sinusoidal curve projection format. It offers a daily interval time resolution and a 1 km spatial resolution. At 1 km resolution, each grid cell measures exactly 0.928 km resolution. The MOD11A1 V6.1 dataset consists of 12 bands. Among them, the "LST_Day_1km" and "LST_Night_1km" bands provide LST data for daytime and nighttime, respectively, and the "QC_Day" and "QC_Night" bands serve as quality control indicators for daytime and nighttime LST data, respectively. To en-sure the use of high-quality pixels in our analysis, we performed pixel quality control separately for daytime and nighttime LSTs by selecting pixels with a bitmask value of 0 in the "QC_Day" and "QC_Night" bands. After the pixel quality control process, we selected the corresponding processed daytime and nighttime high-quality LST images for the specific years 2000, 2009, and 2019, using the "LST_Day_1km" and "LST_Night_1km" bands. Subsequently, we calculated the seasonal average LST values for each season of these three years based on the seasonal division described in Section 2.2. This process resulted in the creation of 24 images of quarterly average daytime and nighttime LSTs for the years 2000, 2009, and 2019, all with a spatial resolution of 1 km. To ensure consistency with other datasets regarding spatial resolution, we performed batch resampling on the 24 LST images using the nearest neighbor method in the ENVI 5.3 software.”

2.4. Seasonal NDVI values (lines 264–279)

“The seasonal NDVI values for the Guangzhou-Foshan metropolis were obtained from the MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid (MOD13Q1 V6.1) product. This dataset is commonly used for acquiring NDVI and Enhanced Vegetation Index (EVI) data. It has a 16-day temporal resolution and a 250-meter spatial resolution. The "NDVI" and "Summary QA" bands are among the 12 bands that make up the MOD13Q1 V6.1 product. The "NDVI" band provides NDVI data, and the "Summary QA" band is the quality control band associated with the NDVI data. To ensure data quality, when selecting the MOD13Q1 V6.1 dataset in the GEE platform, the first step was to perform pixel-level quality control by selecting pixels with a bitmask value of 0 in the "Summary QA" band. This process removed low-quality pixels and retained only high-quality ones for further analysis. Next, the "NDVI" band was selected to obtain NDVI data that has undergone quality control. Following the seasonal division described in this study, the corresponding seasonal average NDVI values for the years 2000, 2009, and 2019 were calculated. This process resulted in the generation of 12 images of seasonal average NDVI for the specified years. The resulting NDVI images had a spatial resolution of 250 m, ensuring compatibility and consistency with other datasets used in the study.”

5. The classification process of LCZs should be described in more detail. How were the LCZs identified and classified? Were there any challenges or limitations in the classification process? Discuss these aspects in greater detail.

Response: Thank you for your suggestion. In the revised version, we have provided a more detailed description of the LCZ classification process in Section 2.5. Specifically, we mentioned that the LCZ map used in this study was generated by Xie et al. (2022).

 In Section 2.5, we included an introduction to the method and specific procedures employed by Xie et al. (2022) for LCZ identification and classification (lines 289–297).

“The Guangzhou–Foshan metropolis’ LCZ maps were taken from the GBA’s LCZ maps, which were produced by Xie et al. (2022) [29]. According to the standards for LCZ classification training areas on the WUDAPT, Xie et al. (2022) used Google Earth Pro to select over 2000 sample points representing different LCZ types for each year [29]. After data conversion and cleansing, they employed the machine-learning random forest classifier on Landsat or Sentinel imagery, as well as other data in the GEE platform [29]. The resulting LCZ maps for 1999, 2009, and 2019 achieved overall accuracies of 91.6%, 76.0%, and 93.0%, respectively [29].”

Furthermore, in Section 4.4, we have expanded the discussion on the challenges and limitations of LCZ classification based on insights drawn from relevant literature (lines 697–708). This revised version provides a more comprehensive exploration of the difficulties and constraints associated with LCZ classification.

“Furthermore, accurate LCZ classification is essential for delineating urban–rural gradients, which, in turn, is influenced by the quality of the adopted remote sensing data. Factors such as the climate background and atmospheric conditions can impose certain limitations on data quality [60]. Additionally, although different LCZ types have standardized frameworks and definitions [16], the selection of classification samples is inevitably subject to human subjective cognition, which can also impact the precision of LCZ classification [60]. Accurately classifying LCZs is challenging in areas with diverse land use types and frequent surface changes, such as agricultural land. The presence of agricultural land with varying crop types and cultivation practices adds complexity to the classification process and can affect the accuracy of the results. Human management activities, such as changes in land surface cover resulting from forestry management and agricultural practices, also present a significant challenge to LCZ classification [29].”

6. The statistical methods used to analyze the data should be clearly explained. Specify the statistical tests or techniques employed for assessing the correlations between variables.

Response: Thank you for your suggestion. In the revised Section 2.8, the content of the statistical analysis has been reorganized to present the different statistical methods in the order they were mentioned in the article. We have included a dedicated paragraph explaining the calculation of average values (lines 338–346). Furthermore, we have expanded and provided more detailed explanations for the methods of logarithmic regression (lines 347–358), Pearson partial correlation coefficients (lines 359–371), and comparison analysis (lines 372–381).

“We calculated the average values to compare the variations in the LST for different LCZ types, urban–rural gradients, and LCZ-LC types. The same approach was also employed to compare variations in the NDVI and the DEM. Before calculating the average values, we performed data filtering to correct any potential inaccuracies or outliers, thereby enhancing the robustness and completeness of the research results. Calculating average values is a fundamental method employed throughout this study. It ensures a comprehensive and reliable data analysis, facilitating meaningful comparisons and insights into the relationships between different variables in the context of urban heat and vegetation analysis.

In this study, a logarithmic regression method was employed to model the relationship between the LST and the urban–rural gradients, aiming to determine the variation pattern of the LST along the urban–rural gradient. The formula used for this regression is as follows: LST = a * ln(Distance) + b, where "a" and "b" are coefficients determined through the regression analysis. The R² and p-value were used to evaluate the fitting performance. The regression results provide a coefficient, "a", that reflects the nature of the relationship between the two variables. A negative value of a indicates that the LST decreases with an increasing urban–rural gradient, while a positive value indicates the opposite. As the absolute value of a increases, the slope of LST variation with the urban–rural gradient becomes steeper. This logarithmic regression approach offers insights into the sensitivity of LST changes regarding urban–rural gradients and helps to characterize the spatial pattern of the urban thermal environment in relation to urbanization intensity.

Given that the DEM and the proportion of built areas also influence LST variations, we calculated Pearson partial correlation coefficients to assess the relationship between NDVI values and daytime and nighttime LSTs. Specifically, 40 gradients of less than 10 km were selected, and the DEM, proportion of built areas, and quarterly average NDVIs and LSTs (daytime and nighttime) for each gradient were calculated to form an array. The partial correlation analysis described above was performed on the array. The resulting coefficients range between -1 and 1. A value close to -1 indicates a strong negative correlation, while a value close to 1 indicates a strong positive correlation. On the other hand, a value close to 0 suggests no significant correlation between the NDVI and the daytime or nighttime LST. By employing this method, we evaluated the direct relationship between the NDVI and the LST, independent of the effects of the DEM and the proportion of built areas. This enabled a more accurate analysis of the impact of vegetation on the urban thermal environment in the study area.

To compare the cooling effects of the subdivided vegetation types, a comparison analysis approach was employed. The average LST of LCZs A–D was used as the reference, and the discrepancies between the average LST of LCZs 1–10 and the reference values were calculated. Likewise, the differences between the nine subdivided vegetation types and the reference values were determined. For the results comparing LCZs A–D with LCZs 1–10, positive values indicate that the vegetation exhibits a warming effect, while negative values suggest a cooling effect. Meanwhile, for the results between LCZs A–D and the nine specific vegetation types, positive values indicate that a certain vegetation type has a lower cooling ability compared with the average level, while negative values indicate that a certain vegetation type has a higher cooling ability than the average level.

It should be noted that all the calculations and data visualizations for the statistical analysis methods described in this section were performed using the R 4.4.2 programme.”

Results:

7. The results section presents a substantial amount of data and figures. However, the interpretation of the results is somewhat limited.

Response: Thank you for your suggestion. In the revised version, we have made changes to the textual structure of the results. We changed the presentation of each picture and table to a structure of first summary and then narration. The summary first makes the results more intuitive, and then the substatement elaborates the interpretation of the results more fully and adds more detail. Specific changes can be seen in the corresponding paragraphs of the results section. Besides, we moved the content related to Figure 5 to Part 3.2 and made a comprehensive analysis of the results presented in Figure 5 and Figure 6.

At the same time, we further explored the calculation results. For example, in lines 520 to 525, we added the ranking of vegetation cooling capacity based on LCZ-CZ classification and gave specific values: “Meanwhile, the remaining seven LCZ-LC types generally exhibited higher LST values compared with LCZs A–D, indicating weaker cooling capacities compared with the average cooling level of the vegetation. From strongest to weakest, the ranking of cooling capacities was as follows: LCZ C₂ (0.14℃) > LCZ B₁ (0.18℃) > LCZ B₂ (0.28℃) > LCZ C₁ (0.39℃) > LCZ C₃ (0.72℃) > LCZ D₁ (0.75℃) > LCZ D₂ (1.26℃).”

Below is a list of the most significant changes in the results section.

3.1. Spatial variations in LST depending on local climate zone scheme and urban-rural gradients

“Figure 3 shows the average LST for the 18 LCZs within the Guangzhou–Foshan metropolis. The analysis was performed for both daytime and nighttime across four seasons spanning the years 2000, 2009, and 2019 using the LCZ classification. The results in Figure 3 demonstrate a close correlation between LST and LCZ types, while seasonal and diurnal variations significantly influence the warming or cooling effects. Overall, over the past two decades, there has been a subtle upward trend in seasonal LSTs. The highest temperatures were observed during the summer, while the lowest temperatures occurred in winter, with daytime temperatures significantly higher than nighttime temperatures.

Based on the average LST derived for the different LCZ types, three key observations emerged. Firstly, the LST of LCZs A–H generally exhibited lower magnitudes compared with LCZs 1–10, showing a cooling effect associated with land cover types relative to built areas. Secondly, built types with higher heights and building densities exhibited higher LSTs. For instance, LCZs 1–7 generally showed a decreasing trend in the LST. Particularly, LCZ 8 and LCZ 10 demonstrated anomalous warming effects, while LCZ 1 exhibited a slightly lower LST than LCZ 2. Moreover, these two anomalous phenomena were more pronounced during the daytime compared with the nighttime. Thirdly, LCZs A–D displayed remarkable cooling effects during both the daytime and nighttime across diverse land cover types, with particularly pronounced cooling effects during the daytime. Specifically, LCZ A exhibited the most significant cooling effect, followed by LCZ B and LCZ C, while LCZ D exhibited the least pronounced cooling effect. However, LCZs E–H only showed various degrees of cooling effects during the daytime, with LST values at night resembling those of the built types. In addition to diurnal variations, seasonal changes also influenced the warming and cooling effects. Specifically, during the winter season, the cooling effect of LCZs A–D and the warming effects of LCZ 8 and LCZ 10 tended to be relatively weak. However, in the summer season, which experiences the highest average LSTs, more pronounced warming or cooling effects were observed.

Figure 4 and Table 3 present the relationship between the LST and the urban–rural gradients, along with the results of the logarithmic regression fitting. Overall, the LST exhibited a logarithmic decrease with increasing urban–rural gradients, with the rate of decrease gradually slowing down until approaching zero. The variation in the LST along the urban–rural gradients was also influenced by seasonal and diurnal changes. Specifically, during the daytime, the rate of LST decrease showed a downward trend, with a notably faster decrease observed within 2 km. A similar trend was observed for nighttime LST variations, although these were not as pronounced as during the daytime. At 15 km, a slight increase in the LST was observed during the daytime, and the variation curve appeared concave between 15 km and 20 km. In the nighttime curve, the LST also increased near 15 km, but there was no distinct concave feature between 15 km and 20 km. Notably, at approximately 35 km, abnormal fluctuations in LST were observed during both the daytime and nighttime, which can be attributed to the limited number of eligible pixels available.

Moreover, the seasonal differences are more evident in Table 3. The maximum absolute value of coefficient "a" was often observed during the summer or autumn, which revealed that the LST experienced a faster decline along the urban–rural gradients during the summer and autumn. The season-specific variations in the regression coefficients suggest that LST responses to urbanization intensity vary throughout the year, with the most pronounced responses occurring during the summer and autumn.”

3.2. Correlations between the LST and NDVI values

“Figure 6 separately illustrates the Pearson partial correlation coefficient results of the NDVI with both the daytime and nighttime LSTs. To achieve accurate and logical results, the average LST and NDVI of the vegetated LCZ types (LCZ A–D), as well as the unique average LST for each type, were chosen (Figure 6). The research results re-vealed an overall negative correlation between NDVI values and LSTs for vegetated LCZ types. When studying specific vegetated LCZ types individually, the negative correlation was more pronounced during the daytime, while nighttime correlations showed a predominantly positive trend. Additionally, the relationship between the NDVI and the LST was influenced by the different vegetated LCZ types. Figure 6a and Figure 6b show partial correlation results between the NDVI and the average LST of all the vegetated land cover types. For all seasons and years, except for the daytime in the spring of 2000, there were consistently significant negative correlations between these two variables. For LCZ A, the partial correlation results exhibited distinct characteristics between daytime (Figure 6c) and nighttime (Figure 6d). During the daytime, all groups showed negative correlations, except for the spring of 2000. At night, significant positive correlations between NDVI values and LSTs were observed in the springs of both 2009 and 2019, while the other tested groups exhibited negative correlations. In the case of LCZ B, significant negative correlations were found during the daytime for all years and seasons, except for the summer of 2019 (Figure 6e). During the nighttime, negative correlations were observed for all four seasons in 2000, while the springs and summers of 2009 and 2019 showed somewhat positive correlations (Figure 6f). The correlation results for LCZ C indicated a negative correlation between daytime NDVI values and the LST of LCZ C (Figure 6g). Except for the fall of 2009, which failed the significance test, positive associations were seen throughout the night in all seasons of 2009 and 2019 (Figure 6h). The results for LCZ D showed significant variations be-tween daytime and nighttime (Figure 6i and Figure 6j). During the daytime, all tested groups, except for the summer of 2009, exhibited significant negative correlations, as confirmed by the significance test. Conversely, during the nighttime, positive correlations were observed for all tested groups, except for the winter months of 2000 and 2019.”

3.3. Mitigation effect of subdivided vegetation types on land surface temperature based on LCZ-LC classification

“Figure 8 depicts the average cooling capacities of LCZs A–D and the cooling capacities of different vegetation types based on LCZ-LC classifications relative to the average cooling level of LCZs A–D. Figure 8a shows that vegetation exhibited a lower LST compared with the built types, revealing its significant cooling effect. This cooling effect was particularly pronounced during the daytime rather than during the nighttime. Additionally, the cooling effect of the vegetation gradually increased over the years.

Figure 8a–8j display the cooling capacities of different LCZ-LC types compared with the average cooling level of vegetation. The results show that LCZ A₁ and LCZ A₂ consistently demonstrated stable and superior cooling abilities compared with the average cooling level of vegetation, with LCZ A₁ exhibiting more pronounced effects. On average, over the three years, LCZ A₁ and LCZ A₂ had lower LST values by 0.94℃ and 0.89℃, respectively, compared with LCZs A–D. Meanwhile, the remaining seven LCZ-LC types generally exhibited higher LST values compared with LCZs A–D, indicating weaker cooling capacities compared with the average cooling level of the vegetation. From strongest to weakest, the ranking of cooling capacities was as follows: LCZ C₂ (0.14℃) > LCZ B₁ (0.18℃) > LCZ B₂ (0.28℃) > LCZ C₁ (0.39℃) > LCZ C₃ (0.72℃) > LCZ D₁ (0.75℃) > LCZ D₂ (1.26℃). Among these, LCZ C₃, LCZ D₁, and LCZ D₂ consistently showed cooling effects lower than the average cooling level of vegetation. While LCZ C₂, LCZ B₁, LCZ B₂, and LCZ C₁ had higher average LST values than LCZs A–D, they often exhibited stronger cooling effects than the average cooling level of vegetation during the nighttime. The cooling capacities of different vegetation types were influenced by seasonal and diurnal variations. For dense trees (LCZ A₁ and LCZ A₂), the daytime cooling capacities were generally superior to nighttime. Additionally, the daytime–nighttime difference in cooling capacity was greater for LCZ A₁ compared with LCZ A₂. Meanwhile, the remaining seven types tended to exhibit stronger cooling capacities during the nighttime.

The cooling capacity of vegetation is also affected by season. For LCZs A–D, the cooling capacity in summer and autumn was generally stronger than that in spring and winter, and the cooling capacity in summer was often the most prominent. This phenomenon was also observed in the six subclasses (LCZ A₁–C₂). However, for the other three subclasses of vegetation with weaker cooling abilities (LCZ C₃–D₂), summer and autumn tended to have weaker cooling abilities than spring and winter.”

Discussion:

8. The discussion section should provide a comprehensive analysis and interpretation of the results. Connect the findings with the stated objectives of the study and explain how they contribute to the existing knowledge on the topic.

Response: Thank you for your suggestion. We have revised the content of the discussion section according to your suggestion. First, we have added a new paragraph to the discussion section, which begins with a brief description of the contributions and assistance of the findings in this paper. (lines 545–552)

“Based on an urban–rural LCZ scheme, we studied the regulating effect of vegetation on LSTs in a subtropical metropolis. Our findings on LST spatial distributions, especially rural–urban changes, are conducive to fully under-standing the spatial characteristics of the metropolitan thermal environment. At the same time, we emphasized the direct relationship between the NDVI and the LST and the difference in the cooling capacities of different LCZ-LC vegetation types, which can provide theoretical support for ecological management and development planning for subtropical metropolis.”

Second, we use parentheses at the end of the sentence about an important finding in the discussion to indicate which part of the result the conclusion comes from. For instance, “The cooling capacity of LCZ A₁ and LCZ A₂, which are subdivided vegetation types, is significantly higher compared to the average cooling capacity of vegetation (Figure 8. b–c)” (lines 644–646). This approach achieves the goal of linking the results of this article to the purpose.

Conclusion:

9. The conclusion should provide a concise summary of the main findings and their implications. Currently, it is too brief and does not sufficiently address the research objectives and outcomes. Consider revising and expanding the conclusion to provide a more comprehensive summary of the study's contributions.

Response: Thank you for your suggestion. In the revised version, the first paragraph of the discussion section summarizes the main conclusions of this paper (lines 724–752). We have made some additions to the previous conclusions to fully present the new findings and key points of this paper.

“We used an urban–rural LCZ scheme to evaluate the mitigating impact of vegetation on the urban thermal environment of the Guangzhou–Foshan metropolis. The following are the study's main conclusions. First, the LST was greater in the constructed portions of the study area. The built LCZ types showed a decreasing trend in the average LST from LCZ 1 to 7, indicating a cooling effect, while LCZ 8 and LCZ 10 exhibited a warming effect. These trends were more pronounced during the day than at night. Second, the four vegetated types (LCZs A–D) showed varying degrees of cooling effects, with an average cooling effect temperature of 1.92℃. In comparison with spring and winter, the cooling capability was more significant in the summer and autumn, with LCZ A demonstrating the strongest cooling effect. Third, the average LST decreased spatially with increasing distance from the urban centers, following a logarithmic regression relationship. Summer and autumn experienced a quicker decline in LSTs than spring and winter. Fourth, NDVI values were negatively correlated with the average LST in LCZs A–D, influenced by vegetation types, seasons, daytime, and nighttime. For the single vegetated LCZ type, the NDVI values were positively correlated with the LST in the daytime and negatively correlated with the LST at night. Vegetation plays a significant role in cooling, with different vegetation types exhibiting varying cooling capacities. Last, among the subdivided vegetation types based on the LCZ-LC classifications, LCZ A₁ showed the most noticeable cooling effect, with the LST being 0.94℃ lower than that of the average vegetation. LCZ A₂ closely followed with a cooling effect of 0.90℃. The cooling capacity of the other seven subclasses was generally lower than the average cooling level of vegetation, and the cooling capacities were ranked as follows: LCZ C₂ (0.14℃) > LCZ B₁ (0.18℃) > LCZ B₂ (0.28℃) > LCZ C₁ (0.39℃) > LCZ C₃ (0.72℃) > LCZ D₁ (0.75℃) > LCZ D₂ (1.26℃). LCZ A₁ and LCZ A₂ showed a stronger cooling ability during the daytime, while the other types tended to exhibit stronger cooling capacities during the nighttime. The cooling abilities of LCZs A₁–C₂ in summer and autumn were higher than in spring and winter, while LCZs C₃–D₂ showed an opposite trend. These findings provide scientific support and a theoretical foundation for urban and rural planning and management, underscoring the significance of different vegetation types, seasons, and times of day in influencing the cooling capacity of urban areas.”

The second newly added paragraph makes a comprehensive summary of the main ideas and methods, key conclusions, applications, limitations, and future improvement directions of this paper, and emphasizes the innovation and practical significance of this study (lines 755–772).

“In summary, this study introduced a more refined approach to urban–rural division by using the LCZ classification, allowing for a detailed analysis of LST changes concerning urban development dynamics. By focusing on areas with significant built types, this study explored the correlation between LCZ-based NDVIs and LSTs. Meanwhile, this study attempted to combine its LCZ scheme with land cover data to establish an LCZ-LC vegetation classification system and assess the cooling capacities of different types based on this classification. Seasonal and diurnal differences were also analyzed in this study. The findings highlight the importance of preserving dense tree cover, particularly evergreen broadleaf forests, and avoiding the fragmentation of natural habitats, as they are effective measures in mitigating the UHI effect in relation to expanding the Guangzhou–Foshan metropolis. These conclusions have practical significance for urban planning and development, providing valuable insights into sustainable and climate-resilient urban designs. However, we also acknowledge limitations, such as the use of MODIS data with limitations in spatial resolution and the challenge of accurately classifying LCZs in the presence of complex land use types. The study area's subtropical environment and size may impact the specific findings and conclusions. The links between LCZ types, urban–rural gradients, vegetation, and ur-ban LSTs would be better understood with additional studies conducted in additional climatic zones and wider spatial scales.”

10. Language and Structure:

The paper needs significant improvements in language and structure. There are several grammatical errors, awkward sentence structures, and unclear phrases throughout the manuscript. Proofread the entire paper carefully to improve clarity and readability.

 Response: Thanks for your suggestion. The article was edited by MDPI Services, and we hope it could meet your requirements.

Comments on the Quality of English Language

The paper needs significant improvements in language and structure. There are several grammatical errors, awkward sentence structures, and unclear phrases throughout the manuscript. Proofread the entire paper carefully to improve clarity and readability.

 Response: Thanks for your suggestion. The article was edited by MDPI Services, and we hope it could meet your requirements.

Author Response File: Author Response.pdf

Reviewer 3 Report

This study investigated the effects of vegetation on urban surface temperature based on urban-rural local climate zones. Overall, the study is well-written and the research is of some significance. I have several comments for the authors, please see the details below.

1) Line 129-131. The authors should also tell readers the major approaches for urban-rural gradient analysis and why the authors choose the LCZ classification to establish the gradient.

2) Research gaps should be stated carefully in the Introduction section.

3) Figure 2. Should MOD13A1 be MOD13Q1? Please check it.

Author Response

Our response is also submitted as an attachment for your convenience.

Comments and Suggestions for Authors

This study investigated the effects of vegetation on urban surface temperature based on urban-rural local climate zones. Overall, the study is well-written and the research is of some significance. I have several comments for the authors, please see the details below.

 Response: Thanks for your recognition of our work. Based on the comments of reviewer #3, as well as reviewer #1 and reviewer #2, the revised version has been concisely organized.

According to reviewer #3’s suggestion, we have explained in more detail about the urban-rural gradient and research gaps in the introduction.

1) Line 129-131. The authors should also tell readers the major approaches for urban-rural gradient analysis and why the authors choose the LCZ classification to establish the gradient.

Response: Thank you for your suggestion. In the revised version, we have explained the rationality of using LCZ to establish the urban-rural gradient: “Within the LCZ classification system, compact high-rise areas (LCZ 1) are characterized by densely distributed buildings with dozens of stories, lacking significant vegetation cover [16]. These areas have the highest impervious surface ratio among all built types and are primarily composed of concrete, steel, glass, and similar materials. Xie et al. (2022) reported that LCZ 1 was always densely distributed in urban centers, so a rural–urban gradient of 250m was established in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), with LCZ 1 as the center [29]. Using the rural–urban gradient data developed by Xie et al., this study carried out an analysis of the rural–urban change in the LST of the study area.” (Lines 136-144)

In section 3.7 of the Methods section, we provided detailed information about the rural-urban gradients (lines 326-336): “To investigate the spatial gradient changes in the LST of the Guangzhou–Foshan metropolis, we used the urban–rural gradient data of the GBA for the year 2019, generated by Xie et al. (2022) [29]. This dataset provides a comprehensive understanding of the urban–rural transition within the region, with LCZ 1 as the central reference point. The data was generated using the ArcGIS 10.8 software and employed the buffer distance for urban land cover analysis in China using a 250m gradient. To analyze the spatial gradient changes in the LST of the Guangzhou–Foshan metropolis, we processed the data using ENVI 5.3. By performing cutting and processing, we obtained gradient data with a spatial resolution of 250m. This approach considered the influence of surrounding cities, ensuring a more realistic and consistent representation of the thermal environment within the study area.”

In section 3.8 of the Methods section, we introduced how to use urban and rural gradient data for analysis (lines 338-339): “We calculated the average values to compare the variations in the LST for different LCZ types, urban–rural gradients, and LCZ-LC types.” By calculating the average value of each gradient (LST, NDVI, etc.), the gradient changes of each factor were analyzed in this study.

2) Research gaps should be stated carefully in the Introduction section.

Response: Thank you for your suggestion. In the introduction section of the revised version, we logically pointed out the research gaps one by one and summarized them again in the last paragraph of the introduction (lines 175-178): “The LCZ scheme has performed well in the study of urban thermal environments, but the relevant studies still lack comprehensive considerations of the seasons and daytime/nighttime, as well as accurate measurements of continuous urban and rural changes relying on LST scheme. In addition, analyses of vegetation cooling capacities solely based on the LCZ scheme ignore the influence of vegetation types”.

3) Figure 2. Should MOD13A1 be MOD13Q1? Please check it.

Response: Thank you for pointing out this mistake. MOD13Q1 is right. We have corrected this error in Figure 2.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The revisions made by the authors to the paper are commendable, and they have indeed enhanced the quality and readability of the manuscript. However, there are still some aspects that require further refinement:

1.         In the introduction, the authors have provided a relatively clear introduction to the research background and significance. However, there is room for expansion, particularly in elaborating on the urban thermal environment and the role of vegetation regulation.

2.         In the discussion section, further exploration of the consistency and disparities between the research findings and existing literature would be valuable. Additionally, providing more insights into comparisons with similar studies and discussing the practical applications of this research in urban planning and climate adaptation could enhance the paper's overall quality.

3.         Throughout the manuscript, there are instances of lengthy sentences that result in complex expression. Consider breaking down these longer sentences for improved readability. Moreover, ensuring smoother transitions between paragraphs would enhance the overall coherence of the paper.

 Throughout the manuscript, there are instances of lengthy sentences that result in complex expression. Consider breaking down these longer sentences for improved readability. Moreover, ensuring smoother transitions between paragraphs would enhance the overall coherence of the paper.

Author Response

We have also uploaded the response content as an attachment, hoping to provide you with some conveniences.

Comments and Suggestions for Authors

The revisions made by the authors to the paper are commendable, and they have indeed enhanced the quality and readability of the manuscript. However, there are still some aspects that require further refinement:

 Response: Thanks for your recognition of our work. The revised version has been organized concisely following comments from reviewer.

According to the recommendations of the reviewer, we have expanded the introduction section to further emphasize the theme of vegetation's cooling effects. Additionally, we have made adjustments to the discussion section to highlight the distinctiveness and innovation of our research results compared to similar studies. Furthermore, we have elaborated on the practical applications of our findings. Lastly, we have divided lengthy sentences into shorter ones and incorporated transitional phrases between paragraphs to enhance readability.

1. In the introduction, the authors have provided a relatively clear introduction to the research background and significance. However, there is room for expansion, particularly in elaborating on the urban thermal environment and the role of vegetation regulation.

Response: Thanks for your suggestion. The revised version has been organized concisely following comments from reviewer. In the revised version, we have underscored the theme of the article in the introduction section.

 Firstly, we have incorporated a discussion on the cooling capacity of vegetation: “Vegetation in urban areas, with its canopy shading, albedo, and evapotranspiration　(ET), plays a critical role in regulating urban temperatures and enhancing the overall urban climate [9]. The vegetation canopy intercepts and reflects solar radiation and contributes to transpiration, effectively lowering the land surface temperature (LST). Research has demonstrated that higher albedo and increased ET lead to lower LSTs [10–12]. Li Yan et al. (2015) employed global satellite data to study the biophysical effects of forests and elucidated the cooling or warming effects of forests across different latitudes driven by the competition between albedo and ET [12]. They highlighted the seasonal variations in these effects, with tropical forests exhibiting stronger cooling impacts throughout the year [12]. Estoque et al. (2017) reported that in Southeast Asian cities, green spaces exhibit an average LST 3°C lower than impervious surfaces [13]. These studies underscore the significant role of vegetation in mitigating urban heat, and emphasize the strong correlation between vegetation distribution and temperature levels.” (Lines 62–74)

Secondly, in the introduction section regarding LCZ, we have supplemented information about the cooling capacity of vegetation based on the LCZ scheme: “Based on the LCZ scheme, the cooling effect of vegetation has been widely observed, with the cooling effect of LCZ A being particularly pronounced [24–26].” (Lines 118–120)

2. In the discussion section, further exploration of the consistency and disparities between the research findings and existing literature would be valuable. Additionally, providing more insights into comparisons with similar studies and discussing the practical applications of this research in urban planning and climate adaptation could enhance the paper's overall quality.

Response: Thanks for your suggestion. In the revised version, we have made adjustments to the language in the Discussion section to emphasize the nuances and contrasts between our results and similar studies, as well as to highlight the innovations of our research. For instance, in the Discussion section 4.1, our spatial analysis of LST based on the LCZ scheme aligns with some previous studies: “Shi et al. (2021) also reported a negative correlation between building density and height with LST [26].” (Lines 571–572), while also diverging from certain research: “It is worth noting that Cai et al. (2018) discovered that LCZ 1 exhibited the highest LST of all the built types in the Yangtze River Delta [46].” (Lines 574–575).

Our findings also align with peer’s research in the following aspects: “Therefore, the cooling impact hierarchy found in this study (i.e., LCZ A > LCZ B and LCZ C > LCZ D) supports previous studies indicating that LCZ A exhibits particularly prominent cooling ability [26,50]. A prior study also ranked the severity of the SUHI effect among different vegetated LCZ types as follows: LCZ D > LCZ C > LCZ B > LCZ A [51].” (Lines 599–603); “The negative association between NDVI values and the average LST of the LCZs A–D was consistent with the notion that a lower LST is a result of more vegetation greenness rather than less (Figure 6).” (Lines 637–639), and so on. Meanwhile, this study differs from similar research in the following aspects: “However, the cooling effect of LCZ B on the SUHI is more advantageous than LCZ A in urban ventilation corridors [51].” (Lines 603–604); “However, these studies had not considered day and night differences.” (Lines 641–642), and so on.

Simultaneously, we have underscored the innovative aspect of our study by emphasizing the novel approach of LST spatial analysis based on urban-rural gradients: “In contrast to previous studies that quantified urban–rural LST differences using differential approaches, this study adopted a methodology based on urban–rural gradients to assess the continuous variation of average LST between urban, suburban, and rural areas.” (Lines 612–615). Similar to this, the presentation of results based on the LCZ-LC scheme analysis is as follow: “Compared to previous urban thermal environment studies based on the LCZ scheme, the LCZ-LC approach established in this study further refines urban vegetation types and quantifies their cooling capabilities.” (Lines 670–672).

Furthermore, we have added several brief sentences in the Discussion section regarding the practical applications of our findings in urban planning and management. “The aforementioned findings suggest that controlling the expansion of built types (represented by LCZ 8 and LCZ 10) would be beneficial in mitigating the increase in LST levels in a subtropical metropolis.” (Lines 586–589). “These findings underscore the role of vegetation in mitigating SUHI, placing particular emphasis on the notable cooling capacity of LCZ A. This highlights the importance of considering the strong cooling potential of LCZ A in urban green space planning.” (Lines 608–611). “This finding indicates that alleviating the high LSTs in near-gradient urban areas holds greater significance for the planning and development of metropolitan regions.” (Lines 621–623). “Hence, addressing the SUHI effect during the warmer summer and autumn seasons, when the average LST is higher, holds greater importance in subtropical metropolitan areas compared to other seasons.” (Lines 632–635). “In conclusion, the results based on the LCZ-LC approach further confirm the strong cooling capabilities of LCZ A, particularly LCZ A₁. This highlights the potential benefits of increasing the area of dense forests, especially evergreen broadleaf forests, in the planning of green spaces within subtropical metropolises, thereby aiding in the mitigation of the SUHI effect. Moreover, the pronounced cooling effect of LCZ C₂, sur-passing that of LCZ B₁ and LCZ B₂, should also be duly acknowledged.” (Lines 714–719).

3. Throughout the manuscript, there are instances of lengthy sentences that result in complex expression. Consider breaking down these longer sentences for improved readability. Moreover, ensuring smoother transitions between paragraphs would enhance the overall coherence of the paper.

Response: Thanks for your suggestion. In the revised version, we have broken down the lengthy sentences that might pose comprehension difficulties into shorter ones. For instance, we have divided the original lengthy sentence “The rapid growth of global cities has resulted in a worrisome concentration of populations, resources, and capital within urban areas, which poses a significant threat to the urban ecological environment and people's physical and mental health” into two separate sentences “The rapid growth of global cities has resulted in a worrisome concentration of populations, resources, and capital within urban areas. This concentration poses a significant threat to the urban ecological environment and people's physical and mental health” for better readability and clarity. The other modifications can be readily identified in the revised version. (Lines 48–50).

Furthermore, we have improved the coherence between paragraphs by incorporating additional transitional words and phrases (such as “however”, “furthermore”, “meanwhile” etc.). Meanwhile, we have also made revisions to the wording of the opening sentences in certain paragraphs, resulting in a more seamless transition. For instance, “Regarding land cover types, the vegetated types (LCZs A–D) exhibited substantial cooling effects because of their larger permeable surface areas and the transpiration and shading provided by vegetation (Figure 3).” (Lines 590–592).

Comments on the Quality of English Language

Throughout the manuscript, there are instances of lengthy sentences that result in complex expression. Consider breaking down these longer sentences for improved readability. Moreover, ensuring smoother transitions between paragraphs would enhance the overall coherence of the paper.

Response: Thanks for your suggestion. Please refer to our response to comment 3.

Author Response File: Author Response.pdf