Seasonal Cooling Effect of Vegetation and Albedo Applied to the LCZ Classification of Three Chinese Megacities

Round 1

Reviewer 1 Report

The manuscript explores the seasonal cooling effects of vegetation and surface albedo based on the LCZ data in Beijing, Shanghai, and Guangzhou. This paper is interesting with extensive work and thorough analysis. I recommend it for publication with minor revisions.

- L76, "[22] showed ...". I suggest using the author's name instead of the reference number.  Same for the latter.

- L110-117. This paragraph should be intergrated into the  previous paragraphs in Page 2.

- L132 and 133. The format of the atitude and longitude coordinates of Beijing and Guangzhou should be corrected.

- Detailed descriptions on LCZ are required, especially the LCZ types in the three cities.

- L224. Albedo should be calculated from atmospherically corrected surface reflectance, not TOA.

- The font size of figure 3-5 is too small to read.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

The manuscript "Seasonal cooling effect of vegetation and albedo applied to the LCZ classification of three Chinese megacities" investigates the surface urban heat island (SUHI) intensity and its variation over various Local Climate Zones (LCZ) in response to the vegetation and albedo in three megacities of China including Beijing, Shanghai, and Guangzhou. The study uses simple linear regression and Pearson's correlation to analyze the impact of vegetation and albedo on SHUI over different LCZs, taking low plants (LCZ D) as a reference surface. The topic is interesting and has the potential to attract the reader's attention. However, the study lacks many aspects that must be overcome before publication.

1.      LCZs have never been defined but are only numbered and are referred to by their assigned numbers in the manuscript. It is useless to analyze any impact without knowing the details of the corresponding LCZ. LCZ codding and their description shall be listed as a table. Also, the methodology lacks the process of defining certain LCZ. How did you mark/map them, what are the parameters, what are the processing steps in determining LCZ, and at what scale these LCZs have been defined? The subsection 2.2.1 fails to answer these fundamental questions.

2.      You used an empirical formula to estimate surface albedo that shall be validated using some well-established albedo product. Although, in the discussion, you mentioned this as a study limitation, however, it is a huge limitation that can not be ignored.

3.      You used two statistical methods to conclude, which are somewhat close in their nature. Also, for analyzing a complex phenomenon such as SUHI, finding a mere Pearson's correlation is not enough to conclude which factor has a decisive impact on surface heat. For the documenting coefficient values, they shall be labeled with their significance and confidence level (p-value).

4.      Albedo denotes the fraction of incoming solar radiation reflected from the surface. The higher the albedo, the less energy will be absorbed by the surface, leading to a low temperature. But in your study, albedo is positively correlated with SUHI at most places, pointing towards their similar polarity (except for a few LCZs in summer and autumn, where albedo and SUHI are inversely correlated). This result is highly doubtful and must be rechecked, and explained with scientific logic and reference to the published literature.

5.      The results are not supported by sufficient discussion. Please include a more related discussion that explains the results in detail.

6.      Figure 4 (a,b, and c) includes extra columns/rows that are not required. In the x and y axes, one shall contain the SUHI and the other shall contain the influencing factor. Figure 5 (a,b, and c), what each dot represents? Is it a random sampling pixel, or the combined average of all pixels or any other? Please explain in the caption. Table 2 (a,b, and c) contains codding labels such as bj, sh and gz, which are expected to denote Beijing, Shanghai and Guangzhou. But it is not required as these cities are mentioned in their captions, also, this coding is not defined in the caption.

7.      You defined different seasonal definitions. It usually is winter as December, January, and February, spring as March, April and May, summer as June, July and August and Autumn as September, October and November. Will you please explain the choice of your seasonal definition?

The writing of the manuscript contains many typos, poor sentence structure and premature explanations. The English language needs extensive improvements.

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

l  Overall Comments

I think it is well organized as a paper. There are no major problems with the structure, content, logical progression, or description. A more creative way of presenting the results would be even better than the current paper. With minor modifications, I judged it acceptable.

l  Individual Comments

The order in which the results are displayed should be consistent with the order in Figure 1 (Beijing, Shanghai and Guangzhou). Specifically, a, b and c in Figure 3, Figure 4 and Figure 5 should correspond to Beijing, Shanghai and Guangzhou, respectively.

The schematic diagram in Figure 5 of [38] Zhu et al. (2022), which shows land use in 17 different LCZs, is very clear. It would be better for the reader's understanding if this schematic diagram was directly quoted in the paper.

In relation to Figure 1, it would be easier to understand the frequency distribution of LCZs in each region.

L299 “excess heat t [48].” What is “t”?

The information needed in this paper is SUHI versus albedo and SUHI versus NDVI for the same season in Figure 4. Extra information not referenced in the paper should be removed. The correlation between albedo and NDVI is also of interest but not referenced in the text. Another example, the correlation between UHI spring and NDVI summer-winter is listed, but why did you include it? Does it make sense to include it?

Figure 4 is too small. Since the upper right and lower left are diagonal targets, it would be easier to see if one of them was omitted and the figure was enlarged a bit more.

Replace L360 (Figure 5 a-c) and L361 (Table 2 a-c).

L365 and L371

"regression coefficients" is correct, not "correlation coefficients".

The number of significant digits of the coefficients in Table 2 is sufficient to two decimal places.

Table 2 lists only the regression coefficients. It is necessary to determine whether the regression coefficients are significant or not, and then bold the significant ones. Significance judgments are made, for example, by correlation coefficients.

It is very difficult to understand the explanation in the text by looking at Table 2. I think it is necessary to devise a method of presenting the results so that they can be clearly seen by looking at the Table 2.

For example, as shown in the attached file, the results for each of the three cities for each of the four seasons and for each LCZ are shown in a single table. The table is also color coded according to the positive and negative regression coefficients (heating and cooling effects). It would be interesting to discuss the differences in the seasonal changes between the three cities, the differences in the LCZ factors between the three cities, the differences in the effects of NDVI and albedo, etc. based on this table.

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The authors have extensively revised the manuscript and addressed all the issues to my satisfaction. I particularly commend the inclusion of Table 1 and the updated Figures 5 to 7. I recommend this manuscript for acceptance in its present form.

The English language is fine.