Urban Heat Mapping Strategies for Predicting Near-Surface Air Temperature in Unsampled Cities in Iowa

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper introduces the data-driven method for predicting urban air temperatures in areas without direct measurements, using a thorough approach that contributes meaningfully to urban heat island studies. The writing is clear, and the structure flows logically. Here are specific suggestions for improvement:

1. The introduction doesn’t fully describe Iowa’s climate or urban features, which makes it harder for readers to see why this region works well as a case study.
2. In the methods section, there’s no clear explanation, based on existing research, for choosing these five statistical models over alternatives like Gradient Boosting or Bayesian approaches.
3. The description of how landscape variables are calculated could be sharper— for instance, it’s unclear how NDVI is derived from aerial imagery.
4. The results point out overfitting in the Random Forest model but don’t back this up with numbers, such as a comparison of training versus testing R-squared values.
5. Figures 6-7 in the results section seem underused; with just two maps and limited explanation, they don’t fully reveal the spatial patterns of the findings.
6. The discussion doesn’t dig deeply into why the Population CV strategy outperforms others, and it could benefit from stronger physical or statistical reasoning.

Author Response

We appreciate the reviewer's thoughtful and constructive comments and provide a full response in attached document. 

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The authors present strategies for determining urban heat mapping  for cities in Iowa.  The results are well presented and the different approaches are clearly justified.  The main problem with the manuscript is the quality of the English throughout.  There are several grammar and English mistakes which should have been checked better before submission. 

Major Comments:

1) Does elevation impact your conclusions?

2) Define Acronyms before using them

3) Only define acronyms once

4) fit should be fitted

5) Never start a sentence with and, so, or then.

6) Be consistent with using either night, night-time, or nighttime.

7) Use capital letters correctly 

8) Provide a summary of what is in each section at the end of the introduction.

9) Some of the paragraphs are too long.  Start a new one when the subject changes.

Minor Comments:

1) Line 21: there should be a colon after models and a comma after series

2) Line 48: and the other quoted parts why is the first letter in square brackets?

3) Line 224: I may be mistaken but isn't there only 4 variables listed here?

4) Figures 3a,b: what do the dash lines represent? They are not defined in the caption, but also what is the point of figure 3b? 

5) Line 379: the is missing before second.  This mistake is made more than once in the manuscript.  Please correct all instances.

6) Line 381: The sentence that is started her does not make sense.

7) Line 454: Colon needed before a) as you are starting a list in a sentence.

Comments on the Quality of English Language

There are several grammar and English mistakes throughout that need to be addressed.

Author Response

We appreciate the reviewer's thoughtful and constructive comments and provide a thorough response in the attached document

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

Comments on the manuscript attached as a PDF file.
The manuscript is original and interesting. I only make a few observations that may improve understanding of the manuscript. The discussion seems more like conclusions; I suggest changing the subtitle.

Comments for author File: Comments.pdf

Author Response

We appreciate the reviewer's thoughtful and constructive comments and provide a thorough response in the attached document

Author Response File: Author Response.docx