A Multitemporal and Multilevel Land Surface Temperature Regional Attribute Change Analysis in Henan, China, Using MODIS Imagery

Round 1

Reviewer 1 Report

This scientific article that we have appraised is a high quality work.

It is well written, clear, precise, relevant in its form and especially in terms of its demonstration. The methodology is clearly presented, the demonstration is of quality and allows a valid answer to the questions asked. The figures are numerous and really support the demonstration.

The bibliographical references are numerous, recent, in short it is a very relevant scientific analysis.

This excellent quality research work deserves to be validated as it is.

Author Response

Thank you very much for your evaluation and recognition of this paper, which also encourages our confidence in scientific research.

Reviewer 2 Report

Remote sensing is a method of obtaining large-scale LST data. The manuscript displays the LST from remote sensing in Henan Province, and analyzes attribute changes and correlations of LST data in different periods and at different temperature levels.

1.       The authors believe that the LST data were obtained under the same climatic conditions at different times, and the influence of climatic conditions on the LST data was excluded. The premise of all subsequent discussions needs a fuller argument.

2.       Based on the data of only 5 days with a time span of more than 10 years, the number of samples is too small. The negative/positive correlation between temperature and the vegetation index/the built-up index maybe reasonable according to overall trend of data, but attribute information of connected regions in different districts requires a further trend discussion.

3.       In the line 263, ‘while on May 11, 2010’ should be ‘while on May 1, 2010’.

Author Response

Please see the attachment

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

The authors have made appropriate improvements according to the revision suggestions. Take Henan as an example, land surface temperature data is calculated from remote sensing images and determined relationship with surface type. Threshold superposition analysis is performed to generate temperature-connected regions of different levels. Attribute changes and correlations between different times and levels are analyzed. Negative correlation/positive correlation exists between temperature and the vegetation index/the built-up index. Correlation between temperature and the surface feature type index decreased with temperature increase. There are still a few things that need to be improved in the manuscript, such as the several tables’ format should be unified, etc.

Author Response

Thank you for your suggestions for this manuscript.

Regarding the inconsistency of the table format you raised, we have carefully checked the article and unified the table format in the article according to the requirements of the journal. In addition, the format of some pictures has also been modified.