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Peer-Review Record

Multi-Model Comprehensive Inversion of Surface Soil Moisture from Landsat Images Based on Machine Learning Algorithms

Sustainability 2024, 16(9), 3509; https://doi.org/10.3390/su16093509
by Weitao Lv 1, Xiasong Hu 1,*, Xilai Li 2, Jimei Zhao 2, Changyi Liu 1, Shuaifei Li 1, Guorong Li 1 and Haili Zhu 1
Reviewer 1: Anonymous
Reviewer 2:
Reviewer 3:
Reviewer 4: Anonymous
Sustainability 2024, 16(9), 3509; https://doi.org/10.3390/su16093509
Submission received: 4 January 2024 / Revised: 28 March 2024 / Accepted: 28 March 2024 / Published: 23 April 2024

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1.      There is no author shown coming from the second affiliation of “Academy of Agriculture and Forestry, Qinghai University, Xining, Qinghai 810016, China;”. Please double-check this.

2.      The paper writing needs extensive editing.

3.      Tables 2, 3, and 4 are hard to read and compare. I suggest making figures at least for Tables 3 and 4 or adding color gradients to Tables 3 and 4, which would help reading and comparing.

 

 

Comments on the Quality of English Language

The paper writing needs extensive editing.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Inversion of soil moisture by remote sensing data is very important and economic for regional and global environmental management. The authors conducted inversing model using machine learning method to monitor the soil moisture at two typical sites in the upper Yellow River.

I have a major question about this work.

Since the combined model is proper than other three model, RF, SVM and BPNN, why it can be applied only on the Xijitan, but not on the Xiazantan? Due to the differences of the two different landslide distribution areas, is the combined model better than others in Xiazantan? What are the results ?

Minor questions

(1)       abstract:words in the first two sentences are somehow repeated, so please change the sentences. Such as ”ecological stability and sustainable development in the upper Yellow River region”

(2)       R2 should be R2

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This manuscript is “Multi-model comprehensive inversion of surface soil moisture from Landsat images based on machine learning algorithms”. Some detailed comments are as follows:

(1) Abstract: The abstract of the manuscript lacks disclosure of relevant new mechanisms.

(2) Introduction: The research background has not been analyzed from a global perspective. So, it is not comprehensive enough.

(3) The “2 Study area” and “3. Materials and Methods” are suggested to be merged into one chapter.

(4) The author needs to explain in detail the importance of the research area.

(5) Tables 1, 2 and 4 should avoid being distributed on page 2.

(6) Fig. 8 should avoid being distributed on page 2.

(7) Discussion: This is only a limited discussion.

(8) Conclusions: The conclusions are only the research result, and the author needs to further summarize.

(9) The format of the references did not meet the requirements of the journal. The journal names in the references were not abbreviated as required.

(10) A proof reading by a native English speaker should be carefully conducted to improve both language and organization quality.

Comments on the Quality of English Language

The English language of this manuscript needs improvement.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The study reflects the need to establish for different regions the most relevant indicators, i.e. the best combinations of independent variables, which can be used to develop more relevant soil moisture models. Congratulations to the authors for the research undertaken and the data and results obtained.

Considering the authors' statements that the development of vegetation, is an indispensable element in maintaining the stability and sustainable development of the ecosystem and the fact that 6 vegetation indices were used, it would be interesting and relevant for this study to indicate the dominant species, a more detailed presentation of their morphology (root, height).

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

This manuscript is “Multi-model comprehensive inversion of surface soil moisture from Landsat images based on machine learning algorithms””. Some detailed comments are as follows:

(1) The author should revise the English language throughout the entire text.

(2) The author needs to typeset the entire text.

Comments on the Quality of English Language

The English language of this manuscript needs improvement.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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