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Peer-Review Record

Changes in Vegetation Greenness and Their Influencing Factors in Southern China

Remote Sens. 2022, 14(14), 3291; https://doi.org/10.3390/rs14143291
by Hao Li 1,2, Kunxi Li 1,2, Xiang Zhao 1,3,* and Jiacheng Zhao 1,3
Reviewer 2:
Remote Sens. 2022, 14(14), 3291; https://doi.org/10.3390/rs14143291
Submission received: 26 May 2022 / Revised: 4 July 2022 / Accepted: 5 July 2022 / Published: 8 July 2022
(This article belongs to the Section Ecological Remote Sensing)

Round 1

Reviewer 1 Report

Dear Authors,

The paper 'Changes in vegetation greenness and their influencing factors in southern China' by Li et al. is well written and treats an actual problem ( influencing factors of vegetation greenness dynamics) and provides suggestions for the development and construction of new cities and improvement of ecological environment quality

Title and abstract: The title is appropriate for the content of the article. The abstract is concise and accurately summarizes the essential information of the paper.

Introduction

The paper summarizes very well recent research on the topic: studies that shown that spring temperature in the context of global warming enhances the intensity of soil moisture acquisition by vegetation or studies that attributed and quantified the factors influencing the change in vegetation greenness and there degree of influence, which are mainly classified into three types:  natural factors, anthropogenic factors, and mixed natural and anthropogenic effects.

Moreover, it provides a clear idea of why the research was undertaken and the topicality of the manuscript : the study contributes to an in-depth understanding of the mechanisms of the action of phenological factors in the region

Although there are also more or less similar works, they do not refer to the same study area (for instance Xue et al, 2021. Dynamics of Vegetation Greenness and Its Response to Climate Change in Xinjiang over the Past Two Decades. Remote Sensing, 13(20), p.4063.)

Materials and Methods

There are enough data to ensure that the data is accurate: Study Area, Data Sources. Next, Calculation of vegetation greenness change, Predicting future vegetation changes, Attribution and time-lag effects analysis, the steps that ensure a robust research indeed.

 Results, a critical analysis of the data collected, include Temporal and spatial variation of vegetation greenness, Spatial distribution of meteorological factors and its lag effect on vegetation, Attribution of the variation in vegetation greenness;

Discussion: Authors discuss vegetation change in South China in the context of global climate change

Conclusions reflect very well the aims.

However I am not very happy with the opening statement which is then found in the first part of the conclusions: 'the change in vegetation greenness is a (key) factor in evaluating the stability of the ecosystem'. There is a factor indeed but I am not convinced it is a key factor.



Author Response

Please see the attachment, thanks.

Author Response File: Author Response.docx

Reviewer 2 Report

The paper is interesting but needs to be rewritten by showing in a more clear and direct way the reasons to have applied the selected methods. I add the paper in PDF with indicated in red the parts that are not clear.

 

The paper deals with a very interesting topic and it would deserve publication in the Journal, however in the present state it is not written in a way that can not be easily understood. It is based on 20 years data (2000-2020) and it uses the simple linear regression to calculate greeness change, the Hurst exponent for trend analysis and the Random Forest Regression method for “attribution and time lag effect analysis” (the meaning of the term attribution is not clear!) . The methods are presented without a clear explanation of why they are used after the linear regression who gave already a good picture of the positive changes of NDVI in Fig. 2 b. I do not see future trends since the data cover all the period 2000-2020 and the “model” does not do projections,  may be what the authors would put in evidence is the deviation from what we could expect from 2000 to 2020 in a scenario of “businness as usual”.  The results of the methods show the contribution to NDVI changes by each considered factor and this is a very useful output of the paper, but the text does not stress that this was one aim of the paper. I appreciate the end of the chapter “Discussion” from line 413-425 (I signed in Yellow).   I suggest to rewrite the paper stressing in a more simple and direct way  its aims and clarifying the reasons of having applied the selected methods. In Red I signed the parts that are unclear and that need to be rewritten (e.g. lines 67-69, 73-79, 129-133). In the lines 101-107 there are some notes on the biogeography of the area, in this respect it should be useful to put some references.

Comments for author File: Comments.pdf

Author Response

Please see the attachment, thanks.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

The presentation of the paper improved. In yellow I signed some points that are not clear. The lines 235-237 should go in the methods. In attach the file of the new version of the paper. I did not check the references, please look if they are complete. 

Comments for author File: Comments.pdf

Author Response

Please see the attachment, thanks.

Author Response File: Author Response.docx

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