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Article
Peer-Review Record

Variations of Terrestrial Net Ecosystem Productivity in China Driven by Climate Change and Human Activity from 2010 to 2020

Forests 2024, 15(9), 1484; https://doi.org/10.3390/f15091484 (registering DOI)
by Mei Xu 1, Bing Guo 1,* and Rui Zhang 2,*
Reviewer 1:
Reviewer 2:
Reviewer 3: Anonymous
Forests 2024, 15(9), 1484; https://doi.org/10.3390/f15091484 (registering DOI)
Submission received: 19 July 2024 / Revised: 15 August 2024 / Accepted: 22 August 2024 / Published: 24 August 2024
(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

I commend the authors for tackling this crucial theme and conducting this comprehensive study. Estimating carbon concentration across China is vital given the country's rapid development in recent decades. The article's findings on the spatial distribution patterns of mean NEP and dominant factors influencing the spatiotemporal evolution of NEP can contribute substantially to a better understanding of the carbon circle. 

To further strengthen the article, I recommend incorporating critical references that address contrasting findings on NPP/NEP in different regions, and alternative methods for carbon estimation. For example, in my first search, I found a recently published article (https://doi.org/10.3390/su16010381) on factors influencing NPP in the Yellow River Basin.

 

In sub-section 2.2, a more detailed explanation of how NEP was derived from the MODIS17A3H dataset would be valuable. Similarly, clarification on how temperature, precipitation, and duration of sunshine were obtained is needed. Were these also derived from MODIS17A3H or were additional datasets used?

In subsection 3.2 (page 7) the phrase “Among them, the average annual NEP in East China, Northwest China, Central South China and Southwest China decreased greatly, which might be related to the frequent occurrence of extreme climate, resulting in the change of precipitation pattern and the increase of temperature, thus affecting the growth of vegetation and the function of ecosystem, resulting in the decrease of NEP.” need some literature references to demonstrate the change in precipitation and temperature.

Subsection 3.7. To improve data visualization in Figure 12, I suggest using a consistent color palette throughout the graph, making it easier to identify patterns and differences between regions. Why there are changes in the interactions between natural pairwise driving factors like soil type, elevation, and slope?  These are factors that do not modify over 10 years.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper analyses the NPP in China derived from the MODIS17A3H data set in relation to environmental variables and human impact. In general the paper is well written and scientifically sound, the detailed presentation of results seems a bit lengthy.

Major comments:

In the material section a link to the land use data of the Chinese academy of sciences and a description of land use classes used is missing. It remains totally unclear from the method section how human impact was measured and coded (Results 3.4). This needs to be clarified

 

Minor comments:

For formula 3 the variables are missing in the .pdf

page 18: "with" instead of "wit"

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

General comments:

The authors analyzed the Theil-Sen median trend, Mann-Kendall method and Geodetector model to analyze the variation patterns and its dominant factors of NEP in China from 2010 to 2020.  The Cokriging interpolation method of ArcGIS 10.2 was used to obtain the gridded data of temperature, precipitation and sunlight from 2010 to 2020, with a spatial resolution of 1 km. However, they do not describe the procedure, e.g., how many observations and which data source (climate stations) were used to generate the interpolation and the model error.

A regression analysis is mentioned, however, the variables explaining human activities and climate in the changes of vegetation carbon source/sink in China NEP are not explained, the adjustment statistics are not explained and the results are not discussed. On the other hand, variables that were not explained throughout the manuscript appear in the correlation analysis. In the discussion, the assertions that are made lack more substantiation, for example "In contrast, Northern China had a lower forest coverage, high population density, frequent industrial and agricultural activities, and relatively dry climate, leading to weaker carbon sequestration capacity", it is clear that low density and high population density and other factors are synonymous with a weaker carbon sequestration, more emphasis should be placed on the level of impact.

The manuscript is presented as a descriptive analysis of the highest and lowest values of the spatial behavior of the NEP, in this sense what is the scientific contribution of this work and the social, economic and environmental importance, these aspects should be substantiated in the discussion of the results.

 

Specific comments:

Page 3. Define what is DEM?

Page 6. A good adjustment statistic of R2=0.0067 is mentioned, compared to what?

Page 6. The R2 is superscript, also the term (R2) should be homogenized.

Page 20. Define what is GDP?

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

Update the year (2022 to 2024) in the Forest templete.

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