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Peer-Review Record

Relationship between CO2 Fertilization Effects, and Stand Age, Stand Type, and Site Conditions

Remote Sens. 2023, 15(17), 4197; https://doi.org/10.3390/rs15174197
by Shaojie Bian, Bin Wang, Mingze Li *, Xiangqi Kong, Jinning Shi, Yanxi Chen and Wenyi Fan
Reviewer 1: Anonymous
Reviewer 2:
Reviewer 3: Anonymous
Reviewer 4: Anonymous
Remote Sens. 2023, 15(17), 4197; https://doi.org/10.3390/rs15174197
Submission received: 23 June 2023 / Revised: 20 August 2023 / Accepted: 22 August 2023 / Published: 26 August 2023

Round 1

Reviewer 1 Report

This paper has good in depth research but I suggest a number of changes as listed in the enclosed document.

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Dear editor 

I have reviewed the manuscript named “Relationship between CO2 fertilization effects and stand age, stand type, and site conditions” written by Shaojie Bian, Bin Wang*, Mingze Li*, Xiangqi Kong, Jinning Shi, Yanxi Chen and Wenyi Fan. 

They use emerging remote sensing products to explore the relationship between CFE and stand age changes and compared the changes in CFE among different stand types and site conditions based on random forest algorithm, which proves the feasibility of machine learning in this kind of research. The structure of the manuscript is reasonable, hence, I recommend minor work for this manuscript. Detailed comments are shown as follows:

1.     The full name of some abbreviations requires attention, such as GPP.

2.     The last paragraph of the Introduction about the formula for quantifying CFE should be placed under Methods.

3.     Figure 7 layout is confusing, plus its better to line up Figure 2, Figure 3 and Figure 4 together.

4.     Formula (5): What does t mean? Please specify.

5.     Please add gross primary productivity in the section of Introduction . The following article may helps you.

1.Quantifying CO2 uptakes over oceans using LIDAR: A tentative experiment in Bohai bay[J]. Geophysical Research Letters, 2021

2.Quantifying factory-scale CO 2 /CH 4 emission based on mobile measurements and EMISSION-PARTITION model: cases in China

3. Retrieving CH 4 -emission rates from coal mine ventilation shafts using UAV-based AirCore observations and the genetic algorithm–interior point penalty function (GA-IPPF) model

 

I have no comments on the quality of English language.

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

I kindly ask the authors to consider these minor remarks:

- line 82: instead of a period, put a semicolon after "...CFE with stand age"

- line 83: instead of a period put a semicolon and "and" after "...the different types"

- line 98: the measurement unit hm2 is often used in English, please change it to hectares

- line 100: same remark

- line 101: same remark

- line 124: after "0.5" probably the degree marker is missing

- picture 6: if the limit of the number of pages of the article allows, please enlarge the picture

- picture 7: same remark

- picture 9: same remark

- line 294: set number 2 as index in "CO2".

- line 398: put a colon after "Our study found that"

- line 401: put a semicolon after "...stabilize".

- line 402: after "...higher maximum" put a semicolon and "and"

Author Response

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Author Response File: Author Response.pdf

Reviewer 4 Report

In this paper, authors used remote sensing GPP products EC-LUE 10 GPP and NIRv GPP with a random forest model to explore the CO2 fertilization effect trends with stand age in a coniferous forest and a broad-leaved forest in Heilongjiang province, China.

To apply random forest model, first of all, it is necessary to determine the importance and sensitivity of the input parameters, and whether the change in CO2 concentration has an adequate response to GPP. Secondly, the CO2 concentration by the Mauna Loa Observatory at the annual scale is difficult to reflect the actual CO2 concentration in the study area. Third, random forest model for estimating GPP are hardly convincing if they do not take into account vegetation index. The GPP difference is caused by vegetation growth, and how to distinguish the effect of CO2 fertilization in vegetation growth is difficult, and the method in this study is not convincing.

 1. What is the meaning of the site class index (SCI), how to define and calculate it? What is the rule for determining the different levels?

 2. Line 165-167, saturated and actual water vapor pressure is recommended to use a conventional letter.

 3. Line 231, Figure 5 is the wrong reference, should be figure 6.

 4. Line236-239, what is the regression equation, and what is meaning for the regression coefficient?

 5. Fig7a, is it the same as Fig6c,f?

 6. According to Fig7,  βGPP tended to be stable for mature forest (age>50), while Fig9 showed a significant downward trend for each condition. Whether the conclusions are contradictory.

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The introduction of CO2 effect should be emphasized, such as follows: 

1. A XCO2 retrieval algorithm coupled spatial correlation for the Aerosol and Carbon Detection Lidar. Atmospheric Environment,119933,2023.

2. Quantifying factory-scale CO 2 /CH 4 emission based on mobile measurements and EMISSION-PARTITION model: cases in China.

No comments

Author Response

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Author Response File: Author Response.docx

Reviewer 4 Report

The author has made a good response to the review comments, and the article has been greatly improved. There are still a few issues to be aware of.

1. It is still recommended to conduct sensitivity analysis for the input parameters of the random forest method. Although the article in Science applies these five input parameters on a global scale, it may not be suitable for regional applications. For this study area, the input parameters still need to be screened.

2. It seems far-fetched to explain the small difference in atmospheric CO2 concentration between the Chinese regional sites and Mauna Loa Observatory. Obviously, in the boreal forest, the summer CO2 concentration is lower than the global average, so what's the point of carbon satellites if you use the same value all over the world.

3. It is recommended to delete Fig. 7c and f and retain the figure in Fig.8a to reduce information redundancy.

4. The meaning of the size of the regression coefficient in Table2 is not explained in the paper. Moreover, correlation coefficient and RMSE have been reflected in Fig7.Table2 can be deleted.

5. Although the authors explain their differences of Fig.8 and Fig. 10, they are still confusing for the same letter of β and age, suggesting more explanation in the text, and using different expressions to distinguish them.

Author Response

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Author Response File: Author Response.docx

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