Seasonal Effect of the Vegetation Clumping Index on Gross Primary Productivity Estimated by a Two-Leaf Light Use Efficiency Model
, Chenxia Wang 1,2, Siyang Yin 1,2, Jing Guo 1,2, Yidong Tong 1,2, Ge Gao 1,2, Zheyou Tan 1,2 and Sizhe Chen 1,2
Axel R. Von Müller
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis study proposed a method for estimating GPP with the seasonal variation of vegetation clumping index considered. The proposed method could substantially decrease the uncertainty of vegetation GPP estimation, which would help better understand the terrestrial carbon cycle in the context of climate change.
Some detailed comments:
Line 249: Please explain in more detail what SimLab is.
Line 304: Please pay attention to the citation style in the parentheses.
Line 318: Please rephrase the section heading.
Line 346: Please give the full names of the land cover types in the figure caption. Either red-point lines or black bars are needed in the plots.
Figure 4: over which region were the uncertainties of CI evaluated? Please specify.
Figure 5: how was the sensitivity defined? Please elaborate in the corresponding text sections.
The first paragraph of the Discussion is too long, please divide it into several paragraphs, so that they are easier to follow for the readers.
Please recommend in the Conclusion whether the proposed method could be incorporated in future MODIS GPP estimation operational algorithms.
Comments on the Quality of English LanguageMinor editing of English language required.
Author Response
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Author Response File: 
Author Response.docx
Reviewer 2 Report
Comments and Suggestions for AuthorsThis study proposes a TL-CLUE model that considers the seasonal difference in the CI based on the TL-LUE model to characterize general changes in canopy seasonality. This method composites monthly CI values into 2 or 3 Ω values to capture the seasonal changes in CI while attempting to reduce the potential uncertainty caused during CI inversion. The one-year cycle of leaf life is divided into 2 (leaf-off and leaf-on) or 3 seasons (leaf-off, leaf-scattering, and leaf-gathering) according to MCD12Q2, and the mean CI of each corresponding season for each vegetation class is computed to smoothen the uncertainties within each seasonal section. With these 2 or 3 seasonal Ω values as inputs, the TL-CLUE model is run and validated based on observations from 84 eddy covariance (EC) tower sites across North America. The research view is clear, but there are still the following problems.
1. What does EVI2 mean? Please explain in the text. What is the basis for selecting the EVI2 values for the four periods? And please add the reasons. In addition, EVI2 values may vary with climate change in different seasons, so each of the four periods should be different.
2. I am skeptical of the results for Ω values for different vegetation types at different times in Table 3. Please add the calculation or extrapolation process and the corresponding reasons.
3. It is proposed to merge Figures 6-Figure 8 and add the results of the consistency comparison between the LGS and LSS periods.
4. It is recommended that Figures 10 and 11 complement the spatial distribution of the simulated values of GPP for both the LGS and LSS periods.
5. The discussion should focus on the methodology and results of the TL-CLUE model, and it is recommended that this section be rewritten.
6. Please ensure the images and the font sizes are consistent throughout the text.
7. Please check the references throughout the text. The references in lines 304-305 are not labeled in the manuscript.
Comments on the Quality of English LanguageSuggest changes to the tenses throughout the text. Please check the use of abbreviations throughout the text.
Author Response
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Author Response File: Author Response.docx
Reviewer 3 Report
Comments and Suggestions for AuthorsIn the “Abstract” section, the authors write, “With these 2 or 3 seasonal Ω values as inputs, the TL-CLUE model is run and validated 31 based on observations from 84 eddy covariance (EC) tower sites across North America” (Page 1, lines 31 to 32). In the “Model and Methods” section, nothing is detailed about the observed field values for validation, where does GPP data come from? Methodology for determination?
Author Response
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Reviewer 4 Report
Comments and Suggestions for AuthorsThis manuscript presents an introduction, study area, methodology, results, discussion, and bibliography. The authors used figures to help the readers and Tables that help to understand the results. The tables and figures are explicit. The study is very interesting and shows an application of remote sensing in the GPP estimation. The conclusions are coherent and very detailed. Are very good. The references are appropriate.
The file attached has some suggestions.
Comments for author File: 
Comments.pdf
Author Response
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Author Response File: Author Response.docx
Round 2
Reviewer 2 Report
Comments and Suggestions for Authors1. Suggested changes to Figure 6 for overlap and Figure 7 for dimensions.
2. Please double-check text editing errors.
3. Correct references in the text and the reference list according to the journal's format.
Author Response
Response 1: Thanks for this comment. The overlap party has been already removed from the Figure 6 position. Additionally, in Figure 7, the vertical axis has been moved from the left to the right so that readers can easily understand its meanings.
Response 2: Thanks for this comment. We checked the editing errors throughout the manuscript, again.
Response 3: Thank you for your suggestion. All references were corrected according to the MDPI standard format of Remote Sensing, and ‘DOI’ is mainly added to the reference.
