Three Decades of Gross Primary Production (GPP) in China: Variations, Trends, Attributions, and Prediction Inferred from Multiple Datasets and Time Series Modeling

Round 1

Reviewer 1 Report

Accurate monitoring of gross primary productivity is central to the study of ecology and ultimately to the survival of almost all living organisms on earth. Providing tools to support such ecological studies is vital. This paper examines the issues involved in monitoring gross primary productivity and proposes an approach to time series monitoring using multiple data sources.

I found this paper to be very clear, well-written and well-structured. The methodology is sensible and well-explained. The diagrams and tables are very clear. It describes a great deal of work that has clearly been done with great care, and it's work of considerable importance. I have very few criticisms of this paper.

The abstract is excellent and gives a clear overview of the work that was done on the project and how it is described in the paper. I liked the introduction too - the background to the project is explained well and a clear justification was given for carrying out this research. It's nice to see the objectives of the research so clearly specified.

The statistical methodology seems sound and is explained well. I really liked the conclusions section - clearly written and well-identified main findings.

Comprehensive references too.

There are just a few minor issues that I noticed:

line 260 - he -> The

lines 296-299 - the paper is full of abbreviations! Why not say here that when you say spring in the paper, it means March, April and May, etc. Then you can replace all occurrences of MAM, JJA, SON and DJF. But I don't feel strongly about this!

line 385 - phoneme -> phenomenon

lines 406-407 - avoid line breaks between sign and the number

line 509 - .12 -> 12

line 533 - google earth engine -> Google Earth Engine

Author Response

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Reviewer 2 Report

Please find the attachment for my review. 

Comments for author File: Comments.pdf

Author Response

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Reviewer 3 Report

Summary: This paper describes a study of the spatiotemporal variability and trends of GPP and its associated climatic and anthropogenic factors in China from 1982 to 2015, mainly based on the optimum light use efficiency (LUEopt) product. Th authors also use the autoregressive integrated moving average (ARIMA) model to forecast the monthly GPP for a one-year lead time. The study is aimed at refining the estimation of gross primary productivity (GPP) in order to understand plant carbon sequestration and grasp the quality of the ecological environment.  The authors show that GPP underwent an increase in the study period, and that of the GPP dynamics are concurrently affected by climate factors and human activities. Furthermore, the ARIMA model achieves satisfactory prediction performance in most areas though the accuracy is influenced by both the data values and data quality.

Comments: I suggest a detailed copyediting of all the manuscript, as for instance in Section 2.3, in which for instance some words are repeated in the same sentence, and CO2 should be written as CO₂.

 

Author Response

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Author Response File: Author Response.pdf