Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method

Round 1

Reviewer 1 Report

This study used the EC-LUE model to estimate GPP and further converted to maize yield. Compared to the widely used machine learning algorithm, the proposed yield estimation framework has a physical mechanism. Besides, the manuscript has a clear structure and reasonable results. There are some concerns that should be addressed before publication.

(1) MODIS GPP product was also generated with a light use efficiency model. What is the difference between MODIS product and this study used EC-LUE model? What is the accuracy of yield estimation if directly using MODIS GPP product?

(2) There are some empirical parameters, e.g., AR and RS, in the yield estimation model. How does parameter uncertainty affect the accuracy of yield estimation?

(3) Whether the estimation framework can be used in other areas?

Author Response

Thanks for your comments concerning our manuscript. Those comments are all valuable and very helpful for revising and improving our paper.We have made our best efforts to improve our paper very carefully following comments and suggestions.Our point by point response to the comments made by Reviewer are given below. We hope the revised manuscript will be acceptable to the Reviewer. If still there are concerns, we will be happy to take care once.

Author Response File: Author Response.docx

Reviewer 2 Report

This manuscript is designed to estimate the yield of summer maize in the North China Plain by combining the light use efficiency model and the region-specific harvest index. For the Chinese farming system, it is difficult to obtain crop harvest indices in a large area. In this manuscript, it is a good method to use the estimated GPP and statistical yield of the previous year to calculate the harvest index of this year, taking into account the changes in space and time of the harvest index. And, the estimated results have a good fitting relationship with the statistical yield. Overall, this manuscript is well done and contains publication-worthy results. A few minor revisions are list below.

1.      There are some problems with the pictures in the manuscript. I suggest add a color bar for the density of points in Figure 4 (a) and Figure 5 (a). Figure 6 has no legend, and Figure 7 is blurred compared to other figures.

2.      There is a little problem with the typesetting of the manuscript. In the second section, the third-level headings need to strictly follow our style.

3.      The manuscript needs to be strengthened in writing details. For example, "where" on line 172 should not be capitalized and indented.

4.      Please clarify some expressions in the manuscript. It is better to change "the spatial variation in yield" in line 23 to yield space-time variation or yield variability.

Minor editing of English language required

Author Response

Thanks for your comments concerning our manuscript. Those comments are all valuable and very helpful for revising and improving our paper.We have made our best efforts to improve our paper very carefully following comments and suggestions.Our point by point response to the comments made by Reviewer are given below. We hope the revised manuscript will be acceptable to the Reviewer. If still there are concerns, we will be happy to take care once.

Author Response File: Author Response.docx

Reviewer 3 Report

• What is the main question addressed by the research?

 The research addresses the estimation of yield using GPP extracted from images using the EC-LUE model.

• Do you consider the topic original or relevant in the field? Does it address a specific gap in the field?

Yes, the topic is original. It focuses on the development of EC-LUE model and its efficiency in estimating yield.

• What does it add to the subject area compared with other published material?

The different indices have been studied on how the model helped in estimating the yield of the corn crop. It has been compared with the existing models and shows better performance. 

• What specific improvements should the authors consider regarding the methodology? What further controls should be considered?

Some other vegetative indices should be used along with the existing ones in predicting the productivity of maize. In future, the efficiency of the model in predicting the yield in other field crops should be displayed.

• Are the conclusions consistent with the evidence and arguments presented and do they address the main question posed?

 Yes, they do but in future, the efficiency of the model in predicting the yield in other field crops should be addressed.

• Are the references appropriate?

 Yes

• Please include any additional comments on the tables and figures.

No, they are sufficient.

It is well written. Quality of English is fine.

Author Response

Thanks for your comments concerning our manuscript. Those comments are all valuable and very helpful for revising and improving our paper.We have made our best efforts to improve our paper very carefully following comments and suggestions.Our point by point response to the comments made by Reviewer are given below. We hope the revised manuscript will be acceptable to the Reviewer. If still there are concerns, we will be happy to take care once.

Author Response File: Author Response.docx

Reviewer 4 Report

This research incorporated the eddy covariance light use efficiency model to derive estimates for gross primary productivity, which was subsequently converted into yield using the harvest index. This work enriches the prevailing academic literature. The manuscript is articulated clearly, with sections that are insightful, and findings that align well with the theme presented.

I do have a few points of clarification and suggestions related to the manuscript:

(1) Regarding the handling of missing values in section 2.2.1: Could you detail the validation process for this method? How do you ensure this is accurate in the context of this study? It would be beneficial to understand its accuracy in this specific context.

(2) In section 2.2.2, there's a portion that reads: "The Savitzky-Golay (S-G) filter, which has the potential to remove random noise, was used to smooth the NDVI series in accordance with the "Quality reliability of VI pixels" metric, which describes the quality of pixels in the data. Then, the high-quality pixels from the original sequence were used to replace the pixels at corresponding locations in the new sequence." Could you specify what's meant by "original sequence" and "new sequence"? Also, could you clarify the rationale behind this replacement? From the context, it seemed the pixels were synonymous with NDVI values？

(3) On Line 207: Please ensure consistency in abbreviations throughout the manuscript, such as changing "dr" to align with its formula representation (d_r).

(4) How can the superior accuracy of the model be demonstrated? For instance, does a MAPE of less than 20% indicate a notable improvement over prior methods? It might be worthwhile to provide context to the MAPE value.

(5) In the 'results and discussion' section, perhaps incorporating additional references might further substantiate the concepts and objectives laid out in the manuscript.

(6) Minor spelling and grammar check is needed.

Author Response

Thanks for your comments concerning our manuscript. Those comments are all valuable and very helpful for revising and improving our paper.We have made our best efforts to improve our paper very carefully following comments and suggestions.Our point by point response to the comments made by Reviewer are given below. We hope the revised manuscript will be acceptable to the Reviewer. If still there are concerns, we will be happy to take care once.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

My concerns have been addressed.

Reviewer 4 Report

The authors made revised according to the comments in the previous revision.