Dynamic Influence Elimination and Chlorophyll Content Diagnosis of Maize Using UAV Spectral Imagery

Round 1

Reviewer 1 Report

General Comments:

I made efforts to review the manuscript carefully and found that authors have done an excellent job. Here I am giving brief remarks on the current form of a paper.
Qiao et al. have studied the three segmentation methods to remove the background noise in the UAV captured multispectral images of maize canopy. Further, this approach was calibrated and validated against in situ data collected at the study site.  Uncertainty of in situ data mainly due to radiometric calibration and data processing were verified by comparing the daily mean PAR values against Monte Carlo radiative transfer simulated values. This study has comprised UAV remote sensing and ground data. Further, this study tested the three segmentation methods on the estimation of chlorophyll content in a maize canopy.  The modelled derived chlorophyll content compared to the measured chlorophyll content.  Thus, it is an encouraging study that presents the comparison of three different methods for removing the background signal from the UAV acquired images.

Please see the comments and suggestions included in the following paragraph.

Abstract
I suggest the authors improve the abstract though its very well written but there is scope to improve for better clarity.

Statistical analysis results are shown for the different methods. Rc2 = 0.5431, RMSEF = 4.2184; Rv2 = 0.5894, and RMSEP = 4.6947. What is the difference between the Rc2 and Rv2 and RMSEF and RMSEP. If Rc2= Rv2= correlation coefficient and RMSEF=RMSEP represents the Root Mean Square Error, then I suggest authors to simply use R2 and RMSE instead of using different notations.

Introduction

The introduction part is very well presented. The authors have carefully cited the work of the most recent research done in this area. 

Materials and Methods

This section is greatly presented and comprises the full details of the data source.  Samples collected in the field and analyzed in the for chlorophyll-a concentration requires more detail. How was the sample stored?

The authors should cite the method used to derive chlorophyll a.  

 

The authors have used the ENVI image processing software, but which version of the software used is not mentioned. Hence please provide the software version such as ENVI 5.8 or something else.  

Results

This section is very well written. Results are very well presented and supported by the neat and clean plots with the statistical matrices.

However, the modeling results are not robust and didn’t provide the robust estimation of the chlorophyll content.

 
3.3.1 Spectral Reflectance Response: The authors have extracted the DN values of the pixels of the image and converted them into the reflectance. I suggest authors to use the spectroradiometer to measure the reflectance and compare against the UAV based reflectance values,

 

 3.4. Modeling and Analysis of Maize Canopy Chlorophyll Content:

 

Statistical parameters are used to assess the accuracy of models is very limited. Authors should also include statistical parameters like Slope, Intercept, and Bias to prove the model performance.

 

Table 4 summarized the statistical analysis results for the three segmentation methods. None of the methods achieve the R2 above 0.67 and RMSE is high which means modeled values are overestimating and underestimating against the measured values. It is clear from figure 9.

 

Discussion

Under the discussion section, the significance of the results is discussed very well in this manuscript. However, the limitation of the present study to be discussed clearly because the model with such a low R2, high RMSE and low slope cannot be accepted for the publication.

Conclusions

This section is very well written. The findings of the present study are summarized in a logical and organized manner.

Overall, the authors have used a good standard of English language and presented all the sections and subsections in a logical and organized manner. This study cannot be published in the present form due to a lack of model accuracy against the measured values of chlorophyll content. The modeling results of this study is proving the model with moderate performance without considering detailed statistical analysis. Therefore, I recommend revising the manuscript based on my comments on the result section to improve the model performance.

Author Response

Dear reviewers：

    Thank you for your advice. It will greatly improve the quality of the paper. For your suggestions, we have made a serious and detailed reply, please see the attachment.

Reviewer 2 Report

Excellent research but presentation of the study is rater low. The manuscript need several improvements.

One of the main issue is the inconsistent use of language which can easily be improved may be by using professional services.

The study used 20 vegetation indices, however, which indices had performed better and why is missing in the entire study.

The entire discussion and most of the conclusion is a summary of the earlier stated findings and need major revision.

Please see my comments on the attached file for more details.

Comments for author File: Comments.pdf

Author Response

Dear Reviewer:
    Thank you for your Suggestions, which will greatly improve the quality of this paper. According to your suggestions, there are three main problems of the paper with format modification, improvements of result expression and explanation, and the optimal of the content about discussion. We have made serious and detailed major amendments. Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

I made efforts to review the revised manuscript carefully and satisfied with authors response. Authors have carefully addressed most of my comments on the previous version of the manuscript. The present version of manuscript is greatly improved and clear to me. Hence, the manuscript can be accepted in the present form.

 

Author Response

Dear Reviewer:

    Thank you very much for processing our manuscripts. Your suggestions greatly improve the quality of the paper. In the second round of revision, we further revised the previous revision and typeset the paper. At the same time, we modify the format in the paper according to the requirements of Remote Sensing paper format. We would like to resubmit the manuscript to the special issue of UAVs for Vegetation Monitoring.

    Really appreciate your comments and suggestions. Thank you for your    consideration.

    Best regards.

Author Response File: Author Response.docx

Reviewer 2 Report

Thank you very much for making significant improvement has been mad in the manuscript. Please proof read manuscript for minor improvements.

Author Response

Dear Reviewer:

    Thank you very much for processing our manuscripts. Your suggestions greatly improve the quality of the paper. In the second round of revision, we further revised the previous revision and typeset the paper. At the same time, we modify the format in the paper according to the requirements of Remote Sensing paper format. We would like to resubmit the manuscript to the special issue of UAVs for Vegetation Monitoring.

    Really appreciate your comments and suggestions. Thank you for your consideration.

    Best regards.

Author Response File: Author Response.docx