Utilization of UAV Remote Sensing in Plant-Based Field Experiments: A Case Study of the Evaluation of LAI in a Small-Scale Sweetcorn Experiment

Round 1

Reviewer 1 Report (Previous Reviewer 2)

Dear appreciated Authors,

The manuscript “Utilization of UAV remote sensing in plant-based field experiments: a case study of the evaluation of LAI in a small-scale 3 sweet corn experiment” was found interesting, appears to be scientifically sound and the topic provides identification the most useful spectral vegetation indices to asses LAI and yield and also provides important information to improve field and plant based investigation.

However, there is a need to improve discussion. In investigation was mentioned that was applied different treatments with different N fertilizer level. Therefore, in paper missing discussion of obtained results within individually treatments. After discussion results within each treatment within each year separately, the overall results could be presented. It is not enough just to mention: "the various effects of treatments could be detected since all of the individual plant data were taken" - because different yield, LAI and Spectral Vegetation Indices can be obtained within different treatment. Therefore, I suggest to authors to make major revision and develop discussion separately for control treatment, as well as for another two levels of nitrogen application and than discuss overall results.

Best regards,

NL

 

Author Response

Dear appreciated Authors,

The manuscript “Utilization of UAV remote sensing in plant-based field experiments: a case study of the evaluation of LAI in a small-scale 3 sweet corn experiment” was found interesting, appears to be scientifically sound and the topic provides identification the most useful spectral vegetation indices to assess LAI and yield and also provides important information to improve field and plant based investigation.

- We really appreciate the reviewer on the positive and insightful assessment for our manuscript.

 

However, there is a need to improve discussion. In investigation was mentioned that was applied different treatments with different N fertilizer level. Therefore, in paper missing discussion of obtained results within individually treatments. After discussion results within each treatment within each year separately, the overall results could be presented. It is not enough just to mention: "the various effects of treatments could be detected since all of the individual plant data were taken" - because different yield, LAI and Spectral Vegetation Indices can be obtained within different treatment. Therefore, I suggest to authors to make major revision and develop discussion separately for control treatment, as well as for another two levels of nitrogen application and than discuss overall results.

 

Best regards,

 

NL

 

- We hereby appreciate again the reviewer on the positive comments and insightful suggestions. We must admit the revisions of our manuscript, so have added the discussion for the detection of the experimental treatments. (L351-354)

Reviewer 2 Report (Previous Reviewer 3)

In data processing, the model used was too single, which was raised in the previous review and has not been modified. It should be done to illustrate.

If the corresponding method of obtaining a single plant is used as the innovation point of the article, the description should be strengthened.

Author Response

In data processing, the model used was too single, which was raised in the previous review and has not been modified. It should be done to illustrate.

- We thank the reviewer for the suggestion. we must admit the regression model we used is simple, but we think model simplicity is absolutely important. The purpose of analyses in Fig. 5 and 6 was to find the most useful VI and buffer size in sweetcorn. We think the analyses in Fig. 5 and 6 follow the reliable previous study (Kang et al. 2016 [40]). Kang et al. (2016) tested relationships between diverse VIs and LAI on various crops and suggest that regression model was most suitable. In addition, we have added the description of RMSEs for SR. This result supports the availability of simple regression analyses. (L255-258, L327-329, L337-338)

A simple regression model in Fig.7 shows the relationship between the LAI predicted by plant-based SR and the actual yield. In this result, we high determination coefficient (over 0.7) even using simple analysis. (L262-265). We really think to use advanced models for next analyses, although we used the simple regression analyses in this manuscript.

If the corresponding method of obtaining a single plant is used as the innovation point of the article, the description should be strengthened.

 

- We thank for the reviewer’s suggestion. We have added the innovative point of our method to Discussion. (L354-355)

Reviewer 3 Report (New Reviewer)

Some points must be improved and highlighted to enable the publication of the manuscript to the proposed journal.

The LAI estimation method by vegetation indices obtained by UAVs is already recurrent in the literature and the way it was presented in the study needs to be better described and explored.

Some important points about the collection of aerial images were not described, such as characteristics and configurations of the flight plan.

The processing of images collected in the study area was not commented on.

The use of a radiometric calibration plate to standardize the images and the orthomosaic generated to obtain reflectance data was not mentioned (this is essential, especially in studies with temporal data).

About the plants studied, the form of identification should be better evidenced, the use of marking plates or the collection of geolocation points for extracting further information was not mentioned.

The results and discussion need to be better performed and presented based on the existing literature.

Author Response

Some points must be improved and highlighted to enable the publication of the manuscript to the proposed journal.

- We appreciate the reviewer on the comments and kind suggestions. We must admit the revisions of our manuscript according to the reviewer’s suggestion.

 

 

The LAI estimation method by vegetation indices obtained by UAVs is already recurrent in the literature and the way it was presented in the study needs to be better described and explored.

 

- We used already-existed vegetation indices as the reviewer’s indication. We reviewed several vegetation indices and selected three vegetation indices from that. And, we validated the relationship between each vegetation index and actual LAI and we indicated SR was most suitable in three vegetation indices. We have described the method. (L162-174, L186-227). We have added the description of RMSEs for SR. This result supports the availability of SR. (L255-258)

 

 

Some important points about the collection of aerial images were not described, such as characteristics and configurations of the flight plan.

- We thank the reviewer for pointing out. We think the explanations of aerial data collection by UAV were insufficient. So, we have summarized the plan or characteristics of UAV flight in Table.1 and described it in Materials and method 2.2. (L131-135, L146-156)

 

The processing of images collected in the study area was not commented on.

- We thank the reviewer for pointing out. We have been able to find that the description for image data collection in study area was not mentioned in detail. we have added Table.1 to describe the process of image data collection for study area and made clear in Materials and method 2.2. (L131-135, L146-156)

 

The use of a radiometric calibration plate to standardize the images and the orthomosaic generated to obtain reflectance data was not mentioned (this is essential, especially in studies with temporal data).

- We thank the reviewer’s kindly suggestion, and we have added explanation about radiometric calibration and orthomosaic procedure. We have been indicated the radiometric calibration information such as Panels’ name, standard reflectance, and time for taking panel picture in Table.1. In addition, we have described radiometric calibration circumstance and the image processing software for orthomosaic. (L156-160)

 

About the plants studied, the form of identification should be better evidenced, the use of marking plates or the collection of geolocation points for extracting further information was not mentioned.

- We thank the reviewer for these crucial observations, and we have strongly agreed with comment. We have strengthened the description for generating point data in Materials and methods 2.3.1. We have commented about the collection of sweetcorn coordinate using QGIS software. (L188-201)

 

The results and discussion need to be better performed and presented based on the existing literature.

 

- We thank the reviewer’s suggestion. We have added the description to Discussion and improved the description as the reviewer suggestion with the other reviewers’ indications.(L311-369)

 

Round 2

Reviewer 1 Report (Previous Reviewer 2)

Dear appreciated Authors,

Dear appreciated Editors,

The manuscript "Utilization of UAV remote sensing in plant-based field experiments: a case study of the evaluation of LAI in a small-scale sweetcorn experiment" appears to be significantly improved. 

The topic provides identification the most useful spectral vegetation indices to assess LAI and yield, scientifically sound and provides important information to improve field and plant based investigation.

Just in Line 86: Please precise the accurate number of sweetcorn experimental fields instead: "over a thousand "

However, the author's paper should be accepted for publication, because the paper represents contribution to science.

Best regards,

Dr. Nataša Ljubičić

Author Response

Dear appreciated Authors,

Dear appreciated Editors,

The manuscript "Utilization of UAV remote sensing in plant-based field experiments: a case study of the evaluation of LAI in a small-scale sweetcorn experiment" appears to be significantly improved.

The topic provides identification the most useful spectral vegetation indices to assess LAI and yield, scientifically sound and provides important information to improve field and plant based investigation.

- We really appreciate the reviewer on the positive and insightful assessment for our manuscript.

 

Just in Line 86: Please precise the accurate number of sweetcorn experimental fields instead: "over a thousand "

- We thank the reviewer’s kindly suggestion, and we have been able to find that the description for accurate number of sweetcorn was not mentioned in detail. We have added the exact number of sweetcorn in both year. (L86-88)

 

However, the author's paper should be accepted for publication, because the paper represents contribution to science.

Best regards,

Dr. Nataša Ljubičić

- We hereby appreciate again the reviewer on the positive and insightful assessment for our manuscript.

 

Reviewer 3 Report (New Reviewer)

The corrections greatly improved the work, clarifying important observations that were previously not presented in the body of the text of the work.

However, I believe that some more small improvements can be made to realize the potential of the proposed study.

-It would be interesting to present the reason for choosing these 3 vegetation indices, whether due to a specific spectral band to highlight some characteristic, or another reason.

-It is also interesting to standardize the treatments, in the methodology it is described as 'control' 'N1' and 'ND' and in the figure with the yield it is appearing as 'C', 'T1' and 'T2'. Perhaps a caption in figure 7 would also be good, which would avoid having to go back in the methodology to verify the present treatments.

-Another point that I think is important to highlight is the fact that the treatments were disregarded when estimating the LAI by the UAV and later when analyzing the yield data for the SR, the treatments were considered. I think it would also be important to highlight/justify the reasons for this, as it is confusing when reading this differentiation when treatments are grouped and when treatments are not grouped.

-In discussing the results, it would be interesting to highlight the reason why the SR was superior to the other vegetation indices and for this reason was chosen for use in estimating the LAI. Lines 346-349 mention the conditions of solar radiation, but how did this have an impact when standardizing the data using the radiometric calibration plate?

Author Response

The corrections greatly improved the work, clarifying important observations that were previously not presented in the body of the text of the work.

However, I believe that some more small improvements can be made to realize the potential of the proposed study.

- We appreciate the reviewer on the comments and kind suggestions. We must admit the revisions of our manuscript according to the reviewer’s suggestion.

 

It would be interesting to present the reason for choosing these 3 vegetation indices, whether due to a specific spectral band to highlight some characteristic, or another reason.

- We thank the reviewer for pointing this out. We totally agree and have strengthened the reason why the 3 vegetation indices were selected for our study. (L88-96)

 

-It is also interesting to standardize the treatments, in the methodology it is described as 'control' 'N1' and 'ND' and in the figure with the yield it is appearing as 'C', 'T1' and 'T2'. Perhaps a caption in figure 7 would also be good, which would avoid having to go back in the methodology to verify the present treatments.

- We thank the reviewer’s kindly suggestion, and we have been able to find that the abbreviation for treatment in Figure 7 unmatched compared with description in Materials and methods. We have checked again the abbreviation for treatment and corrected the abbreviation in Figure 7. (L286-287)

 

Another point that I think is important to highlight is the fact that the treatments were disregarded when estimating the LAI by the UAV and later when analyzing the yield data for the SR, the treatments were considered. I think it would also be important to highlight/justify the reasons for this, as it is confusing when reading this differentiation when treatments are grouped and when treatments are not grouped.

- We thank the reviewer for pointing this out. We agree with the reviewer. We think the regression analysis between VIs and LAI needs to be carried out regardless of treatment because the LAI prediction by VIs is important. According to this, we have added the description for avoiding any confusion from readers. (L225-227)

 

In discussing the results, it would be interesting to highlight the reason why the SR was superior to the other vegetation indices and for this reason was chosen for use in estimating the LAI. Lines 346-349 mention the conditions of solar radiation, but how did this have an impact when standardizing the data using the radiometric calibration plate?

- We thank the reviewer for pointing this out. We think the mention for the solar radiation made the confusion of description. We followed Hashimoto et al (2019) about the analysis. We have ruled out the mention of solar radiation and clarified the usability of SR in the LAI changes. (L331-334)

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

1. References should be updated.More than half of the literature was published ten years ago.

2. The introduction needs to be greatly improved. The research on inversion of LAI based on remote sensing multi-spectral data is widely carried out on various underlying surfaces around the world. The article should further discussed the research status and existing problems, and put forward the purpose and highlight of the research.

3. At present, there are dozens of commonly used spectral indices to invert LAI. It is not sufficient to use only three spectral indices for comparison. The existing research basis should be referred to and more indices should be discussed.

4. It is necessary to conduct sensitivity analysis for the control group experiment, including but not limited to fertilization status, planting density, and in different growth periods.

5. In the discussion part, it is suggested to compare the accuracy of satellite remote sensing and UAV at different pixel scales.

Reviewer 2 Report

Dear appreciated Authors,

The manuscript “Utilization of UAV remote sensing in plant-based field experiments: a case study of the evaluation of LAI in a small-scale 3 sweet corn experiment” was found interesting, appears to be scientifically sound and the topic provides identification the most useful spectral vegetation indices to asses LAI and yield and also provides important information to improve field and plant based investigation.

However, there are a few details which should be considered (see below) and some revisions have to be made and before it can reach a publishable value.

Line 14: In the crop production, which largely dependent on the environmental conditions, various attempts at environmental or social changes have been highlighted, and many field experiments are needed for them.

Line 29:  Among the three VIs, index SR was found the most promising in the estimation of the LAI of the individual sweetcorn plants, providing the strongest correlation of yield with SR.

Line 40: However, environmental problems such as the overuse of agrochemicals and soil accumulation caused by modern agricultural production processes, mentioning that changes to agricultural production should be made [1].

Line 53-54: In recent time, unmanned aerial vehicles (UAVs) are gaining popularity and it is expected to be used in various research fields.

In agriculture, UAVs have gained widespread use for seeding, fertilizer application and monitoring plant growth [5-7].

Line 77:  Sweetcorn cultivar, namely "Nmetomigi" was cultivated during two consecutive vegetation seasons of 2018 and 2019.

Line 105:  Tasseling is an important growth stage in maize breeding and seed production, because the maize tassel is a typical sign indicating the transition from vegetative to reproductive growth and the most sensitive to temperatures [20-22].

Line 108: Each dataset involved five spectral bands: blue, green, red, red edge, and near infrared?

Line 112: Delete point

Line 114: Since that measurements of passive reflectance sensors are influenced by time of day and solar elevation angle, it requires to consider the angular variation in reflectance and ambient light luctuations [Souza et al. 20121, Erdle et al., 2011]. Therefore, the RGB and multispectral image data collection using UAVs measurements were made close to noon, between 10:00 am and 2:00 pm on cloud-free and sunny days.

 For reference:

 de Souza, R.; Buchhart, C.; Heil, K.; Plass, J.; Padilla, F.M.; Schmidhalter, U. Effect of Time of Day and Sky Conditions on Different Vegetation Indices Calculated from Active and Passive Sensors and Images Taken from UAV. Remote Sens. 2021, 13, 1691. https://doi.org/ 10.3390/rs13091691

Erdle, K.; Mistele, B.; Schmidhalter, U. Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crop. Res. 2011, 124, 74–84.

Line 135-136: To generate the plant-based data, the locations of existing sweetcorn in the field were taken as the point data using QGIS software version 3.4.3.

Line 279: Difference between the border section and the outskirt section in this study, was found important in field experiments. The gradual spread of pest and insect damage in border sections indicated that side rows of each plots should serve as guard rows.

Based on all, author's paper should be accepted for publication, after minor revisions, because the paper represents a significant contribution to for the field investigations, as well as for the science.

Best regards,

NL

 

Comments for author File: Comments.pdf

Reviewer 3 Report

This paper describes the assessment of leaf area and yield of individual maize plant based on remote sensing of UAV and the exploration of differences between regional center and side lines by setting vegetation buffer. After studying the paper, I believe that this paper needs to be further modified in the following aspects:

1. The whole content is too colloquial, so it is suggested to strengthen the written writing.

2. The Introduction part and the test content of the article is relatively tight, the point of view is not clear, and the purpose and significance of the test is not clearly indicated.

3. 2.2 Part introduces data collection, the whole section is stacked, the entries are not clear, and part of the chart is missing to explain.

4. 2.3 The description of the buffer zone of single plant and generating plant is not clear.

5. The data processing method is too simple, using simple regression statistical analysis.