Hyperspectral Vegetation Indices to Assess Water and Nitrogen Status of Sweet Maize Crop

Round 1

Reviewer 1 Report (Previous Reviewer 1)

The ecophysiological responses of sweet corn under different water and nitrogen input conditions were described by full spectrum analysis and vegetation index calculation. The results demonstrate the importance of the red edge vegetation index in assessing the condition of sweet corn. However, there is few innovative points in the article. Both the vegetation indices and the analysis methods already existed. Besides, several similar papers have already been published on Agronomy, which all have higher quality than this one:

[1]Sellami, M.H.; Albrizio, R.; Čolović, M.; Hamze, M.; Cantore, V.; Todorovic, M.; Piscitelli, L.; Stellacci, A.M. Selection of Hyperspectral Vegetation Indices for Monitoring Yield and Physiological Response in Sweet Maize under Different Water and Nitrogen Availability. Agronomy 2022, 12, 489.

[2]Ma, L.; Chen, X.; Zhang, Q.; Lin, J.; Yin, C.; Ma, Y.; Yao, Q.; Feng, L.; Zhang, Z.; Lv, X. Estimation of Nitrogen Content Based on the Hyperspectral Vegetation Indexes of Interannual and Multi-Temporal in Cotton. Agronomy 2022, 12, 1319.

[3]Li, H.; Li, D.; Xu, K.; Cao, W.; Jiang, X.; Ni, J. Monitoring of Nitrogen Indices in Wheat Leaves Based on the Integration of Spectral and Canopy Structure Information. Agronomy 2022, 12, 833.

Author Response

The ecophysiological responses of sweet corn under different water and nitrogen input conditions were described by full spectrum analysis and vegetation index calculation. The results demonstrate the importance of the red edge vegetation index in assessing the condition of sweet corn. However, there is few innovative points in the article. Both the vegetation indices and the analysis methods already existed. Besides, several similar papers have already been published on Agronomy, which all have higher quality than this one:

[1]Sellami, M.H.; Albrizio, R.; Čolović, M.; Hamze, M.; Cantore, V.; Todorovic, M.; Piscitelli, L.; Stellacci, A.M. Selection of Hyperspectral Vegetation Indices for Monitoring Yield and Physiological Response in Sweet Maize under Different Water and Nitrogen Availability. Agronomy 2022, 12, 489.

[2]Ma, L.; Chen, X.; Zhang, Q.; Lin, J.; Yin, C.; Ma, Y.; Yao, Q.; Feng, L.; Zhang, Z.; Lv, X. Estimation of Nitrogen Content Based on the Hyperspectral Vegetation Indexes of Interannual and Multi-Temporal in Cotton. Agronomy 2022, 12, 1319.

[3]Li, H.; Li, D.; Xu, K.; Cao, W.; Jiang, X.; Ni, J. Monitoring of Nitrogen Indices in Wheat Leaves Based on the Integration of Spectral and Canopy Structure Information. Agronomy 2022, 12, 833. Open Review

We would like to thank the reviewer for the positive comment and useful suggestions. We have also quoted the suggested papers in our manuscript.

Although we are completely aware of the high scientific quality of the above-mentioned papers, we would like to highlight that our manuscript is specifically focused on investigating the response of selected vegetation indexes to water and N stress at the most sensitive phenological stages of sweet maize grown under Mediterranean environment.

Author Response File: Author Response.docx

Reviewer 2 Report (New Reviewer)

The study assesses the possibility of real-time monitoring of maize growth, water stress, and nitrogen deficiency using hyperspectral vegetation indices, important in N and water management, as goal of precision farming.

Lines 205-206 “However, in our study, the criteria for index selection were done on their previously successful use in numerous studies, and they are presented in Table 1.” Given that sweet maize is a C4 type plant and the experimental design included N fertilization, irrigation and determination of stomatal conductance to water vapour, a relevant index in assessing water and nitrogen status, in order to establish practical solutions for farmers (fertilization- irrigation), would have been WUE.

Conclusions should reflect the most relevant findings and recommendations specific to the cultivation of sweet corn.

Author Response

The study assesses the possibility of real-time monitoring of maize growth, water stress, and nitrogen deficiency using hyperspectral vegetation indices, important in N and water management, as goal of precision farming.

Lines 205-206 “However, in our study, the criteria for index selection were done on their previously successful use in numerous studies, and they are presented in Table 1.” Given that sweet maize is a C4 type plant and the experimental design included N fertilization, irrigation and determination of stomatal conductance to water vapour, a relevant index in assessing water and nitrogen status, in order to establish practical solutions for farmers (fertilization- irrigation), would have been WUE.

We would like to thank the reviewer for his/her precious comments and we have included and discussed yield water use efficiency in the paragraphs 2.2.7, 3.1. and 5.

Conclusions should reflect the most relevant findings and recommendations specific to the cultivation of sweet corn.

The conclusions have been improved as indicated by the reviewer, by reporting findings on sweet maize response to water and N stress.

Author Response File: Author Response.docx

Reviewer 3 Report (New Reviewer)

Considering the impact of climate change and possible shortage of different input for farming, a deployment of novel technologies in the field of precision farming has introduced in this article. It is being claimed that the DATT index based on near-infrared and red-edge wavelengths performed better than other indices in explaining variation in chlorophyll content, whereas the Double Difference Index (DD) showed the greatest correlation with the leaf- gas exchange. The Modified Normalized Difference Vegetation Index (NNDVI) and the ratio of Water Band Index and Normalized Difference Vegetation Index (WBI/NDVI) showed the highest capacity to distinguish the interaction of irrigation x nitrogen, while the best discriminating capability of those indices was under low nitrogen level. Moreover, red-edge-based indices had higher sensitivity to nitrogen levels, compared to structural and water band indices. This study highlighted that it is critical to choose proper narrow-band vegetation indices for monitoring plant eco-physio logical response to water and nitrogen stresses. The following points may consider to improve the draft of manuscript.

1.       Two different inputs were consider (Water and Nitrogen) for crop production. It is suggested that the Carbon with Nitrogen (C: N) may add to check the soil profile and wilting point to grow the real condition of the plant.

2.       In section Material and Method, the climate data of 30 years were considered to predict the climate change impact. The seasonal variation or monthly trend may add to evaluate the impact of temporal change.

3.       Maize is an important crop, two times harvest in a year. The comparison of both harvested crop may add to evaluate the climate impact on it.

4.       The results of Figure 04 needs to elaborate and compare of different parameters with evidence of literature.

5.       Figure 05 graphics are not visible.

6.       It was recommended that Multivariate techniques and machine learning may overcome certain limitations in assessing vegetation parameters under different stresses and they should be investigated in future works. Both are broader range of tools, it is suggested that the author may specify the future work.

Author Response

Considering the impact of climate change and possible shortage of different input for farming, a deployment of novel technologies in the field of precision farming has introduced in this article. It is being claimed that the DATT index based on near-infrared and red-edge wavelengths performed better than other indices in explaining variation in chlorophyll content, whereas the Double Difference Index (DD) showed the greatest correlation with the leaf- gas exchange. The Modified Normalized Difference Vegetation Index (NNDVI) and the ratio of Water Band Index and Normalized Difference Vegetation Index (WBI/NDVI) showed the highest capacity to distinguish the interaction of irrigation x nitrogen, while the best discriminating capability of those indices was under low nitrogen level. Moreover, red-edge-based indices had higher sensitivity to nitrogen levels, compared to structural and water band indices. This study highlighted that it is critical to choose proper narrow-band vegetation indices for monitoring plant eco-physio logical response to water and nitrogen stresses. The following points may consider to improve the draft of manuscript.

1. Two different inputs were consider (Water and Nitrogen) for crop production. It is suggested that the Carbon with Nitrogen (C: N) may add to check the soil profile and wilting point to grow the real condition of the plant.

We would like to thank the Reviewer for this comment. We have included the average values of the main soil properties in M&M (paragraph 2.1).

2. In section Material and Method, the climate data of 30 years were considered to predict the climate change impact. The seasonal variation or monthly trend may add to evaluate the impact of temporal change.

We would like to point out that our manuscript is not focused on prediction of climate change impact. In M&M (line 122), we referred to 30 years average climate data only to characterize the climatic conditions of our experimental site. 

3. Maize is an important crop, two times harvest in a year. The comparison of both harvested crop may add to evaluate the climate impact on it.

We would highlight that sweet maize crop (summer crop) was harvested only once in our experimental conditions.

4. The results of Figure 04 needs to elaborate and compare of different parameters with evidence of literature.

We added a discussion on Fig. 4 in the paragraph 4.1.

5. Figure 05 graphics are not visible.

We believe that it was a problem only of the visualization of the figure, anyway a TIFF version will be provided as well in the submission of the revised version.

6. It was recommended that Multivariate techniques and machine learning may overcome certain limitations in assessing vegetation parameters under different stresses and they should be investigated in future works. Both are broader range of tools, it is suggested that the author may specify the future work.

We improved the conclusions session and we also specified possible data analysis methods.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report (Previous Reviewer 1)

1 Line174: net photosynthetic CO2 assimilation, (A,... However, there is no A in Figure 5.

2 Line288: DAS should be described before Figure 1.

Author Response

We would like to thank the Reviewer for these further comments. We have reported both in our revised manuscript.

Reviewer 3 Report (New Reviewer)

The manuscript has improved from the authors and seems fine to me. It may further consider for next process.

Author Response

We would like again to thank the Reviewer.

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

The ecophysiological responses of sweet corn under different water and nitrogen input conditions were described by full spectrum analysis and vegetation index calculation. The results demonstrate the importance of the red edge vegetation index in assessing the condition of sweet corn. However, there is few innovative points in the article. Both the vegetation indices and the analysis methods already existed. The authors could consider to submit it to other journals.

Reviewer 2 Report

The authors did a field study to assess the use of hyperspectral-based vegetation indices in evaluating sweet maize response to water and nitrogen stress. The manuscript has been written in a good and understandable language, results are presented well, although some improvements are needed, and conclusions are drawn logically from the obtained results. However, there are some parts of the study and manuscript that could be improved. The main drawback is that the authors used only one genotype in a study (if I understood correctly). And one season, as well. The authors should address this in the discussion section. Also, spectral data have been collected only five times during the season, which, in a high-throughput phenotyping era sounds poor. Therefore, I feel a bit of a novelty missing in the study. There are other points that are not clear, specifically in the Materials and Methods part. I think that the English could also be checked once more. Detailed comments and suggestions are given in the attached pdf file.

Comments for author File: Comments.pdf

Reviewer 3 Report

Dear Authors, thank you for your contribution. The topic of using remote sensing in precision agriculture and evaluation of level of minerals and vegetation stress is very important not only for farming, but also for environmental protection.

In presented research authors used differrent vegetation indices for minerals level evaluation. However, in presented paper I am missing more experments for different zones and years. Only then it would be possible to use presented method. Please emphasize that your results are valid for that specific place and time.