Improving Soil Moisture Assessment of Turfgrass Systems Utilizing Field Radiometry

Round 1

Reviewer 1 Report

This study is relevant and important in the context of selecting an appropriate index for water stress detection and water management in turf grasses. I found the paper to be overall well written and felt confident that the authors performed careful and thorough analysis. The data from this greenhouse study provides valuable information on sensor-based irrigation management. The strength of this paper is in the manner that several existing water stress indices have been tested and compared against for better interpretation. However, I came across with few suggestions that I think need might help the readers to understand this study better.

1. It might have been better to give the explanation of dry-down treatment and the requirement for it in few sentences at the beginning of the methodology section or where the dry-down greenhouse was first mentioned.  
2. Wind tunnel fan was used to mimic field conditions, however, in the field, factors such as elevation and slope could also influence the distribution of soil water content. This aspect of field research is missing in the discussion though differences in light conditions have been mentioned.
3. Lines 277-278: Result not shown in Table 1. Is there another table?
4. Line 492: Citation Format?
5. Information on the amount of irrigation applied to the turf grasses is missing. It can provide more insight into the relationship between soil type, grass type, and associated reflectance.
6. Information on Lines 393-394, Lines 446-449, and Lines 488-491 seem contradictory and confusing. Maybe few more sentences in the discussion section about what were the overall pros and cons of GRI compared to WBI could make this less confusing and more relevant to the objective of this study.
7. Line 505-512: Could turfgrass managers use this information for grass selection? Linking this information with the climate of the study area could provide some recommendations for the turfgrass managers, especially in the context of precision irrigation management.
8. Line 561-562: How will UAS improve data collection?

Author Response

Response to Reviewer 1 Comments

Overall Changes:

• Please keep in mind the lines I mention in responses might not match up to previous lines mention with concerns due to the significant things added or taken away. Several things have shifted due to new content added and the major changes to graphical data representation per another reviews request
• Based on several citation concerns it was noticed a few citations were not correct and the in-text numbering citations were significantly off so do not be alarmed with the numerous citation changes.
• The document did not reflect the corresponding author as this changed during the editorial and submission process but was not reflected by the document.
• Per a second reviewer's request, all the figures were updated and enhanced for clarity and quality.

Point 1: It might have been better to give the explanation of dry-down treatment and the requirement for it in few sentences at the beginning of the methodology section or where the dry-down greenhouse was first mentioned.

Response 1: Lines 152 – 161 add some clarity of why we did the dry-downs within the greenhouse to help control or mitigate certain variable factors that are found in the real world and field conditions

Point 2: Wind tunnel fan was used to mimic field conditions, however, in the field, factors such as elevation and slope could also influence the distribution of soil water content. This aspect of field research is missing in the discussion though differences in light conditions have been mentioned.

Response 2: Lines 497-503 address the missing content of what factors are observed in the field that causes variability and why we conducted isolated dry downs before looking at incorporating other factors that manipulate soil moisture stress.  

Point 3: 
 Lines 277-278: Result not shown in Table 1. Is there another table?

Response 3: Line 301 this was a typo and overlooked. There is no other table that is missing and the actual table 1 that shows the Pearson correlation table results does not apply to this portion of the paper.

Point 4: Line 492: Citation Format?

Response 4: Lines 761,761 a different source was found to address this citation issue however, there are several other citation updates within this paper because there were several other issues found while updating this correction. The in-text citation numbers were corrected and several references removed that were not done so from previous edits.

Point 5: Information on the amount of irrigation applied to the turf grasses is missing. It can provide more insight into the relationship between soil type, grass type, and associated reflectance.

Response 5: Line 159 addresses the total water amounts that were applied over the top of the experimental units before the initiation of each run through overhead greenhouse irrigation.

Point 6: Information on Lines 393-394, Lines 446-449, and Lines 488-491 seem contradictory and confusing. Maybe few more sentences in the discussion section about what were the overall pros and cons of GRI compared to WBI could make this less confusing and more relevant to the objective of this study.

Response 6: Lines 487-494, 554-562 are highlighted with a comment as I think this section which was already in the paper with a few revisions already explains the differences between the GRI and WBI.  Maybe further clarification is needed but it talks about the different wavelengths of light related to each index and why one might be more beneficial than the other.

Point 7: Line 505-512: Could turfgrass managers use this information for grass selection? Linking this information with the climate of the study area could provide some recommendations for the turfgrass managers, especially in the context of precision irrigation management.

Response 7: Lines 516-520 provide a short excerpt of how this information could be applied within a real-world scenario. This information could not be used at this time for this purpose, but the capability is there with further research experiments. This is noted within this portion of the paper

Point 8: Line 561-562: How will UAS improve data collection?

Response 8: Lines 566-574 address this comment by means of data collection speed. While further research is needed, once established, the idea is unmanned aircraft system will allow rapid spectral data collection correlated with soil moisture status and be a non-destructive means of assessment.

 

 

 

Author Response File: Author Response.docx

Reviewer 2 Report

The authors demonstrated how three vegetation indices can be used to estimate and predict moisture stress in several turfgrass canopies, even prior to wilting symptoms. The authors showed that index based on visible light may represent cost-effective method for estimating moisture stress in turfgrasses, even better than popular, but relatively “expensive to obtain” NDVI. The precise description of the methodology deserves attention and appreciation. That allows other researchers to repeat the research, which is not often the case in nowadays articles. The only drawback of this article is the poor quality of all figures. I believe that after this technical imperfection is corrected, the text can be published without correction.

Author Response

Response to Reviewer 2 Comments

Overall Changes:

• All figures have been updated and enhanced in quality, information, labels, and figure captions
• Based on citation concerns it was noticed a few citations were not correct and the in-text numbering citations were significantly off so do not be alarmed with the numerous citation changes.
• The document did not reflect the corresponding author as this changed during the editorial and submission process but was not reflected by the document.

Point 1: The authors demonstrated how three vegetation indices can be used to estimate and predict moisture stress in several turfgrass canopies, even prior to wilting symptoms. The authors showed that an index based on visible light may represent a cost-effective method for estimating moisture stress in turfgrasses, even better than popular, but relatively “expensive to obtain” NDVI. The precise description of the methodology deserves attention and appreciation. That allows other researchers to repeat the research, which is not often the case in nowadays articles. The only drawback of this article is the poor quality of all figures. I believe that after this technical imperfection is corrected, the text can be published without correction.

 Response 1: All figures have been updated through an original file and then copied through a screenshot into the revised document. If there are any other updates, suggestions, or critiques, it will be much quicker now that all graphs have been remade. Some of the graphs have limited changes but below is a list of things that were updated and keep in mind no changes to tables were made.

• Figure 1:
  • Gridlines removed
  • Symbols changed to make it more legible to identify the key parameters (lower and upper asymptotes and inflection point).
  • Treatment updated to reflect creeping bentgrass sand (CBGS).

• Figure 2:
  • Gridlines removed
  • Trendlines not as thick and color changed to be more uniform
  • The correlation coefficient was added to the ledged of the graph
  • Treatment abbreviations were updated to reflect properly with the figure caption.
  • Inflection points markers removed the make the graph cleaner, we think it is not needed since the previous figure established where the inflection point is observed, and a general inference can be made where this value should be located.

• Figure 3:
  • Borderlines of the graphs were removed.
  • P values were updated because it was noticed that the calculated probability was used from the effects test and not the means ANOVA separation and this discrepancy was corrected. This update did not change any outcome of the treatment separations.
  • Connecting letter reports were removed to clean up the graph and the standard error remains.

• Figure 4:
  • Gridlines removed
  • Correlation coefficient added to the figure ledged
  • The critical upper value used to calculate wilt prediction was changed to red to draw the attention of its location.
  • Trendline thickness is minimized in size to make it a less cluttered graph.
  • Figure caption and the figure itself was updated to represent creeping bentgrass in loam soil (CBGL)

• Figure 5:
  • Color changed of GRI from green to gray because the green color was used for bentgrass-associated treatments throughout the document. I wanted to try and keep everything uniform.
  • Connecting letter reports removed and only used the standard error to represent vegetation index separation
  • Figure caption updated to reflect the indices, abbreviations.
  • Borderlines of the graph were removed all the way around.

• Figure 6:
  • The color of each bar in the graph were updated to reflect the different grass species and letters for each soil type on the graph itself.
  • Legend was updated to reflect the grass species
  • P-value like figure 3 used the effects test and not means ANOVA separation and updated in the figure and text to reflect this discrepancy.
  • Figure caption updated to reflect correct treatments within the study.

Author Response File: Author Response.docx