Use of Geostatistics for Multi-Scale Spatial Modeling of Xylella fastidiosa subsp. pauca (Xfp) Infection with Unmanned Aerial Vehicle Image

Round 1

Reviewer 1 Report

This is a very intersting work in the field of exploring the multi-scale spatial modeling through geostatistics. It is well structured and explained. 

Some minor comments and suggestions:

Figure 1 (localization of the study site) needs a zoom out map, e.g Europe map, because some readers cannot locate the Apulia region. A scale bar is totally needed as well.

Line 160: Can you inform about some indicator of the accuracy of the geometrical correction, RMS of GCPs or others?

Line 171: Can you add one reference to support the selection of the Maximum Likelihood classification method for a few classes?

Line 184: Can you explain why a morphological filter is needed?

Line 191: Can you give more details about the manual edition of the polygons? Can you show a figure with the polygon before and after this edit? How many polygons do you need to edit? How many time did you use?

Figure 3: I suggest one short title “green”, “red”, “red-edge”, “NIR” in the plots a) b) c) and d). I need to see at the first time the difference between four plots.

Line 264: Can you provide the formula (or a reference) of the sincard model? I usually work with other models.

Results and discussion sections are very interesting. However, it is not clear what are results and what is a discussion. As an example, in my opinion Figure 6 is a result. I suggest merge two sections in one, or improve the distribution of the content in both sections. Please, the content is fine.

Lines 396: Some suggestions are very relevant, for instance you mentioned the needs of additional sensors with SWIR and IR. Can you provide references to support these suggestions?

Author Response

We appreciate the time and effort that the reviewer dedicated to providing feedback on our manuscript and are grateful for the insightful comments on and valuable improvements to our paper. We have incorporated the suggestions made by the reviewers. Those changes are highlighted within the manuscript. Please see below, in red, for a point-by-point response to the reviewers’ comments and concerns.

In attached file, in red, for a point-by-point response to the reviewers’ comments and concerns.

Author Response File: Author Response.docx

Reviewer 2 Report

The complete method is introduced and explained in detail in this paper. It's easy for the reader to implement, which is what makes this article particularly good.

The use of drones in agriculture makes a lot of sense. In fact, there are many applications of remote sensing in traditional remote sensing. It is suggested that the author can add some of these contents to enrich the article. The flying of drones is subject to harsh weather conditions. Remote sensing is much better. Is the method in the paper applicable? Whether similar things can be done with drone images at sufficient resolution is not likely to be mentioned in the paper.

Author Response

We appreciate the time and effort that the reviewer dedicated to providing feedback on our manuscript and are grateful for the insightful comments on and valuable improvements to our paper. We have incorporated the suggestions made by the reviewers. Those changes are highlighted within the manuscript. Please see below, in red, for a point-by-point response to the reviewers’ comments and concerns.

In attached file, in red, for a point-by-point response to the reviewers’ comments and concerns.

"Please see the attachment."

Author Response File: Author Response.docx

Reviewer 3 Report

Using UAV image to monitor pests and diseases is one of the main research contents of Precision Farming. The objective of this work was to investigate the potential of UAV images in the fight against Xylella pest for olive trees. I think the key to solving this objective is the sensitivity of UAV optical remote sensing images in detecting Xylella pest for olive trees. However, the author focuses on use the method of Geostatistical Analysis to explore the effect of scale difference, and the analysis of variogram model is not specific and clear enough to be persuasive. Even the numerical meaning of many important result graphs is not specific, for example, in figures 5 and 6, What does the legend value mean?

  The Geostatistical Analysis method is mostly used in traditional point sampling applicability detection. UAV remote sensing data has the characteristics of high resolution. Under such a high resolution (multi-scale) observation data, is it still necessary to use Geostatistical Analysis method to detect the applicability of the data? The answer is definitely not. It is suggested that the author should pay more attention to the methods of UAV remote sensing image identification of pests and diseases in the later work.

Author Response

We appreciate the time and effort that the reviewer dedicated to providing feedback on our manuscript and are grateful for the insightful comments on and valuable improvements to our paper. We have incorporated the suggestions made by the reviewers. Those changes are highlighted within the manuscript. Please see below, in red, for a point-by-point response to the reviewers’ comments and concerns.

In attached file, in red, for a point-by-point response to the reviewers’ comments and concerns.

"Please see the attachment." 

Author Response File: Author Response.docx

Reviewer 4 Report

Dear Authors,

Thank you for submitting your manuscript that you have investigated the infection of Xylella on olive trees using multi-spectral drone modelling. I have detected some significant issues that should be addressed in your revised manuscript. You can find all my comments in the attached file, but some can be listed as follows:

1- The need for a literature survey should be integrated with your sentences in the Introduction part. I understand that your team has some work in this field, but there is extensive literature applying drones for agricultural purposes with similar scopes to your study. 

2- The details of the methodology you have performed need to be included. This is especially essential for the ML classification algorithm you utilized for classifying the different objects (soil, canopy, and shadow). Please see my comments on the attached file.

3- In Figures 3, 5, and 6, the scales of colour bars should be fixed as the same so that a better comparison can be achieved. Similarly, it needs to include how you determine the low, medium, and high zones that have varied in each figure with different ranges. Please clarify this point. 

Considering all, I have suggested a major revision for the publication of your manuscript. I hope my comments will help you to advance your study.

Best Regards

Comments for author File: Comments.pdf

Author Response

We appreciate the time and effort that the reviewer dedicated to providing feedback on our manuscript and are grateful for the insightful comments on and valuable improvements to our paper. We have incorporated the suggestions made by the reviewers. Those changes are highlighted within the manuscript. Please see below, in red, for a point-by-point response to the reviewers’ comments and concerns.

In attached file, in red, for a point-by-point response to the reviewers’ comments and concerns.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

NOTHING

Reviewer 3 Report

The author has made good modifications or replies to the comments

Reviewer 4 Report

Dear Authors,

Thank you for submitting your revised version of the manuscript and for mostly addressing the points that I had raised in my previous review. 

I am now convinced that your manuscript can be published in its present form. 

Best Regards,