Precision Agriculture: Temporal and Spatial Modeling of Wheat Canopy Spectral Characteristics

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. The current development of agricultural hyperspectral remote sensing is relatively mature, and the methods adopted by others in similar studies need to be comprehensively reviewed in this paper to provide a basis for later experiments.

2. In the spectral algorithm, the operation between bands should also have more complex operations similar to the vegetation index. What is the basis for these combinations?

3. What is the basis for selecting these variables in Table 3?

4. Figure 3 and Figure 4 should be revised and optimized, and should be combined with the table. The figure can express limited information, and the figure is rough and not beautiful enough.

Author Response

Response to Reviewer 1 Comments

Point 1: The current development of agricultural hyperspectral remote sensing is relatively mature, and the methods adopted by others in similar studies need to be comprehensively reviewed in this paper to provide a basis for later experiments.
Response 1: 
I accept this opinion.
Thank you for pointing this out. We have expanded the review of existing studies on agricultural hyperspectral remote sensing, focusing on methodologies used in similar contexts. This includes a detailed comparison of hyperspectral data acquisition techniques, band combinations, and algorithms employed for crop growth monitoring. By incorporating these insights, we aim to provide a more robust basis for our experimental framework. Relevant discussions have been added to the Introduction and Literature Review sections.
Thank you very much for your revision.

Point 2: In the spectral algorithm, the operation between bands should also have more complex operations similar to the vegetation index. What is the basis for these combinations?
Response 2: 
I accept this opinion.
We appreciate your suggestion and have elaborated on the basis for the spectral band combinations. Specifically, we now discuss how these combinations are derived from biophysical and biochemical principles of crop physiology, such as chlorophyll absorption, canopy structure, and water content dynamics. Examples of vegetation indices and their theoretical underpinnings have been added to the Spectral Algorithm section, with references to studies that validate these combinations in remote sensing applications.
Thank you very much for your revision.

Point 3: What is the basis for selecting these variables in Table 3?
Response 3: 
I accept this opinion.
Thank you for raising this question. The variables in Table 3 were selected based on their established relevance in remote sensing and image analysis for crop monitoring. These include metrics like entropy, contrast, and texture variance, which capture canopy health, structural complexity, and dynamic changes in growth stages. We have updated the manuscript to clarify the rationale for including each variable and cited relevant references to support their selection.
Thank you very much for your revision.

Point 4: Figure 3 and Figure 4 should be revised and optimized, and should be combined with the table. The figure can express limited information, and the figure is rough and not beautiful enough.
Response 4: 
I accept this opinion.
In response to feedback, we have made several improvements to Figure 3 and Figure 4 to enhance their clarity and readability. For Figure 3, we added color coding to distinguish between different spectral bands and combinations, ensuring that each combination is easily identifiable. Additionally, we have included clear annotations and labels to provide context for each data set, making it easier for readers to interpret the spectral changes across different growth stages. These changes help highlight the key trends in the data while maintaining a visually clean and informative layout.
For Figure 4, we introduced a highlighted line to emphasize the most significant variations in spectral characteristics across the growth stages. The line highlights the critical trends in canopy health, chlorophyll content, and stress levels, making it easier for readers to track the most notable changes over time. Furthermore, we refined the color scheme and added annotations to better explain the meaning of the data presented on the radar chart. These modifications were made to improve the overall visual accessibility of the figures, ensuring that the information is conveyed clearly and effectively, thereby enhancing the interpretation of the crop’s growth dynamics.
Figure 3 and Figure 4 cannot be combined due to the different types of data they present and their distinct purposes.
Figure 3 focuses on the comparison of different spectral band combinations (such as single-band, two-band, three-band, and four-band combinations) in crop monitoring, showcasing how these combinations react to changes in crop growth stages. The primary goal of this figure is to highlight the performance of different band combinations and how they are sensitive to changes in crop health and other characteristics. On the other hand, Figure 4 concentrates on the changes in spectral features at different growth stages of the crop, presented through radar charts. It emphasizes the variations in spectral reflectance at each growth stage and compares the characteristics of different stages.
The reason these two figures cannot be merged is that they serve different purposes: Figure 3 compares spectral band combinations, while Figure 4 examines spectral changes across growth stages. Their presentation styles are also incompatible—Figure 3 compares multiple band combinations, while Figure 4 uses radar charts to display stage-based data. Merging them would lead to overlapping information, making the charts more complex and difficult to interpret. Thus, keeping them separate ensures that each chart maintains its clarity and focus.
The left panel of Figure 3 shows the normalized calculation results of different feature values. The purpose of this visualization is to standardize the feature values, eliminating the impact of varying scales or units between different variables. By normalizing the values, it becomes easier to compare the relative changes in each feature, regardless of their original magnitudes. This approach allows for a clearer understanding of how each feature behaves over time and provides a uniform basis for comparing different spectral characteristics. Normalization ensures that the data's trends and patterns are not skewed by differences in their value ranges, making the analysis more reliable and interpretable.
The right panel, on the other hand, displays the normalized calculation results of different feature values across the seven growth stages (1–7). This visualization is crucial because it illustrates how the spectral characteristics of the crop evolve at each specific growth stage. By presenting the normalized feature values for each growth stage, this panel provides insights into the temporal dynamics of canopy health, leaf area, and stress levels. The comparison across growth stages allows for a better understanding of the crop's physiological changes throughout its life cycle, helping to pinpoint critical periods for intervention (e.g., fertilization, irrigation) and stress monitoring. Overall, this approach enhances the ability to track crop growth with higher precision and supports targeted agricultural management strategies.
 
Figure 4 illustrates the spectral characteristics of wheat across different growth stages, represented through a radar chart. Each segment of the radar chart corresponds to a specific growth stage (1-7), with the various axes reflecting different spectral features or variables, such as the reflectance values from different spectral bands (Green, Red, Red-Edge, Near-Infrared). The chart displays how these features evolve over time as the crop progresses through its growth stages, providing a clear visual representation of changes in the spectral properties of wheat at each critical growth phase.
The purpose of Figure 4 is to facilitate the comparison of spectral changes at different growth stages, highlighting key shifts in crop characteristics like canopy health, leaf area, and stress levels. By using a radar chart, the figure effectively communicates the multidimensional nature of crop growth dynamics and helps identify important growth transitions. This enables a better understanding of how the crop’s spectral features evolve, which is crucial for precision agriculture and supports decisions related to intervention timing and stress detection.
In Figure 4, the highlighted line emphasizes the key trends or significant variations in spectral characteristics at each growth stage. This line allows readers to easily track the most notable shifts in the crop’s spectral features, such as changes in canopy health, chlorophyll content, or plant stress levels. It also helps identify critical growth stages where substantial transitions occur, facilitating a more focused analysis of the crop’s development and making it easier to correlate spectral data with physiological changes for informed crop management decisions.
 
Thank you very much for your revision.

Thanks again for the sincere opinions of the experts and the hard work of the editors!
Sincerely yours,

Dr. Donghui Zhang
Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing, China 100095
email: [email protected]
tel: +8613381126130     Webpage: https://sciprofiles.com/profile/2204570

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The authors present an analysis of the spectral characteristics inherent to durum wheat cover during seven critical phenological phases, using multispectral data captured by drone. In order to define effective and efficient management strategies, a comprehensive spatio-temporal monitoring framework was developed. The results show that the Green, Red-edge and near-infrared spectral bands are sensitive to dynamic changes in wheat.

The manuscript is well organised, but the research objectives do not seem to be sufficiently detailed and innovative.

Currently, research on multispectral analysis of wheat is well advanced, and the main innovative element of the study is the ‘time’ variable.

The data analysis is correct and well structured. Figures, diagrams and tables are well organised, and of great detail. 

The methodologies applied are well described.

I suggest extending the discussion, with particular reference to the advantages over satellite and hyperspectral data.

It would be essential to specify the drone's flight parameters, flight conditions and data acquisition more clearly. I suggest expanding the introductory discussion on the topic of drones in precision agriculture (there are many interesting and topical reviews on the subject).

I would expand the discussion on limitations, particularly with reference to flight operating conditions.

The bibliography is up-to-date and consistent with the topic addressed.

The article is relevant to the field of agronomy and precision agriculture and consequently to the aim of the journal.

Author Response

Response to Reviewer 2 Comments

Point 1: The authors present an analysis of the spectral characteristics inherent to durum wheat cover during seven critical phenological phases, using multispectral data captured by drone. In order to define effective and efficient management strategies, a comprehensive spatio-temporal monitoring framework was developed. The results show that the Green, Red-edge and near-infrared spectral bands are sensitive to dynamic changes in wheat.
Response 1: 
I accept this opinion.
Thank you very much for your revision. We have incorporated your suggestions into the manuscript and updated the sections accordingly. The analysis now provides a more detailed and comprehensive explanation of the spectral characteristics and dynamic changes of wheat across various growth stages, improving the clarity and depth of the findings. Additionally, the spatio-temporal monitoring framework has been further developed to enhance the applicability of the research, supporting more effective decision-making in precision agriculture. We hope that these revisions address the points raised and contribute to the overall quality and coherence of the manuscript.
Thank you very much for your revision.

Point 2: The manuscript is well organised, but the research objectives do not seem to be sufficiently detailed and innovative.
Currently, research on multispectral analysis of wheat is well advanced, and the main innovative element of the study is the ‘time’ variable.
The data analysis is correct and well structured. Figures, diagrams and tables are well organised, and of great detail. 
The methodologies applied are well described.
I suggest extending the discussion, with particular reference to the advantages over satellite and hyperspectral data.
It would be essential to specify the drone's flight parameters, flight conditions and data acquisition more clearly. 

Response 2: 
I accept this opinion.
Thank you very much for your revision. We appreciate your valuable feedback and have made the necessary adjustments to the manuscript. To address your concerns, we have expanded the research objectives to more clearly outline the novel aspects of this study, particularly the role of the "time" variable in multispectral analysis. We also emphasize the advantages of UAV-based multispectral remote sensing compared to satellite and hyperspectral data, particularly in terms of spatial resolution, cost-effectiveness, and the ability to capture detailed temporal changes in wheat growth. 
Additionally, we have clarified the drone's flight parameters, including flight altitude, sensor specifications, and data acquisition protocols. This additional information enhances the transparency and reproducibility of the study, ensuring that the methodologies are well-defined for future research and applications. We hope these revisions meet your expectations and contribute to the manuscript’s overall quality.
Thank you very much for your revision.

Point 3: I suggest expanding the introductory discussion on the topic of drones in precision agriculture (there are many interesting and topical reviews on the subject).
I would expand the discussion on limitations, particularly with reference to flight operating conditions.
The bibliography is up-to-date and consistent with the topic addressed.
The article is relevant to the field of agronomy and precision agriculture and consequently to the aim of the journal.
Response 3: 
I accept this opinion.
Thank you very much for your revision. In response to your suggestion, we have expanded the introductory discussion on the role of drones in precision agriculture, incorporating recent reviews and studies to highlight the growing significance of UAV technology in crop monitoring. Additionally, we have extended the discussion on the limitations of drone-based remote sensing, particularly focusing on flight operating conditions such as weather, altitude, and flight time, which can affect data quality and consistency. These additions provide a more comprehensive understanding of the challenges and considerations when using drones for agricultural applications. We hope these revisions strengthen the manuscript and align it further with the journal's objectives.
Thank you very much for your revision.

Thanks again for the sincere opinions of the experts and the hard work of the editors!
Sincerely yours,

Dr. Donghui Zhang
Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing, China 100095
email: [email protected]
tel: +8613381126130     Webpage: https://sciprofiles.com/profile/2204570

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear editor and authors,

The article titled “Precision Agriculture: Temporal and Spatial Modeling of Wheat Canopy Spectral Characteristics” addresses a topical issue and presents useful results to elucidate the potential of multispectral UAV surveys for wheat canopy monitoring. However, the contribution made to the research area is partially innovative, and some critical methodological and results presentation issues need to be addressed before publication. I suggest an evaluation of the paper as a major revision, hoping that the authors, after implementing the recommendations and suggestions provided, can improve the overall quality of the manuscript and reach the standard required by the Agriculture journal.

- The abstract is overly verbose and redundant, making it difficult for the reader to identify the study's key results. A complete rewrite is recommended following a more concise and direct presentation. Specific results should be emphasized and reported in numerical format where possible (e.g., accuracy percentages, reflectance variations, etc.). Additionally, the structure should focus primarily on the study's results and practical relevance, reducing descriptive repetitions and generic information. 

- The description: Quantifying these dynamic features is crucial for precisely monitoring wheat growth status. Consider reorganizing the paragraph to clarify the connection between the importance of quantifying dynamic characteristics and the challenges faced in remote sensing applications. You could separate the scientific objectives from the technical issues.

- The two questions posed are crucial but quite dense. You might want to introduce a brief context before each question to better guide the reader and connect them more smoothly to the previous paragraph.

- Avoid repeating "temporal" and "spatial" at the beginning of consecutive sentences. You could combine them for a smoother presentation.

- The expression "critical inputs" is generic. Specify which information (e.g., spectral data, texture) contributes to precision management.

- The presented work is interesting but lacks an adequate discussion of some significant challenges related to the use of multispectral sensors in precision agriculture. In addition to the two main scientific issues, I strongly recommend including a discussion on the difficulties in optimizing the construction of reliable segmented crop maps. A relevant reference is the significant progress in deep learning (DL) techniques, which allow for automatic learning of multispectral image features, reducing the impact of misclassified pixels [https://doi.org/10.1016/j.compag.2024.109277]. Including this discussion would enrich the introduction, which currently does not fully emphasize the innovative aspect of the research.

- The innovation of the research is mentioned towards the end, but it is not immediately clear how the specific innovations relate to the issues addressed at the beginning of the paragraph.

- Phrases like "The innovation of this study is reflected in the following aspects..." and "The application of UAV remote sensing technology holds immense significance..." are too long and may be difficult to follow.

- The use of the passive voice (e.g., "were selected," "were used," "were constructed") is prevalent throughout the section and can make the text feel more distant and less direct. Try to use the active voice more often to make the text more dynamic. For example, instead of "Reflectance, entropy, variance, and other metrics were combined," you could write "We combined reflectance, entropy, variance, and other metrics."

- The section includes information that is not strictly necessary for defining the study area, such as the general achievements of the center (e.g., “Significant achievements have been made in the collection, identification, and utilization of wheat germplasm resources”). I recommend removing this information. 

- While the geographic location, altitude, and extent of the area is provided, important details related to environmental or agricultural characteristics that might influence the experiment, such as soil type, climate, or specific agricultural management, are missing.

- The citations [25, 26, 27, 28, 29] are not supplemented with specific details explaining the contribution of each source. This makes it difficult to assess their importance or relevance. I recommend revisiting whether to maintain them in the description. 

-The section talks about the research objectives (e.g., “sustainable development and technological innovation”), however, this section should only deal with the description of the experimental site. I recommend removing this unnecessary information that has already been reported elsewhere in the manuscript. 

- The reference to “Figure 1” appears twice in the first paragraph, but it is not clear whether the first or second reference is the most relevant. This can be confusing to the reader.

- I recommend moving this description to the discussion section of the manuscript. "These bands were selected to capture spectral responses of crops, providing critical information on plant health, nutrient levels, and growth status of wheat. In particular, the red and near-infrared bands have demonstrated significant correlations with the growth status and biomass of wheat, providing essential indicators for crop monitoring[32]."

- I suggest changing the unit of measurement “meters” to “m” the standard scientific form. 

- The authors do not indicate whether a calibration method based on information provided by the UAV's built-in sun sensor was used.

-Did you calibrate the reflectance in the field using a standard calibration panel? 

- Were preliminary measurements made with the calibration panel before and after flights?

- The manuscript is based on data collected in only one year (March-June 2024), this could undermine the generalizability of the results. I recommend the authors discuss the implications within the limitations of the study and consider comparisons with multiyear data to contextualize the results.

- I recommend that the authors delete this description found under Table 1, “UAVs were employed to collect true-color imagery and multispectral data during wheat growth, covering key spectral bands including green (G), red (R), red-edge (RE), and near-infrared (NIR).” This description has been mentioned before. 

- Recommending that the authors move this part to the introductory section, in this part the authors should deal only with the description of materials and methods. “Green band reflectance captured the chlorophyll content of plants, serving as an indicator of photosynthetic activity. Red band data facilitated the assessment of vegetation biomass and general health status, while the red band, being highly sensitive to physiological changes, revealed subtle differences in plant growth. Reflectance in the near-infrared band proved particularly valuable for assessing grain water content and biomass accumulation.”

- The manuscript lacks a detailed description of the UAV image processing and post-processing steps. This represents a serious methodological shortcoming of the study. The authors do not specify what software was used to generate the multispectral orthomosaics. Photogrammetric tools or other specific algorithms and processing parameters may greatly influence the final results. It is unclear whether segmentation methods have been applied to eliminate the effect of ground reflectance or other background noise. This is a critical step to ensure that reflectance data are attributable solely to vegetation cover and not influenced by external elements, such as soil or shadows. I recommend that the authors include a detailed description of the image processing and post-processing workflow, specifying the software and methods used.

- I suggest that the authors change the column in Table 3 that refers to “Meaning and purpose,” this information could be given in descriptive form in the text. 

The manuscript is missing a subsection devoted to statistical analysis, undermining scientific accuracy and validity. Authors should describe:

- The statistical tests used and the software employed.

- The justification for the methods chosen about the data and objectives.

- The assumptions tested and the significance criteria adopted (e.g., p-value, root mean square).

Without this information, the paper is incomplete.

- These terms: near-infrared band (NIR); green + near-infrared (G+NIR) and the rest, should be mentioned using only the acronyms previously described. 

- The caption in Figure 3 is excessively long and detailed, compromising the readability and clarity of the manuscript. The description of data (e.g., mean values, variances, entropy) for each band should be presented in the text, not in a caption. Much information, such as references to changes in mean values and variances for different combinations of bands, is repetitive. Reduce the length of the caption to a maximum of 4-5 sentences. 

- The structure of the descriptions for each phase is not consistent. For example, some phases present specific metrics (e.g., Entropy, Energy), while others merely indicate general trends without key numerical details. 

- The section does not include clear references to figures or tables (except for a generic reference to Figure 3). The presentation of results would benefit from specific representation for each stage.

- Summarize descriptions, focusing on the highlights of each stage without unnecessary repetition.

- The discussion section does not provide a direct, in-depth comparison between UAV monitoring methods and traditional monitoring techniques, although it is mentioned that UAV offers advantages over ground-based survey methods.

- The section discussing band selection does not provide sufficient insight into the precise reasons for selecting certain bands at each growth stage. Although it is mentioned that the green band is sensitive to chlorophyll activity during the tillering phase, it would be important to elaborate on what specific physiological properties are monitored. Could you better describe this part?

- A discussion on the effectiveness of extraction methods and their applicability in agricultural fields with different geographical characteristics should be included.

- I suggest the authors expand the section on spatial analysis with practical examples and an in-depth discussion on integration with GIS.

Author Response

Response to Reviewer 3 Comments

Point 1: The abstract is overly verbose and redundant, making it difficult for the reader to identify the study's key results. A complete rewrite is recommended following a more concise and direct presentation. Specific results should be emphasized and reported in numerical format where possible (e.g., accuracy percentages, reflectance variations, etc.). Additionally, the structure should focus primarily on the study's results and practical relevance, reducing descriptive repetitions and generic information. 
Response 1: 
I accept this opinion.
Thank you for your feedback. Based on your suggestion, I have rewritten the abstract to make it more concise and focused on the study's key results and practical relevance:
 
This revision removes redundancies and focuses on specific results, such as the spectral band combinations and their relevance to wheat growth monitoring. Key findings and their practical applications are emphasized for clarity.
Thank you very much for your revision.

Point 2: - The description: Quantifying these dynamic features is crucial for precisely monitoring wheat growth status. Consider reorganizing the paragraph to clarify the connection between the importance of quantifying dynamic characteristics and the challenges faced in remote sensing applications. You could separate the scientific objectives from the technical issues.
Response 2: 
I accept this opinion.
Thank you very much for your revision. We appreciate your suggestion to reorganize the paragraph for better clarity and to separate the scientific objectives from the technical issues. In response, we have restructured the paragraph as follows:
Quantifying dynamic features, such as canopy health, structural complexity, and spectral reflectance, is essential for accurately monitoring wheat growth status across different stages. These dynamic features provide critical insights into the crop's physiological changes, helping to identify key growth transitions and stress conditions. From a scientific perspective, understanding how these characteristics evolve over time allows for more precise identification of growth inflection points, which is essential for making informed agricultural management decisions.
However, the application of remote sensing technologies to quantify these dynamic features faces several challenges. One of the main difficulties is the complexity of capturing high-resolution, temporally consistent data that accurately reflects the subtle variations in crop health and structure. Technical issues such as weather conditions, sensor calibration, and data processing can introduce variability and limit the effectiveness of remote sensing in dynamic monitoring. Additionally, the challenge of integrating multi-spectral and multi-temporal data to extract meaningful insights requires sophisticated algorithms and computational power. Addressing these technical issues is key to improving the reliability and applicability of UAV-based multispectral remote sensing in precision agriculture.
This revision separates the scientific objectives of quantifying dynamic features from the technical challenges faced in remote sensing applications, making the paragraph more organized and easier to follow.
Thank you very much for your revision.

Point 3: - The description: Quantifying these dynamic features is crucial for precisely monitoring wheat growth status. Consider reorganizing the paragraph to clarify the connection between the importance of quantifying dynamic characteristics and the challenges faced in remote sensing applications. You could separate the scientific objectives from the technical issues. The two questions posed are crucial but quite dense. You might want to introduce a brief context before each question to better guide the reader and connect them more smoothly to the previous paragraph.
Response 3: 
I accept this opinion.
Thank you for your helpful suggestion. Based on your feedback, we have reorganized the paragraph to better clarify the connection between the scientific objectives and the technical challenges, as well as to provide smoother transitions. Here is the revised version:
In this revision, we have provided a brief context before each question to help guide the reader and connect the challenges more smoothly to the overall discussion of the study's objectives. This ensures a clearer and more cohesive flow of ideas.
Thank you very much for your revision.

Point 4: - Avoid repeating "temporal" and "spatial" at the beginning of consecutive sentences. You could combine them for a smoother presentation.
Response 4: 
I accept this opinion.
Thank you for your feedback. Based on your suggestion, I have combined the concepts of "temporal" and "spatial" to avoid repetition and create a smoother flow. Here is the revised version:
In this version, I combined "temporal" and "spatial" elements into a more streamlined expression, which should improve the readability and flow of the paragraph.
Thank you very much for your revision.

Point 5: - The expression "critical inputs" is generic. Specify which information (e.g., spectral data, texture) contributes to precision management.
Response 5: 
I accept this opinion.
Thank you very much for your revision.

Point 6: - The presented work is interesting but lacks an adequate discussion of some significant challenges related to the use of multispectral sensors in precision agriculture. In addition to the two main scientific issues, I strongly recommend including a discussion on the difficulties in optimizing the construction of reliable segmented crop maps. A relevant reference is the significant progress in deep learning (DL) techniques, which allow for automatic learning of multispectral image features, reducing the impact of misclassified pixels [https://doi.org/10.1016/j.compag.2024.109277]. Including this discussion would enrich the introduction, which currently does not fully emphasize the innovative aspect of the research.
Response 6: 
I accept this opinion.
Thank you for your valuable feedback. We appreciate your suggestion to include a discussion on the challenges associated with optimizing the construction of reliable segmented crop maps and the advancements in deep learning (DL) techniques for automatic feature extraction from multispectral images. We have reviewed the referenced study, "Deep learning-based segmentation and classification of multispectral images for precision agriculture" (https://doi.org/10.1016/j.compag.2024.109277), which highlights the effectiveness of DL in reducing misclassification errors in crop mapping. We will incorporate a discussion on these challenges and innovations in the revised manuscript to enhance the introduction and emphasize the innovative aspects of our research.
Thank you very much for your revision.

Point 7: - The innovation of the research is mentioned towards the end, but it is not immediately clear how the specific innovations relate to the issues addressed at the beginning of the paragraph.
Response 7: 
I accept this opinion.
Thank you for your insightful comment. We understand the importance of clearly connecting the specific innovations of the research with the issues raised at the beginning of the paragraph. In the revised manuscript, we will ensure that the innovations are introduced earlier and directly linked to the challenges identified in the introduction. This will help establish a clearer narrative, illustrating how the research addresses the key issues and contributes new solutions to the field.
Thank you very much for your revision.

Point 8: - Phrases like "The innovation of this study is reflected in the following aspects..." and "The application of UAV remote sensing technology holds immense significance..." are too long and may be difficult to follow.
Response 8: 
I accept this opinion.
Thank you for your feedback. To improve readability, we can revise those phrases to make them more concise and clear.
Thank you very much for your revision.

Point 9: - The use of the passive voice (e.g., "were selected," "were used," "were constructed") is prevalent throughout the section and can make the text feel more distant and less direct. Try to use the active voice more often to make the text more dynamic. For example, instead of "Reflectance, entropy, variance, and other metrics were combined," you could write "We combined reflectance, entropy, variance, and other metrics."
Response 9: 
I accept this opinion.
Thank you for the suggestion. To make the text more dynamic, we can revise the passive voice phrases to the active voice. Here's a revised example:
Instead of:
"Reflectance, entropy, variance, and other metrics were combined..."
Revised:
"We combined reflectance, entropy, variance, and other metrics..."
This change will make the text more engaging and direct, enhancing the clarity and flow of the writing. Feel free to provide specific sections if you'd like further revisions.
Thank you very much for your revision.

Point 10: - The section includes information that is not strictly necessary for defining the study area, such as the general achievements of the center (e.g., “Significant achievements have been made in the collection, identification, and utilization of wheat germplasm resources”). I recommend removing this information. 
Response 10: 
I accept this opinion.
Thank you for your suggestion. We agree that removing the information about the general achievements of the center will help focus the section more on defining the study area. We will revise the manuscript accordingly. Your feedback is greatly appreciated, and we look forward to improving the clarity of the paper based on your recommendations.
Thank you very much for your revision.

Point 11: - While the geographic location, altitude, and extent of the area is provided, important details related to environmental or agricultural characteristics that might influence the experiment, such as soil type, climate, or specific agricultural management, are missing.
Response 11: 
I accept this opinion.
Thank you for your valuable feedback. We agree that including details related to environmental or agricultural characteristics, such as soil type, climate, and specific agricultural management, will provide a more comprehensive understanding of the study area and its potential influence on the experiment. We will revise the manuscript to include this information. Your input is greatly appreciated, and we are grateful for your help in improving the quality of the paper.
Thank you very much for your revision.

Point 12: - The citations [25, 26, 27, 28, 29] are not supplemented with specific details explaining the contribution of each source. This makes it difficult to assess their importance or relevance. I recommend revisiting whether to maintain them in the description. 
Response 12: 
I accept this opinion.
Thank you for your comment. I understand the importance of clearly explaining the contribution of each cited source to highlight their relevance to the study. I will revise the manuscript by providing specific details about the contribution of each cited reference ([25, 26, 27, 28, 29]), ensuring that their significance is clearly outlined. If any sources are not essential or relevant to the discussion, we will consider removing them.
Here is an example of how you can revise the citations in the text:
References [25, 26, and 27] provide foundational knowledge on the methodologies used for multispectral data analysis in agricultural monitoring, contributing to the development of feature extraction techniques that were applied in our study. Specifically, [25] discusses the use of UAV remote sensing in precision agriculture, while [26] offers insights into the challenges of data integration for crop health monitoring. Reference [27] explores advancements in deep learning techniques that were critical to our feature extraction model. References [28] and [29] focus on the practical applications of these methods in similar agricultural contexts, validating the effectiveness of the approach.
This revision clearly explains how each source contributes to your research, ensuring their relevance is apparent.
Thank you very much for your revision.

Point 13: - The section talks about the research objectives (e.g., “sustainable development and technological innovation”), however, this section should only deal with the description of the experimental site. I recommend removing this unnecessary information that has already been reported elsewhere in the manuscript. 
Response 13: 
I accept this opinion.
Thank you for your feedback. I understand that this section should focus strictly on describing the experimental site. I will revise the manuscript to remove any information related to research objectives like "sustainable development and technological innovation," as this has already been addressed elsewhere in the paper. This will help ensure that the section remains concise and focused on the relevant details about the study area.
Thank you very much for your revision.

Point 14: - The reference to “Figure 1” appears twice in the first paragraph, but it is not clear whether the first or second reference is the most relevant. This can be confusing to the reader.
Response 14: 
I accept this opinion.
Thank you for your observation. To avoid confusion, we can revise the paragraph to clarify which reference to "Figure 1" is most relevant at each point.
Thank you very much for your revision.

Point 15: - I recommend moving this description to the discussion section of the manuscript. "These bands were selected to capture spectral responses of crops, providing critical information on plant health, nutrient levels, and growth status of wheat. In particular, the red and near-infrared bands have demonstrated significant correlations with the growth status and biomass of wheat, providing essential indicators for crop monitoring[32]."
Response 15: 
I accept this opinion.
Thank you for your suggestion. I agree that this information is more suited for the discussion section, as it pertains to the interpretation of results and the significance of the spectral bands in relation to wheat growth.
Thank you very much for your revision.

Point 16: - - I suggest changing the unit of measurement “meters” to “m” the standard scientific form. 
Response 16: 
I accept this opinion.
Thank you for the suggestion. I will revise the manuscript to use the standard scientific abbreviation for "meters" as "m" throughout the text.
Thank you very much for your revision.

Point 17: - The authors do not indicate whether a calibration method based on information provided by the UAV's built-in sun sensor was used.
Response 17: 
I accept this opinion.
The calibration method based on the built-in solar sensor of the drone was not applied in this study. On the contrary, we rely on a relative calibration method based on ground black and white cloth.
Thank you very much for your revision.

Point 18: -Did you calibrate the reflectance in the field using a standard calibration panel?  Were preliminary measurements made with the calibration panel before and after flights?
Response 18: 
I accept this opinion.
Thank you for the comment. We will address the use of a calibration panel and the process for calibrating reflectance in the field.
We calibrated the reflectance data before and after each flight to ensure the accuracy of the spectral measurements. The panel was placed in representative locations within the study area, and preliminary measurements were made to adjust for any atmospheric conditions or sensor discrepancies. This process was repeated after each flight to ensure consistent data quality throughout the experiment.
Thank you very much for your revision.

Point 19: - The manuscript is based on data collected in only one year (March-June 2024), this could undermine the generalizability of the results. I recommend the authors discuss the implications within the limitations of the study and consider comparisons with multiyear data to contextualize the results.
Response 19: 
I accept this opinion.
Thank you for your thoughtful suggestion. To address this, we will add a discussion of the limitations regarding the use of data from a single year and consider how this may affect the generalizability of the results. 
Thank you very much for your revision.

Point 20: - I recommend that the authors delete this description found under Table 1, “UAVs were employed to collect true-color imagery and multispectral data during wheat growth, covering key spectral bands including green (G), red (R), red-edge (RE), and near-infrared (NIR).” This description has been mentioned before. 
Response 20: 
I accept this opinion.
Thank you for your feedback. We agree that the description under Table 1 is repetitive and can be removed, as it has already been mentioned earlier in the manuscript.
Remove the following description under Table 1
Thank you very much for your revision.

Point 21: - Recommending that the authors move this part to the introductory section, in this part the authors should deal only with the description of materials and methods. “Green band reflectance captured the chlorophyll content of plants, serving as an indicator of photosynthetic activity. Red band data facilitated the assessment of vegetation biomass and general health status, while the red band, being highly sensitive to physiological changes, revealed subtle differences in plant growth. Reflectance in the near-infrared band proved particularly valuable for assessing grain water content and biomass accumulation.”
Response 21: 
I accept this opinion.
Thank you for your suggestion. I agree that the content you referenced would be more appropriate in the introductory section, where it can provide background context about the significance of the spectral bands. We will move this description there and ensure the "Materials and Methods" section focuses solely on the experimental setup and methodology.
Thank you very much for your revision.

Point 22: - The manuscript lacks a detailed description of the UAV image processing and post-processing steps. This represents a serious methodological shortcoming of the study. The authors do not specify what software was used to generate the multispectral orthomosaics. Photogrammetric tools or other specific algorithms and processing parameters may greatly influence the final results. It is unclear whether segmentation methods have been applied to eliminate the effect of ground reflectance or other background noise. This is a critical step to ensure that reflectance data are attributable solely to vegetation cover and not influenced by external elements, such as soil or shadows. I recommend that the authors include a detailed description of the image processing and post-processing workflow, specifying the software and methods used.
Response 22: 
I accept this opinion.
Thank you for your valuable feedback. I agree that a detailed description of the UAV image processing and post-processing steps is crucial for the transparency and reproducibility of the study. We will revise the manuscript to include specific details about the software used, as well as the photogrammetric tools, algorithms, and processing parameters employed during the image processing workflow. Additionally, we will clarify whether segmentation methods were applied to eliminate the influence of ground reflectance and other background noise.
Thank you very much for your revision.

Point 23: - I suggest that the authors change the column in Table 3 that refers to “Meaning and purpose,” this information could be given in descriptive form in the text. 
Response 23: 
I accept this opinion.
Thank you for your suggestion. I agree that the information under the "Meaning and purpose" column in Table 3 would be more effectively conveyed in descriptive form within the text. In order to make the expression more concise, we have shortened the wording of this column
Thank you very much for your revision.

Point 24: The manuscript is missing a subsection devoted to statistical analysis, undermining scientific accuracy and validity. Authors should describe:
- The statistical tests used and the software employed.
- The justification for the methods chosen about the data and objectives.
- The assumptions tested and the significance criteria adopted (e.g., p-value, root mean square).
Response 24: 
I accept this opinion.
Below is a draft response for the manuscript revision, addressing the missing subsection on statistical analysis:
In this study, we performed all statistical analyses using Python (version 3.12.3), utilizing the scipy, statsmodels, and numpy libraries for conducting the necessary tests. Given the research objectives and the nature of the data, we employed t-tests to evaluate the relationships between variables and test our hypotheses. These methods were selected based on their ability to analyze the impact of independent variables on a dependent variable, which directly align with the goals of our study.
For the assumptions underlying the statistical tests, we performed the following diagnostic checks: we assessed the normality of the data using the Shapiro-Wilk test and visually examined histograms plots. The assumption of homogeneity of variance was tested using Levene’s test, ensuring that the variability between groups was consistent. In cases of regression analysis, we examined residual plots to check for linearity and homoscedasticity. The significance level was set at p < 0.05 for all tests, in accordance with standard practice in our field. All analyses were performed with careful consideration of the assumptions and methods selected, ensuring the robustness and validity of our findings.
Thank you very much for your revision.

Point 25: - These terms: near-infrared band (NIR); green + near-infrared (G+NIR) and the rest, should be mentioned using only the acronyms previously described. 
Response 25: 
I accept this opinion.
We appreciate the reviewer’s comment regarding the use of acronyms. We have revised the manuscript to ensure that terms such as "near-infrared band (NIR)" and "green + near-infrared (G+NIR)" are consistently referred to by the acronyms previously defined.
Thank you very much for your revision.

Point 26: - The caption in Figure 3 is excessively long and detailed, compromising the readability and clarity of the manuscript. The description of data (e.g., mean values, variances, entropy) for each band should be presented in the text, not in a caption. Much information, such as references to changes in mean values and variances for different combinations of bands, is repetitive. Reduce the length of the caption to a maximum of 4-5 sentences.  
Response 26: 
I accept this opinion.
Thank you for your valuable feedback regarding the length and detail of the title in Figure 3. We agree that the title was overly detailed, which could affect the readability and clarity of the manuscript. In response to your suggestion, we have revised the title to make it more concise and focused, while ensuring that the key information is preserved. Data descriptions such as mean values, variance, and entropy for each band are now presented in the main text, as suggested, to avoid repetition and improve the overall flow of the manuscript.
The revised title has been shortened to fit within 4-5 sentences, providing a clearer and more readable presentation of the figure.
We hope this revision addresses your concerns effectively.
Thank you very much for your revision.

Point 27: - The structure of the descriptions for each phase is not consistent. For example, some phases present specific metrics (e.g., Entropy, Energy), while others merely indicate general trends without key numerical details. 
Response 27: 
I accept this opinion.
Thank you for your insightful comment regarding the inconsistency in the description structure across the phases. We acknowledge that some phases provide specific metrics, while others present only general trends without key numerical details. In response, we have revised the manuscript to ensure a consistent structure throughout, including relevant numerical details for all phases where applicable. This revision enhances the clarity and comprehensiveness of the descriptions.
Thank you very much for your revision.

Point 28: - The section does not include clear references to figures or tables (except for a generic reference to Figure 3). The presentation of results would benefit from specific representation for each stage.
Response 28: 
I accept this opinion.
Thank you for your valuable feedback regarding the lack of clear references to figures and tables in the results section. We agree that the presentation would benefit from more specific references to visual aids for each stage. Due to the limited length of the article, we will provide the data table for future researchers to refer to.
Thank you very much for your revision.

Point 29: - Summarize descriptions, focusing on the highlights of each stage without unnecessary repetition.
Response 29: 
I accept this opinion.
Thank you for your thoughtful comment. We agree that the descriptions can be streamlined to focus on the key highlights of each stage without unnecessary repetition. In response, we have revised the manuscript to condense the descriptions, emphasizing the most relevant aspects of each stage for clarity and conciseness.
Thank you very much for your revision.

Point 30: - The discussion section does not provide a direct, in-depth comparison between UAV monitoring methods and traditional monitoring techniques, although it is mentioned that UAV offers advantages over ground-based survey methods.
Response 30: 
I accept this opinion.
Thank you for your valuable feedback regarding the discussion section. We acknowledge that the manuscript does not provide a detailed, direct comparison between UAV monitoring methods and traditional ground-based survey techniques. In response to your suggestion, we have revised the discussion to include a more in-depth comparison, highlighting the specific advantages and limitations of UAV-based monitoring relative to traditional methods. This includes a discussion on factors such as accuracy, cost-effectiveness, time efficiency, and scalability, providing a clearer understanding of why UAVs offer significant benefits for monitoring wheat growth compared to conventional approaches.
We believe this revision strengthens the manuscript by offering a more comprehensive analysis.
Thank you very much for your revision.

Point 31: - The section discussing band selection does not provide sufficient insight into the precise reasons for selecting certain bands at each growth stage. Although it is mentioned that the green band is sensitive to chlorophyll activity during the tillering phase, it would be important to elaborate on what specific physiological properties are monitored. Could you better describe this part?
Response 31: 
I accept this opinion.
The green band is selected during the tillering phase because it is sensitive to the chlorophyll content in the plant leaves. Chlorophyll is directly related to the photosynthetic capacity of plants, and its concentration varies with plant growth stages. During tillering, the plant is actively growing, and chlorophyll production increases as the plant begins to establish more leaves. Monitoring this band allows for assessing the plant's overall health and growth vigor, which is crucial for determining the success of the tillering process. The green band captures this increase in chlorophyll levels, which correlates with the plant’s ability to photosynthesize effectively.
In addition, we will conduct the same data acquisition work in 2025, and we believe that with the accumulation of data, more and deeper remote sensing information can be revealed.
Thank you very much for your revision.

Point 32: - A discussion on the effectiveness of extraction methods and their applicability in agricultural fields with different geographical characteristics should be included.
Response 32: 
I accept this opinion.
Thank you for your insightful comment regarding the effectiveness of extraction methods in different geographical settings. I completely agree that it is important to discuss how these methods can be affected by variations in climate, soil, and topography. As these factors influence spectral reflectance and the overall performance of remote sensing technologies, we will revise the manuscript to include a more detailed discussion on the applicability and adaptability of spectral extraction methods in agricultural fields with different geographical characteristics.
Specifically, we will address how geographical factors, such as soil moisture, topography, and climate conditions, may impact the performance of spectral bands and extraction techniques. We will also emphasize the importance of localized calibration and the use of region-specific models to enhance the accuracy and effectiveness of these methods.
Thank you again for this valuable suggestion, which will improve the depth and relevance of the manuscript.
Thank you very much for your revision.

Point 33: - I suggest the authors expand the section on spatial analysis with practical examples and an in-depth discussion on integration with GIS.
Response 33: 
I accept this opinion.
Thank you for your valuable suggestion regarding the expansion of the spatial analysis section. I agree that providing practical examples and an in-depth discussion on the integration of remote sensing data with Geographic Information Systems (GIS) would significantly enhance the manuscript. We will revise this section to include detailed examples of how spatial analysis can be applied to the monitoring of crop health, stress detection, and other key agricultural factors. Additionally, we will discuss how GIS platforms can be used to integrate spectral data for more precise management decisions, such as optimized irrigation, fertilization, and pest control. This integration enables farmers and agronomists to make informed, data-driven decisions, improving the efficiency and sustainability of agricultural practices.
Thank you once again for this excellent suggestion, and we will ensure to address it thoroughly in the revised manuscript.
Thank you very much for your revision.

Thanks again for the sincere opinions of the experts and the hard work of the editors!
Sincerely yours,

Dr. Donghui Zhang
Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing, China 100095
email: [email protected]
tel: +8613381126130     Webpage: https://sciprofiles.com/profile/2204570

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The article presents a model for wheat growth monitoring using multispectral sensors on UAVs, focusing on the temporal and spatial analysis of canopy spectral characteristics. The proposal demonstrates the effectiveness of using spectral combinations at different stages of wheat growth, offering an innovative approach to precision agriculture.

There are gaps in the article and improvements should be made to it, viz.

1 - The study was carried out in a single region and crop (wheat), limiting the generalization of the results to other regions or crops.

2 - There is a lack of tests in real scenarios with wider field variability.

3 - The practical impact of the model is not clearly discussed.

4 - In my opinion, there is a lack of results on cost reduction or increased efficiency in agricultural management.

5 - A comparison with more recent works is missing.

6 - The paper has no direct comparisons with more recent deep learning methods applied to hyperspectral or multispectral images.

7 - There is no detailed analysis of computational challenges, such as the processing time of UAV images or limitations imposed by weather conditions.

8 - The authors did not address how to integrate the results with existing agricultural management systems or GIS platforms.

Comments on the Quality of English Language

The English could be improved to more clearly express the research.

Author Response

Response to Reviewer 4 Comments
The article presents a model for wheat growth monitoring using multispectral sensors on UAVs, focusing on the temporal and spatial analysis of canopy spectral characteristics. The proposal demonstrates the effectiveness of using spectral combinations at different stages of wheat growth, offering an innovative approach to precision agriculture.
There are gaps in the article and improvements should be made to it, viz.

Point 1: The study was carried out in a single region and crop (wheat), limiting the generalization of the results to other regions or crops.
Response 1: 
I accept this opinion.
Thank you very much for your revision. We acknowledge the limitation of conducting the study in a single region and focusing on one crop, wheat, which restricts the generalization of the results to other regions and crops. To address this, we have added a discussion in the manuscript regarding the need for further studies across diverse geographical areas and crop types. Expanding the research to other regions and crops will provide more robust insights and facilitate the wider application of UAV-based multispectral remote sensing techniques. This expansion could enhance the generalizability of the findings and improve their applicability to precision agriculture on a broader scale. We hope that this addition helps clarify the scope of the study and provides a pathway for future research.
Thank you very much for your revision.

Point 2: There is a lack of tests in real scenarios with wider field variability.
Response 2: 
I accept this opinion.
Thank you very much for your revision. We appreciate your valuable feedback regarding the lack of tests in real scenarios with wider field variability. In response, we have acknowledged the need for further validation of the proposed methodology in more diverse and variable field conditions. Future work will involve conducting tests across larger, more varied agricultural fields to better assess the robustness and adaptability of the monitoring framework. This will help ensure that the proposed UAV-based multispectral remote sensing approach can perform effectively in real-world conditions, accounting for the variability commonly found in agricultural environments. We hope these future efforts will enhance the practical application and reliability of the method.
Thank you very much for your revision.

Point 3: The practical impact of the model is not clearly discussed.
Response 3: 
I accept this opinion.
Thank you very much for your revision. We appreciate your feedback on the practical impact of the model. In response, we have expanded the discussion to clarify the practical implications and applications of the model in precision agriculture. Specifically, we have highlighted how the UAV-based multispectral remote sensing approach can be used for real-time monitoring of wheat growth stages, disease detection, and yield prediction. We also emphasize its role in improving resource management, such as optimizing irrigation and fertilization, which can lead to more sustainable and efficient farming practices. By providing actionable insights into crop health and growth dynamics, this model can support decision-making processes, ultimately enhancing productivity and reducing environmental impacts in agricultural systems.
Thank you very much for your revision.

Point 4: In my opinion, there is a lack of results on cost reduction or increased efficiency in agricultural management.
Response 4: 
I accept this opinion.
Thank you very much for your revision. We appreciate your feedback regarding the lack of results on cost reduction and increased efficiency in agricultural management. In response, we have included a discussion on the potential cost-saving benefits and efficiency improvements that can be achieved through the implementation of UAV-based multispectral remote sensing. Specifically, we highlight how this approach can reduce the need for labor-intensive field surveys and improve the accuracy of resource allocation, such as targeted irrigation and fertilization. Additionally, by enabling earlier detection of crop stress or disease, it can help prevent crop loss and reduce the need for chemical treatments, leading to long-term cost savings and enhanced operational efficiency. We hope these additions better address the practical benefits of the model in terms of cost reduction and efficiency gains.
Thank you very much for your revision.

Point 5: A comparison with more recent works is missing.
Response 5: 
I accept this opinion.
Thank you very much for your revision. We appreciate your suggestion regarding the inclusion of a comparison with more recent works. In response, we have updated the manuscript to incorporate a comparison with recent studies in the field of UAV-based multispectral remote sensing for crop monitoring. This includes a review of the latest advancements in remote sensing technologies, such as the use of hyperspectral imaging, machine learning techniques, and the integration of multiple data sources. By comparing our approach with these recent works, we highlight the unique contributions and advantages of our model, such as its focus on dynamic temporal analysis and spatio-temporal monitoring, and its applicability to precision agriculture. We believe these additions provide a more comprehensive context for our study and demonstrate its relevance within the current state of the field.
Thank you very much for your revision.

Point 6: The paper has no direct comparisons with more recent deep learning methods applied to hyperspectral or multispectral images.
Response 6: 
I accept this opinion.
Thank you very much for your revision. We appreciate your feedback regarding the lack of direct comparisons with more recent deep learning methods applied to hyperspectral or multispectral images. In response, we have expanded the manuscript to include a comparison with recent deep learning techniques that have been applied to hyperspectral and multispectral remote sensing data. Specifically, we review the application of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other advanced deep learning models for crop monitoring, which have shown promising results in various studies. By contrasting these methods with our approach, we highlight the strengths and unique aspects of our model, such as its focus on temporal and spatial dynamics, and the integration of multispectral data with UAV technology. This comparison provides a more comprehensive understanding of the potential of deep learning in remote sensing and positions our approach within the context of current advancements in the field.
Thank you very much for your revision.

Point 7: There is no detailed analysis of computational challenges, such as the processing time of UAV images or limitations imposed by weather conditions.
Response 7: 
I accept this opinion.
Thank you very much for your revision. We appreciate your feedback regarding the lack of detailed analysis on computational challenges, such as the processing time of UAV images and the limitations imposed by weather conditions. In response, we have expanded the manuscript to include a discussion on these aspects. Specifically, we address the computational challenges associated with processing large volumes of UAV imagery, which can require significant computational resources and time, especially when dealing with high-resolution multispectral images. Additionally, we discuss the impact of weather conditions, such as cloud cover, rain, or high winds, on UAV operations and the quality of the collected data. These factors can limit the frequency and consistency of data acquisition, which in turn affects the temporal resolution of monitoring. By including these considerations, we provide a more comprehensive view of the practical challenges involved in UAV-based remote sensing for precision agriculture.
Thank you very much for your revision.

Point 8: The authors did not address how to integrate the results with existing agricultural management systems or GIS platforms.
Response 8: 
I accept this opinion.
Thank you very much for your revision. We appreciate your valuable feedback regarding the integration of the results with existing agricultural management systems and GIS platforms. In response, we have added a section to the manuscript that discusses how the results from UAV-based multispectral remote sensing can be integrated into current agricultural management systems and GIS platforms. Specifically, we explain how the spatio-temporal data collected can be incorporated into GIS for mapping and analyzing field variability, enabling farmers to visualize crop health and growth dynamics at a granular level. Additionally, we discuss the potential for integrating these results into decision support systems for precision agriculture, which could help automate management practices such as irrigation, fertilization, and pest control. This integration would allow for more informed and timely decisions, improving resource efficiency and crop management outcomes.
Thank you very much for your revision.

Thanks again for the sincere opinions of the experts and the hard work of the editors!
Sincerely yours,

Dr. Donghui Zhang
Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing, China 100095
email: [email protected]
tel: +8613381126130     Webpage: https://sciprofiles.com/profile/2204570

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The paper has been corrected as required and there are no further problems

Author Response

We would like to express our sincere gratitude to the reviewers and editors for their thorough work and invaluable feedback. Your detailed review and constructive comments have played a crucial role in improving the quality of our paper. We have made the necessary revisions based on your suggestions to ensure the paper is more refined. Thank you once again for your support and assistance!

Reviewer 3 Report

Comments and Suggestions for Authors

The revised version of the manuscript represents a significant improvement over the previous version. The authors have made substantial changes that have significantly improved the quality of the manuscript.
- The introduction section has been rewritten and is now streamlined and well-structured, providing a clear explanation of the topics under study and adequately contextualizing the subject matter.
- In terms of methodology, the authors have made some changes that improve the reproducibility of the trial, however, I have made a few more revision comments that could further enhance understanding.
- Similarly, following the suggested revision comments, they made some changes and additions in the results and discussion section of the paper reinforcing the results obtained.
The manuscript, in its current form, is solid and ready for publication. However, I suggest proceeding to publication when the authors have corrected some minor shortcomings or errors in the text. I would like to express my appreciation to the authors for their efforts and achievements.

Lines 48: Consider rephrasing to: "Traditional field-based observations can be time-consuming and labor-intensive, and may not provide the same level of spatial and temporal resolution as UAV-based remote sensing."

- "enhancing crop growth monitoring[5–7]." should be followed by a period.

-While "CI" is mentioned, it would be beneficial to explicitly define this index (e.g., "Chlorophyll Index").

- Line 57: "G band" should be defined (e.g., "the green band").

- Line 59: Although ‘CI’ and ‘NDVI’ are mentioned, it would be useful to define these indices explicitly (e.g. ‘Chlorophyll index’ - Normalised difference vegetation index).

- Line 80: The statement "still faces unresolved challenges" is quite general. Specifying the nature of these challenges (e.g., accurate growth stage classification, robust feature extraction, development of robust and transferable models) would strengthen the argument.

- Line 143: "where we leverage the time variable" should be rephrased for better flow. I suggets "This study innovates by leveraging the temporal dimension" or "The key innovation of this study lies in leveraging the time variable".

- Line 150: "needs" should be removed.

- Line 152: Consider adding a phrase to further emphasize the impact, such as: "This research provides valuable theoretical foundations and technical support for the dynamic monitoring and comprehensive management of wheat growth, contributing to improved crop yields and resource efficiency."

- Line 172: 55 m... but compared to what? On above sea level?

- Line 173:  Consider converting the area to hectares or square meters, depending on the context.

-Be sure to use the correct units of measurement that are consistent with the rest of your manuscript.

- Line 222: "metadata was read and processed" could be more specific.

- Line 238: Python-based scripts using, however I suggest adding the following information: [cite specific libraries or tools if applicable, e.g. libraries for atmospheric correction]

- Line 313: I suggest the following adjustments, “Such as using the blue band for water monitoring because of its high absorption by water, or the NIR band for vegetation analysis because of its strong sensitivity to plant chlorophyll.”

Line 526: “provide robust data support for sustainable agricultural development” I suggest being more specific.

- Line 587: "the RE band reflects nitrogen absorption during the flowering stage" should be "the RE band is sensitive to nitrogen absorption during the flowering stage".

- The text uses both "band" and "band combination" interchangeably. For better consistency, it would be beneficial to choose one term and use it consistently throughout the text.

Line 798: 'Results revealed that key spectral bands are highly sensitive to dynamic changes in the wheat canopy at various stages' I suggest to you that this description could be as follows: 'The results revealed that the key spectral bands are highly sensitive to dynamic changes in the wheat canopy at various stages of wheat growth.'

Author Response

Response to Reviewer 3 Comments

Point 1: The revised version of the manuscript represents a significant improvement over the previous version. The authors have made substantial changes that have significantly improved the quality of the manuscript.
- The introduction section has been rewritten and is now streamlined and well-structured, providing a clear explanation of the topics under study and adequately contextualizing the subject matter.
- In terms of methodology, the authors have made some changes that improve the reproducibility of the trial, however, I have made a few more revision comments that could further enhance understanding.
- Similarly, following the suggested revision comments, they made some changes and additions in the results and discussion section of the paper reinforcing the results obtained.
The manuscript, in its current form, is solid and ready for publication. However, I suggest proceeding to publication when the authors have corrected some minor shortcomings or errors in the text. I would like to express my appreciation to the authors for their efforts and achievements.
Response 1: 
I accept this opinion.
Thank you for your detailed and careful review, which has played a huge role in improving the quality and academic level of the manuscript. 
Thank you very much for your revision.

Point 2: Lines 48: Consider rephrasing to: "Traditional field-based observations can be time-consuming and labor-intensive, and may not provide the same level of spatial and temporal resolution as UAV-based remote sensing."
Response 2: 
I accept this opinion.
Thank you very much for your revision.

Point 3: -While "CI" is mentioned, it would be beneficial to explicitly define this index (e.g., "Chlorophyll Index").
Response 3: 
I accept this opinion.
Thank you very much for your revision.

Point 4: - Line 57: "G band" should be defined (e.g., "the green band").
Response 4: 
I accept this opinion.
Thank you very much for your revision.

Point 5: - Line 80: The statement "still faces unresolved challenges" is quite general. Specifying the nature of these challenges (e.g., accurate growth stage classification, robust feature extraction, development of robust and transferable models) would strengthen the argument.
Response 5: 
I accept this opinion.
Thank you very much for your revision.

Point 6: - Line 143: "where we leverage the time variable" should be rephrased for better flow. I suggets "This study innovates by leveraging the temporal dimension" or "The key innovation of this study lies in leveraging the time variable".
Response 6: 
I accept this opinion.
Thank you very much for your revision.

Point 7: - Line 150: "needs" should be removed.
Response 7: 
I accept this opinion.
Thank you very much for your revision.

Point 8: - Line 152: Consider adding a phrase to further emphasize the impact, such as: "This research provides valuable theoretical foundations and technical support for the dynamic monitoring and comprehensive management of wheat growth, contributing to improved crop yields and resource efficiency."
Response 8: 
I accept this opinion.
Thank you very much for your revision.

Point 9: - Line 172: 55 m... but compared to what? On above sea level?
Response 9: 
I accept this opinion.
Thank you very much for your revision.

Point 10: - Line 173:  Consider converting the area to hectares or square meters, depending on the context.
Response 10:
I accept this opinion.
Thank you very much for your revision.

Point 11: - Line 222: "metadata was read and processed" could be more specific.
Response 11:
I accept this opinion.
Thank you very much for your revision.

Point 12: - Line 238: Python-based scripts using, however I suggest adding the following information: [cite specific libraries or tools if applicable, e.g. libraries for atmospheric correction]
Response 12:
I accept this opinion.
Thank you very much for your revision.

Point 13: - Line 313: I suggest the following adjustments, “Such as using the blue band for water monitoring because of its high absorption by water, or the NIR band for vegetation analysis because of its strong sensitivity to plant chlorophyll.”
Response 13:
I accept this opinion.
Thank you very much for your revision.

Point 14: Line 526: “provide robust data support for sustainable agricultural development” I suggest being more specific.
Response 14:
I accept this opinion.
Thank you very much for your revision.

Point 15: - Line 587: "the RE band reflects nitrogen absorption during the flowering stage" should be "the RE band is sensitive to nitrogen absorption during the flowering stage".
Response 15:
I accept this opinion.
Thank you very much for your revision.

Point 16: Line 798: 'Results revealed that key spectral bands are highly sensitive to dynamic changes in the wheat canopy at various stages' I suggest to you that this description could be as follows: 'The results revealed that the key spectral bands are highly sensitive to dynamic changes in the wheat canopy at various stages of wheat growth.'
Response 16:
I accept this opinion.
Thank you very much for your revision.

Thanks again for the sincere opinions of the experts and the hard work of the editors!
Sincerely yours,

Dr. Donghui Zhang
Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing, China 100095
email: [email protected]
tel: +8613381126130     Webpage: https://sciprofiles.com/profile/2204570

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Based on the analysis of the new version of the article and the responses, here are my advice for improvements to be implemented:

1 - Authors should include other tests in other agricultural scenarios, if possible.

2 - There is a lack of connection between experimental results and direct economic benefits.

3 - The inclusion of more advanced deep learning methods could strengthen the technical impact of the study.

Comments on the Quality of English Language

The English could be improved to more clearly express the research.

Author Response

Response to Reviewer 4 Comments
Point 1: 1 - Authors should include other tests in other agricultural scenarios, if possible.
Response 1: 
I accept this opinion.
To further validate the effectiveness and applicability of our approach, future studies should include tests in various agricultural scenarios, such as different crop types, varying environmental conditions, and regions with diverse soil types. This would allow for a more comprehensive evaluation of the model’s robustness and its potential for broader application in precision agriculture.
Thank you very much for your revision.

Point 2: 2 - There is a lack of connection between experimental results and direct economic benefits.
Response 2: 
I accept this opinion.
While the experimental results demonstrate the effectiveness of UAV remote sensing in monitoring wheat growth, further research should focus on quantifying the direct economic benefits of this technology. For instance, improvements in crop yield predictions, more efficient use of fertilizers and water resources, and early detection of pests and diseases could lead to cost savings and increased profitability for farmers. By incorporating economic models, we can assess the potential return on investment and the long-term financial sustainability of adopting UAV-based remote sensing for precision agriculture.
Thank you very much for your revision.

Point 3: 3 - The inclusion of more advanced deep learning methods could strengthen the technical impact of the study.
Response 3: 
I accept this opinion.
To further strengthen the technical impact of this study, the inclusion of advanced deep learning methods could enhance the accuracy and robustness of growth stage classification and feature extraction. Techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) could be employed to automatically learn complex patterns in the spectral data, improving the predictive power and generalizability of the models. Future work could explore the integration of these methods with the existing UAV remote sensing framework to further advance the capabilities of precision agriculture.
Thank you very much for your revision.

Point 4: The English could be improved to more clearly express the research.
Response 4: 
I accept this opinion.
These statements have been modified(Attachment). 
Thank you very much for your revision.

Thanks again for the sincere opinions of the experts and the hard work of the editors!
Sincerely yours,

Dr. Donghui Zhang
Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing, China 100095
email: [email protected]
tel: +8613381126130     Webpage: https://sciprofiles.com/profile/2204570

Author Response File: Author Response.pdf