Non-Destructive Drone-Based Multispectral and RGB Image Analyses for Regression Modeling to Assess Waterlogging Stress in Pseudolysimachion linariifolium
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis study uses drone-mounted hyperspectral and RGB imagery technology to evaluate the physiological response of Pseudolysimachion linariifolium under waterlogging stress. Through non-destructive monitoring methods, the authors use vegetation indices combined with soil moisture data to analyze the effects of waterlogging on plant growth and establish a regression model to predict the relationship between moisture changes and plant stress. While the technology itself is not innovative, it provides a new approach for monitoring waterlogging stress, and its application to specific plant species and environmental conditions has practical value. However, the limitations of experimental conditions, the low predictive accuracy of the regression model, and the lack of validation in actual urban environments restrict the broader applicability of the study's findings. Specific issues are as follows:
- Has the manuscript conducted monitoring validation of Pseudolysimachion linariifolium in urban environmental conditions? I believe this is extremely important. The differences between greenhouse conditions and actual urban environments are significant. In urban environments, drone hyperspectral imaging will be subject to environmental interference, something that greenhouse conditions cannot replicate. It is recommended to add monitoring validation in actual urban green spaces.
- Lack of specific details on waterlogging treatment, such as the duration and intensity of waterlogging. The manuscript only provides a general description. It is important to quantify the parameters and indicators of waterlogging treatment.
- The R² value of 0.745 is quite low for a regression model. Typically, such low predictive accuracy is insufficient to meet the demands of precise monitoring and prediction of plant waterlogging stress. What caused this low R² value? Is there an issue with the model structure or are there missing variables in the data? I recommend further analysis of the regression model and discussion of any potential issues.
- Recommend providing the rationale for selecting indices such as NDVI and GNDVI. An explanation of why these specific indices were chosen should be included in the manuscript.
- The literature is outdated, with at least 30 references that are over five years old or even older. I recommend updating the references to include more recent studies, particularly from the past five years.
- I noticed small tables in Figures 3f and 4b. Are these tables necessary? If not, please remove them. If they are necessary, the font is too small to be readable. Additionally, Figure 7 has text that is too small, and there is text overlap in Figure 11. It would be helpful to adjust the size for clarity.
Author Response
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Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThis study uses drone-based multispectral and RGB images to evaluate waterlogging stress in Pseudolysimachion linariifolium, and has obtained some interesting results. Currently, UAV sensor data are an important means for monitoring urban vegetation and provide valuable support for optimizing urban management. This research is innovative in applying UAV data and control experiments to analyze the waterlogging response of Pseudolysimachion linariifolium. However, several issues still need to be addressed:
The introduction section needs to be reorganized, and some paragraphs can be merged. In addition, it is necessary to review the existing research on Pseudolysimachion linariifolium and highlight the key scientific questions in order to emphasize the innovation of this study.
There are many vegetation indices available in practice. The reasons for selecting the particular indices used in this study should be explained.
Is it possible to analyze how waterlogging stress manifests across different phenological stages of Pseudolysimachion linariifolium?
This study only used multispectral and RGB image data for analyzing waterlogging stress in Pseudolysimachion linariifolium. Would incorporating thermal infrared data further improve the accuracy of the evaluation?
Although UAVs can capture fine-scale ground observations, the acquisition cost is relatively high. It is recommended to upscale the results of this study to the satellite data scale (e.g., Planet data). This would enable analysis and assessment at high spatial and temporal resolution.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsI have thoroughly reviewed the revised manuscript and believe that the necessary revisions have been adequately addressed. The author has responded to the reviewers' comments and made significant improvements.
Reviewer 2 Report
Comments and Suggestions for AuthorsThanks for the revision
