Prediction of Site Index and Age Using Time Series of TanDEM-X Phase Heights
Round 1
Reviewer 1 Report
Reviewed manuscript “Prediction of Site Index and Age Using Time-Series of
Tan-DEM-X Phase Heights” is an original and interesting study. Authors comprehensively demonstrated/calculated the data of Site Index. Study is very interesting and it would add scientific contribution in literature. Authors collected lot of data with strong reasoning. I would suggest minor revision.
Following are some suggestions for further improvements:
First few lines of the abstract should be about the importance of study area.
Lines 36-42: Consider revising the lines.
Lines 80-85: Please rewrite these lines.
Lines 337-344: Consider revising/rewrite the sentences.
Improve the Figures quality.
Introduction and Discussion section needs to further strengthen by latest studies on the subject. Improve the discussion section. Discussion is a little bit long, please summarize it.
At some places in the text, there are grammatical mistakes that need to be corrected by some native English colleague. Please summarize the conclusion.
To further strengthen introduction and discussion latest studies etc. are suggested to cite for Site Index and Age Using Time-Series.
Please cite the latest studies to further improve the article.
English must be improved.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The manuscript focuses on predicting the Site Index (SI) and age of specific plots. The paper discusses the use of TanDEM-X top height, which is the 90th height percentile of bias-corrected phase heights, to capture the canopy top height. The paper presents the results of two prediction cases:
Case (a) involves predicting both SI and age, where the prediction biases were relatively small and statistically insignificant.
Case (b) involves predicting SI when age is provided from field data. The results showed a very low Root Mean Square Error (RMSE) and bias, indicating a good fit.
The paper also discusses the correlation between severe underestimations of SI and overestimations of age. It mentions that silvicultural treatments, from pre-commercial thinning to clear-cutting, lead to underestimation of the slope of the Height-Diameter Curve (HDC), leading to underestimation of SI, and overestimation of age.
To improve the article, consider the following suggestions:
1. Provide More Context: The summary provided does not give a clear understanding of the context or the problem the study is trying to solve. Providing more background information could help readers understand the significance of the research.
2. Clarify Technical Terms: The document uses a lot of technical terms like SI, HDC, and TanDEM-X. Providing definitions or explanations for these terms could make the article more accessible to a wider audience.
3. Include More Visuals: The paper seems to include several figures and tables. Including more visuals like graphs, charts, or diagrams could help illustrate the findings more effectively.
4. Discuss Limitations and Future Work: The summary does not mention any limitations of the study or suggestions for future research. Discussing these aspects could provide a more balanced view of the research.
Separately in the introduction or in a separate chapter, attention should be paid to explaining why TanDEM-X and X-band in general should be used for such works, what is the difference from L and C, why Sentinel-1 data, which are available in the open access, are not suitable for such works.
general proofreading required
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
I have reviewed the manuscript "Prediction of Site Index and Age Using Time-Series of Tan-DEM-X Phase Heights", Manuscript ID: remotesensing-2503149. In this paper, the authors analyze the possibility to use radar time series provided by the radar satellite TanDEM-X in order to estimate site index and stand age.
The Manuscript ID: remotesensing-2503149 is interesting. However, if the authors make an effort to take into account the recommendations from the specific comments, they will arrive at an article that can bring interesting insights to the current state of knowledge. I consider that the article will benefit if the authors take into account the following remarks by addressing the signaled issues within the text of the manuscript and reflecting them clearly point-by-point within the cover letter:
Remark 1: the main strong point of the manuscript consists in the fact that it approaches a very interesting topic for the experts in the field.
Remark 2: the main weak point of the manuscript under review consists in the overreliance on TanDEM-X data. The model appears to overly rely on the TanDEM-X data without incorporating other potentially useful remote sensing or field data. The proposed method depends entirely on the accessibility and reliability of TanDEM-X data. The authors do not sufficiently address strategies to overcome potential data gaps, or how other types of data could be used to supplement or validate the TanDEM-X data. Although the authors mention the requirement for an accurate Digital Terrain Model (DTM) for reliable canopy heights from phase heights, the possible incorporation of other remote sensing technologies such as LIght Detection and Ranging (LIDAR) or multispectral imagery could provide additional, valuable insights, specifically regarding vegetation structure complexity and species variability. Therefore, the authors should consider revising the model to include other factors influencing stand structure or complement the TanDEM-X data with other remote sensing or field data. The authors should address why they have chosen to solely rely on TanDEM-X data and discuss the potential benefits and drawbacks of data fusion.
Remark 3: inadequate consideration of vegetation structure complexity. One of the significant weaknesses of this study lies in the insufficient attention given to the complex interactions between various factors that influence forest stand age and site index, such as interactions between species type, soil quality, and environmental conditions. While the authors have managed to apply time-series interferometric TanDEM-X data to estimate the forest stand age and site index, it seems they have failed to fully consider the aforementioned factors that can significantly impact stand structure in ways that may not be discernible in the TanDEM-X data.
Remark 4: assumption of homogenous forest conditions can limit the model applicability. The study asserts that the proposed method is only appropriate for forest stands that are even-aged and relatively homogenous. Nevertheless, such a situation is not always the case in real-world forest conditions where multi-age stands and mixed species compositions are common. It is necessary for the authors to provide insights within the manuscript regarding the potential solutions when dealing with these complex stand structures. The authors' methods seem to rely on the assumption of homogenous and even-aged forest stands, but real-world conditions can be quite heterogeneous. The penetration depth of radar signals can be significantly affected by stand structure, species composition, and seasonal changes, among other things. These aspects need to be taken into account when applying the method to real-world scenarios, especially considering the manuscript's focus on time series data spanning seven growth seasons.
Remark 5: lack of details on the environmental factor assessment. The manuscript does not thoroughly address how environmental factors that can significantly affect stand age and site index are incorporated into the model. For instance, factors such as precipitation, temperature, and light conditions can drastically influence forest growth rates, yet it's unclear how these factors are accounted for within the model and the overall predictions.
Remark 6: insufficient handling of heterogeneity in the study area. The manuscript, especially the "Discussion" section suggests that some degree of heterogeneity is present within the study site, both in terms of species composition and edge effects from stand boundaries and roads. Nevertheless, the approach of the authors to this inherent complexity is somewhat dismissive. Further exploration of these elements, particularly through sensitivity analyses or other modeling strategies, could strengthen the applicability and robustness of the model. Incorporating a more robust treatment of spatial heterogeneity and edge effects, as well as extending the model's applicability to mixed and uneven-aged stands, could significantly enhance the manuscript's value.
Remark 7: dataset scope and accessibility. An important aspect that must be considered is the availability and accessibility of the data. The authors acknowledge the potential limitations imposed by restricted availability of TanDEM-X data for a specific period or region, but the manuscript lacks concrete discussion on how this limitation was managed, or how future studies might circumvent such challenges. The authors should provide a clearer strategy to address potential issues surrounding the access and availability of TanDEM-X data.
Remark 8: generalizability of results. The authors have tested their method in a specific location, Remningstorp in southern Sweden, where data availability may not be a pressing issue. It would be beneficial if the authors could expand on how their method could be applied in different geographical regions where the TanDEM-X data might be limited or unavailable.
Remark 9: predictive performance in different conditions. The authors claim that the method presented is suited for producing wall-to-wall estimates over large areas of forest. Nevertheless, they do not thoroughly discuss how the potential data limitations might affect the model's prediction accuracy. It would be useful if the authors could explore the performance of their model under various data availability scenarios.
Remark 10: practical approach for overcoming potential difficulties in obtaining data. While the authors speculate that longer time series data would increase the prediction quality, they do not propose a practical approach to overcome potential difficulties in obtaining this data. Given the potential for gaps in TanDEM-X data, the authors should discuss how future work could overcome these challenges.
Remark 11: practical implementation. From a practical perspective, it would be beneficial if the authors discussed how the methodology could be operationalized in a real-world setting where not all the conditions are as ideal as in the test site. This discussion could address issues like cost and time requirements for data acquisition and processing, as well as the necessity for ground truthing. The potential benefits of fusing TanDEM-X data with other readily available data sources, like national forest inventory data, could also be considered here.
Remark 12: heterogeneity of forest stands. The study focuses on homogenous, even-aged forest stands, which certainly simplifies the analyses but also limits the applicability of the findings. The real-world forest environment is often a mosaic of different stand ages, species, and management histories. The authors should address how their methodology would cope with more diverse forest landscapes, and whether integration of additional data sources could assist in dealing with such heterogeneity.
Remark 13: limited consideration of radar penetration depth variability. Throughout the manuscript, the authors have seemingly given minimal attention to the radar penetration depth variability issue. This is a significant concern as the penetration depth of the radar signal in the forest canopy is a function of frequency, polarization, and forest conditions. Each of these factors can significantly introduce variations in phase height measurements. Consequently, a more comprehensive consideration and explanation of this critical issue would strengthen the overall methodological framework and the reliability of the results.
Remark 14: unclear explanation of penetration depth correction. The authors mention applying a penetration depth correction to the phase height in the manuscript, but the exact details about how this correction was implemented and its effects on the final results are lacking in the main body of the paper. Given the importance of this correction, a deeper exploration and explanation would be beneficial. Furthermore, how this correction has been computed or estimated in a real environment with varying conditions needs to be adequately addressed to validate the correction's effectiveness.
Remark 15: incomplete analysis of radar penetration depth impact on predictions. The manuscript should further investigate how changes in radar penetration depth across different growth seasons, due to varying forest conditions, impact the predicted site index and stand age. Given the aim of the paper, it is essential to account for these factors as they could introduce bias into the results. The authors' current approach leaves a gap in the understanding of how this variability might have affected the predictions.
Remark 16: risk for over/underestimation of heights. While the paper concludes that the TanDEM-X top height captures the canopy top height reasonably well, this claim does not fully address the potential impact of varying radar penetration depths. If the radar signal penetrates more deeply into the canopy under certain conditions, this might lead to underestimation of the canopy's true height. Conversely, less penetration could result in overestimation. Therefore, a more comprehensive analysis on the role of radar penetration depth in height estimation is needed.
Remark 17: limited consideration of biotic disturbances. While the manuscript thoroughly explains the usage of TanDEM-X data to predict site index and forest stand age, it does not adequately discuss or consider the impact of diseases or pest outbreaks on forests. These biological disruptions could significantly affect the TanDEM-X phase height measurements and consequently influence the model's predictions. A more detailed consideration of these potential biological disturbances would have greatly enhanced the manuscript's relevance and applicability in a real-world environment.
Remark 18: underestimation of variation. The assumption that the study area consisted of intensely managed productive forest is limiting. As the authors rightly note, this may lead to the underestimation of the slope of the height development curve and therefore, an underestimation of the site index (SI) and an overestimation of age. This assumption could be more problematic in areas with greater heterogeneity due to diseases or pests. The authors should attempt to include more varied and potentially diseased or pest-infested sites in their analysis to obtain a more realistic model that can be applied across a broader range of scenarios.
Remark 19: mitigation measures for disease and pest effects. The manuscript would have benefited from discussing potential mitigation measures or adjustments to account for the effects of disease or pest outbreaks. For example, how does the model account for rapid changes in forest stands due to these factors? Could the model integrate other data sources to detect and adjust for these occurrences? These considerations would not only improve the model but also make it more applicable to real-world forest management.
Remark 20: extrapolation limitations. While the authors suggest the method could be used for producing wall-to-wall estimates over large areas, this could be problematic if the studied area does not accurately represent the variety of conditions present across broader landscapes, such as different levels of disease and pest pressures. Therefore, some discussion around the limits of extrapolation would be useful.
Remark 21: need for complementary pest data sources. In the case of disease and pest outbreaks, TanDEM-X data alone might not be sufficient for accurate prediction of site index and stand age. The authors should consider and discuss the potential for integrating complementary data sources that might provide early detection or measure the impact of such disturbances, ensuring the robustness of the model predictions.
Remark 22: seasonal variations. Regarding the manuscript, how were the considerable seasonal variations experienced by hemi-boreal forests, consisting of both temperate and boreal species, considered in interpreting the TanDEM-X data? Were there any provisions made to mitigate the significant effects these variations could have on forest structure, growth, and hence, the data accuracy?
Remark 23: atmospheric and weather effects. With atmospheric and weather conditions known to introduce phase errors in TanDEM-X acquisitions, potentially affecting height estimation, how were these atmospheric and weather effects managed to ensure the accuracy of the height estimations?
Remark 24: modeling and algorithm development. Could the authors elaborate on their approach to modeling and algorithm development, particularly on how multiple growth-influencing factors were accounted for when designing their proposed approach?
Remark 25: ensuring accuracy despite species composition. Given the significant variation in species composition within hemi-boreal forests, potentially affecting how forest structure is depicted in TanDEM-X data, how was this diversity accounted for in the analysis? Could the authors strengthen the explanation for ensuring the accuracy of the estimations despite the potential influence of species composition?
Remark 26: issues regarding temporal decorrelation. It would be helpful if the authors could clarify how the study accounted for the potential errors in height estimation resulting from temporal decorrelation, which occurs due to changes in the forest's scattering properties between the repeat-pass interferometric TanDEM-X acquisitions?
Remark 27: sudden, non-natural changes in forest growth. The study seems to take a natural course of forest growth into account. Nevertheless, how does it account for sudden, non-natural changes in forest growth brought about by logging, forest management activities, and environmental policies? Could such events distort the interpretation of data and modelling?
Remark 28: non-linear growth patterns. It is understood that trees don't grow at constant rates, thereby introducing non-linearity into the growth patterns. Could the authors strengthen how this particular aspect was handled while modeling and predicting forest stand age and site index from time-series data?
Remark 29: discussion on multi-layer canopy structure. Considering the complexity of multi-layer canopy structure common in hemi-boreal forests, how does the model capture these details using TanDEM-X data? Could this complexity pose challenges in predicting forest stand age and site index accurately?
Remark 30: issues regarding phase discontinuities. The study uses TanDEM-X phase height measurements extensively. Could the authors elaborate on how they have handled potential phase discontinuities arising from volume scattering, which could lead to errors in these measurements? It would be beneficial to know how the study managed the complex radar interactions with different layers of the canopy, understory and ground, which can scatter the signal in multiple directions.
Remark 31: consideration of resource availability. The analysis of large-scale data in the study requires significant computational resources, storage capacity, and specific technical expertise. Could the authors discuss the practicality and feasibility of their proposed approach, considering the resource constraints that may be present in real-world applications?
Remark 32: effect of topography. Could the authors provide more details regarding the potential impact of site topography on TanDEM-X data? Given that topography can significantly influence the data, particularly in complex terrains, it would be beneficial to know how this challenge was addressed during the correction process.
Remark 33: biomass estimation. How does the study address the complex relationship between forest stand parameters such as age, site index, and biomass, considering they may not be accurately captured solely by TanDEM-X data? Clarifying this could shed light on the effectiveness of the chosen approach in real-world scenarios.
Remark 34: scale mismatch. The integration of multiple data sources can be problematic due to potential spatial scale mismatches between TanDEM-X data, ground truth data, and other auxiliary data. Could the authors expound on how they addressed this challenge in their study?
Remark 35: temporal coverage. Even though the study covers seven growth seasons, there might be limitations due to insufficient temporal coverage for modelling long-term forest growth and changes. How did the authors plan to mitigate these limitations in a real-world setting?
Remark 36: influence of climatic events. Could the authors clarify how their model addresses the potential impacts of severe weather events and climatic anomalies (e.g., storms, droughts, extreme heat) on the forest structure and growth? Given that these unforeseen changes can affect the predictive ability of the model, understanding how they have been accounted for would be valuable.
Remark 37: data processing workflow. Could the authors shed light on how they established a consistent, accurate, and efficient data processing workflow, considering the complexity and volume of TanDEM-X data? The ways in which they intended to handle these challenges in a practical environment would be of particular interest.
Remark 38: interpretation of complex radar signals. Given that radar signals can interact differently with various parts of trees and the ground depending on the forest structure, creating a complex signal, could the authors elucidate how they approached the interpretation of these complex signals in their study? The explanation of their real-world problem-solving strategies would be beneficial.
Remark 39: explicit statement of assumptions. Can the authors provide more details regarding the particular assumptions applied in the formulation of height development curves? The insufficient details regarding these assumptions could potentially lead to misinterpretation of the findings.
Remark 40: quantification of treatment bias. The study acknowledges an underestimation of Site Index (SI) and an overestimation of age for treated plots, with the deviation said to increase in tandem with the treatment's intensity. Could the authors provide a quantitative model or measure to better elucidate the relationship between treatment intensity and prediction bias?
Remark 41: assessment of uncertainty. While uncertainties in predictions, due to factors such as edge effects and heterogeneity, are admitted, there seems to be an absence of their quantification or statistical representation. Could the authors consider providing this to offer a more robust understanding of the uncertainties?
Remark 42: reassessment of sample size. The study used a small sample size of 91 plots, which may not be sufficient to generalize the results to other forest stands. With a relatively limited sample size of 91 plots, the authors should make use of a larger sample size to reinforce the validity and reliability of the findings in a real-world environment.
Remark 43: integration of validation data. There appears to be a lack of model validation with independent datasets. Could the authors consider cross-validation of the model with a separate dataset, thereby enhancing the robustness and reliability of the model?
Remark 44: in-depth analysis of the causal factor behind the incorrect predictions. The manuscript identifies certain plots that exhibited substantial deviations between predictions and reference values, yet the discussion regarding the causal factors behind these discrepancies appears to be lacking. Could the authors provide a more in-depth exploration of the elements contributing to these errors, thereby offering valuable insights into potential model improvements?
Remark 45: insufficient discussion of ALS calibration. The manuscript refers to the use of ALS (Airborne Laser Scanning) calibrated TanDEM-X heights in a previous study but does not elaborate on a detailed comparison or analysis of this method in relation to their own. Would a more comprehensive discussion of these comparative methodologies enhance the contextual understanding and significance of their work?
Remark 46: exploring alternate models. Were alternate predictive models or machine learning algorithms considered during the study, and if so, why were they not selected? A discussion exploring this could potentially provide a broader context for the chosen approach.
Remark 47: implications of climate change. Given the period of TanDEM-X data collection (2011-2018), could the authors elucidate on whether they accounted for the possible effects of climate change on forest growth? It would be interesting to understand how such influential factors could have shaped the growth rate and other model parameters.
Remark 48: prospects for future research. The manuscript will benefit from a segment detailing potential avenues for future research, taking into account the discoveries and limitations of the present investigation. The authors should consider outlining such possibilities.
Remark 49: discussion on model assumptions. The authors must strengthen the assumptions underpinning their model, including their implications and the model's resilience when these assumptions are not fulfilled. This additional perspective could offer readers a deeper comprehension of the scenarios where their model might not be fitting.
Remark 50: reliance on TanDEM-X data. With respect to the dependency on TanDEM-X data in estimating forest stand age and site index, could the authors elucidate on its comparative advantages or disadvantages vis-à-vis other remote sensing techniques? This would offer a broader understanding of the technique's application within the context of the study.
Remark 51: the equations. The equations used within the manuscript should be explained, demonstrated or cited, as there are some equations that have not been introduced in the literature for the first time by the authors and that are not cited (for example, equations (1)-(3)).
Remark 52: the details regarding the used dataset. In Subsection 2.2, the authors have provided a series of details regarding the datasets used in their paper. It will benefit the manuscript if the datasets that the authors have used in their experimental setup are provided as supplementary materials to the manuscript as the authors should provide all the necessary details in order to allow other researchers to verify, reproduce, discuss and extend the obtained published results of the authors. Other researchers should not have to obtain and concatenate the datasets using various methods, risking in obtaining different datasets or datasets that have been normalized differently than the datasets on which the authors have performed their experiments and analysis on.
Remark 53: discussing the obtained results – insight. The paper will benefit if, after having discussed the obtained results, the authors make a step further, beyond their approach and provide an insight regarding what they consider to be, based on the obtained results, the most important, appropriate and concrete steps that all the involved parties should take in order to benefit from the results of the research conducted within the manuscript.
Remark 54: discussing the obtained results – the cost-benefit analysis. The authors should analyze the computational costs and the costs associated with data storage and processing. A cost-benefit analysis will help elucidate the possibility of implementing the proposed approach in a day-to-day working environment.
Remark 55: data availability statement and the comprehensive details regarding the datasets. The data availability statement is missing. The data availability statement is an important part of the manuscript and provides essential information for readers. Without the data availability statement, it is difficult for readers to assess the validity of the results presented in the manuscript. Consequently, I would suggest that the authors provide access to the datasets in the data availability statement (that does not exist within the current form of the manuscript). It is of paramount importance that the authors provide this information in order to assure the scientific rigor of their manuscript and allow other researchers to verify, reproduce, discuss and extend the obtained published results.
Other remarks:
· Lines 10-21, the "Abstract" of the manuscript. The manuscript will benefit if the authors provide a structured abstract, that covers the following aspects: the background (in which the authors should place the issue that the manuscript addresses in a broad context and highlight the purpose of the study), the methods used to solve the identified issue (that should be briefly described), a summary of the article's main findings followed by the main conclusions or interpretations. In the abstract the authors must also declare and briefly justify the novelty of their work. The authors must present in a clearer manner the above-mentioned aspects: the background, the methods, the main findings, the conclusions, as in the actual form of the manuscript, the abstract offers information related only to some of these aspects and even so, their delimitation is unclear.
· Lines 25-92, the "Introduction" section. In its current form, the "Introduction" section contains 26 cited papers. I do not contradict the value of these papers, or their relevance in this context, but I consider that the article under review will benefit if the authors improve this section. I consider that the literature review should be enhanced by performing a careful analysis of the cited works. I consider that it is not appropriate for the manuscript to cover 26 scientific works in 68 lines of text (like the authors have did) just for the sake of obtaining an appropriate size of the "References" section. The purpose of the literature survey is to highlight exactly, for the main involved referenced papers the main contribution that the authors of the referenced papers have brought to the current state of knowledge, the method used by the authors of the referenced papers, a brief presentation of the main obtained results and some limitations of the referenced article. This is the only way to contextualize the current state of the art in which the authors of the manuscript position their paper and address aspects that have not been tackled/solved yet by the existing studies.
· The gap in scientific literature. After having performed a critical survey of what has been done up to this point in the scientific literature, the authors must identify and state more clearly in the paper a gap in the current state of knowledge that needs to be filled, a gap that is being addressed by their manuscript. This gap must also be used afterwards, when discussing the obtained results as well, when the authors should justify why their approach fills the identified gap in rapport with previous studies from the literature. In the "Introduction" section the authors must also declare the novel aspects of their work.
· Previewing the structure of the paper. After declaring the novel aspects of their work, at the end of the "Introduction" section, the authors should present the structure of their paper, under the form: "The rest of the paper is structured as follows: Section 2 contains…".
· The citation's order. Paper [7] is cited for the first time at Line 56, while paper [6] is cited for the first time at Line 60, after paper [7]. Please renumber and reorder the references.
· The "References" section. The paper [28] from the "References" section has not been cited in the manuscript at all. Please address this issue either by citing this paper in the manuscript if it has been used in documenting the paper, or by deleting it in the opposite case.
· Issues regarding the title of the manuscript, "Prediction of Site Index and Age Using Time-Series of Tan-DEM-X Phase Heights". Acronyms and abbreviations must be avoided in the title, even if they are widely known. Regarding the other acronyms used in the manuscript, they should be explained the first time when they are introduced.
· Issues regarding the "Tan-DEM-X" and "TanDEM-X" abbreviations. Within the manuscript, the authors make use of two different abbreviations for the same notion, namely "Tan-DEM-X" in the title of the manuscript and "TanDEM-X" in the rest of the paper. The authors should address this inconsistency.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
The authors did not emphasize the changes made, so it is not possible to trace what was actually done and what was not.
At first glance, the new version of the manuscript raises fewer questions.
The manuscript can be published after final reading by the editor.
minor proofreading is required.
Author Response
We are very sorry about this. The upload of a non-highlighted version of the document was a mistake, and we have now uploaded a version with changes highlighted.
Reviewer 3 Report
After having assessed the revised version of the Manuscript ID: remotesensing-2503149-peer-review-v2, with the revised title "Prediction of Site Index and Age Using Time-Series of TanDEM-X Phase Heights", coupled with the insights from the cover letter provided by the authors, several aspects need to be emphasized:
1. Highlighting of Manuscript Revisions: One of the fundamental expectations in academic scholarship is that any updates made in a revised manuscript should be transparently communicated. Such practices ensure clarity for reviewers, allowing them to efficiently assess which concerns have been really addressed and which have not. In the absence of highlighted or track-changed revisions in the manuscript, reviewers are left with the task of having to comb through the entire document to discern the revisions. This can potentially lead to overlooked changes or misinterpretations, neither of which serves the purpose of the peer-review process. It is not just a matter of convenience but a foundational principle of clarity, thoroughness, and academic consideration towards the peers.
2. Data Availability and Reproducibility: Considering the "Data Availability Statement: The data used in this study are available upon reasonable request." inserted by the authors in the revised version as response to my previous remark of the initial review report, I cannot assess with a high degree of certainty the reproducibility and therefore the complete validity of the claims. Making the data available from the very beginning of the submission process in order to verify the claims during peer-review (for example by using the unpublished materials sections of the submission section) is more than a reasonable enough expectation. The essence of scholarly research lies in the reproducibility and verification of its claims. As such, making the data readily available, especially during the peer-review phase, is of paramount importance. According to the TOP Guidelines, the Data Transparency Level is I out of III (if the request is fulfilled post-publication and the terms "reasonable request" along with "legal" mentioned in the coverletter are not being used as barriers for it). By relegating data availability to "reasonable request" the authors introduce a degree of subjectivity that may obstruct unfettered access. This becomes even more salient in light of the TOP Guidelines, which clearly indicate the hierarchy of data transparency. The current manuscript's Data Transparency Level at I out of III, with conditions like "reasonable request" and "legal" potentially being used as hindrances post-publication, is not the zenith of transparent academic research, but unfortunately is being widely used today. Without direct access to this data, the validity of the manuscript's claims remains uncertain, undermining the core principle of scholarly validation. There is not more that I can assess on this front.
3. Improvements in the Manuscript: Procrastinating or postponing necessary revisions could be perceived as a lack of commitment to excellence or thoroughness on the authors' part. Even if the authors have actually procrastinated, postponed some of the signaled aspects, overall the manuscript has been improved when compared to the previous submission.
Author Response
- We are very sorry about this. The upload of a non-highlighted version of the document was a mistake, and we have now uploaded a version with changes highlighted.
- As some of our financiers do not want all data to made publicly available, we cannot publish them outright. However, we can provide the data on request, for precisely the reasons of transparency that you underline. The "reasonable" formulation is just to shield the authors from non-serious requests. Say that some parties would for some reason start to request data from research papers in an automated fashion in order to collect data for some purpose other than verification of results. This could overwhelm the authors with obviously non-serious requests, which we would not want to feel obliged to accede to. If you wish to verify results, we can provide you with any data that you might request. In such case, it would be helpful if you specify which data you are interested in obtaining. We agree that this is a less than optimal level of transparency, but it is the level that we are able to accede to in this study, and we feel that the methodology is still described in enough detail as to be reproducible.
- We are sorry that the revision process was somewhat delayed. This was not due to procrastination or postponing, but the fact that all authors were on annual leave when the manuscript was returned.
