Remote Sensing Perspective on Monitoring and Predicting Underground Energy Sources Storage Environmental Impacts: Literature Review
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsOverall Comments:
The topic of this manuscript is highly forward-looking and of significant practical importance. Against the backdrop of the global energy transition, underground energy storage (UGS), particularly carbon capture and storage (CCS) and underground hydrogen storage (UHS), has become a key technology for ensuring energy security and achieving climate goals. However, the potential environmental impacts (e.g., ground subsidence, gas leakage, microseismicity) also present significant safety challenges. This paper systematically reviews the current state of research on using remote sensing to monitor and predict these environmental impacts. The content is comprehensive, the structure is clear, and it provides an extremely valuable reference for researchers and administrative bodies in this interdisciplinary field.
The contribution of the article is significant. However, to further enhance its academic rigor and impact, I recommend that the authors revise the manuscript according to the following comments before publication.
Major Comments
- As a review article, its core value lies in the scientific and systematic nature of its methodology. The current description in Section 2 is overly brief and may lead readers to question the comprehensiveness and objectivity of the literature selection. The statement "without the assistance of Al-based review generators" [line: 103] is superfluous and non-academic.
- The criterion of limiting the search to the "500 most relevant results according to WoS" [line: 110] is arbitrary. The authors should explain why 500 results are considered sufficient and whether the WoS "relevance" ranking might overlook important earlier or less-cited studies. Please provide the complete and exact search query string used in the Web of Science database, including all Boolean operators, to enhance reproducibility.
- The screening criterion "did not include surface monitoring" [line: 112] is too general. Please clearly define the Inclusion and Exclusion criteria for literature screening. For instance, were purely subsurface geophysical monitoring studies excluded? Were there further requirements regarding the language or publication year of the literature? This would significantly improve the rigor of the review.
- Section 4 is key to elevating the paper from a 'list' to a 'synthesis', but some of the analysis currently remains at a macroscopic level. For example, the strengths and weaknesses of remote sensing techniques mentioned in the SWOT analysis (such as high temporal resolution, dependence on weather) are common knowledge in the field and could be more deeply analyzed in the specific context of UGS monitoring needs.
- When discussing the weaknesses of InSAR, beyond mentioning low coherence in vegetated areas, the authors could delve deeper into the challenges posed by the non-linear, three-dimensional deformation characteristic of UGS sites, and the limitations of existing techniques (e.g., MSBAS) in resolving such complex deformation.
- When discussing the opportunities for multispectral/hyperspectral imaging, in addition to detecting vegetation stress [line: 670s], the authors could further explore its potential and challenges in quantifying soil geochemical anomalies or identifying mineral alterations caused by micro-leakage, providing a more direct comparison with existing ground-based geochemical sampling methods.
- Section 3.4 provides a very comprehensive overview of modeling methods, but the connections and comparisons between the various methods could be more prominent. The data-driven models section, in particular, lists several machine learning algorithms but lacks a sufficient comparative analysis. I suggest adding a short paragraph that critically compares the suitability of different machine learning algorithms (e.g., RF, LSTM, CNN) for handling specific types of UGS monitoring data (e.g., time-series InSAR data, hyperspectral imagery). For example, a discussion on the advantages of CNNs for image-based data like interferograms and the strengths of LSTMs for time-series data like ground subsidence or reservoir pressure would be beneficial.
- Building on Figure 5, the authors could further discuss how data-driven approaches can be integrated with traditional empirical or theoretical models to form hybrid prediction models, which may be a key future direction for the field.
Minor Comments
Incorrect (reversed?) Notations of Tables 2 and 3, The authors should carefully check all figure and table citations throughout the manuscript for accuracy.
The paper uses both "underground gas storage (UGS)" and "geological storage". While the former is a subset of the latter, it is recommended to briefly define the scope and relationship of these terms in the introduction and maintain consistent usage throughout to avoid potential confusion for the reader.
The three research questions posed in the introduction [lines: 80-84] are somewhat basic. Consider rephrasing them to be more analytical and in-depth. For example, question 3 could be revised to: “What are the critical gaps and future research directions for integrating multi-source remote sensing data and data-driven models to create a holistic monitoring framework for UGS sites?”
Author Response
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Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsPlease see the attachment.
Comments for author File: Comments.pdf
Author Response
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Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsI have reviewed the revised manuscript and am pleased to confirm that the author has thoroughly and successfully addressed all of the major concerns raised in my initial review. The manuscript is now a strong, clear, and valuable contribution. I am happy to recommend its acceptance for publication and have no further comments
Reviewer 2 Report
Comments and Suggestions for AuthorsThe revised version shows a significant improvement. I recommend it for publication.
