Prospecting Prediction for the Yulong Metallogenic Belt in Tibet Based on Remote Sensing Alteration Information and Structural Interpretation
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors1. The discussion section is not in-depth enough, for the geological indication significance of altered mineral combinations should be supplemented.
2. The iron staining alteration did't extracted from the GF-5 and ZY1-02D satellite hyperspectral data, it is recommended to supplement.
3. The ore deposit points in Fig.6 are suggested to be graded, and the Fig.9c and Fig.9d should be carefully considered.
Comments on the Quality of English LanguageThe English expression is not authentic enough, it is recommended to polish it.
Author Response
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Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for Authors1. Figure 1(a) significantly lacks information, failing to display a complete and accurate geographical representation at the national level. It is challenging to imagine that a researcher from China would lack the awareness to portray their country's territorial integrity comprehensively. Should there be further revisions, it is hoped that the author provides a satisfactory explanation for this issue. Meanwhile, the location information in Figure 1(b) appears to be unrelated to that of Figure 1(a), leaving its specific location unclear. Lastly, the use of blue in Figure 1(c) is unconventional for color-coding in the field of geology, where it is typically reserved to denote water bodies.
2. The discussion on the robustness of PCA in line 193 requires careful consideration by the author. Robustness usually refers to an algorithm's sensitivity to noise, outliers, and deviations from model assumptions. Being a linear transformation-based method, Principal Component Analysis (PCA) is relatively sensitive to outliers, which can significantly impact the covariance matrix of the data, thereby affecting the computation of principal components and the final outcome. There is substantial research addressing this issue, for instance, Robust PCA, a variant of PCA, which is better equipped to handle outliers and noise in data. It is in such contexts that terms like "high robustness" are typically employed.
3. The author displays common mineral spectral curves in Figure 2, providing a clear visualization. However, having discussed various sensor bands earlier, aligning these bands with the mineral spectral curves would more explicitly demonstrate how the author intends to use specific sensor bands, such as those of ASTER, for alteration information extraction.
4. Regarding Tables 3 and 4, it remains unclear whether a PCA analysis result exists for each scene. Did the author conduct PCA analysis on all data combined or only present it for a single scene? Many details, especially concerning data preprocessing and band processing, such as for GF-5, are omitted, likely to avoid excessive redundancy. However, during revision, it would be beneficial for the author to include some of these processing steps.
5. While the author extensively describes various sensors, there is no clear indication of how these were effectively integrated for alteration information extraction. This raises doubts about the coherence of the extensive work presented.
6. In the validation results section, the author mentions field surveys, but the mere two photos provided are insufficient to truly validate the results. Remote sensing in mineral exploration has been extensively researched, yet many studies fail to address how their results are validated. For publication in journals of higher standards like Remote Sensing, clarifying this aspect, including an accuracy concept (e.g., the number of ground truth sampling points and their concordance with remote sensing results), is essential.
7. Figures 9(a) and 9(b) showcase the alteration information in the validation area, but it is unclear what the depicted frames represent. Are they indicating field surveys? If so, the author needs to address why no samples were collected from the darker, more extensive areas shown in the center of these images, where alteration appears more intense. Additionally, the actual conditions of these areas and the extent to which remote sensing-detected alteration discrepancies mirror reality need clarification. The question of whether stronger remote sensing-detected alteration signals indicate a higher probability of mineralization in the region should also be considered.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsRemote sensors are the method used in predicting prospecting in specific areas, in this case it is the metallogenic search for copper in Tibet. Remote sensors allow access to information about the area's plate tectonics from afar due to their limited accessibility. The data used are multispectral and hyperspectral, with data from the Sentinel-1A and Landsat-8 24 OLI radar.
All this makes prospecting in these isolated areas easier.
It is a magnificent work of application to mining and has been presented in a concise and clear way.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
Comments and Suggestions for AuthorsAgree to receive in its current form
