Can Imaging Spectroscopy Divulge the Process Mechanism of Mineralization? Inferences from the Talc Mineralization, Jahazpur, India
Round 1
Reviewer 1 Report
General comments
This is an interesting paper dealing with the use of AVIRIS-NG images to map and characterize talc mineralization the Jahazpur, India. It is a good illustration of what can be achieved when using spectroscopy to understand geological processes and requires minor adjustments before publication.
The paper would greatly benefit from a little reorganization in the description of the method and part of the results (see detailed comments) and some answers to the following questions and requests:
1) Regarding the methodology, are you using the USGS Tetracorder (ref # 40) or you own software which would be very similar? This needs to be clarified.
The methodology followed to produce the mineral maps should be more detailed. I could not find the explanation for the “combination” of minerals / intimate mixtures??? It is necessary to describe how you select the spectral subsets from which you remove the continuum since this should change depending on the minerals of interest and the location of their diagnostic absorption features. Moreover, it is also necessary to specify that the absorption depth have to be adjusted/normalized between reference spectra from the USGS spectral library and image spectra since there is a difference between absorption depth of pure minerals (reference) and from image where pixels are not composed of pure mineral but of mineral mixtures.
2) Regarding spectra of mixtures, what is the source of these reference spectra? The USGS spectral library? If so, what are the proportions for each constituent leading to the selected reference spectra? The concept of “intimate mixture” has a specific physical meaning: it corresponds to a physical mixture of minerals, describes what is happening at a microscale and is a non-linear mixture of the spectral signatures of “endmembers” by comparison to an aerial mixture which mainly describes what can be observed at a macroscale (linear combination of spectral signatures of endmembers). This must be clarified as the interpretation of the spectral signatures is different.
3) May be reorganize section 4.2 by including justifications of lines 216-232 in the description of each results (see detailed comments)?
Finally, there are a few remaining typos and possible improvements to the English (see detailed comments below).
Detailed comments
Line 13: spatial pattern talc of mineralization
Line 15: Do you mean an intimate mixture of kaolinite and muscovite, the two other minerals been possibly present? This is not clear throughout the paper and needs to be clarified.
Line 35: of the presence
Line 36-37: ASTER has one single band centered at ~ 2.33 µm, with a 35 nm bandwidth, therefore preventing the observation of the diagnostic doublet of talc.
Line 39-40: of the VNIR and SWIR spectral region in the 0.35-2.5 μm wavelength range
Line 84: as part of a joint collaboration
Line 85-86: National Aeronautics and Space Administration (NASA) in the 0.37-2.5 μm wavelength range with 425 spectral channels at a spectral resolution of ~ 5 nm and a spatial resolution of 8.1 meters. The radiance received by the sensor
Line 97: gaussian
Line 97: “an exponentially sloping curve called continuum”??? The continuum is defined as the envelope or global shape of the reflectance spectrum (see Clark and Roush, 1984) and is not exponential if you consider the full 400-2500 nm spectral range. Are you considering the full spectral range or only a subset? This should be clearly specified as the results of continuum removal will be different depending on the spectral range considered. Moreover, the continuum removal has to be the same for image and library spectra.
Are you using the USGS Tetracorder (ref # 40) or your own software which would appear to be very similar? This needs to be clarified. It is also necessary to describe how you select the spectral subsets from which you remove the continuum since this should change depending on the minerals of interest and the location of their diagnostic absorption features. It is also necessary to specify that the absorption depth have to be adjusted/normalized between reference spectra from the USGS spectral library and image spectra since there is a difference between absorption depth of pure minerals (reference) and from image where pixels are not composed of pure mineral but of mineral mixtures. Prevents for proper fitting using least-squares.
Line 124: Capital letters for all minerals or for none.
Line 133: located every 5 nm
Line 135: which is a (or one) hundred in this case.
Line 149: showing diagnostic absorption features in the 2.0-2.5 μm spectral range.
Line 156-157: intimate mixtures of kaolinite and muscovite, kaolinite, halloysite, and dickite. All these minerals have spectral signatures that are very similar (common diagnostic absorption at ~ 2200 nm) and only differ by the strength of the secondary absorption at 2160 nm (except for muscovite which has a secondary absorption at ~2350 nm).
Line 157: I don’t think epidote is a carbonate… (= sorosilicate)
Line 171: are considered
Line 176-177: the presence of some clay mineral along with calcite.
Line 177: a relatively higher continuum slope
Line 184-185: However, dolomite can be distinguished from calcite based on a subtle shift in absorption feature position, near 2.31 μm for dolomite versus 2.33 μm for calcite.
Fig 5: Why not show in Fig 5 the comparison between continuum removed spectra since this is how the identification is performed? Continuum removal normalizes the spectra thus focusing on absorptions only. Other parameters influencing the spectral signature such as grain size, surface roughness, illumination, etc. can no longer be estimated so I do not understand the reason for interpreting the results including possible changes in “continuum slope” as this has no impact on the identification due to the approach used here.
Line 190: intimate mixture of kaolinite and dolomite (i). There seems to be a difference between the figure caption and the annotation on the figure itself = mixture of kaolinite and muscovite or kaolinite and dolomite?
Line 194: by a doublet near 2.31 μm
Line 194-201: Which USGS talc spectrum was selected? Based on the online library, there are several spectra available, and it seems that spectrum of talc GDS23 would be a better match with a clearer doublet.
Differences between kaolinite and halloysite appear very subtle, especially if you consider poorly crystallized kaolinite. This is exactly what you explain lines 216-222 so why not mention it here?
Line 202: Library image spectra of dolomite-calcite-talc? I assume you mean library spectra?
I am not convinced Fig 5d shows a good match. Seems to me there is only a lower concentration of talc (the doublet and the absorption at 2.35 µm are present but not as strong, and there are no obvious other absorption feature) or a mixture with something that does not sign in the spectral range. Moreover, the positions and shapes of the absorptions are changing depending on the proportions of the constituents of the mixture. You need to give an indication of the proportion of each constituent which lead to the spectral signature you are using as a reference.
Line 205: What proportion of calcite, dolomite and talc in your mixture???
Line 206-207: “The mineral map also showed abundance of various clay minerals such as kaolinite, intimate mixture of kaolinite and muscovite, muscovite, halloysite, and dickite (Fig. 4).” Not clear what the mixtures are. Same comment as for previous mixtures: how are they determined? By comparing to mixture spectra provided in the USGS spectral library? What are the proportions of each mineral?
It is hard to identify the presence of muscovite mixed with kaolinite as they both have an absorption feature at 2200 nm just like smectites (like montmorillonite). The main difference is the secondary absorption at 2.35 µm for muscovite or possibly the difference in depth between the 2.16 and 2.2 µm of the kaolinite doublet. Which, again is what you explain lines 225-232 so why not do it here.
Line 211-212: The contamination of image spectra by atmospheric residuals at these 211
wavelengths, makes the inter-comparison ambiguous.
Compensation for atmospheric water vapor absorptions is impossible (not only ambiguous) as the amount of water vapor changes through space and time too quickly to be corrected properly.
Line 222: to the kaolinite group of minerals
Line 235: can be used
Line 245-246: hematite… intimate mixture of kaolinite and muscovite
Line 283: “black colored line”?
Line 289: “shown as rectangle in Fig. 8a and b”???
Line 297: indicative of the presence of iron-rich heavy minerals
Line 300: as an important guide
Line 330: part of the basement
Line 331: his may be a plausible source
Author Response
Please see the attachment
Author Response File: 
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Reviewer 2 Report
Dear authors,
Thank you for submitting this interesting manuscript. I enjoyed reading the manuscript, however, I see some room for improvement and to make your method and approach clearer to the reader. I will go into more detail below but in general terms, I would advise adding more methodology description and what data products you are creating based on AVIRIS-NG data and how you are interpreting them. A couple of things are unclear to me and need added explanation:
- What spectral matching algorithm was used?
- What spectral library was used and how were the endmembers chosen? How did you convolve the spectral library endmembers to the image specs?
- What minerals are you actively looking for in these areas and what minerals and surface content do you use as a proxy for talc mineralization patterns?
- How did you confirm iron content in the areas where IBD methods point towards iron enrichment?
- How was the AVIRIS data processed apart from the atmospheric correction? Smoothing? Vegetation and water masking? Etc.
Please add these sections and make sure the reader is able to understand clearly what process took place to create the mineral maps and index maps.
What I also see is a higher amount of self-citations as well as a lack of relevant citations for the methodology or references used. To me, the title could become clearer as well. In its current state, the title leads the reader to believe that the manuscript will go into more detail on the imaging spectroscopy for talc mineralization whereas the manuscript applies two general methods to AVIRIS data and then validates the results of this basic mineral classification. Generally, I would change the title and refer to hyperspectral imaging instead of “imaging spectroscopy” as well as mention the VNIR-SWIR wavelength range that is used here. As you’re asking a question in the title I would like to see a clear answer or discussion of the results in context with this question.
I apologize for the large number of comments. I hope that they can help elevate this manuscript. More details are found in the attached PDF.
All the best!
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Author Response
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