Dear Prof. Antonio J. Plaza

The responses to critics and suggestions made by the reviewers have been addressed in the revised version of the manuscript (attached) and explained bellow.

-----------Dear Mr. Asadzadeh:Your manuscript TGRS-2015-00602 A review on spectral processing methods for geological remote sensing has been reviewed by the Editorial Board of the IEEE Transactions on Geoscience and Remote Sensing (TGRS) and found to be not acceptable without major revisions.It is recommended that you revise your paper and resubmit it in accordance with the TGRS Editorial Board comments given below.Please remember that TGRS has mandatory excessive page length charges. The policy regarding those charges was described in a previous email that acknowledged receipt of your initial manuscript submission. Should your manuscript be accepted for publication, the excessive page length charges will be based on the final version, after all revisions are complete.Along with the revised manuscript, please provide an item-by-item response to reviewers' comments, including:* Which suggested changes were accepted and made.* Which were ignored (these should be indicated and justified).* Where the changes were made in the manuscript (this should include all changes with detailed information).Please make sure these responses are not submitted in the space designated for confidential comments to the Editor in Chief.TGRS policy requires your revised manuscript to be returned by 12-Oct-2015. Please note that this is the date on which the option to revise will have expired, so your revision should be submitted by the day before. After this date, the manuscript will be considered as a new submission.You may submit your revised manuscript by logging in to your Author Center on IEEE ScholarOne Manuscripts at https://mc.manuscriptcentral.com/tgrsand using the "Click here to submit a revision" link, or by using the link below to submit without logging in.https://mc.manuscriptcentral.com/tgrs?URL_MASK=fa701b80739d4295a9433c3a1e953f6b

Note that if you begin to create a revision but do not fully complete the submission, the paper will then be located in the "revised manuscripts in draft" queue of your Author Center.**PLEASE BE SURE TO FIRST DETAIL THE CHANGES YOU HAVE MADE IN RESPONSE TO THE REVIEWERS COMMENTS BEFORE UPLOADING YOUR REVISION.**If you have any difficulty, please contact:Natalie CiceroEmail: tgrs-editor@ieee.org732-562-5397Sincerely,

Prof. Antonio PlazaEditor, IEEE Trans. on Geoscience and Remote Sensing-----------

Associate Editor Comments:This review paper received positive reviews in general from 4 highly experienced researchers. As they pointed out it is a challenging task to provide a full review on the broad topic. A number of suggestions have been provided from different aspects to improve the manuscript. The authors please address them in the revised version.-------

Reviewers Comments:

Reviewer 1:

This paper is a review of spectral analysis tools and techniques for hyperspectral data with emphasis on geologic applications. It is clear that the authors have done a lot of literature analysis to assemble more than 200 papers on a wide range of processing techniques. This kind of review papers are very useful as a resource for those attempting to start their own research in geologic remote sensing. I warmly welcome publication of this paper with minor revision. Below some comments and suggestions for improvement:

-the paper addresses geologic applications. I feel the authors should make it more explicit what kind of specific information geologists seek in spectroscopic data and why this is so specific for geology and thus why the techniques are not tuned to other applications. One can argue that geologists like to derive lithologic information, mineral composition, maybe chemistry of a sample or derivative parameters such as crystallinity or pressure-temperature of a metamorphic system. Can this be achieved?

To what extend can this be done qualitatively or quantitatively? Somehow a table that links the methods to these key requirements for geology would be helpful.

We have added a paragraph and a table (table 2) to address briefly the geological applications of remote sensing. We have discussed that four different maps can be created from spectral data (e.g. abundance and composition). The geological parameters are extracted directly or inferred indirectly from these four basic maps (table 2) in a qualitative to semi-quantitative way.

An important part of section 4 is dedicated to the capability of spectral processing techniques in providing these basic maps. Also, in section 7, we have concluded that more advanced techniques are still required to address key geologic variables.

-linked to this the introduction is not very well focused. It would be interesting to sketch a bit of the history of the field including some of the key papers that led to paradigm shifts. Now the meaning of the introduction is not clear; what is the key take home message?

There is a recent paper published by Van der Meer et al (2012) that discusses the history of the field and several paradigm shifts. We avoided repeating that in our paper and instead cited that article and several similar ones. Since the focus of our work is the review and categorization of the processing techniques, in the introduction part, we called the attention for the already published review papers related to algorithmic development, the classification scheme, and some basics concepts of spectroscopy and remote sensing.

-in addition; what are the key application areas in geology for spectroscopic data? Is the industry using this?

We have dedicated a full paragraph and a table (table-2) to address the applications of spectral technology in geology. We specified “Spectral solution has been widely acknowledged by the mining industry and to a lesser extent the energy sector, and the interest in this technology in geological application is steadily growing”.

-the test data set is well described I do wonder how representative this is for all geologic applications. It is a rather specific data set.

Though we had access to different airborne hyperspectral datasets in our collections taken over a variety of geologic targets, we used a close-range dataset collected by the sisuCHEMA imaging system for several reasons:

- The readers of this paper will probably be geologists as well as algorithm developers; hence, we avoided to use an airborne dataset to keep the paper simpler. Earlier review papers have used a great length of their page space to explain aspects of the test areas. This was avoided here using a simple and straightforward case study

- The selected sample is well constrained as regards its composition; in the SWIR region, the spectra is dominated by montmorillonitic clay and hydrocarbon (and probably adsorbed water) features, while in the VNIR, it is almost featureless.

- Because the mineralogic content of the sample was already known (from spectroscopy and XRD analysis), there was no need for “field data” to verify the results of spectral analysis.

- The binary mixture between montmorillonite and hydrocarbon is unique, while in airborne datasets, it is very rare to have such a uniform binary mixture.

- The use of a smaller sample size and two endmembers helped reducing the length of the paper. For a scene with several potential targets, it would be difficult to implement and compare the results yielded by numerous different algorithms.

- The tar-sand sample also highlights the broader area of HSI and spectral analysis in geologic remote sensing.

-the division of methods into data and knowledge driven requires a bit more clarification as this for some methods is rather arbitrary.

We agree that some of the assignments may seem arbitrary, but given the fact that the proposed scheme organizes the diverse range of methods and helps explain the relationships between the categories, it was used as a basis for the review and comparison among the methods.

We have defined a third broad category known as hybrid methods (not shown in Fig.2) to describe those methods that inherit their characteristics from two or more of the specified and basic groups/categories. For example, the spectral feature fitting (SFF) is admitted to be a hybrid method, because it combines user knowledge, continuum removal, and regression analysis to implement its processing; however, it is discussed in data-driven block. In fact, such methods are exceptions and the majority of the methods seems to fit well within the knowledge-driven and data-driven framework.

-the description of the algorithms is rather qualitative which is understandable given the length of the paper. However there are some that are explained with formula. Why? Why others not?

In the earlier versions of the manuscript, we included formulas for most of the methods. But given the length of the paper, they were reduced to a minimum. In the new version of the paper, we kept only two of them, where they were absolutely necessary or more difficult to explain in words.

-the results in fig 3 and fig 5 are difficult to digest. Can you validate the results?

We have validated the mineralogic content of the sample using XRD analysis. Apart from bitumen, there is quartz and montmorillonite, along with minor phases like titanomagnetite, brushite and orthoclase. The outcome of the processing was visually validated against the physical sample and its high-resolution image. The maps are meaningful regarding the materials identified. However, the boundary of the bitumen vary between methods.

To enlarge individual images, we have also changed the layout of these figures.

-what are hybrid methods? Do you mean ensemble classifiers?

As we explained above, hybrid methods are those that inherit their characteristics from two or more of the specified groups/categories (Fig. 2). For example, according to the provided categorization, Tetracorder is considered a hybrid method, because it combines user knowledge, decision-making system, regression analysis, and simulation of mixing between minerals to perform mineral analysis. Other examples may be the joint use of unmixing and continuum removal in the canonical variates analysis (CVA) technique for mineral identification and abundance estimation. Hopefully, with the proposed framework, many diverse techniques were justified to be a hybrid of two or more techniques (section 5). Based on this categorization, potential hybrid methods were also mentioned.

-in the discussion is seriously missed a more future look. What are the lessons learnt from all this? What are do’s and don’t’s. what are trends? What is missing? The paper now reads like an overview and not a review.

One of the key messages of this paper is the importance to conceive and use hybrid techniques. We have developed the two categories and then have shown that some of the advanced and most successful systems like Tetracorder are hybrid in their essence. We do believe the same idea can be applied to develop the next generation of processing techniques. We have also discussed the shortcoming in the field (e.g. the lack of a system for “lithology identification”, and some new trends such as multi-wavelength processing.

However to provide a more substantial future vision, we revised our conclusion and included several other ideas.

The new version of the manuscript seems now is better aligned and is complementary to earlier geologic remote sensing review papers (e.g. Van der Meer et al., 2012).

-the final conclusion seems to suggest that field data are needed. That seems rather trivial.

We eliminated this paragraph altogether. New future look was added to this section of the paper.

-I did not understand the reference to ore sorting. This is not discussed in the paper.

In the introduction (section 1), we mentioned ore sorting as one of the key applications of hyperspectral imagery: “This technology has also evolved as a tool for field spectroscopy [18, 19], drill core and chips logging [20-22], wall-rock imaging [23-26], and sensor-based mineral sorting [27]”. For this reason, we chose to cite ore sorting references.

Reviewer 2:

Overview: This paper provides a wide-ranging overview of spectral processing methods with a focus on hyperspectral data and geological applications.

General comments:

Any kind of review paper that takes on such a broad topic will necessarily have to omit many details. However, the authors have done a good job of categorizing the multiple analytical techniques that are available into broad categories based on how the methods are applied (e.g., knowledge based versus data driven).

This is a really good way to organize the disparate methodologies and the authors are to be commended on managing to provide a reasonable framework for these methodologies. The authors have also managed to capture and discuss the techniques that I am aware of. The reference list is comprehensive and complete – another commendable feature and necessary for readers who want to delve into a particular technique in detail.

The demonstration of the techniques using an example (oil sands) is also useful because it shows how the different processing techniques compare to each other, at least in a qualitative way.

I would recommend publication of this manuscript. I have only a couple of significant comments and found only a few grammatical errors as follows:

Comments, typos, and grammar:

1. Section 2 Test dataset. Could you discuss whether you found any other phases in the oil sand sample besides montmorillonite and bitumen. Particularly did you find any other phases in the sample that may contribute spectral features? You should be able to determine this from X-ray diffraction analysis of the sample. I assume that quartz forms the bulk of the mineral phases in the bitumen-rich parts of the sample, and you should point out in the discussion that quartz has no significant spectral features in the wavelength region that you examined, so it should not lead to erroneous results for bitumen and clay abundances. However, quartz may have a second order effect because it will change overall reflectance. Therefore, would this have an effect on any of the classification techniques?

We ran XRD analysis for the selected sample (shown below). The mineralogy turned out to be majorly quartz and montmorillonite with titanomagnetite, brushite and orthoclase as the minor phases.

We added a phrase in the introduction as following: “The bitumen and montmorillonite are the only spectrally (SWIR) active compounds of the sample and the image spectra are dominated by their diagnostic absorption features (Fig. 1b)”.

As shown in Fig. 4a, the overall reflectance of the sample is very low and on average, the bituminous part is only 10% reflective. We believe that due to the bitumen coating effect the high reflectivity of quartz has a minor effect on the overall reflectance of the sample.

2. Section 2 Test dataset. Can you briefly describe what you used as a reflectance standard? I assume you used a spectralon panel?

As truly noted, we used the Spectralon panel as a standard. The following sentence was added to the manuscript to clarify this: “To check out the validity of the results, we collected 9 representative spectra using an ASD FieldSpec spectrometer [17], and a Spectralon panel as a reference to convert the measurements into reflectance”.

3. Section 3-1-2-3. Absorption quantification, lines 2-3. Actually intensity is directly linked to more than one factor, including grain size (smaller powders have shallower absorption bands), and the abundance of the absorbing species (which could be either abundance of a particular mineral or of a particular atom (such as ferrous iron which can cause an absorption band near 1 micron). You may want to reference something like Gaffey et al. (1993) which has a good discussion of these various factors.

We have revised the whole paragraph into the following: “There are correlations between spectral feature characteristics (wavelength position, shape, and asymmetry) of absorption bands and the mineralogic content of a target. The wavelength is related to the chemistry of a mineral, whereas the intensity (depth) of the feature is proportional to the abundance of the compound [6, 74, 89]. Typically, the abundance of a material is quantified by calculating the depth of its diagnostic absorption feature relative to the continuum background [61, 74, 79]. However, there are several drawbacks to the continuum band-depth (CBD) technique for abundance estimation: (i) the depth of an absorption is as well proportional to particle size and amount of opaque materials [1, 9], (ii) the parameter may become saturated for certain minerals [90, 91], (iii) it may behave nonlinearly in relation to areal/weight percentage due to intimate mixing [18, 92, 93], and (iv) it is likely for the absorption bands to overlap each other [61]. Even so, the CBD is still the most accepted spectroscopic-based method for abundance quantification (Fig. 3o, 3p) (e.g. [94])”.

4. Section 3-1-2-3 Absorption quantification. Second last line of this section: change “is” to “can be”.

Done.

5. Section 3-1-4 Spectral denonvolution: in this section you should mention and discuss the work by Pompilio et al. (2009, 2010) which uses mathematical parameters to recognize and quantify overlapping absorption bands and band saturation. On the last line of the first paragraph asymmetry can also be caused by saturated absorption bands.

The following paragraph was added to this section to reflect this work:“ A recent variant of the MGM is called exponential Gaussian optimization (EGO), which is designed to account for non-Gaussian behavior of the absorption features, and alike the MGM, it decomposes a spectrum into several EGO models superimposed on a continuum. The technique is shown to be able to model band asymmetry and flattening due to saturation effect and nested bands [102, 103]”.

6. Section 3-2-1, 3rd line of second paragraph: change “causing” to “resulting”.

Done.

7. Section 3-2-4, 4th line: add “it” after “once”.

Done.

8. Section 3-2-7, 2nd paragraph, line 7: change “inevitably” to “inevitable”.

Done

9. Section 3-2-7, 4th paragraph, line 1: change “to” to “giving”.

Done.

10. Page 24, 5th line of text: change “especial” to “special”.

Done.

11. Page 25, 3rd line of text: delete “up”.

The “add up” was changed to “add in”.

12. Page 26, 6th line of text: add “the” before “derivative”.

Done.

13. Page 28, 4th line of text: change “is” to “are”.

We changed the phrase into the following: “…a hybrid method of the second species has not yet been developed”.

14. Page 28, section 6. last line of first paragraph: delete “is”.

Done.

15. Page 29, 4th line of text: I do not understand what you mean by “basic”.

Here we meant that while for “compositional mapping” there are simple as well as sophisticated methods (as discussed in section 3-1-2-3 and 3-1-4 of the manuscript), the crystallinity mapping still relies on simple techniques like band ratioing. To convey the same meaning, we replaced the “basic” with “embryonic”.

16. Page 29, 3rd full paragraph. You may want to expand this paragraph a bit. Point out that SWIR-VNIR data are mostly sensitive to composition at the atomic level (e.g., crystal field transitions in ferrous iron) as well as combinations and overtones of stretching, bending, and libration modes in molecules. The TIR probes a target at the moleculer level (e.g., Si-O stretching in silica tetrahedral; H-O bending and stretching in water molecules).

Thank for the helpful comment. We have incorporated this comment in two different parts of the manuscript. The same paragraph was changed into following: “While VNIR-SWIR data are valuable to study the alteration mineralogy, LWIR data are able to provide information on the composition of the rocks and rock forming minerals, as they have their fundamental vibrational bands (e.g. Si-O stretching in silica tetrahedral) in this region”.

In the introductory part of the paper, we have also added comments on the electronic and vibrational transitions as follow: “Minerals, rocks, and other terrestrial compounds like hydrocarbons exhibit diagnostic absorptions features in the visible-near infrared (VNIR) (0.4-1.0 µm), shortwave infrared (SWIR) (1.0-2.5 µm), mid infrared (MIR) (3-5 µm), and/or longwave infrared (LWIR) (8-14 µm) wavelength ranges due to electronic and vibrational processes, as well as overtones and combinations of the fundamentals”.

References cited in this review:

Gaffey, S.J., L.A. McFadden, D. Nash, and C.M. Pieters (1993) Ultraviolet, visible, and near-infrared reflectance spectroscopy: Laboratory spectra of geologic materials. In: Remote Geochemical Analysis: Elemental and mineralogical composition (C.M. Pieters and P.A.J. Englert, editors). Cambridge University Press), pp. 43-77.

Pompilio, L., G. Pedrazzi, E.A. Cloutis, M.A. Craig, and T.L. Roush (2010) Exponential Gaussian approach for spectral modeling: The EGO algorithm II. Band asymmetry. Icarus, 208, 811-823.

Pompilio, L., G. Pedrazzi, M. Sgavetti, E.A. Cloutis, M.A. Craig, and T.L. Roush (2009) Exponential Gaussian approach for spectral modeling: The EGO algorithm, I. Band saturation. Icarus, 201, 781-794.

--

Reviewer 3:

This paper is a generally well written comprehensive summary of approaches to spectral processing of imaging spectrometry (hyperspectral or HSI) data that provides a great deal of detailed background information and an extensive reference list. The review portion categorizes and summarizes what has become a diverse set of analysis approaches, algorithms, and techniques. Supplementing the review by the use of analysis examples for the HSI dataset and discussion of results in the context of selected processing techniques helps to illustrate the similarities and differences between the methods, however, the paper could be enhanced, by further discussion and additional direct comparison of the specific research results shown.

Substantially, there are a few factual areas that have been misstated or need clarification. These have been annotated in the comments below. Additional references have been offered where appropriate. There are probably others that could be used in these areas.

The proposed references and several new publications were used within the manuscript. The detail of such changes is provided below.

Editorially, there are a large number of mismatched agreements between singulars/plurals. The manuscript needs to be reviewed in detail to remove these. Suggestions below have been offered to fix some of these (where found). There may be others. Additional editorial comments are also offered in the text below.

Besides the suggested corrections, we reviewed the entire manuscript for such singular/plural mismatching and fixed them.

The comprehensive reference section was not reviewed for spelling, typos, conformation to a specific consistent format, etc. We leave that to the author and editors.

The citation and references of this manuscript was prepared using the EndNote software; we have checked all references again and we now believe it is conforming to the journal standards.

Specific Comments:

Pg 2, Line 9 and throughout the paper: Authors refer to 8-14 micrometer range as “TIR”. In fact TIR also includes 3-5 micrometer range and this isn’t discussed. Authors might more appropriately refer to the 8-14 micrometer region as “Longwave Infrared” or “LWIR”

Within the manuscript, we changed “thermal infrared” and “TIR” into “Longwave Infrared” or “LWIR”.

Pg 2, Line 27: additional pertinent references for core and wall rock imaging:

We greatly appreciate these suggestions. Actually, we have compiled a very large number of references, but to be concise with the citation, we limited ourselves to a few for each subject. Since this wall-rock technology is emerging in geosciences we have included the most comprehensive study by Kruse, et.al. Another relevant reference from Murphy et al. was cited as well for wall-rock imaging.

Murphy, R. J., Monteiro, S. T., and Schneider, S., "Evaluating Classification Techniques for Mapping Vertical Geology Using Field-Based Hyperspectral Sensors," Geoscience and Remote Sensing, IEEE Transactions on, vol. 50, 3066-3080,(2012).

Kruse, F. A., Bidell, R. L., Taranik, J. V., Peppin, W. A., Weatherbee, O., and Calvin, W. M., "Mapping alteration minerals at prospect, outcrop, and drill core scales using imaging spectrometry," International Journal of Remote Sensing, (http://www.tandfonline.com/doi/pdf/10.1080/01431161.2011.600350 ), vol. 33, 1789-1798,(2012).

Pg 2, Line52: Additional recent key reference (or source for more key papers) for unmixing:

The noted reference was cited.

Plaza, A., Du, Q., Bioucas-Dias, J. M., Jia, X., and Kruse, F. A., "Foreword to the Special Issue on Spectral Unmixing of Remotely Sensed Data," Transactions on Geoscience and Remote Sensing (TGARS), Special Issue on Spectral Unmixing of Remotely Sensed Data, vol. 49, 4103-4109,(2011).

Pg 3, Line 24 “currently” available

“Currently” was added to the sentence.

Pg 3, Line 52: citation needed for sisuCHEMA instrument (currently just web link)

We provided a citation for the sisuCHEMA instrument.

Pg 4, Line 20: ditto above – need reference(s) for ASD FieldSpec

We provided a citation for the ASD spectrometer.

Pg 4, Line 23: currently reads “As it can be seen, the two series are in good agreement; however the image spectra are more reflective relative to ASD spectra”. Should be updated to discuss specifics of the agreement (e.g. note correspondence in overall spectral shape and specific features at ??, ??, and ??).

We greatly appreciate the comment. We have updated the statement into the following: “These curves are compared with their relevant image spectra in Fig. 1b. Note the correspondence in overall spectral shape between the two series and specific features at 1900, 2200, 2300, and 2350 nm. The image spectra at around 1650 nm, however, have higher albedo and are noisier between 1500 to 1800 nm ranges”.

Figure 2: some of the spectral processing method abbreviations are cut off along the bottoms of the lower boxes in the tree. The Full modeling (Feature Mapping) portion of the tree needs some revision. E.g. CR is not an analysis method, but a preprocessing approach that is performed before some of the analysis methods. At least one method (SFF) is a hybrid – it does use least-squares-fitting, but usually to the spectral features. Should also probably appear in the Feature Mapping portion of the tree (other similar hybrids?) Some expert systems us the features from specific library spectra (e.g. USGS Tetracorder). This is another case where perhaps a method belongs in two categories (spectral modeling and absorption modeling). The categorization used in the tree needs further review and perhaps revision.

This is one of the difficulties that one may face in devising a universal framework for such diverse processing methods. As we have discussed in Table.1, continuum removal is regarded as a

preprocessing method. To be clear on this, we have removed CR from the chart. Besides, we have re-arranged the chart from left- to-right to match the order of discussions in the manuscript. We have also added some new techniques to the manuscript as well as the chart. The cut off parts of the chart was also corrected.

As we have mentioned in the comments related to Pg 16, Line 4, the SFF is noted to be a hybrid method, but regarding its affinity to “Full modeling”, since it uses reference data (over a feature) to perform its processing, then, we preferred to keep it under the “least square-based” group. However, we consider that these two groups inserted in two different parts of the tree have a very close relationship.

For the Tetracorder and similar systems, we have proposed a hybrid branch and discussed it in detail in section 5.

Pg 7, Line 9: the band characteristic referred to here as “symmetry” is usually described as “asymmetry” in most uses for spectral remote sensing

Done.

Pg 7, Line 31: “mostly used” should be “most-used

Done.

Pg 7, Line 42: it should be made that specific PCAs can not be directly equated to specific features or bands as they are linear combinations

The correction was applied to line 52 of this page as: “While BR and RBD are still in use with both MSI and HSI datasets, PCA has been mostly confined to multispectral imagery, perhaps because it relies merely upon empirically chosen input bands or because of the difficulties in equating PCs to specific features in the imagery”.

Pg 8, Line 38: ditto use of “asymmetry”

Done.

Pg 8, Line46: Continuum needs to be defined before discussion and outlining methods on how to remove it.

Thanks for this reminder. We have updated this part as follow: “To isolate the absorption bands, the continuum should be initially removed. The continuum is the background absorption due to a different process with overall concave shape onto which other absorbing bands are superimposed [75]”.

Pg 9, Line 6: subtraction of the continuum is not a valid operation, See Clark and Roush, 1984 and others.

We have encountered the usage of subtraction operation in the literature, though in practice the division operation has been always used by us. To conform to the statement of Clark and Roush, we removed the “…subtracted from…” from the manuscript.

Pg 9, Line 42: “Since an absorption correspond” should be “Since an absorption corresponds”

Done.

Pg 9, Section 3-1-2-2 Absorption Detection: Detection is a prerequisite to absorption quantification. The quantification methods typically do not use the derivative method to detect the feature, rather searching the continuum-removed reflectance spectra directly for spectral minima with specific characteristics. Some mention and discussion of this is required here or in the quantification section.

Thanks for the comments. We do agree that absorption detection is a prerequisite to absorption quantification. This phrase was added to the end of this section as following: “The continuum removal and absorption detection are prerequisites to absorption quantification”.

As is cited in this section, it is also common to detect the inflations in a spectrum using the derivative analysis (e.g. Huguenin, 1986; Brown, 2006; Piech, 1987, etc.); however to reflect the suggested direct and efficient method, we have corrected the section as follow: “A common routine is to search directly for the local spectral minima using the continuum-removed spectra [76, 82]; however, since an absorption corresponds to an inflection in the spectrum, DA can be a choice for its detection”.

Pg 10, Line 5 currently reads “there is a correlation between shape and the wavelength of absorption bands and the mineralogic content of a target”. As wavelength position of an absorption feature is the initial indicator of a specific mineral, this should more appropriately read something like “there are correlations between spectral feature characteristics (wavelength position, shape, and symmetry) of absorption bands and the mineralogic content of a target”.

Thanks for the suggestion. We changed the statement as following: “There are correlations between spectral feature characteristics (wavelength position, shape, and asymmetry) of absorption bands and the mineralogic content of a target”.

Pg 10, Line 7: “directly” should probably read “proportionally”. The authors also need to discuss factors other than abundance that affect band depth (grain size, opaque materials, intimate mixing, etc). Band depth cannot be taken literally as a direct measure of abundance.

We have revised the whole paragraph as follow: “There are correlations between spectral feature characteristics (wavelength position, shape, and asymmetry) of absorption bands and the mineralogic content of a target. The wavelength is related to the chemistry of a mineral, whereas the intensity (depth) of the feature is proportional to the abundance of the compound [6, 74, 89]. Typically, the abundance of a material is quantified by calculating the depth of its diagnostic absorption feature relative to the continuum background [61, 74, 79]. However, there are several drawbacks to the continuum band-depth (CBD) technique for abundance estimation: (i) the depth of an absorption is as well proportional to particle size and amount of opaque materials [1, 9], (ii) the parameter may become saturated for certain minerals [90, 91], (iii) it may behave nonlinearly in relation to areal/weight percentage due to intimate mixing [18, 92, 93], and (iv) it is likely for the absorption bands to overlap each other [61]. Even so, the CBD is still the most accepted spectroscopic-based method for abundance quantification (Fig. 3o, 3p) (e.g. [94])”.

Pg 10, Line 25: ditto use of “asymmetry” rather than symmetry

Done.

Pg 10, Line 28: introduce the acronym “FWHM” in parentheses

Done.

Pg 10, Line 53: change “viewpoint behind ES” to “objective of spectral ES”

The sentence was changed to: “The objective of expert systems (ES) is”.

Pg 10, Line 55: insert reference #74 after reference #73

Reference was inserted.

Pg 10, Line 59: “solution” should be “solutions”

Done.

Pg 11, Line 2: “technique” should be “techniques”

Done.

Pg 12, Line 27: currently reads “Radiative transfer equation is suggested for describing the scattering behavior of light”. Should more appropriately say something like “Scattering theory utilizes a radiative transfer equation to describe the scattering behavior of light”.

The sentence was changed (as suggested) as follows: “Scattering theory utilizes a radiative transfer equation to describe the scattering behavior of light from particulate media”.

Pg 13, Fig 3 Caption: subpart “q” is an image of relative symmetry of the 2200nm absorption. This parameter, as discussed previously is usually referred to as “asymmetry”.

Done.

Pg 13, Line 30: currently reads “On the other perspective stand DD methods.” This might more appropriately be written “DD methods illustrate an alternative spectral analysis approach requiring only the hyperspectral data themselves and perhaps some additional reference spectra.”

Thanks for the suggestion. Currently the sentence reads: “DD methods illustrate an alternative spectral analysis approach in which only the hyperspectral data themselves and some additional reference data (spectra) are required”.

Pg 13, Line 32, “reference data are”, “training classes”, “endmember sets”

The sentence was changed to the following: “reference data are commonly called training classes, or endmember sets”.

Pg 13, Line 33, “comprised”

Done.

Pg 13, Line 56, Figure 3 caption “ENVI” and “SMACC” acronyms not defined. There is no need to stack the spectra in Figure 3, part b). In fact not doing so would more effectively illustrate the continuum-removal aim and affect.

The caption was changed as”… using the sequential maximum angle convex cone (SMACC) tool embedded in the ENVI (Environment for visualizing images) software from Exelis Visual Information Solutions, Boulder, Colorado”.

Since the two spectra were overlapping each other, in a black and white print it would be difficult to distinguish them; accordingly, we have decided to keep them stacked. In Fig3.a, with real scales, it is shown that the bitumen spectrum has very low albedo.

Pg 14, Line 23: makes it sound like the BE includes an angle. This line in fact pertains to the SAM algorithm below. This could be clarified by changing “can be compared” to “can also be compared”

Done.

Pg 14, Line 26 and equation (3) make it sound like SAM is being measured in two dimensions. This is in fact an n-dimensional angle between the spectral vectors in n-dimensional space (as described for ED) and the description and equation need to be modified to make this clear.

We modified the sentence to: “The reference and test spectrum can also be compared; however, based on the “angle” or the “distance” between them in n-dimensional space”.

Since we decided to omit the formulation for almost all the processing methods, the formula for the SAM was removed and only a written description of the method was used.

Pg 15, Line 54: a key point to be made for all of these methods is that the final classification maps are made by thresholding the similarity measure used and that the thresholds selected are subjective.

The final sentence of this part was modified as: “The major problems associated with similarity measures, however, are their inability to deal with mixed spectra as well as subjective thresholding”.

Pg 16, Line 4: it should be pointed out that spectral feature fitting is really a hybrid method, as it requires definition of the features and continuum removal prior to the least-square-fit. This is covered somewhat in the section discussing the hybrid USGS Tetracorder approach (of which SFF is a small part) but bears mentioning and some discussion here.

That is a good point. According to our definition of the two approaches, SFF is a hybrid method, but since it is similar to the PLSR technique and has outputs very similar to “comparison-based” category, we placed it there. To clarify that, in the initial paragraphs of hybrid methods (section 5), we added a sentence as “The SFF technique discussed in section 3-2-2- is in reality a hybrid method that combines user knowledge of the feature(s) and CR prior to the least-square-fitting [127]”. In addition, we explained the same meaning in section 3-2-2: “In practice, SFF uses the user knowledge of the features and CR procedure to do the regression; hence, it can be considered a hybrid method (section 5) as well”.

Pg 18, Line 19: The reference used for MTMF (#161) is just a short summary. The full paper describing all of the details of MTMF is:

Boardman, J. W. and Kruse, F. A., "Analysis of Imaging Spectrometer Data Using N-Dimensional Geometry and A Mixture-Tuned Matched Filtering (MTMF) Approach," Transactions on Geoscience and Remote Sensing (TGARS), Special Issue on Spectral Unmixing of Remotely Sensed Data, vol. 49, 4138-4152,(2011).

The reference #161 was updated to this new one.

Pg 18, Line 38: reads “In the MTMF, an “infeasibility” image is also calculated for each target signature and then is used to threshold out the MF’s false positive results [161].” This requires more explanation. Something like “The MTMF is a hybrid of the classical MF and spectral unmixing. It produces two parameters that are jointly used to determine the predominant material and abundance at a given pixel. Higher MF scores (from 0.0 to 1.0 = 0 to 100% abundance) indicate that there is more of the target spectrum material in the pixel of interest. Lower Infeasibility Scores (forming 1 sigma and 2 sigma [etc.] cones extending from the composite background to the target) constrain the spectral signature in the context of mixing of the background and target signatures. The best spectral matches can be mapped based on meeting the combined criteria of high MF score and low Infeasibility score”. (cite Boardman and Kruse, 2011 reference).

The part related to MTMF was updated into this: “In the MTMF, beside the MF, an “infeasibility” image is also calculated for each target signature and then the predominant material and its abundance is determined using the combined criteria of high MF and low Infeasibility scores [161]”. The new reference (Boardman and Kruse, 2011) was used as well.

Pg 18, Line 43: That these detectors output one label per pixels is an incorrect statement, The fact is that several of these produce “abundance” images, which can be contoured, thresholded, etc. They often give much more information than just identification. As such, they are actually superior to many of the other approaches described for mineral mapping. (relative abundances are important for targeting “hot spots”, the highest mineral concentrations associated with ore deposits and other targets).

That is a very good explanation. Here we meant that despite the full unmixing in which several scores are calculated for a single pixel (equal to the whole endmember sets), partial unmixing is restricted to produce one single score per pixel. To convey the same meaning, we changed the word “label” into “score”. The sentence now read as: “these detectors carry out a partial unmixing and their output is a single score (abundance of the target) per pixel, which bear some resemblance to similarity measures”.

Pg 19, Line 31: should a priori be italicized?

“a priori” was italicized throughout the text.

Pg 19, Line 47: “end up to” should be “end up with”

Done.

Pg 19 Line 45: mention “missing endmembers”

In page 19, line 47, “the endmembers” was changed to “the selected endmembers”.

Pg 20, Line 23: delete the redundant word “quantity”

Done.

Pg 20, Line 36: change “proper bands” to “specific bands”

Done.

Pg 21, Figure 5: MTMF results not shown?, Figure 5 Caption: authors state abuncance thresholds used are typically 82-99% or 63-99% for montmorillonite and bitumen respectively, however, mnost of the DD approaches don’t actually produce abundance images (e.g. SAM and related). Explain !

Some processing methods including the MF, MTMF, and ACE did not perform well for our selected case study (e.g. Fig. 5L). MTMF performed in the standard way (MNF transformed image as input) was very noisy, and the thresholding based on MF and infeasibility scores was difficult to achieve, because there was no trend in the scatter plot. Accordingly, we restricted the results to MF score only. We repeated this processing again during the revision and the same results were yielded. The reason for such poor performance of partial unmixing algorithm is not known yet and is largely out of the scope of this paper. However, as proposed, it deserves a comprehensive and comparative study of its own for the case of geological applications.

It is true that “abundance” phrase cannot be used with similarity methods. For clarification, we changed the sentence as follows: “The score or abundance thresholds used with color representation is…”.

Pg 23, Table 1: I don’t agree with the authors’ Pro/con for Knowledge-Based methods as “Less Robust”. They are often actually more robust and a big Pro is that they are Physics based. The biggest Cons of the DD approaches are that you often can’t relate the results back to the physics, the outputs are set using arbitrary thresholds, they are highly subjective, and non-reproducible by different researchers/analysts (more of an art than a science). This holds true for some, not all of the DD methods. The partial unmixing, unmixing approaches are more quantitative than many of the others.

We appreciate this comment. The results of KBA (e.g. a simple band ratioing) is more robust with hyperspectral imagery, but with multispectral datasets, it is hardly the case. Routinely we hybridize two techniques from these two approaches to get satisfying results. Therefore, this statement depends on the datasets in hand.

To clarify the statement, we changed it to: “Less robust (esp. with MSI data)”.

Pg 23, line 20: The authors state that spectral smoothing should be incorporated. My contention is NEVER. Instead make the absorption feature based methods more noise tolerant by incorporating spectral variability and other constraints. Spectral smoothing is never a good thing.

We agree that spectral smoothing filters modify the signal and sometimes removes useful information, but with noisy datasets (including the case study we developed in this review using the sisuCHEMA imaging system), it has proved to be helpful, specifically with band calculation methods. In other

instances (e.g. the determination of absorption minima), it is also useful. To “alleviate” the statement, we changed the phrase “should be incorporated” into “may be required to be incorporated”.

Pg 24, Lines 19 and 21: change “TIR” to “LWIR”

This change was made in the noted lines as well as throughout the manuscript.

Pg 24, Line 21: additional references with LWIR MTMF analyses (and some contained references):

We have used the second reference (Kruse, F. A., 2015) to cite for MTMF in this paragraph. The reference was also used in the “multiple wavelength processing” part of the manuscript.

Kruse, F. A., "Integrated visible and near infrared, shortwave infrared, and longwave infrared (VNIR-SWIR-LWIR), full-range hyperspectral data analysis for geologic mapping," Journal of Applied Remote Sensing (in press), 2015).

Kruse, F. A., "Comparative analysis of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperspectral Thermal Emission Spectrometer (HyTES) longwave infrared (LWIR) hyperspectral data for geologic mapping," Proceedings SPIE Defense and Security, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, The International Society for Optical Engineering (SPIE). vol. 9472, 94721F-1 to 94721F-13,(2015).

McDowell, M. L. and Kruse, F. A., "Integrated visible to near infrared, short wave infrared, and long wave infrared spectral analysis for surface composition mapping near Mountain Pass, California," Proceedings SPIE Defense and Security, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, The International Society for Optical Engineering (SPIE), vol. 9472, 94721C-1 to 94721C-14,(2015).

Cone, S. R., Kruse, F. A., and McDowell, M. L., "Exploration of integrated visible to near-, shortwave-, and longwave-infrared (full range) hyperspectral data analysis," Proceedings SPIE Defense and Security, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, The International Society for Optical Engineering (SPIE), vol. 9472, 94721D-1 to 94721D-12,(2015).

Pg 24, Line 23: temperature has no effect on emissivity if emissivity and temperature are properly separated. This isn’t an effect of MESMA, it is improper reduction of the data to separate the two components. This sentence should be deleted.

Many thanks for the statement. Actually, we reflected on this idea from the cited reference, but we agree that it is not sensible, therefore this sentence was deleted. The earlier sentence was changed to: “A case in point is the application of BR [193], least square [193], CR [194], FP [194], PLSR [195], ANN [138], LSU [196], MF [164], MTMF [202], MESMA [164], and WA [197] routines to radiance or emittance thermal datasets”.

Pg 24, Line 33: KB arguably more robust, not less because typically physics based and objective. Best approach is hybrid – use that as lead-in to next section.

We modified the statement as follows: “… is not robust enough, specifically with MSI data”. In addition, as a lead-in, we added the following sentence: “The best solution for geological application may come from the hybridization of these approaches”.

Pg 25, Line 6, “add in” not “add up”

Done.

Pg 25, Line 33, add Boardman MTMF full paper reference

Done.

Pg 26, line 49-50: ”to compare the image (test) spectra with.”, should be “to compare with the image (test) spectra.”

Done.

Pg 26, Line 54: Figure 7 cited out of place/order. This Figure 7 isn’t related to the Kruse or USGS work. See comment below for proper location.

We are sorry for this error. The citation to Fig.7 in this line was removed.

Pg 27, Line 10. This statement is misleading. MICA is not an upgraded version of Tetracorder, just modified to run under Microsoft Windows. The basic structure and algorithms are the same, with an added GUI.

This was changed to: “A modified version of the early Tetracorder with graphical user interface (GUI) designation known as…”

Pg 27, Line 32: insert reference to Figure 7 at end of sentence.

Done.

Pg 28, Line 26: insert comma after “general”

Done.

Pg 28, Line 41: insert “depending on spectral contrast” after “identify”

Done.

This phrase then changed as: “the accuracy of estimation and the detection limit is dependent not only on the algorithm in use, but also on the type of target material and its spectral contrast”.

Pg 29, Line 44, “data are:” not “data is”, “LWIR” not “TIR”, “data provide” not “data provides”

Done.

Pg 29, Line 45, change “Thermal” to “LWIR”

Done.

Pg 30, Line 2: “continuum components” not “continuum component”

Done.

Pg 30, Line 9: “at all scales:” not “in all scales”

Done.

Pg 30, Line 16: “which need” not “which needs”

Done.

Pg 30, Line 26: “ground measurements” not “the ground measurements”

Done.

Pg 30, Line 28: “method” not “methods”

Done.

Pg 30, Line 37: “capable to provide proximal or distal sensing means” should be something like “capable of providing proximal or distal sensing results”

The sentence was changed to:”… capable of providing means for proximal or distal sensing”.

Pg 30, Line 55: change “more accurate estimate of the amounts of abundances” to something like “more accurate estimates of abundances”

Done.

Pg 30, Line 59: change “showing to be able to solve” to something like “able to solve”

We have eliminated this phrase from the manuscript.

Pg 31, Line 7: “happen” not “happens”

Done.

Pg 31, Line 14: change “Hopefully, FP is promising for those solid solutions that manifest themselves as linear wavelength shifts in the absorption minima, but for more complicated overlaps, there are barely a remedy.” to something like “FP is promising for those solid solutions that manifest themselves as linear wavelength shifts in the absorption minima, there are few remedies for more complicated overlaps.”

Done.

Pg 31, Line 18: “measurement” not “measurements”

Done.

Pg 31, Line 22: change “Tetracorder and its successor, still is governed by hard rules.” to something like “Tetracorder and its successor are still governed by hard rules.”

Done.

Pg 31 Line32: change “hybrid method of second species” to something like “hybrid model of additional species”.

Since in page 26, lines 42-46, we have defined two species, by specifying the “second species”, we meant to refer to that saying. However, in this part, we replaced “method” by “model”.

Pg 31, Line 40: change “TIR” to “LWIR”, “data have” not “data has”

Done.

Pg 31, Line 44, “LWIR” not “TIR”

Done.

Pg 31, Line 46: add something like “LWIR hyperspectral data are only now becoming routinely available” and add some references (e.g. SEBASS papers by Vaughan, Calvin, several others; HyTES papers by Hook and Johnson, MAKO papers by Buckland and others (aerospace corp), LWIR HSI integration papers by Kruse and others (see previous suggested added references) above).

This is a good suggestion. Indeed we used the statement in the introduction part in the following form: “Overall, proximal and distal sensing technologies in the VNIR-SWIR have been matured and readily available [14], while the LWIR hyperspectral data are only now becoming routinely available [20, 28, 29]”. We have cited for the HyLogger-3, as well as HyTES and SEBASS.

Pg 31, Line 52: “Such a system” or “Such systems”, not “Such system” “character”, not “characters”

Done.

Pg 31, Line 54: add multiple wavelength range reference(s): e.g. Kruse et al papers above. There are others.

We have cited Kruse, F. A., 2015 in the “multiple wavelength processing” part. To be more concise, we added only one of the proposed citations.

Pg 32, Line 2: “will still remain”, not “will remain still”. Not clear what authors are trying to say with the second part of the sentence. This whole sentence needs to be rewritten to clarify. Maybe they mean that the field measurements won’t be as important as atmospheric compensation algorithms improve? Clarify.

We made several changes in the conclusion session, including the last paragraph and now the noted sentence is omitted.

Pg 32 Acknowledgements: spell out all of the acronyms

Done.

The comprehensive reference section was not reviewed for spelling, typos, conformation to a specific consistent format, etc. We leave that to the author and editors.

--

Reviewer 4:

General Comments

The authors provide a comprehensive overview of the dominant approaches used to analyze spectroscopic data for geological remote sensing. The paper is generally well written and well structured. The list of references is very impressive and draws upon a wealth of seminal papers, newer journal articles and non-refereed proceedings. I can name a few I might have expected to see, but not many. However, there is some room for improvement

1) The examples (Fig 3, 5, 6, 7) are not that compelling. Partly I think this is a product of the very small size of the figures, which are difficult to interpret. In part, it is also a product of the way they are discussed. In most cases, the authors simply refer to a specific frame as an example, yet provide no actual discussion of what it means. I would suggest that (a) the authors could make the examples larger and (b) put some effort into describing what they show and saying something about their meaning. As it is, a reader could simply ignore the examples and not lose much content. In addition, perhaps the authors can better justify their choice of this particular sample as an example? Was this just because it was available, or is there a specific reason it is a good example?

We enlarged individual images by changing the layout of these figures. In addition, the result of each processing was discussed in their relevant part within the text. For example, for similarity-based method we included the following: “In the case study of Fig.5, the performance of similarity metrics considering most methods is very close. The ED has lower performance in delineating the boundary of montmorillonite and bitumen (Fig. 5b), whereas the SID ×sin (SAM) shows better results in describing the boundaries of the target, specifically for bitumen (Fig. 5g)”.

We discussed and compared the results for target detectors, classifications, band calculations, absorption quantification, wavelet analysis, etc.

Regarding the selected case study, though we had access to different airborne hyperspectral datasets in our collections taken over a variety of geologic targets, we used a close-range dataset collected by the sisuCHEMA imaging system for several reasons:

- The readers of this paper will probably be geologists as well as algorithm developers; hence, we avoided to use an airborne dataset to keep the paper simpler. Earlier review papers have used a great length of their page space to explain aspects of the test areas. This was avoided here using a simple and straightforward case study

- The selected sample is well constrained as regards its composition; in the SWIR region, the spectra is dominated by montmorillonitic clay and hydrocarbon (and probably adsorbed water) features, while in the VNIR, it is almost featureless.

- Because the mineralogic content of the sample was already known (from spectroscopy and XRD analysis), there was no need for “field data” to verify the results of spectral analysis.

- The binary mixture between montmorillonite and hydrocarbon is unique, while in airborne datasets, it is very rare to have such a uniform binary mixture.

- The use of a smaller sample size and two endmembers helped reducing the length of the paper. For a scene with several potential targets, it would be difficult to implement and compare the results yielded by numerous different algorithms.

- The tar-sand sample also highlights the broader area of HSI and spectral analysis in geologic remote sensing.

2) I suggest revising figure 2 to place Knowledge Based Approaches on the left, and Data Driven Approaches on the right. The discussion starts with KBA and most western readers will expect to see the content on the left side of the figure, not right.

The figure was revised and recreated to match with their order in the text.

3) There are a great deal of acronyms thrown about. I would suggest (a) getting rid of some if possible (perhaps DD and KBA, as an example). I would also suggest perhaps the authors include a glossary of acronyms as part of an appendix, perhaps with a key citation if relevant.

We replaced all the KB, and DD acronyms with the complete words throughout the manuscript. Since the readers are going to need the glossary of acronyms to apprehend the taxonomic tree of Fig.2, we have placed it as the caption of Fig.2 and have explained this in the caption as: “The acronyms used in the tree and throughout the text are the following”.

4) While the references are good, once in a while they cite a non-refereed source, where a refereed source clearly is superior and exists. A good example is reference 191, to a 2006 conference proceeding. Given Bo-Cai Gao’s publication record, surely a refereed version could be found. I suggest use of non-refereed proceedings only in cases where there really is no other option – the approach is only published in that venue. Where a refereed article exists, I would use it, because that is what readers will be able to access.

From the beginning of the work, we focused our attention to use refereed sources as much as possible. However, we admit that there are cases that we have inadvertently used inappropriate sources, partly due to the extensive references we had to manage, and partly because we may have overlooked the alternative refereed ones. We have inspected the whole reference list and replaced several of them (including the noted reference 191) with peer-reviewed alternatives or omitted them altogether.

5) A number of band selection techniques have been developed (i.e., Stable Zone Unmixing by Somers et al.) that are not mentioned, but probably should be because they can improve fraction accuracy (see Peterson et al., 2015) and detection.Peterson, S.H., Roberts, D.A., Beland, M., Kokaly, R.F., and Ustin, S.L. 2015, Oil detection in the coastal marshes of Louisiana using MESMA applied to band subsets of AVIRIS data, Remote Sens. Environ. 159, 222-231.

We have included the “Stable Zone Unmixing” technique in the feature selection section paragraph at the end of section 3.

Specific Comments

1) Page 1, line 29. Perhaps “are compared and their” vs “comparatively studied”

Done.

2) Page 1, line 56. Revise to read “in the early 70s…”

Done.

3) Page 2, line 7. I would not neglect the Mid-IR (2.5-8 um), especially given that there is some research in that part of the spectrum (including one of the papers cited) and important fundamental vibrational bands there.

Done.

4) Page 2, line 14. Perhaps “Historically, remotely sensed…”. I am not sure what “At length, remotely sensed” means in this context.

In the beginning of the paragraph, “At length” was replaced by “Historically”.

5) Page 2, line 28. Rock-wall imaging.

Done.

6) Page 2, line 34. Use plural for spectra to read “near laboratory-quality spectra for every…”

Done.

7) Page 3, line 28. Revise to read “are compared and their…”

Done.

8) Page 4, line 8. Perhaps “spectra” in place of “cube”.

Done.

9) Page 4, line 15. “striping” not “stripping”.

Done.

10) Page 4, line 24. Revise to read “..image spectra show greater spectral contrast than ASD spectra”. Without an absolute scale, it is not clear they are more reflective.

We changed this phrase into as following: “Note the correspondence in overall spectral shape between the two series and specific features at 1900, 2200, 2300, and 2350 nm. The image spectra at around 1650 nm however, have higher albedo and are noisier between 1500 to 1800 nm ranges. In general, the image spectra show greater spectral contrast than ASD spectra”.

11) Figure 2. See general comments. The figure should match the order of presentation. I suggest discarding with DDA and KBA. This figure does represent a nice glossary of terms, however.

Done.

12) Page 7, line 31. I suggest “… is the simplest and most common”

The “used” was replaced by “common”.

13) Page 7, line 35. I am not sure most ratios are entirely “immune” against scene variation. Perhaps “more resistant?”

The “immune” was replaced by “more resistant”.

14) Figure 2. Where does the decorrelation stretch lie? Given that the authors list PCA on the far right, perhaps it should also be included. The decorrelation stretch was a very common approach used in the analysis of TIMS data, and specific band combinations were very effective at showing variability in silicon content in rocks.

We appreciate this suggestion. In the manuscript (section 3-1-1), we added a sentence as follows: “PCA used together with contrast stretching is comprised in a technique coined “decorrelation stretch” and it has been used to enhance image color and highlight specific targets in MSI data (e.g. silica in TIMS data)”. Since the output of this technique is required to be visually analyzed by an expert, it belongs to enhancement techniques. For this reason, we have not included it in the tree of Fig.2.

15) Page 8, lines 4-5. Perhaps “hence it bears a resemblance to band ratios. The derivative is a parameter that is more sensitive to …”

We changed this part as following: “…hence bear a resemblance to band calculation. Derivative is a parameter that is more sensitive to the shape rather…”. We preferred to keep the “band calculation” phrase, because it justifies why we have discussed this technique within section 3-1-1.

16) Page 8, line 50. Add “The” to read “The continuum…”

Done.

17) Page 9, line 2. Revise to read “In empirical continuum removal ‘’

Done.

18) Page 9, line 31. Revise to read “..pixel spectrum by its”

“to” was replaced by “by”.

19) Page 9, line 42. Add an “s” to read “corresponds”

Done.

20) Page 10, line 30. I suggest “used” in place of “demonstrated”

Instead of “demonstrated”, we used “shown”. The sentence now read: “..is shown to achieve more accurate results..”

21) Page 10, lines 34 and 35. Add an “a” to “a fitted 4th” and “a 2nd order”

Done.

22) Page 10, line 40. Add an “A’ to read “A more general form …”

Done.

23) Page 10, lines 55-59. Several minor corrections are needed here including “In the case of a logical operator”, “Although the LO was developed for …” and “threshold appear to be elusive. A similar…”

This part was corrected as follow: “Although the LO was developed for the analysis of both multi-, and hyperspectral datasets [95, 96], transferable thresholds appear to be elusive. A similar solutions may come from ...”.

24) Page 11, line 15. I would replace “It implies” with “MGM assumes”

Done.

25) Equation (2). Define u (mean?)

It was defined as follow: “The MGM states that for a given absorption, there is a distribution in energy (x) with a standard deviation (δ), center or mean (µ), and amplitude (s)”.

26) Page 12, lines 8-20. Several minor errors are present including “..by the low-scale wavelet…”, “associated with given minerals”, “In the frequency domain”, “to the frequency domain”, “overlapping wavelengths” and “ Radiative transfer has been used to described scattering behavior of light in particulate media[2].”

Many thanks for the observations. All of the listed changes were applied to the text.

27) Figure 3. See general comments. A reader will be tempted to simply skip this figure since it is so small and the results are never discussed.

As we explained earlier, we discussed and compared the results for target detectors, classifications, band calculations, absorption quantification, wavelet analysis, etc.

28) Page 13. I suggest getting rid of “DD” as an acronym. There are already too many of them.

As noted earlier, the DD and KB acronyms were replaced by their complete words.

29) Page 14, line 2. Perhaps “We have divided” …. “which is also called a hard classifier”

Done.

30 Page 15, line 2. Revise to read “The major difference between SAM and SCM is that SCM standardizes”..

The changes applied to the sentence.

31) Page 15, line 40. Add “the” to read “The spectral similarity”

Done.

32) Page 15, line 46. Remove “has” to read “[114] compared…”

Done.

33) Page 16, line 31. “classifiers aim to “

Done.

34) Page 16, line 53. Add “it” to read “once it is trained”

Done.

35) Page 17, line 6. Revise to read “ANN has been used, for example, to …”

The sentence now read as: “In nonlinear scenarios, ANN has been used, for example, to estimate the SiO2 content…”.

36) Page 17, line 36. Revise to read “has been shown to be superior”

Done.

37) Page 18, line 2. Change “is” to “has been” to “It has been shown”

Done.

38) Page 18, lines 12 and 14. I would remove “or target” and “with”

We changed this part as follows:” Instead the aim is to isolate spectral features of interest from the background [154]. In this case, the problem is reduced to the detection of spectral signatures that match the known target”.

39) Page 18 line 24. Add “the” to read “In the OSP detector”. Others include “is minimized by a likelihood ratio”, “CEM utilizes a “, “The TCIMF can be viewed” and “The ACE detector”.

Done.

40) Page 18, line 58. Add “A” to read “A linear mixture model”

Done.

41) Page 19, line 6. Add “the” to read “of the LMM”

Done.

42) Page 19. Lines 18, 20, 24, and 39. Revise to read “has a profound…”, “many studies leading to “, “compared in [112, “, “either unconstrained or …. are often used to solve”. Note, Gramm-schmidt Orthogonalization is also common and Singular Value Decomposition is probably more common than straight constrained or unconstrained least squares.

The corrections related to the first part were implemented. The statement in the other part was updated as follows: “In linear spectral unmixing (LSU), the unconstrained or constrained least-squared inversion, singular value decomposition etc. are used to solve the inversion problem”.

43) Page 19, line 57. There have been many innovations in linear mixture analysis that are more recent than a paper on simulated annealing published in 2006.

That is a good point. Since the unmixing (linear and nonlinear) has been extensively reviewed by Bioucas-Dias (2012) and Heylen (2014), therefore, we have referred to these papers for further details.

44) Page 20, line 22. What is a “non-linear plug in”?

We are sorry for this error. The nonlinear plug-ins are those nonlinear inversion methods that could be included in the linear unmixing chain to complement the processing. They can be plugged-in to full unmixing group in Fig.2. We have corrected this paragraph as follow: “The inaccurate estimate of the abundance quantity could be circumvented by plugging a nonlinear inversion method (like those mentioned earlier [162]), or by including nonlinear regression into the end of the linear unmixing chain (Fig. 2). The bias in the estimation of the abundances is known to be induced by “camouflage” between mineral classes, and hence camouflage (CF) correction is proposed [193]”.

45) Page 20, line 34. Reflectance spectra in a pixel …

Done.

46) Page 21, lines 49-60. This paragraph is poorly written and should be revised.

We have improved the writing of this paragraph and now it reads as follows: “The knowledge-based approach is physical-based and is derived from the concepts of spectroscopy. The techniques in this category can be used in a processing chain without the need for reference data. In contrast, the data-driven approach is mathematical-based and relies directly on reference data for information extraction. It treats each pixel as a n-dimensional vector (with “n” being the number of spectral bands) in feature space, and attempts to model the “whole scene” variation by a set of endmembers [52]. Since n is inter-correlated, the dataset should undergo a feature extraction process prior to the processing. On the other hand, the knowledge-based approach strives to describe the variation observed in a “single spectrum” through absorption band modeling in spectral space, and as a preprocessing step, it only possibly demands the continuum to be removed. While the search for the endmembers (their numbers and types) in the data-driven approach is automated, the detection of absorption (their numbers and positions) in the knowledge-based approach is largely manual and knowledge-based (table 1); though recently, a number of automated algorithms have been proposed to serve this need (e.g. [89])”.

47) Page 24. Decorrelation stretch is a technique developed for the TIR that deserves mention.

As noted earlier, we have mentioned this technique in section 3-1-1 of the manuscript.

48) Page 24, line 23. Temperature does not influence emissivity. The better phrase would be that errors in temperature impact estimated emissivity. Note, Collins et al., (2001) developed TESSMA, which can estimate sub-pixel abundance and temperature in thermal images.

We have made changes to this paragraph as follow: “The majority of the algorithm noted so far may have emerged as VNIR-SWIR data processing tools, but they can handle LWIR data as well. A case in point is the application of BR [201], least square [201], CR [202], FP [202], PLSR [203], ANN [145], LSU [204], MF [171], MTMF [205], MESMA [171], and WA [206] routines to radiance or emittance thermal datasets”.

Collins, E.F., Roberts, D.A., and Borel, C.C., 2001, Spectral Mixture Analysis of Simulated Thermal Infrared Spectrometry Data:

an Initial Temperature Estimate Bounded TESSMA Search Approach, IEEE Trans. Geosci. Remote Sens 39(7) 1435-1446.

49) Page 24, line 44. This is a bit of a weak lead in to the discussion of hybrid techniques. Whis is this known?

The lead in of this section was changed to: “Given the strength and limitations of the individual spectral processing algorithms, it would be favorable to combine (crossbreed) multiple perspectives to yield advanced algorithms”.

50) Page 25, line 12. Continuum removal has been incorporated in to MESMA by Youngetob et al., (2011) who used MESMA as a classifier to discriminate two sub-genera of Eucalyptus with reflectance and CR spectra. They found CR increased classification accuracy.Youngentob, K.N., Roberts, D.A., Held, A.A., Dennison, P.E., Jia, X., and Lindenmayer, D.B., 2011, Mapping two Eucalyptus subgenera using multiple endmember spectral mixture analysis and continuum-removed imaging spectrometry data, Remote Sens. Environ. 115,1115-1128.

Thank you for the suggestion. Since the CR is not included in the structure of MESMA and the input endmembers are only continuum removed, we included this reference in the upper paragraph. The updated statement is now: “A case in point is the attachment of CR to SAM, CCSM, ED, and SID routines [123, 210, 211]. The last uses a weighted combination of continuum intact (CI) and CR for the spectral measurement. The continuum-removed spectra is also used with MESMA approach and is shown to improve its classification performance [212]”.

51) Page 25, line 6. I suggest removing “up” to simply read “to add a shadow”

The sentence was changed to: “some prefer to add in a shadow component..”.

52) Page 25, line 29. Add “the” to read “combines the stochastic …”

Done.

53) Page 25 line 55. Add an ‘s” to “Wavelets can”

Done.

54) Page 26, line 12. Add a “the” to “Eventually, the derivative”. Figure 6 is too small.

Done.

55) Page 28, line 8. “species are not yet developed”

We changed the phrase to: “…a hybrid method of the second species has not been yet developed”.

56) Page 28, line 39. I suggest “… abundance has been detected” …. “As a universal rule, spectroscopic detection limits for bright and ..”

First done.

Second, the sentence was updated to: “As a basic rule, spectroscopic detection limits for bright and dark minerals are considered to be 10 and 20%, respectively”.

57) Page 29, line 2. Perhaps “maps have been shown”

Done.

58) Page 29, lines 10-25. Why the sudden discussion of Rare Earth Elements (REEs – acronym undefined).

The acronym was defined. We changed the order of the paragraphs here, and added more materials to the Discussion section. We discussed that spectroscopy can detect rocks, minerals, and some of the elements; the REEs in a direct way, and others (e.g. transition metals) by regression analysis.

59) Page 29, line 34. Statistically based algorithms

Done.

60) Page 29. I would use VNIR-SWIR, not SWIR-VNIR to read “VNIR-SWIR data are valuable …”

Done.

61) Page 30, line 9. Revise to read “at all scales”

Done.

62) Page 31, line 7. Mixtures happen between

Done.

63) Page 31, line 40. “data have been processed”

Done.

Sincerely,Saeid Asadzadeh