Montmorillonite Estimation in Clay–Quartz–Calcite Samples from Laboratory SWIR Imaging Spectroscopy: A Comparative Study of Spectral Preprocessings and Unmixing Methods
"> Figure 1
<p>(<b>a</b>) Mixed pixel composed of a linear mixture of two surface materials illustrating the “aeral mixture” (green: vegetation, brown: bare soil) and (<b>b</b>) mixed pixel composed of a volumetric distribution of intimate soil constituents illustrating the “intimate mixture”.</p> "> Figure 2
<p>(<b>a</b>) Laboratory setup and (<b>b</b>) RGB composite image of a mixture sample with the region of interest (ROI) selected for the study (black rectangle).</p> "> Figure 3
<p>Spectral mean reflectance of illite, montmorillonite, kaolinite, quartz and calcite samples measured in the laboratory.</p> "> Figure 4
<p>Ternary diagrams of (<b>a</b>) clay mixtures and (<b>b</b>) montmorillonite–quartz/calcite mixtures (unit: wt %).</p> "> Figure 5
<p>Processing chain to estimate montmorillonite abundance. Linear unmixing performances on aeral mixtures are compared with linear and nonlinear unmixing performances.</p> "> Figure 6
<p>Montmorillonite–illite mixtures: from (<b>a</b>) to (<b>c</b>) reflectance spectra and from (<b>d</b>) to (<b>u</b>) application of the 6 spectral preprocessings for aeral mixtures (first column) and intimate mixture (second column), difference between mean spectra of aeral and intimate mixtures (third column; line: mean reflectance, ribbon: mean ± one standard deviation).</p> "> Figure 7
<p>Montmorillonite–calcite: from (<b>a</b>) to (<b>c</b>) reflectance spectra and from (<b>d</b>) to (<b>u</b>) application of the 6 spectral preprocessings from aeral mixtures (first column), intimate mixture (second column), difference between mean spectra of aeral and intimate mixtures (third column; line: mean reflectance, ribbon: mean ± one standard deviation).</p> "> Figure 8
<p>Montmorillonite–quartz: from (<b>a</b>) to (<b>b</b>) reflectance spectra and from (<b>c</b>) to (<b>n</b>) application of the 6 spectral preprocessings from aeral mixtures (first column), intimate mixture (second column; line: mean reflectance, ribbon: mean ± one standard deviation).</p> "> Figure 9
<p>Performances of montmorillonite abundance estimation in illite–montmorillonite aeral mixtures with the (<b>a</b>) fully constrained least square method (FCLS) and (<b>b</b>) multiple endmember spectral mixture analysis (MESMA) unmixing method.</p> "> Figure 10
<p>Histograms of parameter γ (non-linearity contribution of the generalized bilinear model (GBM)) for MK mixtures: (<b>a</b>) reflectance data, (<b>b</b>–<b>d</b>) for each spectral preprocessing (red: average value of γ).</p> "> Figure 11
<p>Histograms of parameter P (non-linearity contribution of multi-linear model (MLM)) for MK mixtures: (<b>a</b>) reflectance data, (<b>b</b>–<b>d</b>) for each spectral preprocessing (red: average value of P).</p> "> Figure 12
<p>Histograms of parameter γ (non-linearity contribution of GBM) for MC mixtures: (<b>a</b>) reflectance data, (<b>b</b>–<b>d</b>) for each spectral preprocessing (red: average value of γ).</p> "> Figure 13
<p>Histograms of parameter γ (non-linearity contribution of GBM) for MC mixtures: (<b>a</b>) reflectance data, (<b>b</b>–<b>d</b>) for each spectral preprocessing (red: average value of γ).</p> ">
Abstract
:1. Introduction
2. Material
2.1. Laboratory Imaging Spectroscopy Setup
2.2. Spectral Database
2.2.1. Pure Clay Mineral Samples
- The absorption feature around 2200 nm is due to the Al–OH vibrational mode. Its accurate location depends on the clay type: 2208 nm for illite, 2212 nm for montmorillonite and 2206 nm for kaolinite. Kaolinite also has a double absorption feature (2160 nm and 2206 nm), which is leftward asymmetric. The typical absorption bandwidth is around 100 nm whatever the clay type.
- The absorption features due to OH-stretching bands combined with lattice vibrations at approximately 2360 nm is shallow for illite and sharp for kaolinite. Kaolinite has also in addition two more absorption features at 2320 nm and 2380 nm.
2.2.2. Synthetic Mineral Aeral Mixtures
2.2.3. Mineral Intimate Mixtures
3. Methods
3.1. Spectral Preprocessings
3.2. Unmixing Methods
3.3. Evaluation Criteria
4. Results
4.1. Comparison between Reflectance and Preprocessed Spectra
4.1.1. Clay Mixtures
4.1.2. Montmorillonite–Calcite/Quartz Binary Mixtures
4.2. Performances of Linear Unmixing Methods
4.2.1. Clay Mixtures
4.2.2. Montmorillonite–Calcite/Quartz Mixtures
4.3. Performances of Non-Linear Unmixing Methods
4.3.1. Clay Binary Mixtures
4.3.2. Montmorillonite–Calcite/Quartz Mixtures
4.3.3. Clay Ternary Mixtures
5. Discussion
5.1. Non-Linearity of Intimate Mixtures Depending on the Mineralogical Composition
5.2. Spectral Variability Reduction with Spectral Preprocessings
5.3. Performance of Linear Unmixing Methods with Spectral Preprocessings
5.4. Performance of Allunmixing Methods with Spectral Preprocessings
- For clay intimate mixtures either binary or ternary and whatever unmixing methods, the best spectral preprocessings were CWT and 1st SGD while for calcite/montmorillonite intimate mixtures, the best results were obtained without the use of spectral preprocessing. Consequently, it was recommended to use quasi-linear spectral preprocessings (CWT or 1st SGD) when the absorption bands of minerals overlap and keep the reflectance in the other cases (calcite). Attention should be paid with the use of SNV leading to higher biases in montmorillonite abundance estimations, and Log(1/R)/Hapke leading to non robust results dependent on the mixture type and the unmixing method. The only best results for Hapke were obtained with MESMA for clay intimate mixtures (IM: RMSE of 6.6%, MK: RMSE of 12.1% and IMK: RMSE of 7.1%) with minerals of similar granulometry (80 µm), which is in agreement with Heylen and Scheunders [31] (RMSE less than 2% for alunite–quartz mixtures having similar grain size). However, with minerals of different granulometry (calcite: 70 µm, quartz: 300 µm), performance was poor because it violated the main assumption of the Hapke algorithm used in this paper [44].
- Similar performances in terms of RMSE were noticed between the use of linear and non-linear unmixing methods. As a result, the use of the simplest linear unmixing method, FCLS, was advised, coupled with the simplest spectral preprocessing, 1st SGD, for clay intimate mixtures and without the use of spectral preprocessing with calcite/montmorillonite.
- The error in montmorillonite abundance estimation achieved for the best couple mentioned in Section 2, a RMSE of 9.2% for IM mixtures, 13.9% in MK mixtures, 10.8% in clay ternary mixtures and 8.8% for MC mixtures. These results were better than those obtained by [12] using a geometrical analysis for montmorillonite–illite–kaolinite mixtures (RMSE 15.5%). For more complex mixtures, the performance gave an RMSE of around 8% using a regression tree for smectite–kaolinite–muscovite–calcite–quartz mixtures [26] and an RMSE of around 3.4% using a multivariate analysis for smectite–illlite–kaolinite–carbonate–quartz mixtures [29].
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Hyspex SWIR–320m–e Camera | |
---|---|
Spectral range (nm) | 1000–2500 |
Spectral resolution (nm) | 6 |
Number of spectral bands | 256 |
Field of view (°) | 13.5 |
Number of pixels across track | 320 |
Preprocessing | Equation | Reference |
---|---|---|
Pseudo absorbanceLog(1/R) | [19] (Rinnan et al., 2009) | |
Hapke Model (W) | [44] (Heylen et al., 2014) | |
Standard Normal Variate (SNV) | [59] (Barnes et al., 1989) | |
Continuum Removal (CR) | [60] (Clark and Roush, 1984) | |
Continuous Wavelet Transform (CWT) | [20] (Rivard et al., 2008) | |
First Savitzky–Golay Derivative (1st SGD) | [61] (Tsai and Philpot, 1998) |
Unmixing | Equation | Reference |
---|---|---|
Fully Constraint Least Square (FCLS) | [37] (Heinz and Chang, 2001) | |
Multiple Endmember Spectral Mixture Analysis (MESMA) | and | [39] (Roberts et al., 1998) |
Generalized Bilinear Model (GBM) | [46] (Halimi et al., 2011) | |
Multi-Linear Model (MLM) | [34] (Heylen and Scheunders, 2016) |
Mixture | Aeral | Intimate | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
FCLS | MESMA | FCLS | MESMA | ||||||||||||||
R2 | MB | STDB | RMSE | R2 | MB | STDB | RMSE | R2 | MB | STDB | RMSE | R2 | MB | STDB | RMSE | ||
IM | REF | 0.66 | 0.1 | 15.3 | 15.3 | 0.95 | −0.2 | 5.2 | 5.2 | 0.14 | 5.7 | 22.8 | 23.5 | 0.90 | 7.1 | 6.9 | 9.9 |
Log(1/R) | 0.67 | −1.7 | 15.0 | 15.1 | 0.96 | −2.3 | 4.8 | 5.3 | 0.23 | 7.2 | 21.1 | 22.3 | 0.27 | 9.2 | 32.4 | 29.5 | |
Hapke | 0.68 | −5.3 | 14.4 | 15.3 | 0.79 | −6.5 | 10.5 | 12.3 | 0.30 | 4.0 | 19.6 | 20.0 | 0.91 | 0.0 | 6.6 | 6.6 | |
SNV | 0.99 | 2.1 | 2.2 | 3.1 | 0.99 | 2.2 | 2.5 | 3.3 | 0.98 | 12.2 | 3.4 | 12.7 | 0.98 | 12.3 | 3.4 | 12.7 | |
CR | 0.98 | 1.7 | 3.4 | 3.8 | 0.94 | 1.3 | 5.3 | 5.4 | 0.94 | 9.4 | 5.3 | 10.8 | 0.95 | 5.9 | 5.1 | 7.8 | |
CWT | 0.99 | 0.1 | 2.5 | 2.5 | 0.96 | 0.1 | 4.6 | 4.6 | 0.96 | 7.1 | 4.5 | 8.4 | 0.80 | 5.5 | 10.9 | 12.2 | |
1st SGD | 0.99 | 0.1 | 2.4 | 2.4 | 0.97 | −0.2 | 4.1 | 4.1 | 0.97 | 8.3 | 4.0 | 9.2 | 0.97 | 8.3 | 4.0 | 9.3 | |
MK | REF | 0.51 | −1.3 | 19.8 | 19.8 | 0.97 | 0.9 | 3.7 | 3.8 | 0.21 | 12.6 | 20.9 | 24.4 | 0.90 | 13.3 | 6.8 | 15.0 |
Log(1/R) | 0.53 | −2.3 | 18.9 | 19.1 | 0.97 | −2.6 | 3.7 | 4.5 | 0.24 | 15.5 | 20.2 | 25.4 | 0.73 | 17.4 | 14.0 | 21.2 | |
Hapke | 0.58 | −3.9 | 16.9 | 17.3 | 0.85 | −7.5 | 9.1 | 11.8 | 0.26 | 11.7 | 20.1 | 23.2 | 0.86 | 8.8 | 8.3 | 12.1 | |
SNV | 0.99 | 2.1 | 2.4 | 3.2 | 0.99 | 2.1 | 2.3 | 3.1 | 0.94 | 14.8 | 5.4 | 15.7 | 0.94 | 14.8 | 5.5 | 15.7 | |
CR | 0.93 | 1.4 | 6.0 | 6.2 | 0.98 | −1.9 | 3.6 | 4.1 | 0.94 | 14.2 | 6.4 | 15.5 | 0.94 | 11.3 | 6.3 | 13.0 | |
CWT | 0.96 | 0.0 | 4.6 | 4.6 | 0.99 | 0.0 | 2.5 | 2.5 | 0.95 | 12.5 | 5.2 | 13.6 | 0.92 | 12.7 | 6.3 | 14.1 | |
1st SGD | 0.98 | 0.0 | 3.5 | 3.5 | 0.99 | 0.3 | 2.5 | 2.6 | 0.94 | 12.8 | 5.3 | 13.9 | 0.94 | 12.8 | 5.3 | 13.9 |
IMK | FCLS | MESMA | |||||||
---|---|---|---|---|---|---|---|---|---|
Montmorillonite | |||||||||
R2 | MB | STDB | RMSE | R2 | MB | STDB | RMSE | ||
REF | 0.21 | 0.8 | 24.3 | 24.3 | 0.86 | 10.8 | 5.5 | 12.1 | |
Log(1/R) | 0.21 | −0.2 | 23.5 | 23.5 | 0.93 | 8.4 | 5.1 | 9.7 | |
Hapke | 0.25 | −2.9 | 20.7 | 20.9 | 0.77 | 2.3 | 6.8 | 7.1 | |
SNV | 0.93 | 13.9 | 5.0 | 14.8 | 0.92 | 13.6 | 5.0 | 14.5 | |
CR | 0.87 | 8.8 | 5.2 | 10.2 | 0.86 | 6.3 | 5.3 | 8.2 | |
CWT | 0.87 | 7.4 | 5.2 | 9.1 | 0.65 | 6.0 | 9.5 | 11.2 | |
1st SGD | 0.89 | 9.7 | 4.8 | 10.8 | 0.88 | 10.2 | 5.1 | 11.4 |
Mixture | Aeral | Intimate | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
FCLS | MESMA | FCLS | MESMA | ||||||||||||||
R2 | MB | STDB | RMSE | R2 | MB | STDB | RMSE | R2 | MB | STDB | RMSE | R2 | MB | STDB | RMSE | ||
MC | REF | 0.96 | 0.2 | 4.8 | 4.8 | 0.99 | 0.3 | 2.5 | 2.5 | 0.92 | –3.8 | 8.0 | 8.8 | 0.94 | 7.6 | 5.4 | 9.3 |
Log(1/R) | 0.96 | −5.2 | 4.8 | 7.0 | 0.98 | −11.5 | 3.6 | 12.0 | 0.93 | −8.5 | 6.7 | 10.8 | 0.99 | 1.6 | 2.2 | 2.5 | |
Hapke | 0.91 | −20.8 | 6.6 | 21.9 | 0.85 | −27.3 | 8.9 | 28.7 | 0.91 | −22.6 | 6.9 | 23.6 | 0.87 | −23.8 | 8.3 | 25.2 | |
SNV | 0.97 | 12.8 | 4.1 | 13.4 | 0.97 | 12.9 | 4.1 | 13.5 | 0.86 | 36.6 | 12.3 | 38.6 | 0.86 | 36.6 | 12.3 | 38.6 | |
CR | 0.98 | −5.2 | 3.6 | 6.3 | 0.98 | −4.2 | 4.1 | 5.9 | 0.95 | 19.1 | 8.6 | 21.0 | 0.95 | 14.3 | 7.0 | 16.0 | |
CWT | 0.98 | 0.0 | 3.3 | 3.3 | 0.98 | 0.0 | 3.3 | 3.3 | 0.94 | 21.7 | 8.3 | 23.2 | 0.91 | 21.4 | 8.9 | 23.2 | |
1st SGD | 0.98 | 0.0 | 2.9 | 2.9 | 0.98 | 0.1 | 3.2 | 3.2 | 0.96 | 21.3 | 7.7 | 22.6 | 0.95 | 22.0 | 8.1 | 23.4 | |
MQ | REF | 0.93 | −0.1 | 6.2 | 6.2 | 0.99 | 0.4 | 2.2 | 2.3 | 0.29 | 55.9 | 17.7 | 58.6 | 0.71 | 53.0 | 15.1 | 55.1 |
Log(1/R) | 0.93 | −3.9 | 6.1 | 7.2 | 0.99 | −7.2 | 2.7 | 7.7 | 0.29 | 55.0 | 17.6 | 57.8 | 0.69 | 53.1 | 14.7 | 54.6 | |
Hapke | 0.92 | −13.2 | 6.5 | 14.7 | 0.83 | −22.4 | 9.3 | 24.2 | 0.32 | 48.7 | 18.0 | 51.9 | 0.35 | 48.4 | 17.8 | 51.6 | |
SNV | 0.86 | 35.5 | 12.3 | 37.5 | 0.86 | 35.5 | 12.4 | 37.6 | 0.00 | 61.8 | 20.9 | 65.2 | 0.00 | 61.8 | 20.9 | 65.2 | |
CR | 0.98 | −5.0 | 3.4 | 6.1 | 0.97 | −6.0 | 3.9 | 7.2 | 0.65 | 51.2 | 14.4 | 53.1 | 0.63 | 52.1 | 14.9 | 54.1 | |
CWT | 0.98 | 0.0 | 3.1 | 3.1 | 0.98 | 0.0 | 3.0 | 3.0 | 0.67 | 53.1 | 15.6 | 55.3 | 0.16 | 51.2 | 20.3 | 55.1 | |
1st SGD | 0.99 | 0.0 | 2.8 | 2.8 | 0.98 | −0.3 | 3.5 | 3.5 | 0.71 | 53.0 | 15.3 | 55.2 | 0.68 | 53.1 | 15.6 | 55.3 |
Mixture | GBM | MLM | |||||||
---|---|---|---|---|---|---|---|---|---|
R2 | MB | STDB | RMSE | R2 | MB | STDB | RMSE | ||
IM | REF | 0.56 | 12.7 | 14.8 | 19.6 | 0.97 | 9.2 | 3.8 | 10.0 |
Log(1/R) | 0.47 | 2.0 | 16.6 | 16.7 | 0.97 | 5.8 | 4.0 | 7.0 | |
SNV | 0.98 | 12.6 | 3.5 | 13.0 | 0.98 | 11.1 | 3.3 | 11.6 | |
CR | 0.95 | 5.9 | 4.9 | 7.7 | 0.94 | 11.4 | 5.9 | 12.9 | |
CWT | 0.96 | 7.7 | 4.4 | 8.8 | 0.96 | 8.2 | 4.5 | 9.4 | |
1st SGD | 0.97 | 8.3 | 4.0 | 9.2 | 0.97 | 9.4 | 4.1 | 10.3 | |
MK | REF | 0.56 | 20.3 | 14.6 | 25.0 | 0.95 | 13.4 | 5.1 | 14.4 |
Log(1/R) | 0.51 | 10.0 | 15.8 | 18.7 | 0.95 | 11.3 | 5.1 | 12.4 | |
SNV | 0.94 | 15.0 | 5.4 | 16.0 | 0.94 | 13.8 | 5.3 | 14.8 | |
CR | 0.94 | 12.3 | 6.4 | 13.9 | 0.94 | 12.7 | 6.1 | 14.1 | |
CWT | 0.95 | 12.3 | 5.2 | 13.4 | 0.95 | 12.4 | 5.3 | 13.5 | |
1st SGD | 0.94 | 12.8 | 5.3 | 13.9 | 0.95 | 12.9 | 5.2 | 14.0 |
Mixture | GBM | MLM | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
R2 | MB | STDB | RMSE | R2 | MB | STDB | RMSE | |||
MC | REF | 0.92 | 5.2 | 6.3 | 8.2 | 0.65 | 26.3 | 13.4 | 29.5 | |
Log(1/R) | 0.93 | −8.5 | 6.7 | 10.8 | 0.88 | 12.3 | 8.1 | 14.7 | ||
SNV | 0.86 | 35.8 | 11.9 | 37.7 | 0.87 | 35.3 | 11.8 | 37.2 | ||
CR | 0.96 | 16.2 | 7.8 | 17.9 | 0.85 | 26.3 | 8.8 | 27.7 | ||
CWT | 0.94 | 21.6 | 8.3 | 23.1 | 0.88 | 25.8 | 8.9 | 27.3 | ||
1st SGD | 0.96 | 21.3 | 7.7 | 22.6 | 0.89 | 26.4 | 8.5 | 27.7 | ||
MQ | REF | 0.38 | 57.4 | 17.2 | 59.9 | 0.70 | 52.8 | 15.0 | 54.9 | |
Log(1/R) | 0.32 | 54.3 | 17.2 | 57.0 | 0.73 | 48.8 | 12.9 | 50.4 | ||
SNV | 0.01 | 61.8 | 20.9 | 65.3 | 0.01 | 61.7 | 20.8 | 65.1 | ||
CR | 0.65 | 52.2 | 14.6 | 54.2 | 0.00 | 56.7 | 23.2 | 61.2 | ||
CWT | 0.67 | 53.1 | 15.6 | 55.4 | 0.36 | 53.9 | 17.4 | 56.6 | ||
1st SGD | 0.71 | 53.1 | 15.3 | 55.2 | 0.03 | 51.5 | 22.5 | 56.2 |
IMK. | GBM | MLM | ||||||
---|---|---|---|---|---|---|---|---|
Montmorillonite | ||||||||
R2 | MB | STDB | RMSE | R2 | MB | STDB | RMSE | |
REF | 0.43 | 12.3 | 15.3 | 19.6 | 0.93 | 12.0 | 4.1 | 12.7 |
Log(1/R) | 0.30 | –3.7 | 18.8 | 19.2 | 0.90 | 8.8 | 4.6 | 9.9 |
Hapke | ||||||||
SNV | 0.91 | 20.5 | 4.5 | 21.0 | 0.93 | 7.6 | 5.5 | 9.3 |
CR | 0.87 | 5.4 | 5.2 | 7.5 | 0.84 | 15.6 | 6.8 | 17.1 |
CWT | 0.88 | 8.9 | 5.1 | 10.2 | 0.89 | 8.3 | 5.3 | 9.9 |
1st SGD | 0.89 | 9.7 | 4.8 | 10.8 | 0.91 | 10.6 | 4.9 | 11.6 |
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Share and Cite
Ducasse, E.; Adeline, K.; Briottet, X.; Hohmann, A.; Bourguignon, A.; Grandjean, G. Montmorillonite Estimation in Clay–Quartz–Calcite Samples from Laboratory SWIR Imaging Spectroscopy: A Comparative Study of Spectral Preprocessings and Unmixing Methods. Remote Sens. 2020, 12, 1723. https://doi.org/10.3390/rs12111723
Ducasse E, Adeline K, Briottet X, Hohmann A, Bourguignon A, Grandjean G. Montmorillonite Estimation in Clay–Quartz–Calcite Samples from Laboratory SWIR Imaging Spectroscopy: A Comparative Study of Spectral Preprocessings and Unmixing Methods. Remote Sensing. 2020; 12(11):1723. https://doi.org/10.3390/rs12111723
Chicago/Turabian StyleDucasse, Etienne, Karine Adeline, Xavier Briottet, Audrey Hohmann, Anne Bourguignon, and Gilles Grandjean. 2020. "Montmorillonite Estimation in Clay–Quartz–Calcite Samples from Laboratory SWIR Imaging Spectroscopy: A Comparative Study of Spectral Preprocessings and Unmixing Methods" Remote Sensing 12, no. 11: 1723. https://doi.org/10.3390/rs12111723
APA StyleDucasse, E., Adeline, K., Briottet, X., Hohmann, A., Bourguignon, A., & Grandjean, G. (2020). Montmorillonite Estimation in Clay–Quartz–Calcite Samples from Laboratory SWIR Imaging Spectroscopy: A Comparative Study of Spectral Preprocessings and Unmixing Methods. Remote Sensing, 12(11), 1723. https://doi.org/10.3390/rs12111723