Mapping of Clay Montmorillonite Abundance in Agricultural Fields Using Unmixing Methods at Centimeter Scale Hyperspectral Images
Abstract
:1. Introduction
- The Al–OH vibrational mode produces the 2200 nm absorption feature with a bandwidth around 100 nm whatever the clay type. Kaolinite also has a double absorption feature (2160 nm and 2206 nm), which is leftward asymmetric.
- OH-stretching bands combined with lattice vibrations produces absorption features at 2360 nm for both kaolinite and illite. This feature is shallow for illite and sharp for kaolinite. Kaolinite has also two absorption features at 2320 nm and 2380 nm.
- (1)
- How can spectral libraries or automatic EM detection benefit from unmixing method performances?
- (2)
- Which combined spectral preprocessing and unmixing algorithm is the most efficient strategy to estimate montmorillonite abundances in soil?
- (3)
- What is the impact of soil composition and other factors influencing montmorillonite abundance accuracy of ploughed fields at the centimeter scale?
2. Materials and Methods
2.1. Site Descriptions
- The knowledge of soil mineral composition:
- Soil swelling hazard maps from the BRGM [8] (Figure 1b) were used to roughly locate the previous XRD measurements. Their three classes (low/medium/high swelling classes) were selected using geotechnical analysis (MBT) and dominant lithology inside stratigraphic formations. The sites were chosen within the swelling class entity where the XRD measurements were sampled.
- An easy access to the selected sites given the field owner agreement.
- A low vegetation cover (less than 20%) observed on agricultural fields (the experiment has been carried out after wheat harvest, on freshly ploughed fields) (Figure 1c–e).
- “Le Buisson” locality in Coinces municipality, hereafter named Coinces (WGS 84, 48.00901°N, 1.734826°E). This site lies on a stratigraphic formation of Quaternary loam and loess, clayey and carbonated. The nearest XRD measurements indicate a composition of 11% kaolinite, 7% illite and 2% smectite, presenting a low swelling risk (Figure 1c).
- “Les Laps” locality in Gémigny municipality, hereafter named Gémigny (WGS 84, 47.95422°N, 1.689848°E). This site lies on a stratigraphic formation lower Pliocene sand and clay with dominant sand and clayey sand with metric clay layers. The nearest XRD measurement indicates 2.9% kaolinite, 5% illite and 43.5% smectite, presenting a high swelling risk (Figure 1d).
- “La Malandière” locality in Mareau-aux-près municipality, hereafter named “Mareau” (WGS 84, 47.83964°N, 1.758915°E). This site lies on a stratigraphic formation of recent Holocene Loire alluvium, mainly siliceous with local imbrications of loam and clay deposits. The nearest XRD measurements indicate 14.6% kaolinite, 6.5% illite and 0% smectite, presenting a low swelling risk (Figure 1e).
2.2. Hyperspectral Data Acquisitions
- Normalized difference vegetation index (NDVI) to detect green vegetation [60]:
- Chlorophyll absorption index (CAI) to detect dry vegetation [61]:
2.3. Field and Laboratory Measurements
2.4. General Methodology
2.4.1. Endmember Selection
2.4.2. Spectral Preprocessings
2.4.3. Unmixing Methods
2.4.4. Evaluation Criteria
2.4.5. Validation Methodology
3. Results
3.1. Endmember Detection and Comparison
3.2. Performance Analysis of Spectral Preprocessing Coupled with Unmixing Methods for Montmorillonite Abundance Estimation
3.3. Analysis of Montmorillonite Abundance Maps
3.3.1. At the Subzone Scale
- Gémigny-SUB14, characterized by a weak spatial variability of the roughness index (median and standard deviation of 0.51 cm and 0.68 cm (Figure 11c) and by a solar elevation angle of 44°.
- Coinces-SUB2, characterized by a large spatial variability of the roughness index (median and standard deviation of 83% and 0.51 cm and 0.68 cm (Figure 12c), and a solar elevation angle of 35.6°.
3.3.2. At the Image Scale
- On the Gémigny map, the estimated abundance values of clays increased inside wheel tracks located in SUB1, SUB2, SUB3, SUB7, SUB8 and SUB9 (Figure 14c). They contained more estimated montmorillonite than the other subzones (from around 4% to 5%).
- On the Coinces map (Figure 15c), the montmorillonite estimation was higher in left subzones (SUB1, SUB2, SUB6, SUB7, SUB11, SUB12) than in the right ones (SUB5 and SUB15). This trend was not present in any validation data but may have been due to the presence of clouds during the acquisitions. In the wet subzone (SUB10), the majority of pixels was wrongly classified as “dry vegetation”, and montmorillonite was estimated at around 40% in bare soils’ pixels.
- On the Mareau map, the montmorillonite estimation was around 20% in the wet subzone (SUB15), whereas the montmorillonite estimation in other pixels was around 15% (Figure 16c).
3.4. Estimation Performances of Other Mineral Abundance
4. Discussion
4.1. Endmembers Selection for Unmixing
4.2. Limitations of Preprocessings and Unmixing Methods for Montmorillonite Abundance Estimation
4.3. Impact of Soil Mineralogical Composition
4.4. Other Factors Influencing Montmorillonite Abundance Mapping
- The obtained performances degraded compared to those obtained over in-lab mixtures as these samples were dried, sieved and crushed to a powder with a flattened surface,
- The centimeter scale of spectroscopic acquisitions tended to exacerbate directional effects, both geometric (no local slope correction has been applied to retrieve surface reflectance) and optical (no anisotropic correction has been applied assuming only Lambertian materials), and so the spectral variability was increased, which could not be accounted by unmixing methods.
- The better the spatial resolution is, the wider the spatial variability is; thus, at a centimeter scale, the variability was very high compared to the reflectance at a meter scale where the soil heterogeneities were more smoothed.
- The presence of residual dry and wet vegetation and of shadows contributed to increasing the number of surface types seen by a pixel and then reducing the quality of the unmixing performances; an improved masking can be targeted in the future.
- The roughness of the ploughed fields induced surface multiple scatterings and variations of illuminations (direct and diffuse downwelling irradiance), which were taken into account in our unmixing models; [36] and, more recently, [88] have proposed a physical approach to take into account these effects. In particular, [88] proposed an extension of MLM to this end. Unfortunately, these approaches have never been tested at a centimeter scale also including intimate mixtures.
- The higher variability of mineral granulometry (10–35% for Gémigny, 10–31% for Mareau and 6–31% for Coinces accounting for textural clay and coarse sand, cf. Figure 4) impacted volume multiple scatterings and induced higher spectral variations, oppositely to the more homogeneous granulometry found for the laboratory mixtures, having either only clay minerals or clay minerals combined with calcite or quartz [12].
- The important contribution of other minerals than clay ones, mainly quartz for Gémigny and Coinces (abundance more than 58%), and quartz, potassium feldspars and plagioclases for Mareau (global abundance more than 50%).
- The presence of quartz in soils, such as noted by [11,21,92], that highlighted the difficulties to retrieve minerals and quantify their abundance when a mixture contains quartz, and also by [12] that confirmed this point in the laboratory where montmorillonite abundance estimation in the presence of quartz was very poor (RMSE more than 50%) whatever the unmixing method and the preprocessing.
- The atmospheric conditions, such as for Coinces, for which the experiment was performed under partially cloudy conditions with varying illumination, while in the laboratory, these conditions were controlled and stable in time.
- The soil water content led to an increase in montmorillonite abundance that could be explained by the decrease of global reflectance level due to the soil moisture content increase and the potential overestimation of the darkest EM (i.e., montmorillonite). However, in our case, the water content was low enough (<18%) to not mask the clay absorption band with soil moisture content, which happens for an SMC of 30% [39].
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Title 1 | Gémigny | Coinces | Mareau | |||
---|---|---|---|---|---|---|
Type of Mineral | Min | Max | Min | Max | Min | Max |
Quartz | 58 | 68 | 58 | 64 | 29 | 31 |
Smectite | 13 | 20 | 18 | 23 | 17 | 20 |
Illite and/or micas | 3 | 5 | 1 | 4 | 6 | 10 |
Kaolinite | 1 | 5 | 2 | 3 | 5 | 7 |
Calcite | traces | traces | traces | 7 | traces | traces |
Potassium Feldspars (Sanidine/Orthoclase) | 9 | 10 | 5 | 7 | 12 | 16 |
Plagioclase Feldspars (Albite) | 4 | 7 | 5 | 6 | 18 | 24 |
Quartz | 58 | 68 | 58 | 64 | 29 | 31 |
REF-MLM | 1stSGD-MLM | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Sample | Average Abundance | XRD Validation | MB | STDB | RMSE | R | Average Abundance | XRD Validation | MB | STDB | RMSE | R |
Coinces sub2 | 15.6 | 21 | −5.4 | 4.3 | 6.9 | 7.6 | 21 | −13.4 | 6.6 | 14.9 | ||
Coinces sub4 | 7.4 | 20 | −12.6 | 4.1 | 13.3 | 3.5 | 20 | −16.5 | 5.1 | 17.3 | ||
Coinces sub6 | 13.2 | 18 | −4.8 | 4.3 | 6.4 | 3.7 | 18 | −14.3 | 3.3 | 14.7 | ||
Coinces sub8 | 10.5 | 23 | −12.5 | 4.0 | 13.1 | 3.6 | 23 | −19.4 | 3.5 | 19.7 | ||
Coinces all subs | 12.8 | 21 | −7.8 | 6 | 9.8 | −0.08 | 4.3 | 21 | −16.3 | 5.4 | 17.1 | 0.05 |
Gémigny sub3 | 27.8 | 20 | 7.8 | 3.4 | 8.5 | 12.2 | 20 | −7.8 | 4.3 | 9.0 | ||
Gémigny sub4 | 25.0 | 18 | 7.0 | 2.9 | 7.5 | 12.3 | 18 | −5.7 | 5.4 | 7.9 | ||
Gémigny sub6 | 25.5 | 20 | 5.5 | 3.8 | 6.6 | 9.5 | 20 | −10.5 | 5.5 | 11.8 | ||
Gémigny sub10 | 23.9 | 13 | 10.9 | 2.5 | 11.1 | 9.3 | 13 | −3.7 | 4.3 | 5.7 | ||
Gémigny sub13 | 25.5 | 14 | 11.5 | 1.8 | 11.7 | 9.5 | −14 | −4.5 | 2.7 | 5.3 | ||
Gémigny sub14 | 24.2 | 15 | 9.2 | 1.9 | 9.4 | 9.2 | 15 | −5.8 | 3.4 | 6.7 | ||
Gémigny all subs | 25.6 | 17 | 8.6 | 3.4 | 9.2 | 0.27 | 10.7 | 17 | −6.4 | 4.6 | 7.9 | 0.23 |
Mareau sub3 | 13.7 | 24 | −10.3 | 1.6 | 10.4 | 6.6 | 24 | −17.4 | 2.3 | 17.6 | ||
Mareau sub4 | 13.6 | 18 | −4.4 | 2.0 | 4.9 | 8.4 | 18 | −9.6 | 8.4 | 12.7 | ||
Mareau sub8 | 14.5 | 17 | −2.5 | 1.8 | 3.1 | 4.7 | 17 | −12.3 | 2.5 | 12.5 | ||
Mareau all subs | 14.0 | 20 | −6.2 | 4.1 | 7.4 | −0.17 | 6.0 | 20 | −14.1 | 4.9 | 15.0 | 0.14 |
All subzones, all sites. | 18.9 | 18 | 0.0 | 9.0 | 9.0 | −0.44 | 18.9 | −11.3 | −11.3 | 6.7 | 13 | −0.17 |
REF-MLM | 1stSGD-MLM | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Sample | Average Abundance | XRD Validation | MB | STDB | RMSE | R | Average Abundance | XRD Validation | MB | STDB | RMSE | R |
Coinces sub2 | 17.6 | 21 | −3.4 | 5.9 | 6.8 | 12.3 | 21 | −8.7 | 5.5 | 10.3 | ||
Coinces sub4 | 6.9 | 20 | −13.1 | 4.7 | 14.0 | 6.2 | 20 | −13.8 | 6.5 | 15.3 | ||
Coinces sub6 | 13.6 | 18 | −4.4 | 6.3 | 7.6 | 7.9 | 18 | −10.1 | 4.3 | 11.0 | ||
Coinces sub8 | 10.2 | 23 | −12.8 | 5.3 | 13.8 | 7.4 | 23 | −15.6 | 4.6 | 16.3 | ||
Coinces all subs | 13.9 | 21 | −6.6 | 7.4 | 9.9 | −0.07 | 8.6 | 21 | −12.0 | 6.4 | 13.6 | 0.05 |
Gémigny sub3 | 32.7 | 20 | 12.7 | 4.2 | 13.4 | 18.4 | 20 | −1.6 | 2.9 | 3.3 | ||
Gémigny sub4 | 28.9 | 18 | 10.9 | 3.6 | 11.5 | 17.9 | 18 | −0.1 | 3.3 | 3.3 | ||
Gémigny sub6 | 29.5 | 20 | 9.5 | 4.7 | 10.7 | 16.1 | 20 | −3.9 | 3.7 | 5.4 | ||
Gémigny sub10 | 27.7 | 13 | 14.7 | 3.2 | 15.0 | 15.0 | 13 | 2.0 | 3.1 | 3.7 | ||
Gémigny sub13 | 29.8 | 14 | 15.8 | 2.2 | 16.0 | 16.1 | 14 | 2.1 | 2.2 | 3.0 | ||
Gémigny sub14 | 28.3 | 15 | 13.3 | 2.4 | 13.5 | 15.5 | 15 | 0.5 | 2.5 | 2.6 | ||
Gémigny all subs | 29.9 | 17 | 12.8 | 4.00 | 13.4 | 0.26 | 16.9 | 17.1 | −0.2 | 3.4 | 3.4 | 0.29 |
Mareau sub3 | 14.6 | 24 | −9.4 | 2.4 | 9.7 | 14.3 | 24 | −9.7 | 5.0 | 10.9 | ||
Mareau sub4 | 14.1 | 18 | −3.9 | 2.9 | 4.9 | 17.3 | 18 | −0.7 | 13.2 | 13.2 | ||
Mareau sub8 | 15.5 | 17 | −1.5 | 2.6 | 3.0 | 11.5 | 17 | −5.5 | 5.3 | 7.6 | ||
Mareau all subs | 14.9 | 20 | −5.3 | 4.5 | 6.9 | −0.11 | 14.9 | 20 | −6.7 | 7.3 | 10.1 | 0.12 |
All subzones, all sites. | 21.4 | 19 | 2.5 | 10.8 | 11.1 | −0.43 | 13.6 | 19 | −5.3 | 7.6 | 9.3 | −0.17 |
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Ducasse, E.; Adeline, K.; Hohmann, A.; Achard, V.; Bourguignon, A.; Grandjean, G.; Briottet, X. Mapping of Clay Montmorillonite Abundance in Agricultural Fields Using Unmixing Methods at Centimeter Scale Hyperspectral Images. Remote Sens. 2024, 16, 3211. https://doi.org/10.3390/rs16173211
Ducasse E, Adeline K, Hohmann A, Achard V, Bourguignon A, Grandjean G, Briottet X. Mapping of Clay Montmorillonite Abundance in Agricultural Fields Using Unmixing Methods at Centimeter Scale Hyperspectral Images. Remote Sensing. 2024; 16(17):3211. https://doi.org/10.3390/rs16173211
Chicago/Turabian StyleDucasse, Etienne, Karine Adeline, Audrey Hohmann, Véronique Achard, Anne Bourguignon, Gilles Grandjean, and Xavier Briottet. 2024. "Mapping of Clay Montmorillonite Abundance in Agricultural Fields Using Unmixing Methods at Centimeter Scale Hyperspectral Images" Remote Sensing 16, no. 17: 3211. https://doi.org/10.3390/rs16173211