Sensitivity of clay content prediction to spectral configuration of VNIR/SWIR imaging data, from multispectral to hyperspectral scenarios
Introduction
Soil provides major services such as provisions of food, fiber, carbon sequestration, water purification and storage, soil contaminant reduction, climate regulation, nutrient cycling, biological habitats and gene pools. However, demographic pressure and climate change impact these key environmental functions which must be monitored, explored and studied. Many models and indicators that represent these functions are now available (Sanchez et al., 2009). To be fully operational for assisting decisions at local, national and global levels, precise spatially referenced soil information is required as input in these models and indicators. To address this situation, hyperspectral visible, near-infrared and short-wave infrared (VNIR/SWIR, 400–2500 nm, with > 100 spectral bands) imagery can be considered as an adequate technology for accurate mapping and monitoring of some key soil surface properties (e.g., Ben-Dor et al., 2002, Selige et al., 2006, Stevens et al., 2010, Gomez et al., 2012a). Accurate local estimates were obtained by hyperspectral VNIR/SWIR imagery over bare soil surfaces for soil properties: i) related to a chemical component that impacts soil surface reflectance through absorption bands (e.g., OH− ions for clay) or ii) highly correlated with the latter (e.g., Cation Exchange Capacity when it is correlated with, for example, clay content) (Ben-Dor et al., 2002). Moreover, recent studies showed that to be predictable, the soil properties have also to have a quite high amount of variability over the study area (Gomez et al., 2012a, Gomez et al., 2012b). Nevertheless hyperspectral VNIR/SWIR imagery cannot be extended to large surface mapping or to temporal monitoring because of the expensive cost and the low availability of hyperspectral VNIR/SWIR imaging data.
Only one hyperspectral VNIR/SWIR satellite sensor exists, which is the HYPERION sensor with a spatial resolution of 30 m, a spectral resolution of 10 nm and a swath of 7.5 km (Folkman et al., 2001). Other existing hyperspectral VNIR/SWIR imaging sensors are airborne sensors, such as the HyMap, AISA-DUAL, Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and HySpex sensors, with spectral resolutions between 5 and 10 nm, spatial resolutions of approximately 2 to 5 m (depending on the flight altitude) and flight prints generally inferior of 400 m2 (depending on the study case). And at least five hyperspectral VNIR/SWIR satellite sensors are planned to be launched next few years (Table 1).
In addition to the hyperspectral imaging sensors, two others categories of VNIR/SWIR imaging sensors exist: multispectral (< 10 bands) and superspectral (10 < bands < 100). Several VNIR/SWIR multispectral satellite sensors are available, such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (Abrams and Hook, 2003), LANDSAT-7 Enhanced Thematic Mapper (ETM +) and LANDSAT-8 Operational Land Imager (OLI) sensors, which were launched in 1999 and 2013, respectively (Masek et al., 2001, Roy et al., 2014). The World-View 3 (Kruse and Perry, 2013) and SENTINEL-2 Multispectral sensor Instrument (MSI) (Baillarin et al., 2012) sensors are both VNIR/SWIR superspectral satellite sensors, which were launched in 2014 and 2015, respectively.
In advance of a diversity of emerging and existing VNIR/SWIR satellite sensors and to confront the lack of soil maps around the world, the potential of VNIR/SWIR satellite sensors for soil properties mapping must be studied. The effect of spectral resolutions effects on minerals and plants identification were studied by Swayze et al. (2003), by simulations of four imaging spectrometers (including AVIRIS sensor). Van Der Meer et al. (2014) demonstrated the relevance of using bands ratios based on simulated SENTINEL-2 MSI data for ferric iron, ferrous iron, laterite, gossan, ferrous silicate and ferric oxides mapping. The effect of coarsening spatial resolution (from 5 m to 60 m) on the accuracy of clay content (defined as the percentage of granulometric fraction inferior to 2 μm by weight of the soil, Baize and Jabiol, 1995) prediction models was studied by Gomez et al. (2015). They found that, up to a spatial resolution of 30 m, clay mapping was still possible, but beyond a spatial resolution of 15 m, clay content variations due to short-scale successions of parent materials were not precisely captured. In addition, spatial resolutions of 60 m or coarser were not suitable for clay content mapping over areas characterized by small short-scale clay content variability and small field sizes. The effect of coarsening spectral resolution (from 1 nm to 200 nm) on the accuracy of soil properties prediction models was studied only from laboratory spectral databases. Castaldi et al. (2016) conducted a study using several laboratory spectral databases to compare the performances of soil texture and soil organic content estimation from present (EO-1 ALI and Hyperion, LANDSAT-8 OLI, SENTINEL-2 MSI) and forthcoming (EnMAP, PRISMA and HyspIRI) multi and hyperspectral sensors. Adeline et al. (2017) used a laboratory spectral database to compare estimation performances of four soil properties (with different spectral absorption features due to their various physico-chemical interactions with soil substrates), clay content, free iron oxides, calcium carbonate and pH, from seven spectral configurations (number of spectral bands decreasing from 328 to 10 and coarsening spectral resolution from 3 nm to 200 nm). Concerning the clay content, Castaldi et al. (2016) and Adeline et al. (2017) demonstrated that coarsening spectral resolution on lab spectra induces a small decrease in prediction model performance, as this soil property has large and pronounced spectral features. Clay content is often used as a tested soil property since its estimation by VNIR/SWIR spectroscopy is driven by both:
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An absorption band centered around 2200 nm, as clay granulometric fractions is correlated to clay minerals which induce an absorption band centered around 2200 nm due to the combination of vibrations associated with the OH bond and the OHAlOH bonds (e.g., Hunt et al., 1971, Chabrillat et al., 2002, Kariuki et al., 2004),
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And the general shape of the spectrum as the particle size influences both spectral intensity and absorption bands depth (Baumgardner et al., 1985, Ben-Dor and Banin, 1995). At fine particle sizes, surface scattering dominates so albedo is high and the expression of absorption features worsens as path length (transmission through particles) in minerals is short. And the more the grain size increases, the more the surface to volume ratio decreases, so albedo decreases and absorption begins to dominate as path length increases in minerals. Thus, spectrum with high content of clay fraction will tend to have higher albedo than spectrum of sandy or loamy soil. Finally, the particle size usually doesn't affect the absorption bands position (Ben-Dor and Banin, 1995).
The present study complements both previous works (Adeline et al., 2017 and Castaldi et al., 2016) by assessing the effect of spectral resolution on clay topsoil property estimation. We simulated both artificial sensors (characterized by regular spectral resolutions) and existing multispectral sensors (characterized by irregular spectral resolutions). All these sensor simulations were based on real airborne hyperspectral VNIR/SWIR data acquired over landscapes at a 5-m spatial resolution (AISA-DUAL hyperspectral sensor). The simulation of sensors allowed us to assess the influence of the spectral resolution on estimated soil property, independently to others specifications (e.g. spatial resolutions, acquisition dates, signal to noise ratio).
Section snippets
Study site
A subarea of the Cap Bon region in northern Tunisia including the catchment named Lebna, was selected as the test area for this study. It covers approximately 300 km2 as pictured in Fig. 1a. For more details, the reader is referred to Gomez et al., 2012b and Gomez et al., 2015. Hereafter, only its main characteristics are included for a better understanding.
This study site was initially chosen for its variety in soil type and soil distribution patterns due to variability in lithology. The soil
Method
A schematic overview of the process required to map the soil clay content using coarsening spectral resolution is displayed in Fig. 2. This process was composed of five steps, described in the following sections: application of a spatial mask (Section 3.1), resampling of the spectra to different configurations (Section 3.2), atmospheric correction (Section 3.3), the band removal (Section 3.4) and the construction of a Partial Least Squares Regression (PLSR) model (Section 3.5). All of the
Preliminary results
A reference PLSR model was built from the AISA-DUAL spectra and observed clay content values associated to the 129 available soil samples. Four spectral outliers were removed from the calibration database of this reference model, and 4 latent variables were selected following the rule of the first local minimum of the RMSECV (Table 4, Fig. 3a). The performance of the model was accurate, with R2cal and R2val values of 0.77 and an RMSEP value of 82 g/kg (Table 4).
This reference PLSR model was
Clay absorption feature as a driver of spectral configuration impact
The presence of the clay absorption feature centered around 2200 nm in the tested spectral configuration seems to be the main driver of clay content prediction performance. From the literature, the width of the clay absorption feature centered around 2200 nm is between 92 nm (Levin et al., 2007) and 126 nm (Lagacherie et al., 2008).
Conclusion
The launch of the forthcoming sensors, in addition to existing ones, will produce an increasing amount of VNIR/SWIR data over the world, and soil surface quality could be mapped over larger areas than it is currently. This work investigated the sensitivity of spectral configurations on clay content estimation and mapping to identify adequate sensor(s) for soil mapping. Performances of the PLSR models built from six simulated sensors (with regular spectral ranges from 5 nm to 200 nm) and four
Acknowledgments
This research was granted by the TOSCA- CNES project “MiHySpecSol - Mission HYPXIM: Impact de la résolution Spectrale pour la cartographie des propriétés pérennes des Sols en milieu Méditerranéen” (2014–2015). This research was also supported by the French National Research Agency (ANR) through the ALMIRA project (ANR-12-TMED-0003). The authors are indebted to UMR LISAH (IRD, France) and to CNCT (Centre National de Cartographie et de Télédétection, Tunisia), for providing the AISA-DUAL
References (58)
- et al.
Predictive ability of soil properties to spectral degradation from laboratory Vis-NIR spectroscopy data
Geoderma
(2017) - et al.
Soil organic carbon mapping of partially vegetated agricultural fields with imaging spectroscopy
Int. J. Appl. Earth Obs. Geoinf.
(2011) - et al.
Prediction of soil attributes by NIR spectroscopy. A critical review of chemometric indicators commonly used for assessing the quality of the prediction
Trac-Trends Anal. Chem.
(2010) - et al.
Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon
Remote Sens. Environ.
(2016) - et al.
Use of hyperspectral images in the identification and mapping of expansive clay soils and the role of spatial resolution
Remote Sens. Environ.
(2002) - et al.
Exploring process data with the use of robust outlier detection algorithms
J. Process Control
(2003) - et al.
Performance of some variable selection methods when multicollinearity is present
Chemom. Intell. Lab. Syst.
(2005) - et al.
Visible-NIR reflectance: a new approach on soil evaluation
Geoderma
(2004) - et al.
Retrieval of apparent surface reflectance from AVIRIS data: a comparison of empirical line, radiative transfer and spectral mixture methods
Remote Sens. Environ.
(1994) - et al.
Partial least-squares regression: a tutorial
Anal. Chim. Acta
(1986)
Regional predictions of eight common soil properties and their spatial structures from hyperspectral Vis–NIR data
Geoderma
Sensitivity of soil property prediction obtained from hyperspectral Vis-NIR imagery to atmospheric effects and degradation in image spatial resolutions
Remote Sens. Environ.
Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements
Remote Sens. Environ.
An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities
Remote Sens. Environ.
Landsat-7 ETM + as an observatory for land cover
Remote Sens. Environ.
Assessment of overland flow variation and blue water production in a farmed semi-arid water harvesting catchment
Phys. Chem. Earth
Applying blind source separation on hyperspectral data for clay content estimation over partially vegetated surfaces
Geoderma
Runoff and water erosion modelling using WEPP on a Mediterranean cultivated catchment
Phys. Chem. Earth
Landsat-8: science and product vision for terrestrial global change research
Remote Sens. Environ.
High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures
Geoderma
Potential of ESA's Sentinel-2 for geological applications
Remote Sens. Environ.
PLS-regression: a basic tool of Chemometrics
Chemom. Intell. Lab. Syst.
ASTER Users Handbook. Version 2. Jet Propulsion Laboratory. 4800 Oak Grove Dr. Pasadena, CA
Sentinel-2 level 1 products and image processing performances
Guide pour la description des sols. INRA édition, Paris
Modified Soil Adjusted Crop Residue Index (MSACRI): a new index for mapping crop residue
Correction to the description of standard normal variate (snv) and de-trend transformations in practical spectroscopy with applications in food and beverage analysis – 2nd edition
J. Near Infrared Spectrosc.
Reflectance properties of soils
Near infrared analysis (NIRA) as a simultaneous method to evaluate spectral featureless constituents in soils
Soil Sci.
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