Estimation of the Relative Abundance of Quartz to Clay Minerals Using the Visible–Near-Infrared–Shortwave-Infrared Spectral Region

Abstract

Quartz is the most abundant mineral on the earth’s surface. It is spectrally active in the longwave infrared (LWIR) region with no significant spectral features in the optical domain, i.e., visible–near-infrared–shortwave-infrared (Vis–NIR–SWIR) region. Several space agencies are planning to mount optical image spectrometers in space, with one of their missions being to map raw materials. However, these sensors are active across the optical region, making the spectral identification of quartz mineral problematic. This study demonstrates that indirect relationships between the optical and LWIR regions (where quartz is spectrally dominant) can be used to assess quartz content spectrally using solely the optical region. To achieve this, we made use of the legacy Israeli soil spectral library, which characterizes arid and semiarid soils through comprehensive chemical and mineral analyses along with spectral measurements across the Vis–NIR–SWIR region (reflectance) and LWIR region (emissivity). Recently, a Soil Quartz Clay Mineral Index (SQCMI) was developed using mineral-related emissivity features to determine the content of quartz, relative to clay minerals, in the soil. The SQCMI was highly and significantly correlated with the Vis–NIR–SWIR spectral region (R2 = 0.82, root mean square error (RMSE) = 0.01, ratio of performance to deviation (RPD) = 2.34), whereas direct estimation of the quartz content using a gradient-boosting algorithm against the Vis–NIR–SWIR region provided poor results (R2 = 0.45, RMSE = 15.63, RPD = 1.32). Moreover, estimation of the SQCMI value was even more accurate when only the 2000–2450 nm spectral range (atmospheric window) was used (R2 = 0.9, RMSE = 0.005, RPD = 1.95). These results suggest that reflectance data across the 2000–2450 nm spectral region can be used to estimate quartz content, relative to clay minerals in the soil satisfactorily using hyperspectral remote sensing means.

In Situ VIS-NIR Spectroscopy for a Basic and Rapid Soil Investigation

“…All models failed to predict sand, which is often difficult to model since quartz is nearly transparent to the NIR radiation and, therefore, presents no significant features in the VIS-NIR range investigated here [ 50 ]. Though the effect of temperature is documented on the VIS-NIR spectra [ 28 , 51 ], there are very few studies that take into account the temperature when modelling.…”

Strategies for the Efficient Estimation of Soil Moisture through Spectroscopy: Sensitive Wavelength Algorithm, Spectral Resampling and Signal-to-Noise Ratio Selection

“…The technology for selecting feature wavelengths can effectively eliminate the phenomenon of spectral information redundancy caused by the large amount of soil hyperspectral data, and it can improve the stability and prediction ability of the soil moisture content inversion model [24][25][26][27]. Since the wavelength selection algorithm proposed in this study is based on the soil radiative transfer model and verified by experimental sample data, the wavelengths selected in this study are effective for large-scale prediction of soil moisture content.…”

Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy

“…To recognize what soil properties with spectral assignments might contribute to the spectral modeling, we followed Ben-Dor's [14] and Viscarra, Rossel, and Behrens' [44] references. Then, these spectral assignments were compared against the feature importance product of the models [12,15,45].…”

“…Reflectance spectroscopy in the visible-near-infrared-shortwave-infrared (VNIR/ SWIR) spectral region across the 400-2450 nm range is a low-cost, rapid, and effective technology for obtaining information on minerals and soil attributes through the creation of spectral-based models [14,15]. As a result, several soil spectral libraries (SSLs) have been developed to estimate soil properties, including soil textural classes (sand, silt, clay), soil organic matter (SOM), calcium carbonates, cation-exchange capacity, and pH, among others [3,[15][16][17][18][19][20]. The spectral-based prediction models can be used to characterize the soil composition via aerial and satellite imaging spectrometers, allowing for the spatial representation of soil properties found in SSLs without expensive and time-consuming laboratory analyses [3].…”