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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., Graphical Abstract

Reflection spectroscopy, in the visible–near-infrared–shortwave infrared region (Vis-NIR-SWIR, 350–2500 nm), is a useful technology to extract chemical and physical properties of materials, but might be useless in identifying the spectral features of transparent or dark opaque liquids. Low reflectance values of a liquid reduce the ability to identify characteristic absorption features at specific wavelengths in the reflectance spectrum. In this study, we present a rapid and easy-to-use method to increase the measured reflectance spectrum and expose characteristic absorption features of a liquid. This was done by mixing the liquid with a white enhanced substance (WES). For this purpose, we used aluminum oxide (Al2O3) powder—a very bright (high albedo) substance and featureless across the entire Vis-NIR-SWIR region. The reflectance spectrum of the mixture—liquid and WES—was measured using a spectroradiometer. This procedure enabled to identify characteristic spectral features of the liquids that would have not been observed in the reflectance spectrum measured from the liquid alone.

Petroleum hydrocarbons are contaminants of great significance. The commonly used analytic method for assessing total petroleum hydrocarbons (TPH) in soil samples is based on extraction with 1,1,2-Trichlorotrifluoroethane (Freon 113), a substance prohibited to use by the Environmental Protection Agency. During the past 20 years, a new quantitative methodology that uses the reflected radiation of solids has been widely adopted. By using this approach, the reflectance radiation across the visible, near infrared–shortwave infrared region (400–2500 nm) is modeled against constituents determined using traditional analytic chemistry methods and then used to predict unknown samples. This technology is environmentally friendly and permits rapid and cost-effective measurements of large numbers of samples. Thus, this method dramatically reduces chemical analytical costs and secondary pollution, enabling a new dimension of environmental monitoring. In this study we adapted this approach and developed effective steps in which hydrocarbon contamination in soils can be determined rapidly, accurately, and cost effectively solely from reflectance spectroscopy. Artificial contaminated samples were analyzed chemically and spectrally to form a database of five soils contaminated with three types of petroleum hydrocarbons (PHCs), creating 15 datasets of 48 samples each at contamination levels of 50–5000 wt% ppm (parts per million). A brute force preprocessing approach was used by combining eight different preprocessing techniques with all possible datasets, resulting in 120 different mutations for each dataset. The brute force was done based on an innovative computing system developed for this study. A new parameter for evaluating model performance scoring (MPS) is proposed based on a combination of several common statistical parameters. The effect of dividing the data into training validation and test sets on modeling accuracy is also discussed. The results of this study clearly show that predicting TPH levels at low concentrations in selected soils at high precision levels is viable. Dividing a dataset into training, validation, and test groups affects the modeling process, and different preprocessing methods, alone or in combination, need to be selected based on soil type and PHC type. MPS was found to be a better parameter for selecting the best performing model than ratio of prediction to deviation, yielding models with the same performance but less complicated and more stable. The use of the “all possibilities” system proved to be mandatory for efficient optimal modeling of reflectance spectroscopy data.

A group of 91 soils from arid-zone regions were used to develop a nondestructive near-infrared analysis (NIRA) procedure for estimating carbonate concentration in soils. Carbonate concentration was measured gasometrically and reflectance in the NIR was measured by a high-resolution FT-NIR instrument. The reflectance data were converted to absorption and derivatized, and statistical procedures were applied to obtain the multivariate regression equation between the various manipulated optical data and carbonate concentration. Reflectance from preheated (600°C) and nonheated soils was compared to test the ability to remove spectral interferences caused by hydroxyl groups. Forty-three soils were used for calibration, and subsets were selected by different strategies which either maximized variation of soil properties (as measured by their specific surface area) in the selected set, or minimized it. A validation set of 48 soils was used to test the prediction-ability of the various calibration equations obtained. Most of the wavelengths entered into the calibration equations were spectroscopically explainable as belonging to carbonate absorption features. Satisfactory prediction was obtained, however, in only one combination of conditions (heated soils; 21 samples in calibration set; second derivative of absorption). Standard error of prediction (SEP) was similar to standard error of calibration (SEC) in this case (7.9% and 5.9%, respectively) if 7 samples belonging to one soil type (Xerochrept) were omitted from the validation set. We conclude that carbonate in soils in the concentration range of 10 to 75% can be satisfactorily estimated for pedological and soil classification purposes by the rapid and nondestructive NIRA procedures.