Visible Near-infrared Reflectance Research Articles

Estimation of Cadmium Content in Lactuca sativa L. Leaves Using Visible–Near-Infrared Spectroscopy Technology

In order to monitor cadmium contamination in lettuce quickly, non-invasively, and accurately, and to understand the growth status of lettuce under cadmium pollution, lettuce was used as the test material to detect and analyze the visible–near-infrared reflectance spectra and leaf cadmium content under different concentrations of cadmium stress. A model for estimating lettuce leaf cadmium content was established. For model establishment, firstly, the original spectra were preprocessed using smoothing (Savitzky–Golay, SG), SG combined with multiplicative scatter correction (MSC), SG combined with standard normal variable transformation (SNV), SG combined with mean normalization (MN), SG combined with the first derivative (FD), SG combined with the second derivative (SD), SG combined with the baseline offset (B), and SG combined with de-trending (D). Then, the principal component analysis (PCA) was applied to perform dimensionality reduction on the data. Finally, the reduced dataset was divided into training and testing sets in a 2:1 ratio, and separate models for estimating the lettuce leaf cadmium content were built using partial least squares regression (PLSR), the backpropagation neural network (BP-NN), and support vector regression (SVR) in combination. The results showed that the accumulated cadmium content in lettuce leaves increased with an increase in the soil cadmium concentration. In the visible light range, the spectral reflectance of lettuce leaves increased with an increase in the cadmium concentration. In the near-infrared range, the spectral reflectance of the lettuce leaves under 10 mg/kg and 20 mg/kg cadmium stress was lower than that of the control group. The PLSR models established using the SG + MSC and SG + SNV preprocessing methods exhibited the strongest estimation capability for lettuce leaf cadmium content, with Rp2 and RMSEp values of 0.92 and 1.53 mg/kg, respectively, for the testing dataset. This study demonstrated that visible–near-infrared spectroscopy has great potential in monitoring cadmium contamination in lettuce.

Experimental transformation of nontronite to kaolinite: implication for ferrallitization of Fe/Mg-rich clays in compositional stratigraphy on Mars

The compositional stratigraphy of Al-rich clays overlying Fe/Mg-rich clays on Mars has been viewed as a window to understanding the atmospheric conditions of early Mars. Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) indicates that nontronite is a main component of Fe/Mg-rich clays. However, the role of the ancient climate in the alteration process, which produced and dissolved these phyllosilicates, remains under debate. The present study experimentally modeled the dissolution of nontronite and precipitation of kaolinite in an acidic solution, which enables us to enhance the interpretation of near-infrared remote sensing data from compositional stratigraphy on Mars. The obtained results show that the transformation started from the losses of both tetrahedral Si and interlayer alkali(earth) cations in nontronite, and Fe3+ in the dioctahedral sheet was released and formed hematite with progressive damage of SiO tetrahedral sheets. TG analyses also show that the dissolution of nontronite and precipitation of kaolinite led to an increase in metal-OH (metal = Al, Fe3+ and Mg) content and a simultaneous decrease in H2O content. Accordingly, the gradual increasing trend of visible/near-infrared reflectance (VNIR) spectra ratios of BD1400/BD1900 (BD, band depth) in the kaolinized nontronite can be linked to a gradual upward transition from nontronite to a mixture of nontronite and kaolinite, and finally into kaolinite and hematite, by comparing their spectral features at different evolution stages. Such a trend suggests a weathering process known as ferrallitization, i.e., partial leaching of Si and enrichment of Fe3+ and Al3+. The observed weathering process is consistent with a warm and wet climate capable of sustaining acidic liquid water on its surface over extended geological periods.

Estimation of Relative Chlorophyll Content in Lettuce (Lactuca sativa L.) Leaves under Cadmium Stress Using Visible—Near-Infrared Reflectance and Machine-Learning Models

Chlorophyll content is a crucial assessment parameter in the growth monitoring of lettuce, particularly in cases when it is affected by disease. Accurate estimation of chlorophyll content is beneficial for early detection and prevention of diseases and holds significant importance in practical production. To construct a model for estimating the chlorophyll content in lettuce leaves under cadmium stress, this study utilized lettuce as the experimental material. The visible–near-infrared reflectance spectra of lettuce leaves, as well as the relative chlorophyll content of the leaves, were detected and analyzed under different concentrations of cadmium stress. Subsequently, an inversion model for estimating the relative chlorophyll content in lettuce leaves was established. First, to determine the optimal spectral preprocessing method, eight techniques are utilized: Savitzky–Golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variable transformation (SNV), mean normalization (MN), baseline offset (B), detrending (D), gap derivatives—first derivative (FD), and gap derivatives—second derivative (SD). These methods are used to preprocess the spectra and establish a partial least squares regression (PLSR) monitoring model. The optimal spectral preprocessing method is then selected. Next, the feature bands are extracted from the preprocessed spectral data using the correlation coefficient method. Finally, the selected feature bands will be combined with support vector regression (SVR) to establish a chlorophyll content estimation model using a training-to-testing set ratio of 4:1. The results showed that the PLSR model established after preprocessing with detrending (D) had the highest accuracy, with the coefficient of determination (Rv2) and root mean squared error (RMSEv) values of 0.87 and 1.16, respectively. The feature bands selected by the correlation coefficient method were used to establish SVR models for estimating the chlorophyll content of lettuce leaves under cadmium stress, with the highest accuracy being achieved by the genetic algorithm (GA)–SVR model. It can be seen that near-infrared spectroscopy technology provides a scientific basis for rapid, nondestructive, and accurate detection of lettuce diseases and stress.

Feasibility of using Vis-NIR spectroscopy and PXRF spectrometry to estimate regional soil cadmium concentration

Conventionally, soil cadmium (Cd) measurements in the laboratory are expensive and time-consuming, involving complex processes of sample preparation and chemical analysis. This study aimed to identify the feasibility of using sensor data of visible near-infrared reflectance (Vis-NIR) spectroscopy and portable X-ray fluorescence spectrometry (PXRF) to estimate regional soil Cd concentration in a time- and cost-saving manner. The sensor data of Vis-NIR and PXRF, and Cd concentrations of 128 surface soils from Yunnan Province, China, were measured. Outer-product analysis (OPA) was used for synthesizing the sensor data and Granger-Ramanathan averaging (GRA) was applied to fuse the model results. Artificial neural network (ANN) models were built using Vis-NIR data, PXRF data, and OPA data, respectively. Results showed that: (1) ANN model based on PXRF data performed better than that based on Vis-NIR data for soil Cd estimation; (2) Fusion methods of both OPA and GRA had higher predictive power (R2) = 0.89, ratios of performance to interquartile range (RPIQ) = 4.14, and lower root mean squared error (RMSE) = 0.06, in ANN model based on OPA fusion; R2 = 0.88, RMSE = 0.06, and RPIQ = 3.53 in GRA model) than those based on either Vis-NIR data or PXRF data. In conclusion, there exists a great potential for the combination of OPA fusion and ANN to estimate soil Cd concentration rapidly and accurately.

A transparent radiative cooling emitter with multi-band spectral regulation for building energy saving

The radiative cooling technology has gained momentum. Maximizing the reflection of incident sunlight has been the primary approach to enhance radiative cooling properties. However, the fully reflection and opacity of the proposed radiative cooling materials significantly limits their application scope. In this study, a transparent radiative cooling emitter was fabricated by optimizing the concentration of SiX nanospheres embedded in PVDF matrix. The emitter has the capability of multi-band spectral regulation, and exhibits outstanding performance in terms of visible light transmission, near-infrared reflectance, and selective infrared emission. In the cooling experiment, the temperature of the emitter was up to 8.5 °C and 15.1 °C lower than with commercial insulation thermal films and common glass, respectively. The emitter prepared by easy process has potential for large-scale production.

Application of visible-near infrared spectroscopy in tandem with multivariate analysis for the rapid evaluation of matcha physicochemical indicators.

Consumer preference for matcha is heavily influenced by its physicochemical properties. The visible-near infrared (Vis-NIR) spectroscopy technology coupled with multivariate analysis was investigated for rapid and non-invasive evaluation of particle size and the ratio of tea polyphenols to free amino acids (P/F ratio) of matcha. The multivariate selection algorithms such as synergy interval (Si), variable combination population analysis (VCPA), competitive adaptive reweighted sampling (CARS), and interval combination population analysis (ICPA) were compared, and eventually, the variable selection strategy of ICPA and CARS hybridization was firstly proposed for selecting the characteristic wavelengths from Vis-NIR spectra to build partial least squares (PLS) models. Results indicated that the ICPA-CARS-PLS models achieved satisfactory performance for the evaluation of matcha particle size (Rp=0.9376) and P/F ratio (Rp=0.9283). Hence the rapid, effectual, and nondestructive online monitoring, Vis-NIR reflectance spectroscopy in tandem with chemometric models is significant for the industrial production of matcha.

Coloration of Polyamide-6, 6 Fabric with Carbon Black Nano Particle for Camouflage Textiles of Simultaneous Spectrum Probe in Visible and Near Infrared

Camouflage textiles on simultaneous spectrum probe in Visible-Near Infrared (Vis-NIR) have been explicated under optical mechanism of reflection and chromatic hue. Polyamide 6,6 (PA-6,6) fabric was experimented for liquid phase formulation and coloration in acidic medium of seven standardized PH (0-6) with Carbon Black Nano Particle (CBNP). International commission on illumination (CIE) color value (L*, a*, b*), Kubelka-Munk (K-M) reflection and reflection (%) of CBNP dyed PA-6,6 fabric was investigated with optical condition of specular reflectance, color measurement spectrophotometer from 400 nm to 700 nm. The K-M reflection and reflection (%) of CBNP dyed PA-6,6 fabric was also measured with optical property of diffuse reflectance, Fourier transform infrared spectroscopy from 1000 nm to 2500 nm. Vis-NIR reflection (%) was observed comparatively lower for PH 4 to 5. Similarly, K-M reflection of simultaneous spectrum probe in Vis-NIR was found higher for PH 4 to 5. Raw CBNP and CBNP modified PA-6,6 fabric was also examined for oxidation property, aggregation property, coloring property and air permeability by scanning electron microscopy. Hence, this experimentation was only focused on color properties of CBNP modified PA-6,6 fabric in terms of camouflage textiles.

Mapping the Spectral and Mineralogical Variability of Lunar Breccia Meteorite NWA 13859 by VNIR Reflectance Spectroscopy

We provide the first visible near-infrared (VNIR) reflectance spectroscopy characterization of a sample from the lunar feldspathic breccia NWA 13859. The sample is a 2 mm thick slab with an approximate area of 35 ± 2 cm2. We characterized the spectroscopic variability by choosing and analyzing representative spots throughout the sample. Our results show a distinct spectral contribution from orthopyroxene, which, according to a preliminary mineralogical characterization, should be a minor phase in the sample. In a second step, we measured several spectra along a transect between a clast and the matrix. In order to oversample the signal, the points analyzed along the sample were partly superimposed to each other (80% areal superposition). The same approach was extended to a grid of points covering an area of about 8.6 cm2, with the goal of obtaining a spatial resolution of the spectral parameters higher than the instrument’s spot size. Using this strategy, we obtained 2D maps of spectral parameters, which allowed us to infer the major mineralogical composition of the sample.

Soil carbon content prediction using multi-source data feature fusion of deep learning based on spectral and hyperspectral images

Visible near-infrared reflectance spectroscopy (VNIR) and hyperspectral images (HSI) have their respective advantages in soil carbon content prediction, and the effective fusion of VNIR and HSI is of great significance for improving the prediction accuracy. But the contribution difference analysis of multiple features in the multi-source data is inadequate, and there is a lack of in-depth research on the contribution difference analysis of artificial feature and deep learning feature. In order to solve the problem, soil carbon content prediction methods based on VNIR and HSI multi-source data feature fusion are proposed. The multi-source data fusion network under the attention mechanism and the multi-source data fusion network with artificial feature are designed. For the multi-source data fusion network based on the attention mechanism, the information are fused through the attention mechanism according to the contribution difference of each feature. For the other network, artificial feature are introduced to fuse multi-source data. The results show that multi-source data fusion network based on the attention mechanism can improve the prediction accuracy of soil carbon content, and multi-source data fusion network combined with artificial feature has better prediction effect. Compared with two single-source data from the VNIR and HSI, the relative percent deviation of Neilu, Aoshan Bay and Jiaozhou Bay based on multi-source data fusion network combined with artificial feature are increased by 56.81% and 149.18%, 24.28% and 43.96%, 31.16% and 28.73% respectively. This study can effectively solve the problem of the deep fusion of multiple features in the soil carbon content prediction by VNIR and HSI, so as to improve the accuracy and stability of soil carbon content prediction, promote the application and development of soil carbon content prediction in spectral and hyperspectral image, and provide technical support for the study of carbon cycle and the carbon sink.

Reconstructing historical time-series of cyanobacteria in lake sediments: Integrating technological innovation to enhance cyanobacterial management

The global rise of cyanobacterial blooms emphasizes the need to develop tools to manage water bodies prone to cyanobacterial dominance. Reconstructing cyanobacterial baselines and identifying environmental drivers that favour cyanobacterial dominance are important to guide management decisions. Conventional techniques for estimating cyanobacteria in lake sediments require considerable resources, creating a barrier to routine reconstructions of cyanobacterial time-series. Here, we compare a relatively simple technique based on spectral inferences of cyanobacteria using visible near-infrared reflectance spectroscopy (VNIRS) with a molecular technique based on real-time PCR quantification (qPCR) of the 16S rRNA gene conserved in cyanobacteria in 30 lakes across a broad geographic gradient. We examined the sedimentary record from two perspectives: 1) relationships throughout the entire core (without radiometric dating); 2) relationships post-1900s with the aid of radiometric dating (i.e., 210Pb). Our findings suggest that the VNIRS-based cyanobacteria technique is best suited for reconstructing cyanobacterial abundance in recent decades (i.e., circa 1990 onwards). The VNIRS-based cyanobacteria technique showed agreement with those generated using qPCR, with 23 (76%) lakes showing a strong or very strong positive relationship between the results of the two techniques. However, five (17%) lakes showed negligible relationships, suggesting cyanobacteria VNIRS requires further refinement to understand where VNIRS is unsuitable. This knowledge will help scientists and lake managers select alternative cyanobacterial diagnostics where appropriate. These findings demonstrate the utility of VNIRS, in most instances, as a valuable tool for reconstructing past cyanobacterial prevalence.

Digital morphometrics and genesis of soils with buried horizons and lithological discontinuities in southeastern Brazil

Soil morphometrics aims to measure attributes observed in soil profiles. The use of digital technologies in the morphological study of soil profiles has yet to be further explored by the scientific community. Particularly, soils with buried horizons, which are common in lowlands and floodplains throughout the world, have never been examined using digital morphometrics. This study aimed to examine the morphology and genesis of three soil profiles with buried horizons in Brazil using digital soil morphometrics combined with wet-chemistry and mineralogical analyses (including X-ray diffraction – XRD). Digital analyses used three proximal sensors – portable X-ray fluorescence spectrometry (pXRF), visible near-infrared (Vis-NIR) and mid-infrared (MIR) diffuse reflectance spectrometry. The soils had buried horizons and were classified as Typic Udifolists (TU), Typic Dystrustepts (TD) and Typic Endoaquents (TE). Each soil profile was sampled in a 15 × 15 cm grid, with 40 samples per profile. All sensors identified the buried soil horizons. Vis-NIR and MIR identified the presence of several key minerals and variations in soil organic matter (SOM) that were not observed in the whole soil pits. PXRF captured discontinuities in the parent material, not only between deposited and buried horizons, but also within horizons. The deposited material above TU and TE was derived from neighboring Fe-rich Oxisols and Ultisols, as shown by high Fe content and absorption features of Fe oxides. Sensor analyses were quick and environmentally friendly. Proximal sensor data enhanced morphological assessment of soil profiles, and provided insights otherwise very difficult or even impossible with only traditional and visual methods.

Optimal zinc level and uncertainty quantification in agricultural soils via visible near-infrared reflectance and soil chemical properties

Zinc (Zn) is a vital element required by all living creatures for optimal health and ecosystem functioning. Therefore, several researchers have modeled and mapped its occurrence and distribution in soils. Nonetheless, leveraging model predictive performances while coupling information derived from visible near-infrared (Vis-NIR) and soils (i.e. chemical properties) to estimate potential toxic elements (PTEs) like Zn in agricultural soils is largely untapped. This study applies two methods to rapidly monitor Zn concentration in agricultural soil. Firstly, employing Vis-NIR and machine learning algorithms (MLAs) (Context 1) and secondly, applying Vis-NIR, soil chemical properties (SCP), and MLAs (Context 2). For the Vis-NIR information, single and combined pretreatment methods were applied. The following MLAs were used: conditional inference forest (CIF), partial least squares regression (PLSR), M5 tree model (M5), extreme gradient boosting (EGB), and support vector machine regression (SVMR) respectively. For context 1, the results indicated that M5-MSC (M5 tree model-multiplicative scatter correction) with coefficient of determination (R2) = 0.72, root mean square error (RMSE) = 21.08 (mg/kg), median absolute error (MdAE) = 13.69 and ratio of performance to interquartile range (RPIQ) = 1.63 was promising. Regarding context 2, CIF with spectral pretreatment and soil properties [CIF-DWTLOGMSC + SCP (conditional inference forest-discrete wavelet transformation-logarithmic transformation-multiplicative scatter correction-soil chemical properties)] yielded the best performance of R2 = 0.86, RMSE = 14.52 (mg/kg), MdAE = 6.25 and RPIQ = 1.78. Altogether, for contexts 1 and 2, the CIF-DWTLOGMSC + SCP approach (context 2) was the best Zn model outcome for the agricultural soil. The uncertainty map revealed a low to high error distribution in context 1, and a low to moderate distribution in context 2 for all models except CIF, which had some patches with high uncertainty. We conclude that a multiple optimization approach for modeling Zn levels in agricultural soils is invaluable and may provide fast and reliable information needed for area-specific decision-making.

Occluded object detection and exposure in cluttered environments with automated hyperspectral anomaly detection.

Cluttered environments with partial object occlusions pose significant challenges to robot manipulation. In settings composed of one dominant object type and various undesirable contaminants, occlusions make it difficult to both recognize and isolate undesirable objects. Spatial features alone are not always sufficiently distinct to reliably identify anomalies under multiple layers of clutter, with only a fractional part of the object exposed. We create a multi-modal data representation of cluttered object scenes pairing depth data with a registered hyperspectral data cube. Hyperspectral imaging provides pixel-wise Visible Near-Infrared (VNIR) reflectance spectral curves which are invariant in similar material types. Spectral reflectance data is grounded in the chemical-physical properties of an object, making spectral curves an excellent modality to differentiate inter-class material types. Our approach proposes a new automated method to perform hyperspectral anomaly detection in cluttered workspaces with the goal of improving robot manipulation. We first assume the dominance of a single material class, and coarsely identify the dominant, non-anomalous class. Next these labels are used to train an unsupervised autoencoder to identify anomalous pixels through reconstruction error. To tie our anomaly detection to robot actions, we then apply a set of heuristically-evaluated motion primitives to perturb and further expose local areas containing anomalies. The utility of this approach is demonstrated in numerous cluttered environments including organic and inorganic materials. In each of our four constructed scenarios, our proposed anomaly detection method is able to consistently increase the exposed surface area of anomalies. Our work advances robot perception for cluttered environments by incorporating multi-modal anomaly detection aided by hyperspectral sensing into detecting fractional object presence without need for laboriously curated labels.

Detection and Relative Quantification of Neodymium in Sillai Patti Carbonatite Using Decision Tree Classification of the Hyperspectral Data.

A recent increase in the importance of Rare Earth Elements (REEs), proportional to advancements in modern technology, green energy, and defense, has urged researchers to look for more sophisticated and efficient exploration methods for their host rocks, such as carbonatites. Hyperspectral remote sensing has long been recognized as having great potential to identify the REEs based on their sharp and distinctive absorption features in the visible near-infrared (VNIR) and shortwave infrared (SWIR) electromagnetic spectral profiles. For instance, neodymium (Nd), one of the most abundant Light Rare Earth Elements (LREEs), has among the most distinctive absorption features of REEs in the VNIR part of the electromagnetic spectrum. Centered at ~580, ~745, ~810, and ~870 nm in the VNIR, the positions of these absorption features have been proved to be independent of the mineralogy that hosts Nd, and the features can be observed in samples as low in Nd as 1000 ppm. In this study, a neodymium index (NI) is proposed based on the 810 nm absorption feature and tested on the hyperspectral images of the Sillai Patai carbonatite samples to identify Nd pixels and to decipher the relative concentration of Nd in the samples based on the depth of the absorption feature. A preliminary spectral study of the carbonatite samples was carried out using a spectroradiometer to determine the presence of Nd in the samples. Only two of the absorption features of Nd, centered at ~745 and ~810 nm, are prominent in the Nd-rich samples. The other absorption features are either weak or suppressed by the featureless spectra of the associated minerals. Similar absorption features are found in the VNIR and SWIR images of the rock samples captured by the laboratory-based hyperspectral cameras that are processed through Minimum Noise Fraction (MNF) and Fast Fourier Transform (FFT) to filter the signal and noise from the reflectance data. An RGB false-color composite of continuum-removed VNIR reflectance bands covering wavelengths of 587.5, 747.91, and 810.25 nm efficiently displayed the spatial distribution of Nd-rich hotspots in the hyperspectral image. The depth of the 810 nm absorption feature, which corresponds to the concentration of Nd in a pixel, is comparatively greater in these zones and is quantified using the proposed NI such that the deeper the absorption feature, the higher the NI. To quantify the Nd-rich pixels in the continuum-removed VNIR images, different threshold values of NI are introduced into a decision tree classifier which generates the number of pixels in each class. The strength of the proposed NI coupled with the decision tree classifier is further supported by the accuracy assessment of the classified images generating the Kappa coefficient of 0.82. Comparing the results of the remote sensing data obtained in this study with some of the previously published studies suggests that the Sillai Patti carbonatite is rich in Nd and associated REEs, with some parts of the samples as high in Nd concentration as 1000 ppm.

Average VNIR reflectance: A rapid, sample-free method to estimate glass content and crystallinity of fresh basaltic lava

The microcrystalline texture in basaltic lava, scoria, and spatter can vary widely from pure glass to holocrystalline due to complex cooling histories after eruption. How quickly a molten rock cools is a function of the environmental surroundings, including water, ice, sustained heat source, and atmospheric conditions. Thus, petrologic texture serves as an indicator of cooling history captured in the rock record. As basalt is a common component of terrestrial bodies across the solar system, relating the abundance of crystalline components to spectral character would allow for a more thorough understanding of the cooling history and emplacement conditions on planetary surfaces. Visible/near-infrared (VNIR) reflectance spectroscopy has been used to examine the absorptions associated with volcanic glass, however, the non-linearity of absorption features in this spectral region requires complex spectral unmixing modeling to achieve modal percentages of minerals. Here we present evidence that average reflectance from 500 to 1000 nm (referred to as R500–1000) of solid surface samples is indicative of the crystal texture and degree of glassiness of basaltic rocks. Several factors, such as sample surface roughness, lichen cover, coatings, weathering, and chemical composition can affect the R500–1000 of a sample. However, our data indicate that these factors can be sufficiently controlled during sample selection to attribute relative glassiness values to basaltic surfaces. This quick and straightforward method requires no sample preparation or modeling and is demonstrated with training data from sixteen rocks from five basaltic flow fields with differing mineralogy, surface qualities, and geochemistry across Idaho and Oregon, USA. We further test our relationship with two published datasets of synthetic and natural basalts, as well as a subset of our own data collected with our methods to examine the sensitivities of the correlation. This method has the potential to broadly identify glassier basaltic lavas across planetary surfaces. This could be applied toward understanding lava eruption temperatures, cooling rates, magma petrogenesis, paleoclimate reconstruction, and astrobiology due to the involvement of water in quenching of lava.

Visible to Mid‐Infrared Optical Constants of Orthopyroxenes

AbstractRadiative transfer models of remotely acquired infrared spectra result in quantitative identification of minerals on planetary surfaces. Optical constants, or the real (n) and imaginary (k) indices of refraction are necessary inputs in such models. Pyroxenes are ubiquitous on the surfaces of terrestrial bodies within our solar system and can be readily used as thermo‐barometers to interpret magmatic histories. However, optical constants for intermediate pyroxene compositions are undetermined. Here, we have determined the optical constants of two natural orthopyroxenes both in the visible/near‐infrared (VNIR) and mid‐infrared (MIR) regions. VNIR reflectance spectra were measured using powdered samples and modeled using a combination of Hapke theory and Kramers‐Kronig analysis. MIR reflectance spectra were measured on oriented single crystal samples with non‐normal incidence and modeled using Lorentz‐Lorenz dispersion theory. The optical constants derived here are available to the scientific community at https://doi.org/10.5281/zenodo.4758045 to be used in the interpretation of remote sensing data.

Crystallinity effects on the vibrational spectral features of saponite: Implications for characterizing variable crystalline phyllosilicates on Mars

X-ray diffraction patterns of Martian mudstones acquired by Chemistry & Mineralogy X-Ray Diffraction (CheMin) aboard Mars Science Laboratory (MSL) suggest that the smectites detected in Gale crater have poor crystallinity. This finding poses an urgent question about the structural ordering of phyllosilicates found globally by Visible/Near-Infrared (VIS-NIR) spectroscopic orbital remote sensing on Mars, linked to their formation conditions. In this study, we synthesized saponite (Nz+x/z[M6][Si8-xAlx]O20(OH)4·nH2O, where M and N correspond to the divalent octahedral cations and the interlayer cations, respectively) with variable crystallinity, which bear structural similarities to the smectite discovered at Gale crater. Synthetic saponite was characterized using Field Emission Scanning Electron Microscopy (FE-SEM), powder X-ray diffraction (XRD), then studied the spectral features of these samples using nuclear magnetic resonance spectroscopy (NMR), Raman spectroscopy, and VIS-NIR reflectance spectroscopy. Our study revealed that the crystallinity of these saponite samples increased (as shown in the FWHM of XRD (060) peak), and is accompanied by (1) improved T-O-T layer stacking along the c axis (specified by the intensity and width of the XRD (001) peak); (2) increased uniformity of the SiO4 unit in tetrahedral sheets (based on the peak widths of Raman spectra and the well-resolved 29Si NMR peaks); (3) improved regularity in the distribution of Mg2+ in octahedral sites and thus the regularity of metal-OH bonds in octahedral sheet (based on the resolution of the NIR 2.2–2.4 μm band and the peak width of the XRD (060,330) peak); and (4) the increased Al in tetrahedral sites and decrease of Al in octahedral sites (the width of 27Al NMR peak), the crystallinity of saponite raised. Based on experimental observations, the first derivative spectra of metal-OH absorptions were proposed as a crystallinity index for smectite on Mars. Our results indicate that different vibration spectroscopy techniques can constrain the structural ordering of smectite on Mars and provide insight into their formation conditions.

Visible/near infrared reflectance (VNIR) spectral features of ion-exchangeable Rare earth elements hosted by clay minerals: Potential use for exploration of regolith-hosted REE deposits

Regolith-hosted ion-exchangeable rare earth element (REE) mineralization in South China dominate the global source of heavy REEs. The lack of studies on spectral features of ion-exchangeable REEs limits the application of visible/near infrared reflectance (VNIR, 350–2500 nm) spectroscopy for rapid identification of regolith-hosted REE deposits in prospective areas. This paper presents a systematic study on VNIR spectral features of ion-exchangeable REEs hosted by a series of synthetic REE-adsorbed clay minerals and natural regolith samples. The results show that ion-exchangeable Nd3+, Dy3+, Ho3+, Er3+ and Tm3+ display diagnostic absorption features at the bands of 730–870, 805, 641, 652, and 684 nm. Specifically, the Nd3+ related absorption bands of regolith samples at ~800 nm show a shoulder at ~802 nm that are distinguishable from unweathered primary REE(−bearing) minerals. The maxima of second derivative curves derived from the Nd3+ absorption at ~802 nm, M802_2nd, can be used to quantify variations of ion-exchangeable REEs in regoliths. Nevertheless, the absorption features of REE3+ ions could be subdued due to an overlapping problem caused by the broad absorption bands of Fe-oxides in the range of 700–900 nm. The obtained VNIR spectra of ion-exchangeable REEs provide critical supplement to spectral library for REE-bearing minerals. This study demonstrates that field-based VNIR spectroscopy can be deployed as a powerful tool for regional and deposit-scale exploration of regolith-hosted ion-exchangeable REE resources.

Orientation Averaged Visible/Near‐Infrared and Mid‐Infrared Optical Constants of Hydrous Ca‐Sulfates: Gypsum and Bassanite

AbstractThe hydrous Ca‐sulfates, gypsum and bassanite, have been detected on Mars using both visible/near‐infrared (VNIR) remote sensing measurements and in‐situ rover‐based mineralogical measurements. Understanding the distributions and abundances of minerals on the Martian surface from remote sensing data is important in evaluating ancient Martian environments. Optical constants are critical input parameters in radiative transfer theory, which allows for the extraction of mineral abundances from a mixture spectrum. We have derived VNIR (0.35–2.5 μm, at 10 nm spectral sampling) optical constants of gypsum and bassanite powders using radiative transfer theory and laboratory reflectance spectra of separated size fractions as well as multiple phase angles. In addition, the MIR (2.5–25 μm, or 4,000–400 cm−1, at 2 cm−1 spectral sampling) orientation averaged optical constants of gypsum and bassanite were generated using the classical Lorentz‐Lorenz dispersion theory. We compare the optical constants of gypsum derived in this work with previously published estimates and note both similarities and differences. The optical constants derived in this work will assist in modeling VNIR reflectance spectra and quantifying mineral abundances from mineral mixtures.

Rapid prediction of soil available sulphur using visible near-infrared reflectance spectroscopy

Rapid and accurate prediction of soil available S, an important secondary nutrient, is crucial for its site-specific management in a cultivated region. Although traditional chemical analysis of any nutrient is an accurate method, but often costly, time-consuming and destructive in nature. Recently visible near-infrared (VIS-NIR) reflectance spectroscopic technique has gained its popularity for rapid, non-destructive and cost-effective assessment of soil nutrients. Hence, a study was carried out in an intensively cultivated region of Katol block of Nagpur, Maharashtra, during 2018-20 for rapid prediction of soil available S using spectroscopic technique. Both spectroscopic and chemical analyses were carried out using 132 georeferenced surface soil samples (0-15 cm depth). The descriptive statistical analysis showed that the available S content varied from 1.09 to 47.88 mg/kg. Multivariate models namely partial least square regression (PLSR) and random forest (RF) were applied to develop spectral models for S prediction from spectral dataset. Several statistical diagnostics like coefficient of determination (R2), root mean square error (RMSE), ratio of performance deviation (RPD) and ratio of performance to interquartile distance (RPIQ) were used to evaluate the performances of two models. The best prediction of S was achieved from nonlinear RF model (R2 = 0.71, RMSE = 8.86, RPD =1.18, RPIQ = 1.69) as compared to linear PLSR model (R2 = 0.53, RMSE = 9.04, RPD = 1.16, RPIQ = 1.66) datasets. Therefore, the result suggested applying non-linear multivariate model (RF) for obtaining best predictability for S from spectroscopic technique.