Visible Near-Infrared Short Wave Infrared Research Articles

Estimation of Malondialdehyde Content in Medicago truncatula under Salt Stress Based on Multi-Order Spectral Transformation Characteristics

Salt stress is a significant abiotic factor affecting the growth and development of alfalfa. Malondialdehyde (MDA) serves as a critical biomarker for assessing alfalfa’s salt tolerance. Traditional methods for measuring MDA are often time-consuming and labor-intensive. Recent advances in remote sensing technology have made non-destructive estimation of metabolites feasible, positioning the accurate estimation of MDA content in alfalfa as a key focus in intelligent breeding. To address the challenge of detecting subtle changes in MDA content, this study developed a partial least squares regression (PLSR) model specifically for Medicago truncatula. This study utilized leaf reflectance hyperspectral data across the visible near-infrared–shortwave infrared (VIR-NIR-SWIR) spectrum, applying multi-order spectral transformation methods, including continuous wavelet transform (CWT), fractional differential (FD), and multi-granularity spectral segmentation (MGSS). Feature selection techniques, such as sequential forward selection (SFS), Least-Squares Boosting (LSBoost), and feature selection using neighborhood component analysis for regression (FSRNCA), were employed to enhance the efficiency of the MDA estimation. The findings revealed that the optimal PLSR model for MDA estimation was achieved by integrating CWT features across orders 1–30 with the SFS method. This model demonstrated robust estimation capabilities under varying salt stress conditions, significantly outperforming the original spectral data (R2 = 0.654, RMSE = 22.567 vs. R2 = 0.242, RMSE = 33.411). A comparative analysis of feature selection methods confirmed that SFS was the most effective for estimating MDA content in alfalfa. These results provide valuable insights and methodologies for MDA estimation and evaluating salt tolerance in alfalfa.

Combined Scanned Macro X-Ray Fluorescence and Reflectance Spectroscopy Mapping on Corroded Ancient Bronzes

Bronze is an alloy composed primarily of copper and tin and since its discovery is widespread in the whole world. This alloy can thus be found in many archaeological sites and its study can give information about the technology of production, the trading routes, or the warfare within a region. However, bronze artefacts can undergo severe alteration processes, and the formation of corrosion layers of different copper minerals can prevent the readability of the artefact or even destroy it, as in the case of the ‘bronze disease’. Their preservation is crucial for maintaining a connection to our cultural heritage. In this paper, we present the study of some corroded bronze artefacts found in different burying conditions. They have been analysed through a scanner system that combines two non-invasive techniques, macro XRF (MA-XRF) and visible, near infrared, short wave infrared (VIS-NIR-SWIR) reflectance, to unravel information about the metal and the patina composition, thickness, and distribution. As the corrosion of bronze depends on the burying conditions and the alloy composition, these data are of the utmost importance to understanding the alteration processes occurring in the archaeological site and to ensure the artefacts’ optimal preservation.

Further to quantification of content, can reflectance spectroscopy determine the speciation of cobalt and nickel on a mine waste dump surface?

Toxic elements released due to mining activities are of the most important environmental concerns, characterised not only by their concentration, but also by their distribution among different chemical species, known as speciation. These are conventionally determined using chemical analysis and sequential extraction, which are expensive and time-demanding. In this study, the possibility of using visible–near-infrared–shortwave infrared (VNIR–SWIR) reflectance spectroscopy was investigated as an alternative technique to quantify the contents of cobalt (Co) and nickel (Ni) in soil samples collected from Sarcheshmeh copper mine waste dump surface, in Iran. As a novel approach, the capability of VNIR–SWIR spectroscopy was also investigated in speciation of those elements. Three machine learning (ML) techniques (i.e., extreme gradient boosting (EGB), random forest (RF) and support vector regression (SVR)) were used to make relationships between soil spectral responses and Co and Ni contents of the samples. For all ML algorithms, the best prediction accuracies were obtained by the models developed on the first derivative (FD) spectra (for Co: RMSEp values of 7.82, 8.03 and 9.22 mg·kg−1, and for Ni: RMSEp values of 9.88, 10.32 and 11.02 mg·kg−1, using EGB, RF and SVR, respectively). Spatial variability maps of elements showed relatively similar patterns between observed and predicted values. Correlation and ML (EGB, RF, SVR)-based methods revealed that the most important wavelengths for Co and Ni prediction were those related to iron oxides/hydroxides and clay minerals, as two main soil properties responsible for controlling their speciation. This study demonstrated that the EGB technique was successful at indirect quantification and spatial variability mapping of Co and Ni on the mine waste dump surface. In addition, it provided an inspiration for implementation of the VNIR–SWIR reflectance spectroscopy as a potentially fast and cost-effective method for speciation studies of toxic elements, especially in heterogeneous soil environments.

Logistic splicing correction for VNIR-SWIR reflectance imaging spectroscopy.

In the field of spectroscopy, a splicing correction is a process by which two spectra captured with different sensors in adjacent or overlapping electromagnetic spectrum ranges are smoothly connected. In our study, we extend this concept to the case of reflectance imaging spectroscopy in the visible-near-infrared (VNIR) and short-wave infrared (SWIR), accounting for additional sources of noise that arise at the pixel level. The proposed approach exploits the adaptive fitting of a logistic function to compute correcting coefficients that harmonize the two spectral sets. This short Letter addresses usage conditions and compares results against the existing state of the art.

Classification of Plant Endogenous States Using Machine Learning-Derived Agricultural Indices.

Leaf color patterns vary depending on leaf age, pathogen infection, and environmental and nutritional stresses; thus, they are widely used to diagnose plant health statuses in agricultural fields. The visible-near infrared-shortwave infrared (VIS-NIR-SWIR) sensor measures the leaf color pattern from a wide spectral range with high spectral resolution. However, spectral information has only been employed to understand general plant health statuses (e.g., vegetation index) or phytopigment contents, rather than pinpointing defects of specific metabolic or signaling pathways in plants. Here, we report feature engineering and machine learning methods that utilize VIS-NIR-SWIR leaf reflectance for robust plant health diagnostics, pinpointing physiological alterations associated with the stress hormone, abscisic acid (ABA). Leaf reflectance spectra of wild-type, ABA2-overexpression, and deficient plants were collected under watered and drought conditions. Drought- and ABA-associated normalized reflectance indices (NRIs) were screened from all possible pairs of wavelength bands. Drought associated NRIs showed only a partial overlap with those related to ABA deficiency, but more NRIs were associated with drought due to additional spectral changes within the NIR wavelength range. Interpretable support vector machine classifiers built with 20 NRIs predicted treatment or genotype groups with an accuracy greater than those with conventional vegetation indices. Major selected NRIs were independent from leaf water content and chlorophyll content, 2 well-characterized physiological changes under drought. The screening of NRIs, streamlined with the development of simple classifiers, serves as the most efficient means of detecting reflectance bands that are highly relevant to characteristics of interest.

Geostatistical Modelling of Soil Spatial Variability by Fusing Drone-Based Multispectral Data, Ground-Based Hyperspectral and Sample Data with Change of Support

Traditional soil characterization methods are time consuming, laborious and invasive and do not allow for long-term repeatability of measurements. The overall aim of this paper was to assess and model spatial variability of the soil in an olive grove in south Italy by using data from two sensors of different types: a multi-spectral on-board drone radiometer and a hyperspectral visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance radiometer, as well as sample data, to arrive at a delineation of homogeneous areas. The hyperspectral data were processed using Continuum Removal (CR) methodology to obtain information about the content and composition of clay. Differently, the multispectral data were firstly upscaled to the support of soil data using geostatistics and taking into account the change of support. Secondly, the data acquired with the two different sensors were integrated with soil granulometric properties by using two multivariate geostatistical techniques: multi-collocated cokriging to achieve a more exhaustive and finer-scale soil characterization, and multi-collocated factor cokriging to extract synthetic scale-dependent indices (regionalized factors) for the delineation of soil in homogeneous zones. This paper shows the impact of change of support on the uncertainty of soil prediction that can have a significant effect on decision making in Precision Agriculture. Moreover, four regionalized factors at two different scales (two for each scale) were retained and mapped. Each factor provided a different delineation of the field with areas characterized by different granulometries and clay compositions. The applied method is sufficiently flexible and could be applied to any number and type of sensors.

Snow surface properties derived from PRISMA satellite data over the Nansen Ice Shelf (East Antarctica)

In this paper, we made use of PRISMA imaging spectroscopy data for retrieving surface snow properties in the Nansen Ice Shelf (East Antarctica). PRISMA satellite mission has been launched in 2019 and it features 239 spectral bands covering the 400-2500 nm interval. These data are promising for cryospheric applications, since several snow and ice parameters can be derived from reflectance in the Visible Near InfraRed - Short Wave InfraRed (VNIR-SWIR) wavelength interval. Here we analyze, for the first time, PRISMA data collected in Antarctica. Our scene was acquired on December 2020 over the Nansen Ice Shelf (NIS). Using PRISMA data we estimated various snow parameters (effective grain diameter, snow specific surface area, snow spectral and broadband albedo, bottom of atmosphere snow reflectance, type of impurities in snow and their concentration), and we compared them with data presented in the scientific literature.

Potential of GPR data fusion with hyperspectral data for precision agriculture of the future

Precision Agriculture (PA) requires accurate spatial and temporal information of soil properties at a very fine scale. Traditional soil characterization methods are time consuming, laborious and invasive and do not allow long-term repeatability of measurements. Ground Penetrating Radar (GPR) appears to be a particularly suitable methodology for characterizing soil and subsurface from a physical property point of view. Visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance spectroscopy has now become a widespread technique in soil analysis. Information on soil variability can be improved by the integration of data from multiple sensors. The overall objective of this paper was to examine the potential of fusing GPR data with hyperspectral data using multivariate geostatistics for delineating the management zones in the soil of an olive grove of centuries-old trees in Italy.A linear model of coregionalization (LMC) was individually fitted for the raw hyperspectral data and for GPR data including for each case a nugget effect and two spherical models at short scale and at longer scale. After that, one data set was obtained from the fusion of the two sensor data sets and a LMC was fitted for the combined data to be then used in factor cokriging. The application of this technique produced a delineation of the field into homogeneous zones, highlighting a wide southern-central zone, characterized by different granulometric and chemical properties. The proposed approach was then effective to discriminate areas with different properties by using multi-sensor data. It then has the potential to be used in PA.

Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method

Soil salinization is a global ecological and environmental problem in arid and semi-arid areas that can be ameliorated via soil management, visible-near infrared-shortwave infrared (VNIR-SWIR) spectroscopy can be adapted to rapidly monitor soil salinity content. This study explored the potential of Grünwald–Letnikov fractional-order derivative (FOD), feature band selection methods, nonlinear partial least squares regression (PLSR), and four machine learning models to estimate the soil salinity content using VNIR-SWIR spectra. Ninety sample points were field scanned with VNIR-SWR and soil samples (0–20 cm) were obtained at the time of scanning. The samples points come from three zones representing different intensities of human interference (I, II, and III Zones) in Fukang, Xinjiang, China. Each zone contained thirty sample points. For modeling, we firstly adopted FOD (with intervals of 0.1 and range of 0–2) as a preprocessing method to analyze soil hyperspectral data. Then, four sets of spectral bands (R-FOD-FULL indicates full band range, R-FOD-CC5 bands that met a 0.05 significance test, R-FOD-CC1 bands that met a 0.01 significance test, and R-FOD-CC1-CARS represents CC1 combined with competitive adaptive reweighted sampling) were selected as spectral input variables to develop the estimation model. Finally, four machine learning models, namely, generalized regression neural network (GRNN), extreme learning machine (ELM), random forest (RF), and PLSR, to estimate soil salinity. Study results showed that (1) the heat map of correlation coefficient matrix between hyperspectral data and salinity indicated that FOD significantly improved the correlation. (2) The characteristic band variables extracted and used by R-FOD-CC1 were fewer in number, and redundancy between bands smaller than R-FOD-FULL and R-FOD-CC5, thus estimation accuracy of R-FOD-CC1 was higher than R-FOD-CC5 or R-FOD-FULL. A high prediction accuracy was achieved with a less complex calculation. (3) The GRNN model yielded the best salinity estimation in all three zones compared to ELM, BPNN, RF, and PLSR on the whole, whereas, the RF model had the worst estimation effect. The R-FOD-CC1-CARS-GRNN model yielded the best salinity estimation in I Zone with R2, RMSE and RPD of 0.7784, 1.8762, and 2.0568, respectively. The fractional order was 1.5 and estimation performance was great. The optimal model for predicting soil salinity in II and III Zone was, also, R-FOD-CC1-CARS-GRNN (R2 = 0.7912, RMSE = 3.4001, and RPD = 1.8985 in II Zone; R2 = 0.8192, RMSE = 6.6260, and RPD = 1.8190 in III Zone), with the fractional order of 1.7- and 1.6-, respectively, and the estimation performance were all fine. (4) The characteristic bands selected by the best model in I, II, and III Zones were 8, 9, and 11, respectively, which account for 0.45%, 0.51%, and 0.63%% of the full bands. This approach reduces the number of modeled band variables and simplifies the model structure.

Raman Spectroscopy Coupled with Reflectance Spectroscopy as a Tool for the Characterization of Key Hydrothermal Alteration Minerals in Epithermal Au-Ag Systems: Utility and Implications for Mineral Exploration.

Raman spectroscopy of fine-grained hydrothermal alteration minerals, and phyllosilicates in particular, presents certain challenges. However, given the increasingly widespread recognition of field portable visible–near infrared–shortwave infrared (Vis-NIR-SWIR) spectroscopy as a valuable tool in the mineral exploration industry, Raman microspectroscopy has promise as an approach for developing detailed complementary information on hydrothermal alteration phases in ore-forming systems. Here we present exemplar high-quality Raman and Vis-NIR-SWIR spectra of four key hydrothermal alteration minerals (pyrophyllite, white mica, chlorite, and alunite) that are common in precious metal epithermal systems, from deposits on the island of Newfoundland, Canada. The results reported here demonstrate that Raman microspectroscopy can accurately characterize pyrophyllite, white mica, chlorite, and alunite and provide details on their compositional variation at the microscale. In particular, spectral differences in the 1000–1150 cm−1 white mica Raman band allows the distinction between low-Tschermak phases (muscovite, paragonite) and phases with higher degrees of Tschermak substitution (phengitic white mica composition). The peak position of the main chlorite Raman band shifts between 683 cm−1 for Mg-rich chlorite and 665 cm−1 for Fe-rich chlorite and can be therefore used for semiquantitative estimation of the Fe2+ content in chlorite. Furthermore, while Vis-NIR-SWIR macrospectroscopy allows the rapid identification of the overall composition of the most abundant hydrothermal alteration mineral in a given sample, Raman microspectroscopy provides an in-depth spectral and chemical characterization of individual mineral grains, preserving the spatial and paragenetic context of each mineral and allowing for the distinction of chemical variation between (and within) different mineral grains. This is particularly useful in the case of alunite, white mica, and chlorite, minerals with extensive solid solution, where microscale characterization can provide information on the alteration zonation useful for mineral exploration and provide insight into mineral deposit genesis.

Mapping Water Infiltration Rate Using Ground and UAV Hyperspectral Data: A Case Study of Alento, Italy

Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties.

Interrelationships in the Gypsum–Syngenite–Görgeyite System and Their Possible Formation on Mars

Calcium sulfates are known to be potential reservoirs of organic compounds and have been detected on Mars. However, not all data that indicate the presence of sulfates collected by the Mars Exploration Rovers (Spirit and Opportunity) and Curiosity rover can be explained by the different calcium sulfate polymorphs, and therefore, mixtures of calcium sulfates with other single sulfates must be considered. In addition, the presence of mixed calcium sulfates supports the data and indicates that the molar ratio of sulfate/calcium is >1. To obtain adequate spectroscopic information of mixed-cation sulfates to be used in the interpretation of data from Mars in the next few years, the thermodynamically stable syngenite (K2Ca(SO4)2·H2O) and görgeyite (K2Ca5(SO4)6·H2O) mixed-cation sulfates have been studied along with the interrelationships in the gypsum-syngenite-görgeyite system to understand their possible formation on Mars. Raman spectroscopy and Visible-Near Infrared-Shortwave Infrared (VisNIR) spectroscopy have been used for their characterization to increase the databases for the two future Mars exploration missions, Mars2020 and ExoMars2022, where both techniques will be implemented. These VisNIR data can also help with the interpretation of spectral data of salt deposits on Mars acquired by the OMEGA and CRISM spectrometers onboard the Mars Express and Mars Reconnaissance orbiters. This work demonstrates that syngenite (K2Ca(SO4)2·H2O) easily precipitates without the need for hydrothermal conditions, which, depending on the ion concentrations, may precipitate in different proportions with gypsum. Furthermore, in this study, we also demonstrate that, under hydrothermal conditions, görgeyite (K2Ca5(SO4)6·H2O) would also be highly likely to form and may also be identified on Mars together with syngenite and gypsum.

Analysing the impact of soil spatial sampling on the performances of Digital Soil Mapping models and their evaluation: A numerical experiment on Quantile Random Forest using clay contents obtained from Vis-NIR-SWIR hyperspectral imagery

It has long been acknowledged that the soil spatial samplings used as inputs to DSM models are strong drivers – and often limiting factors – of the performances of such models. However, few studies have focused on evaluating this impact and identifying the related spatial sampling characteristics. In this study, a numerical experiment was conducted on this topic using the pseudo values of topsoil clay content obtained from an airborne Visible Near InfraRed-Short Wave InfraRed (Vis-NIR-SWIR) hyperspectral image in the Cap Bon region (Tunisia) as the source of the spatial sampling.Twelve thousand DSM models were built by running a Random Forest algorithm from soil spatial sampling of different sizes and average spacings (from 200 m to 2000 m) and different spatial distributions (from clustered to regularly distributed), aiming to mimic the various situations encountered when handling legacy data. These DSM models were evaluated with regard to both their prediction performances and their ability to estimate their overall and local uncertainties. Three evaluation methods were applied: a model-based one, a classical model-free one using 25% of the sites removed from the initial soil data, and a reference one using a set of 100,000 independent sites selected by stratified random sampling over the entire region.The results showed that: 1) While, as expected, the performances of the DSM models increased when the spacing of the sample increased, this increase was diminished for the smallest spacing as soon as 50% of the spatially structured variance was captured by the sampling, 2) Sampling that provided complete and even distributions in the geographical space and had as great spread of the target soil property as possible increased the DSM performances, while complete and even sampling distributions in the covariate space had less impacts, 3) Systematic underestimations of the overall uncertainty of DSM models were observed, that were all the more important that the sparse samplings poorly covered the real distribution of the target soil property and that the dense sampling were unevenly distributed in the geographical space, 4) The local uncertainties were underestimated for sparse sampling and over-estimated for dense sampling while being sensitive to the same sampling characteristics as overall uncertainty.Such finding have practical outcomes on sampling strategies and DSM model evaluation that are discussed.

Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data

Earth observation (EO) has an immense potential as being an enabling tool for mapping spatial characteristics of the topsoil layer. Recently, deep learning based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the processing of EO data. This paper aims to present a novel EO-based soil monitoring approach leveraging open-access Copernicus Sentinel data and Google Earth Engine platform. Building on key results from existing data mining approaches to extract bare soil reflectance values the current study delivers valuable insights on the synergistic use of open access optical and radar images. The proposed framework is driven by the need to eliminate the influence of ambient factors and evaluate the efficiency of a convolutional neural network (CNN) to effectively combine the complimentary information contained in the pool of both optical and radar spectral information and those form auxiliary geographical coordinates mainly for soil. We developed and calibrated our multi-input CNN model based on soil samples (calibration = 80% and validation 20%) of the LUCAS database and then applied this approach to predict soil clay content. A promising prediction performance (R2 = 0.60, ratio of performance to the interquartile range (RPIQ) = 2.02, n = 6136) was achieved by the inclusion of both types (synthetic aperture radar (SAR) and laboratory visible near infrared–short wave infrared (VNIR-SWIR) multispectral) of observations using the CNN model, demonstrating an improvement of more than 5.5% in RMSE using the multi-year median optical composite and current state-of-the-art non linear machine learning methods such as random forest (RF; R2 = 0.55, RPIQ = 1.91, n = 6136) and artificial neural network (ANN; R2 = 0.44, RPIQ = 1.71, n = 6136). Moreover, we examined post-hoc techniques to interpret the CNN model and thus acquire an understanding of the relationships between spectral information and the soil target identified by the model. Looking to the future, the proposed approach can be adopted on the forthcoming hyperspectral orbital sensors to expand the current capabilities of the EO component by estimating more soil attributes with higher predictive performance.

Multispectral and RADAR images integration for geologic, geomorphic, and structural investigation in southwestern Arabian Shield, Al Qunfudhah area, Saudi Arabia

In this article, Western Saudi Arabia underwent remote sensing investigation via Operational Land Imager (OLI), Advanced Land Observing Satellite/ Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR), Sentinal-1 and Shuttle Radar Topography Mission (SRTM). Image-transformation and enhancement techniques such as Band ratios, principal component analysis (PCA), and Minimum Noise Fraction (MNF) were utilized. Applying Sentinel-1 and SRTM DEMs enhanced the perspective views of the terrain. The main results are highlighting of geologic rock units, geomorphic and structural features, and extraction and visualization of dominant lineaments. N–S foliations that are associated with folds N–S tight, isoclinal, and asymmetric (F1), NW-SE trending Najd system strike-slip faults and NNE–SSW, NE–SW NW–SE, and E–W Cenozoic faults are the major identified structural lineaments, which were analyzed and synthesized via rose diagrams. Visible Near Infra-Red/Short Wave Infra-Red (VNIR/SWIR) radar data are very important data set for water resources applications.

Spatial and Temporal Monitoring of Pasture Ecological Quality: Sentinel-2-Based Estimation of Crude Protein and Neutral Detergent Fiber Contents

Frequent, region-wide monitoring of changes in pasture quality due to human disturbances or climatic conditions is impossible by field measurements or traditional ecological surveying methods. Remote sensing imagery offers distinctive advantages for monitoring spatial and temporal patterns. The chemical parameters that are widely used as indicators of ecological quality are crude protein (CP) content and neutral detergent fiber (NDF) content. In this study, we investigated the relationship between CP, NDF, and reflectance in the visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) spectral range, using field, laboratory measurements, and satellite imagery (Sentinel-2). Statistical models were developed using different calibration and validation data sample sets: (1) a mix of laboratory and field measurements (e.g., fresh and dry vegetation) and (2) random selection. In addition, we used three vegetation indices (Normalized Difference Vegetative Index (NDVI), Soil-adjusted Vegetation Index (SAVI) and Wide Dynamic Range Vegetation Index (WDRVI)) as proxies to CP and NDF estimation. The best models found for predicting CP and NDF contents were based on reflectance measurements (R2 = 0.71, RMSEP = 2.1% for CP; and R2 = 0.78, RMSEP = 5.5% for NDF). These models contained fresh and dry vegetation samples in calibration and validation data sets. Random sample selection in a model generated similar accuracy estimations. Our results also indicate that vegetation indices provide poor accuracy. Eight Sentinel-2 images (December 2015–April 2017) were examined in order to better understand the variability of vegetation quality over spatial and temporal scales. The spatial and temporal patterns of CP and NDF contents exhibit strong seasonal dependence, influenced by climatological (precipitation) and topographical (northern vs. southern hillslopes) conditions. The total CP/NDF content increases/decrease (respectively) from December to March, when the concentrations reach their maximum/minimum values, followed by a decline/incline that begins in April, reaching minimum values in July.

Examining the Performance of PARACUDA-II Data-Mining Engine versus Selected Techniques to Model Soil Carbon from Reflectance Spectra

The monitoring and quantification of soil carbon provide a better understanding of soil and atmosphere dynamics. Visible-near-infrared-short-wave infrared (VIS-NIR-SWIR) reflectance spectroscopy can quantitatively estimate soil carbon content more rapidly and cost-effectively compared to traditional laboratory analysis. However, effective estimation of soil carbon using reflectance spectroscopy to a great extent depends on the selection of a suitable preprocessing sequence and data-mining algorithm. Many efforts have been dedicated to the comparison of conventional chemometric techniques and their optimization for soil properties prediction. Instead, the current study focuses on the potential of the new data-mining engine PARACUDA-II®, recently developed at Tel-Aviv University (TAU), by comparing its performance in predicting soil oxidizable carbon (Cox) against common data-mining algorithms including partial least squares regression (PLSR), random forests (RF), boosted regression trees (BRT), support vector machine regression (SVMR), and memory based learning (MBL). To this end, 103 soil samples from the Pokrok dumpsite in the Czech Republic were scanned with an ASD FieldSpec III Pro FR spectroradiometer in the laboratory under a strict protocol. Spectra preprocessing for conventional data-mining techniques was conducted using Savitzky-Golay smoothing and the first derivative method. PARACUDA-II®, on the other hand, operates based on the all possibilities approach (APA) concept, a conditional Latin hypercube sampling (cLHs) algorithm and parallel programming, to evaluate all of the potential combinations of eight different spectral preprocessing techniques against the original reflectance and chemical data prior to the model development. The comparison of results was made in terms of the coefficient of determination (R2) and root-mean-square error of prediction (RMSEp). Results showed that the PARACUDA-II® engine performed better than the other selected regular schemes with R2 value of 0.80 and RMSEp of 0.12; the PLSR was less predictive compared to other techniques with R2 = 0.63 and RMSEp = 0.29. This can be attributed to its capability to assess all the available options in an automatic way, which enables the hidden models to rise up and yield the best available model.

Customized Spectral Libraries for Effective Mineral Exploration: Mining National Mineral Collections

AbstractInfrared (Visible-Near Infrared-Shortwave Infrared (VNIR-SWIR)) spectroscopy is a cost-effective technique for mineral identification in the field. Modern hand-held spectrometers are equipped with on-board spectral libraries that enable rapid, qualitative analysis of most minerals and facilitate recognition of key alteration minerals for exploration. Spectral libraries can be general or customized for specific mineral deposit environments. To this end, careful collection of spectra in a controlled environment on pure specimens of key minerals was completed using the National Mineral Reference Collection (NMC) of the Geological Survey of Canada. The spectra collected from specimens in the ‘Kodama Clay Collection’ were processed using spectral plotting software and each new example was validated before being added to a group of spectra considered for incorporation into the on-board library of the handheld ASD-TerraSpec Halo near-infrared (NIR) mineral identification instrument. Spectra from an additional suite of mineral samples of the NMC containing REE, U, Th, and/or Nb are being prepared for a new, publicly available spectral library. These minerals commonly occur in carbonatite or alkali intrusive deposits, and as such will assist in the exploration for critical metals.

Leaf dorsoventrality as a paramount factor determining spectral performance in field-grown wheat under contrasting water regimes.

The effects of leaf dorsoventrality and its interaction with environmentally induced changes in the leaf spectral response are still poorly understood, particularly for isobilateral leaves. We investigated the spectral performance of 24 genotypes of field-grown durum wheat at two locations under both rainfed and irrigated conditions. Flag leaf reflectance spectra in the VIS-NIR-SWIR (visible-near-infrared-short-wave infrared) regions were recorded in the adaxial and abaxial leaf sides and at the canopy level, while traits providing information on water status and grain yield were evaluated. Moreover, leaf anatomical parameters were measured in a subset of five genotypes. The spectral traits studied were more affected by the leaf side than by the water regime. Leaf dorsoventral differences suggested higher accessory pigment content in the abaxial leaf side, while water regime differences were related to increased chlorophyll, nitrogen, and water contents in the leaves in the irrigated treatment. These variations were associated with anatomical changes. Additionally, leaf dorsoventral differences were less in the rainfed treatment, suggesting the existence of leaf-side-specific responses at the anatomical and biochemical level. Finally, the accuracy in yield prediction was enhanced when abaxial leaf spectra were employed. We concluded that the importance of dorsoventrality in spectral traits is paramount, even in isobilateral leaves.

Identifying the phyllosilicate minerals of hypogene ore deposits in lateritic saprolites using the near-IR spectroscopy second derivative methodology

Infrared field-based reflectance spectroscopy in the Visible - Near-Infrared - Short-wave Infrared (VIS-NIR-SWIR) domain is a useful tool in mining geology particularly efficient for investigating the clay mineralogy of alteration haloes around ore deposits. It is used as a routine technique for the basic identification, mapping and semi-quantification of clay mineral species. However, the use of this technique for prospecting in hypogene deposits at depth in intertropical areas is strongly limited because of the presence of a thick, kaolinite-rich lateritic cover. Due to the strong IR absorption of kaolinite and the overlapping of its IR bands with those of most of the phyllosilicates in the SWIR domain, the use of field based near-infrared spectroscopy does not permit efficient identification and mapping of the phyllosilicates inherited from hypogene alteration that persist in the saprolite of lateritic profiles.In this paper, we propose a methodology to enhance the detection and semi-quantification of hypogene phyllosilicate minerals in kaolinite-rich lateritic saprolites using calibration curves. Those curves are built from the NIR spectra of binary admixtures of kaolinite or smectite with muscovite, Fe-Mg chlorite, clinochlore or talc in different known proportions.For each admixture series, calibration curves were established, based on investigation of two regions of interest within the NIR domain (1350–1470nm and 2080–2500nm) using a field-based spectrometer. For each binary mixture series of phyllosilicates, the second derivative of the NIR spectra was used to enhance the detection of the diagnostic absorption bands of each type of phyllosilicate, and hence to optimize the calculation of the intensity ratios between the diagnostic bands of the phyllosilicate components as a function of their percentage in the mixture. In presence of large amounts of lateritic kaolinite, the detection limit of the major types of hypogene phyllosilicates has been found at ranges from 5 to 10wt% of the total clay content using the second derivative of the NIR spectra acquired with a field-based spectrometer. Above these aforementioned limits of detection, the semi-quantitative data obtained by comparing the NIR reflectance spectra of natural samples with those of the calibration curves could permit to map hypogene alteration haloes directly from the lateritic saprolite.Finally the described approach has been successfully tested on natural samples from the skarn deposits of the Ity gold mine (Ivory Coast).