Derivative analysis of absorption features in hyperspectral remote sensing data of carbonate sediments

Potential of DESIS and PRISMA hyperspectral remote sensing data in rock classification and mineral identification:a case study for Banswara in Rajasthan, India

Remote sensing datasets and methods are suitable for mapping and managing the natural resources like minerals, clean water, and energy and also govern their sustainability nowadays. Hyperspectral (HS) imaging has immense potential for rock type classification, mineral mapping, and identification. This work demonstrates the potential of feature extraction techniques and unsupervised machine learning methods for the space-borne hyperspectral remote sensing data in characterizing and identifying mineral and classifying rock type in Banswara, Rajasthan, India. Feature extraction techniques can reveal variations within the data, which can help identify geological areas, reduce noise, and check the dimensionality of the data. Singular value decomposition (SVD)-based principal component analysis (PCA), kernel PCA (KPCA), minimum noise fraction (MNF), and independent component analysis (ICA) were tested for lithological mapping using recently launched DLR Earth Sensing Imaging Spectrometer Hyperspectral (DESIS) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) data in order to map geologically significant areas. Unsupervised machine learning methods, such as Iterative Self-Organizing Data Analysis Technique (ISODATA) and K-means, were also employed. Vertex component analysis (VCA) was utilized to check for similarity and identify various spectral features. Our work demonstrates the advantages of using feature extraction algorithms such as PCA and KPCA over MNF and ICA in geological mapping and interpretability. We recommend K-means as the preferred method for lithological classification of hyperspectral remote sensing data. Our work highlights the potential of advanced feature extraction algorithms for mineral mapping using hyperspectral data, providing different ways to identify minerals and ultimately leading to better mineral resource management.

Kleptoplast distribution, photosynthetic efficiency and sequestration mechanisms in intertidal benthic foraminifera

Foraminifera are ubiquitously distributed in marine habitats, playing a major role in marine sediment carbon sequestration and the nitrogen cycle. They exhibit a wide diversity of feeding and behavioural strategies (heterotrophy, autotrophy and mixotrophy), including species with the ability of sequestering intact functional chloroplasts from their microalgal food source (kleptoplastidy), resulting in a mixotrophic lifestyle. The mechanisms by which kleptoplasts are integrated and kept functional inside foraminiferal cytosol are poorly known. In our study, we investigated relationships between feeding strategies, kleptoplast spatial distribution and photosynthetic functionality in two shallow-water benthic foraminifera (Haynesina germanica and Elphidium williamsoni), both species feeding on benthic diatoms. We used a combination of observations of foraminiferal feeding behaviour, test morphology, cytological TEM-based observations and HPLC pigment analysis, with non-destructive, single-cell level imaging of kleptoplast spatial distribution and PSII quantum efficiency. The two species showed different feeding strategies, with H. germanica removing diatom content at the foraminifer's apertural region and E. williamsoni on the dorsal site. All E. williamsoni parameters showed that this species has higher autotrophic capacity albeit both feeding on benthic diatoms. This might represent two different stages in the evolutionary process of establishing a permanent symbiotic relationship, or may reflect different trophic strategies.

Nitric oxide detection using principal component analysis spectral structure matching to the UV derivative spectrum

Ultraviolet (UV) spectroscopy is widely applied in real-time environmental monitoring, especially in diesel vehicle nitrogen monoxide (NO) emissions. However, in field experiments, UV absorption spectrum may exist for different degrees of drifts. Spectral jitters may exist for various reasons such as optical power variation, electrical signal drift, and the refractive index jitters of the optical path for an extended period of time, which causes the detection system to be calibrated. And the pulse xenon lamps as the UV source are characterized by specific emission lines that interfere in spectral analysis directly. For these problems, we proposed the spectral structure matching method based on principal component analysis (PCA), which was compared with the conventional polynomial fitting method to observe feasibility and variability. Further, the UV derivative spectrum was applied to the system appropriately, due to the variation of the absorption peak, and was only related to the target gas by using the above method. We validated our method experimentally by performing the NO UV detection system with the calibration and the comparison test. The results suggested that the calibration relative error was less than 9% and the measurement relative error was less than 6% for this wide range by the proposed processes, which optimized the interference of spectral structures and fluctuation to the system and therefore provided better monitoring. This study may provide an alternative spectral analysis method that is unaffected on the specific emission lines of lamps and is not limited to the spectral region and the target gas.

Spectral Derivatives of Optical Depth for Partitioning Aerosol Type and Loading

Quantifying aerosol compositions (e.g., type, loading) from remotely sensed measurements by spaceborne, suborbital and ground-based platforms is a challenging task. In this study, the first and second-order spectral derivatives of aerosol optical depth (AOD) with respect to wavelength are explored to determine the partitions of the major components of aerosols based on the spectral dependence of their particle optical size and complex refractive index. With theoretical simulations from the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) model, AOD spectral derivatives are characterized for collective models of aerosol types, such as mineral dust (DS) particles, biomass-burning (BB) aerosols and anthropogenic pollutants (AP), as well as stretching out to the mixtures among them. Based on the intrinsic values from normalized spectral derivatives, referenced as the Normalized Derivative Aerosol Index (NDAI), a unique pattern is clearly exhibited for bounding the major aerosol components; in turn, fractions of the total AOD (fAOD) for major aerosol components can be extracted. The subtlety of this NDAI method is examined by using measurements of typical aerosol cases identified carefully by the ground-based Aerosol Robotic Network (AERONET) sun–sky spectroradiometer. The results may be highly practicable for quantifying fAOD among mixed-type aerosols by means of the normalized AOD spectral derivatives.

Improving the Transferability of Suspended Solid Estimation in Wetland and Deltaic Waters with an Empirical Hyperspectral Approach

The deposition of suspended sediment is an important process that helps wetlands accrete surface material and maintain elevation in the face of sea level rise. Optical remote sensing is often employed to map total suspended solids (TSS), though algorithms typically have limited transferability in space and time due to variability in water constituent compositions, mixtures, and inherent optical properties. This study used in situ spectral reflectances and their first derivatives to compare empirical algorithms for estimating TSS using hyperspectral and multispectral data. These algorithms were applied to imagery collected by NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) over coastal Louisiana, USA, and validated with a multiyear in situ dataset. The best performing models were then applied to independent spectroscopic data collected in the Peace–Athabasca Delta, Canada, and the San Francisco Bay–Delta Estuary, USA, to assess their robustness and transferability. A derivative-based partial least squares regression (PLSR) model applied to simulated AVIRIS-NG data showed the most accurate TSS retrievals (R2 = 0.83) in these contrasting deltaic environments. These results highlight the potential for a more broadly applicable generalized algorithm employing imaging spectroscopy for estimating suspended solids.

Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts

Chlorophyll a concentration (Chla) is a well-proven proxy of biocrust development, photosynthetic organisms’ status, and recovery monitoring after environmental disturbances. However, laboratory methods for the analysis of chlorophyll require destructive sampling and are expensive and time consuming. Indirect estimation of chlorophyll a by means of soil surface reflectance analysis has been demonstrated to be an accurate, cheap, and quick alternative for chlorophyll retrieval information, especially in plants. However, its application to biocrusts has yet to be harnessed. In this study we evaluated the potential of soil surface reflectance measurements for non-destructive Chla quantification over a range of biocrust types and soils. Our results revealed that from the different spectral transformation methods and techniques, the first derivative of the reflectance and the continuum removal were the most accurate for Chla retrieval. Normalized difference values in the red-edge region and common broadband indexes (e.g., normalized difference vegetation index (NDVI)) were also sensitive to changes in Chla. However, such approaches should be carefully adapted to each specific biocrust type. On the other hand, the combination of spectral measurements with non-linear random forest (RF) models provided very good fits (R2 > 0.94) with a mean root mean square error (RMSE) of about 6.5 µg/g soil, and alleviated the need for a specific calibration for each crust type, opening a wide range of opportunities to advance our knowledge of biocrust responses to ongoing global change and degradation processes from anthropogenic disturbance.

Diagnosis of nasopharyngeal carcinoma from serum samples using hyperspectral imaging combined with a chemometric method

Diagnosing nasopharyngeal carcinoma (NPC) is a significant challenge because of the highly complex process. We proposed an approach to diagnose NPC serum using a combination of hyperspectral imaging and weight-based principal component analysis. Samples were prepared by pressing boric acid into pellets for use as the sera substrate. The sera, collected from 100 healthy volunteers and 60 NPC patients, was dripped onto the surface of the substrate for hyperspectral imaging. The characteristic spectral bands were selected based on the variable weight obtained from a support vector machine (SVM) model, using principal component analysis (PCA) to reduce the dimension in the extracted bands. Obtained results show that the accuracy rate, sensitivity, and specificity between the NPC sera and the sera of the healthy controls reached extremely high levels of 99.15%, 98.79%, and 99.36%, respectively. For the model's consistency evaluation, we found that the Kappa and area under the curve (AUC) of the receiver operating characteristic (ROC) curve were 0.99 and 0.98, respectively. These results suggest that the developed approach could serve as a noninvasive diagnostic and screening tool for highly accurate and consistent detection of NPC. Hence, a combination of hyperspectral imaging (HSI) and a weighted principal component analysis (WPCA)-SVM model represents a powerful and promising tool for NPC diagnosis.

In situ spectral response of the Arabian Gulf and Sea of Oman coastal waters to bio-optical properties

Mapping of Chlorophyll-a (Chl-a) over the coastal waters of the Arabian Gulf and the Sea of Oman using the satellite-based observations, such as MODIS (Moderate Resolution Imaging Spectro-radiometer), has shown inferior performance (Chl-a overestimation) than that of deep waters. Studies in the region have shown that this poor performance is due to three reasons: (i) water turbidity (sediments re-suspension), and the presence of colored dissolved organic matter (CDOM), (ii) bottom reflectance and (iii) incapability of the existing atmospheric correction models to reduce the effect of the aerosols from the water leaving radiance. Therefore, this work focuses on investigating the sensitivity of the in situ spectral signatures of these coastal waters to the algal (chlorophyll: Chl-a), non-algal (sediments and CDOM) and the bottom reflectance properties, in absence of contributions from the atmosphere. Consequently, the collected in situ spectral signatures will improve our understanding of Arabian Gulf and Sea of Oman water properties. For this purpose, comprehensive field measurements were carried out between 2013 and 2016, over Abu-Dhabi (Arabian Gulf) and Fujairah (Sea of Oman) where unique water quality data were collected. Based on the in situ water spectral analysis, the bottom reflectance (water depth<20m) are found to degrade the performance of the conventional ocean color algorithms more than the sediment-laden waters where these waters increase the R_rsat the blue and red ranges. The increasing presence of CDOM markedly decreases the R_rsin the blue range, which is conflicting with the effect of Chl-a. Given the inadequate performance of the widely used ocean-color algorithms (OC3: ocean color 3, OC2: ocean color 2) in retrieving Chl-a in these very shallow coastal waters, therefore, a new algorithm is proposed here based on a 3-bands ratio approach using [R_rs (656) ^-1-R_rs (506) ^-1]×R_rs (661). The selected optimum bands (656nm, 506nm, and 661nm) from this approach can be used to retrieve the Chl-a more accurately in these coastal Case II (turbid) waters which are close to the bands of the current missions such as Sentinel-3 OLCI (Ocean and Land Colour Instrument), MODIS, VIIRS (Visible Infrared Imaging Radiometer Suite) and LandSat 8. However, more uniformly distributed data over the Arabian Gulf is required to have a highly accurate regional model for Chl-a retrieval.

Cost-Effective Hyperspectral Transmissometers for Oceanographic Applications: Performance Analysis

The recent development of inexpensive, compact hyperspectral transmissometers broadens the research capabilities of oceanographic applications. These developments have been achieved by incorporating technologies such as micro-spectrometers as detectors as well as light emitting diodes (LEDs) as light sources. In this study, we evaluate the performance of the new commercial LED-based hyperspectral transmissometer VIPER (TriOS GmbH, Rastede, Germany), which combines different LEDs to emulate the visible light spectrum, aiming at the determination of attenuation coefficients in coastal environments. For this purpose, experimental uncertainties related to the instrument stability, the effect of ambient light and derived temperature, and salinity correction factors are analyzed. Our results identify some issues related to the thermal management of the LEDs and the contamination of ambient light. Furthermore, the performance of VIPER is validated against other transmissometers through simultaneous field measurements. It is demonstrated that VIPER provides a compact and cost-effective alternative for beam attenuation measurements in coastal waters, but it requires the consideration of several optimizations.

DETECTION OF DIATOM XANTHOPHYLL CYCLE USING SPECTRAL REFLECTANCE(1)

Analysis of reflectance spectra was used to monitor the conversion of diadinoxanthin (DD) into diatoxanthin (DT) in two benthic diatom species, Amphora coffeaeformis (C. Agardh) Kütz. and Cylindrotheca closterium (Ehrenb.) J. C. Lewin et Reiman, cultured at high light (HL, 400 μmol · m(-2)  · s(-1) PAR) and low light (LL, 25 μmol · m(-2)  · s(-1) PAR). Cultures were exposed to saturating light for 32 min. HL cultures of both species showed higher (DT + DD) content, whereas LL cultures exhibited higher chl a and fucoxanthin content. DD to DT conversion, measured by HPLC, occurred mainly in the first 2 min (LL) or 5 min (HL) after exposure to saturating light. Nonphotochemical quenching (NPQ), measured by PAM fluorescence, showed the same pattern as DT/(DD + DT), resulting in a linear relationship between these parameters. Addition of dithiothreitol (DTT) blocked the conversion of DD into DT and significantly reduced NPQ induction. Reflectance spectra showed no obvious change after light exposure. However, second derivative spectra (δδ) showed a shift in reflectance from 487 to 508 nm, which was not present for DTT-treated samples. Changes in δδ487 were strongly correlated with changes in DD (r = 0.76), while changes in δδ508 were strongly correlated with changes in DT (r = 0.94). The best index to estimate DD to DT conversion was δδ508 /δδ630 (r = 0.87). This index was very sensitive to minute changes that occurred immediately after exposure to light and was species insensitive. Good relationships were observed between indices for xanthophyll cycle activation (DD to DT conversion and NPQ induction) and the second derivative spectra. With further in situ validation, this index may prove to be highly useful for investigation into aquatic global photoregulation mechanisms in diatom-dominated samples.

Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables

A spectrum-matching and look-up-table (LUT) methodology has been developed and evaluated to extract environmental information from remotely sensed hyperspectral imagery. The LUT methodology works as follows. First, a database of remote-sensing reflectance (Rrs) spectra corresponding to various water depths, bottom reflectance spectra, and water-column inherent optical properties (IOPs) is constructed using a special version of the HydroLight radiative transfer numerical model. Second, the measured Rrs spectrum for a particular image pixel is compared with each spectrum in the database, and the closest match to the image spectrum is found using a least-squares minimization. The environmental conditions in nature are then assumed to be the same as the input conditions that generated the closest matching HydroLight-generated database spectrum. The LUT methodology has been evaluated by application to an Ocean Portable Hyperspectral Imaging Low-Light Spectrometer image acquired near Lee Stocking Island, Bahamas, on 17 May 2000. The LUT-retrieved bottom depths were on average within 5% and 0.5 m of independently obtained acoustic depths. The LUT-retrieved bottom classification was in qualitative agreement with diver and video spot classification of bottom types, and the LUT-retrieved IOPs were consistent with IOPs measured at nearby times and locations.