Short-wave Infrared Spectra Research Articles

Visible, near-infrared, and shortwave-infrared spectra as an input variable for digital mapping of soil organic carbon

This study proposes a novel methodology to employ discrete point spectra as input variable for digital mapping of soil organic carbon (SOC). Accordingly, two SOC modeling approaches were used in three agricultural sites in Czech Republic: i) machine learning (ML) including partial least square regression (PLSR), cubist, random forest (RF), and support vector regression (SVR), and ii) regression kriging (RK) by the combination of ordinary kriging (OK) and PLSR (PLSR-K), cubist (cubist-K), RF (RF-K), and SVR (SVR-K). Models were developed on environmental predictor covariates (EPCs) and thirty genetic algorithms (GA)-selected visible, near-infrared, and shortwave-infrared (VNIR–SWIR) wavelengths spectra, individually and combined. Thirty rasters were then created using interpolation of the selected spectra and served as the input variables – with and without EPCs – to test and compare the developed models and SOC predictive maps with each other and with those retrieved from the third approach: iii) kriging using OK of the measured and ML-predicted SOC. The impact of employing selected wavelengths’ spectra and EPCs on models' performance was investigated using independent test samples and the uncertainty associated with the produced maps. Using interpolated spectra as the only input variable yielded a relatively acceptable accuracy (Nová Ves: RMSE = 0.19%, Údrnice: RMSE = 0.12%, Klučov: RMSE = 0.13%). In comparison, the interpolated spectra coupled with EPCs enhanced the results. Regarding the uncertainty, however, the ML-based SOC maps were more reliable, than RK-based ones. Furthermore, maps produced using both spectra and EPCs showed less uncertainty than those constructed on the individual datasets.

High-Performance Visible-to-SWIR Photodetector Based on the Layered WS2 Heterojunction with Light-Trapping Pyramidal Black Germanium.

This study presents a layered transition metal dichalcogenide/black germanium (b-Ge) heterojunction photodetector that exhibits superior performance across a broad spectrum of wavelengths spanning from visible (vis) to shortwave infrared (SWIR). The photodetector includes a thin layer of b-Ge, which is created by wet etching of germanium (Ge) wafer to form submicrometer pyramidal structures. On top of this b-Ge layer, the WS2 thin film is deposited using pulsed laser deposition. In comparison to conventional germanium, b-Ge absorbs about 25% more light between 850 and 1750 nm wavelengths. The WS2/b-Ge photodetector has a peak photoresponsivity of 0.65 A/W, which is more than twice the photoresponsivity of the WS2/Ge photodetector at 1540 nm. Additionally, it shows better responsivity and response speed compared with other similar state-of-the-art photodetectors. Such an improvement in the performance of the device is credited to the light-trapping effect enabled by the germanium pyramids. Theoretical simulations employing the finite-difference time-domain technique help validate the concept. This novel photodetector holds promise for efficient detection of light across the vis to SWIR spectrum.

Phase change material metasurface loading enables an ultrafast all-optically switchable, compact, narrowband freespace optical filter

Active metasurfaces leveraging phase change materials (PCMs) remain a subject of intense investigation, owing to their inherent tunable properties and successful integration into integrated photonics. By harnessing the ultra-compact form factor of optical metasurfaces, further advancements in switching speeds and reduction in switching energies have the potential to revolutionize applications in free space optical communication, optical signal processing, neuromorphic photonics, quantum photonics, and compact LiDAR. In this context, an ultrafast all-optically switchable narrowband spectral filter employing a distributed Bragg reflector cavity loaded with a PCM-metasurface is proposed. The work involves a comprehensive numerical exploration of its optical properties, encompassing design optimization and anticipating limits in switching speeds and energy requirements. Specifically, focusing on a GST225 metasurface operating in the shortwave-infrared spectrum, the numerical investigations reveal the potential to achieve transmission contrast levels as high as 24 dB, accompanied by Q-factors up to a thousand and less than 1 dB insertion loss. This performance notably outperforms recently reported free-standing PCM metasurfaces and cavities loaded with unstructured PCM thin films. Furthermore, the study predicts full-cycle reconfigurability (transitioning from amorphous to crystalline states and vice versa) with anticipated switching speeds of 91 & 200 MHz and switching fluence of 0.655 & 0.616 mJ/cm2 per cycle for the RESET and SET processes respectively. This investigation holds promise for advancing the state-of-the-art performance of PCM-based metasurfaces.

Non-Destructive Assessment of Stone Heritage Weathering Types Based on Machine Learning Method Using Hyperspectral Data

Abstract. Stone cultural heritage is exposed to various environments, resulting in a diverse range of weathering types. The identification of these weathering types is vital for targeted conservation efforts. In this paper, a weathering type classification method based on hyperspectral imaging technology is proposed. Firstly, fresh sandstones are collected from Yungang Grottoes to carry out the simulated weathering experiments, including freeze-thaw cycles and wet-dry cycles with acid, alkali and salt solutions. Subsequently, the hyperspectral imaging system was used to collect the visible-near-infrared (VNIR) and short-wave infrared (SWIR) images of the sandstone samples with different weathering types and degrees. The surface spectral reflectance of sandstone samples with different weathering types were used as training data, with weathering types serving as the labels. Support vector machine (SVM), K-nearest neighbour (KNN), linear discriminant analysis (LDA) and random forest (RF) were used to establish weathering type classification models. The results show that the SVM model and LDA model based on both VNIR and SWIR spectra exhibit outstanding performance, with a best accuracy of 0.994. The framework proposed in this paper facilitates rapid and non-contact assessment of the weathering types of the superficial layers of stone cultural heritage, thereby supporting more targeted conservation work.

First Nighttime Light Spectra by Satellite—By EnMAP

For the first time, nighttime VIS/NIR—SWIR (visible and near-infrared—shortwave infrared) spectra from a satellite mission have been analyzed using the EnMAP (Environmental Mapping and Analysis Program) high-resolution imaging spectrometer. This article focuses on the spectral characteristics. Firstly, we checked the spectral calibration of EnMAP using sodium light emissions. Here, By applying a newly devised general method, we estimated shifts of +0.3nm for VIS/NIR and −0.2nm for SWIR; the uncertainties were found to be within the range of [−0.4nm,+0.2nm] for VIS/NIR and [−1.2nm,+1.0nm] for SWIR. These results emphasize the high accuracy of the spectral calibration of EnMAP and illustrate the feasibility of methods based on nighttime Earth observations for the spectral calibration of future nighttime satellite missions. Secondly, by employing a straightforward general method, we identified the dominant lighting types and thermal emissions in Las Vegas, Nevada, USA, on a per-pixel basis, and we considered the consistency of the outcomes. The identification and mapping of different types of LED (light-emitting diode) illuminations were achieved—with 75% of the identified dominant lighting types identified in VIS/NIR—as well as high- and low-pressure sodium and metal halide, which made up 22% of the identified dominant lighting types in VIS/NIR and 29% in SWIR and other illumination sources, as well as high temperatures, where 33% of the identified dominant emission types in SWIR were achieved from space using EnMAP due to the elevated illumination levels in the observed location. These results illustrate the feasibility of the precise identification of lighting types and thermal emissions based on nighttime high-resolution imaging spectroscopy satellite products; moreover, they support the specification of spectral characteristics for upcoming nighttime missions.

High-power two-color Kerr frequency comb generation on the gallium phosphide-on-insulator platform at SWIR and MIR spectra

Optical frequency combs (OFCs) covering multiple spectral windows are of great interest as broadband coherent light sources. Pushing into high powers for traditional single OFCs as well as nonlinear frequency translated OFCs led to the narrowing of their bandwidths. Here, we present a hybrid integrated solution on the gallium phosphide-on-insulator (GaP-OI) platform to generate high-power two-color Kerr frequency combs at both the short-wave infrared (SWIR) and the mid-infrared (MIR) spectra. The design consists of a GaP-OI resonator with a partially etched gap for frequency comb generation at the two colors and a modal phase-matched strip waveguide for a second-harmonic generation (SHG). The resonator has a 3.25 µm wide anomalous dispersion window, which is enabled by mode hybridization and higher-order modes waveguide dispersion. The pump light at 1550 nm wavelength is frequency doubled from the 3100 nm wavelength light source, with a normalized SHG conversion efficiency of 793%W−1cm−2. We also propose the ring-bus coupler design to efficiently deliver optical power into the resonator while suppressing the leakage out of the resonator. The simulated two-color combs show a bandwidth of 87 nm above the −30dBm power level at the SWIR spectrum and a bandwidth of 749 nm above the same power level at the MIR spectrum. Our proposed two-color OFC generation scheme levitates the ceiling in terms of high power and broad bandwidth simultaneously on a single platform, paving the way toward monolithic solutions to integrated broadband coherent sources.

Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion.

Efficient, rapid, and non-destructive detection of pesticide residues in fruits and vegetables is essential for food safety. The visible/near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging (HSI) systems were used to detect different types of pesticide residues on the surface of Hami melon. Taking four pesticides commonly used in Hami melon as the object, the effectiveness of single-band spectral range and information fusion in the classification of different pesticides was compared. The results showed that the classification effect of pesticide residues was better by using the spectral range after information fusion. Then, a custom multi-branch one-dimensional convolutional neural network (1D-CNN) model with the attention mechanism was proposed and compared with the traditional machine learning classification model K-nearest neighbor (KNN) algorithm and random forest (RF). The traditional machine learning classification model accuracy of both models was over 80.00%. However, the classification results using the proposed 1D-CNN were more satisfactory. After the full spectrum data was fused, it was input into the 1D-CNN model, and its accuracy, precision, recall, and F1-score value were 94.00%, 94.06%, 94.00%, and 0.9396, respectively. This study showed that both VNIR and SWIR hyperspectral imaging combined with a classification model could non-destructively detect different pesticide residues on the surface of Hami melon. The classification result using the SWIR spectrum was better than that using the VNIR spectrum, and the classification result using the information fusion spectrum was better than that using SWIR. This study can provide a valuable reference for the non-destructive detection of pesticide residues on the surface of other large, thick-skinned fruits.

Classification of tree symbiotic fungi based on hyperspectral imagery and hybrid convolutional neural networks

Hyperspectral imagery and machine learning have proven to be powerful, non-invasive, and chemical-free tools for studying tree symbiotic fungi. However, traditional machine learning requires manual feature extraction (feature engineering) of spectral and spatial features of tree symbiotic fungi. Deep convolutional neural networks (CNNs) can extract self and robust features directly from the raw data. In the current study, a deep CNN architecture is proposed to recognize the isolates of dark septate endophytic (DSE) fungal in hyperspectral images. The performance of different CNN approaches (two-dimensional and three-dimensional CNNs) was compared and evaluated based on two independent datasets collected using visible-near-infrared (VNIR) and short-wave-infrared (SWIR) hyperspectral imaging systems. Moreover, the impact of different spectral pre-processing techniques was investigated. The results show that a hybrid CNN architecture (3D-2D CNN), which combines three and two-dimensional CNNs, achieved the best performance for the classification of fungal isolates on SWIR hyperspectral data compared to the same architecture on VNIR hyperspectral data. The best performance is 100% for precision, recall, and overall accuracy. The results also demonstrate that combining different pre-processing techniques on raw SWIR spectra can significantly improve the performance of the CNN models for fungal classification. The hybrid CNN approach with SWIR hyperspectral data provides an efficient method for classifying fungal isolates, which can contribute to the development of accurate and non-destructive tools for evaluating the occurrence of fungal isolates on trees. Such tools can be beneficial for both sustainable agriculture and preserving fungal diversity.

Estimation of Sugar Content in Wine Grapes via In Situ VNIR-SWIR Point Spectroscopy Using Explainable Artificial Intelligence Techniques.

Spectroscopy is a widely used technique that can contribute to food quality assessment in a simple and inexpensive way. Especially in grape production, the visible and near infrared (VNIR) and the short-wave infrared (SWIR) regions are of great interest, and they may be utilized for both fruit monitoring and quality control at all stages of maturity. The aim of this work was the quantitative estimation of the wine grape ripeness, for four different grape varieties, by using a highly accurate contact probe spectrometer that covers the entire VNIR-SWIR spectrum (350-2500 nm). The four varieties under examination were Chardonnay, Malagouzia, Sauvignon-Blanc, and Syrah and all the samples were collected over the 2020 and 2021 harvest and pre-harvest phenological stages (corresponding to stages 81 through 89 of the BBCH scale) from the vineyard of Ktima Gerovassiliou located in Northern Greece. All measurements were performed in situ and a refractometer was used to measure the total soluble solids content (°Brix) of the grapes, providing the ground truth data. After the development of the grape spectra library, four different machine learning algorithms, namely Partial Least Squares regression (PLS), Random Forest regression, Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), coupled with several pre-treatment methods were applied for the prediction of the °Brix content from the VNIR-SWIR hyperspectral data. The performance of the different models was evaluated using a cross-validation strategy with three metrics, namely the coefficient of the determination (R2), the root mean square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). High accuracy was achieved for Malagouzia, Sauvignon-Blanc, and Syrah from the best models developed using the CNN learning algorithm (R2>0.8, RPIQ≥4), while a good fit was attained for the Chardonnay variety from SVR (R2=0.63, RMSE=2.10, RPIQ=2.24), proving that by using a portable spectrometer the in situ estimation of the wine grape maturity could be provided. The proposed methodology could be a valuable tool for wine producers making real-time decisions on harvest time and with a non-destructive way.

Effect of Chlorophyll Content & Solar Irradiance on Spectral Reflectance of Vegetation Canopies Acquired By Spectro-Radiometer

The aims of the study were: (i) to observe the effect of leaf chlorophyll content, Solar Irradiance and Normalized Difference Vegetation Index (NDVI) on spectral reflectance at Visible(Blue,Green,Red), Near Infrared (NIR) and Short Wave Infrared (SWIR) spectrum for a given number of vegetation types including Rongon (Ixora Coccinea), Hibiscus, Jhau, Grass and Togor(Tabernaemontana Divaricata).(ii) to investigate the relationship of Solar Irradiance with Normalized Difference Vegetation Index (NDVI) for the same number of vegetation types. This study used a five band hand-held spectro-radiometer “Multispectral Radiometer MSR-5” centered at wavelength 485nm, 560nm, 660nm, 830nm and 1650nm corresponding to bands 2, 3, 4, 5, 6 of Landsat 8 operational Land Imager (OLI) sensor. This spectro-radiometer provides solar irradiance and spectral reflectance values in the visible, NIR and SWIR spectrum which indirectly help to calculate Normalized Difference Vegetation Index (NDVI) for the given number of vegetation types. This study also used a Chlorophyll Meter (SPAD 502) to estimate chlorophyll concentration from the leaf of the vegetation types. The result shows that the value of the spectral reflectance correlated linearly with chlorophyll content at wavelength at 560nm and 1650 nm where the coefficient of determination R2 is 0.8761 and 0.6289 respectively. The spectral reflectance correlated inversely with NDVI at wavelength 485nm and 660nm where the coefficient of determination R2 was 0.5317 and 0.6191 respectively. This result also shows that solar irradiance relates inversely with chlorophyll content at wavelength 830nm where the coefficient of determination R2 was 0.8523.Lastly we have found that solar irradiance correlated inversely with NDVI where the coefficient of determination R2 was 0.7617.

Three-Channel Infrared Imaging for Object Detection in Haze

Object detection is of wide application for its capability in recognizing and locating targets in scenes. Under heavy hazy conditions, however, the detection performance on RGB images will greatly degrade since the image contents are polluted. In this work, we propose an imaging system to acquire three-channel images in the shortwave infrared (SWIR) spectrum to facilitate object detection under hazy conditions. The system captures SWIR images in the form of pseudo color that are most suitable for detecting objects, such as pedestrians and vehicles. Two different types of filters, i.e., liquid crystal tunable filter (LCTF) and optical filters, are employed in our imaging system design. We use the LCTF to acquire narrowband hyperspectral images, which are fed into a band simulation model to generate wideband images for optimal band selection. We present a specific measure called recognition and localization (RL) score to evaluate the detection performance of three-band combinations. Based on the measure, optimal bands are determined using an efficient searching algorithm. Then, we customize three optical filters and install them on a filter wheel, with which we can acquire three-channel images in the SWIR spectrum. The effectiveness of our imaging system is evaluated on a self-collected RGB-SWIR image dataset. Experimental results indicate that, compared with the RGB images after haze removal, the three-channel SWIR images acquired by our imaging system are of higher quality and can achieve better object detection performance under hazy conditions.

A Spectrum Extension Approach for Radiometric Calibration of the Advanced Hyperspectral Imager Aboard the Gaofen-5 Satellite

The advanced hyperspectral imager (AHSI) is one of the sensors aboard the Chinese Gaofen-5 (GF-5) satellite, possessing characteristics of high spatial and spectral resolution, as well as width swath. To better understand the radiometric performance of GF-5/AHIS after its launch, this article presents an on-orbit radiometric calibration approach for AHSI visible and near-infrared (VNIR) and shortwave infrared (SWIR) sensors from field automatic observations with a field spectrometer in the absence of SWIR measurements. A spectrum extension method was proposed to extend the retrieved surface hyperspectral reflectance in the VNIR spectral ranges to SWIR by incorporating the historical hyperspectral reflectance library. The radiometric calibration coefficients of GF-5/AHSI were calculated by linear fitting of the observed digital number (DN) values with GF-5/AHSI and predicted at-sensor radiances with MODTRAN 5 based on extended hyperspectral surface reflectance. Comparisons with onboard calibration results were also performed, and the averaged relative differences were within 5% with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\delta $ </tex-math></inline-formula> standard deviations less than 10% for most bands, except for those in the atmospheric absorption and low signal-to-noise ratio bands. The comparison results indicate that the on-site radiometric calibration results are consistent with the onboard results, and the operational on-orbit radiometric calibration approach is reliable in the case that there are no measurements in the SWIR spectra range. The on-orbit radiometric performance of GF-5/AHSI rapidly degraded during the first several months after its launch and then tended to be relatively stable.

Using reflectance spectroscopy and Advanced Spaceborne Thermal Emission and Reflection Radiometer data to identify bauxite deposits in vicinity of Az Zabirah, northern Saudi Arabia

Bauxite samples from deposits in northern Saudi Arabia were identified in the laboratory using spectral reflectance measurements (at 0.3–2.5 μm) and X-ray diffraction (XRD). The XRD results revealed that the bauxite deposits are composed mainly of goethite, gibbsite, and boehmite with small amounts of kaolinite, hematite, and quartz. The bauxite spectra revealed the presence of significant iron oxides (at 0.5 and 0.87 μm) accompanied by water (at 1.4 and 1.9 μm) and aluminum hydroxide (at 2.2 μm). The convolved Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectra of the bauxite samples were characterized by an aluminum hydroxide feature in ASTER band 6 (at 2.2 μm). The results demonstrated that principal components analysis band PC2 is the best component for delineating the bauxite deposits. Fractional abundances of bauxite were derived by using a matched-filtering method. This study demonstrates the applicability of reflectance spectroscopy and ASTER data to provide spectral information for distinguishing economically important minerals from visible and near-infrared and shortwave-infrared spectra in arid and semiarid environments such as in Saudi Arabia.

Hyperspectral sensor: A handy tool to evaluate the efficacy of cleaning procedures

• ASD-FieldSpec® 3 operates VNIR and SWIR reflectance spectral range. • VNIR and SWIR provide ready-to-use information on the degree of sulphation of carbonate stone. • SWIR data allowed information on efficacy of three different cleaning techniques. • The method supports monitoring of stone alteration during restoration. In urban environments, degradation processes affecting both natural and artificial carbonate materials commonly result in the formation of sulphate-based deposits (i.e., black crusts). Hyperspectral techniques may provide ready-to-use information on the degree of sulphation of carbonate stone, hence helping to monitor the conservation state of carbonate stone monuments. In this study, the Short-Wave InfraRed (SWIR) hyperspectral technique was tested on the world-famous marble-made Loggia di Baccio d’Agnolo in Santa Maria del Fiore Cathedral of Florence (Italy), affected by extensive “black crust” formation. A SWIR method based on a portable spectroradiometer (ASD Fieldspec® 3) was applied to verify the efficacy of different cleaning methods (i.e., laser, chemical and microbial). The interpretation of the SWIR spectra was based on a full-profile approach, in which the calcite and gypsum contributions were evaluated by weighing the best fit simulated contributions of reference compounds. The technique was able to provide in real time semi-quantitative determinations of the amount of gypsum developed on the stone surface, demonstrating to be a fast and handy routine tool during restoration of artworks.

Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks

This paper addresses the problem of face presentation attack detection using different image modalities. In particular, the usage of short wave infrared (SWIR) imaging is considered. Face presentation attack detection is performed using recent models based on Convolutional Neural Networks using only carefully selected SWIR image differences as input. Conducted experiments show superior performance over similar models acting on either color images or on a combination of different modalities (visible, NIR, thermal and depth), as well as on a SVM-based classifier acting on SWIR image differences. Experiments have been carried on a new public and freely available database, containing a wide variety of attacks. Video sequences have been recorded thanks to several sensors resulting in 14 different streams in the visible, NIR, SWIR and thermal spectra, as well as depth data. The best proposed approach is able to almost perfectly detect all impersonation attacks while ensuring low bonafide classification errors. On the other hand, obtained results show that obfuscation attacks are more difficult to detect. We hope that the proposed database will foster research on this challenging problem. Finally, all the code and instructions to reproduce presented experiments is made available to the research community.

High-resolution hyperspectral-based continuous mineralogical and total organic carbon analysis of the Eagle Ford Group and associated formations in south Texas

Hyperspectral sensing is used to generate mineral maps of a fine-grained, vertical Eagle Ford Group cored section and underlying Buda Limestone and Del Rio Formation in south Texas. This technology produces a map that shows the distribution of minerals on the core surface. Three different cameras within the hyperspectral core-imaging system were used to image a 99.1-m (325-ft) core: a (1) line scan camera, which produces a high-resolution red–green–blue (120-μm) natural-color photograph of the dry core from the visible light spectrum; (2) short-wave infrared (SWIR) spectrometer (300–500-μm resolution); and (3) long-wave infrared (LWIR) spectrometer (300–500-μm resolution). In 2016, the introduction of a new high-resolution LWIR spectrometer made it possible to identify minerals in cores that were not detected by previous SWIR systems. High-resolution hyperspectral imaging technology using both the SWIR and LWIR spectra provides a significant step toward quantifying mineralogy and total organic carbon. Hyperspectral imaging is a powerful tool for studying textural and fabric relationships because it detects and highlights the major mineralogical changes that occur between depositional beds and among depositional features. Calcite compositional variations and cyclicity appear to be closely related. The Sr-rich calcite appears to indicate a smaller allochem size dominance as well as the slower sedimentation rates that are required for nannofossils to settle from suspension. Finally, the observed diagnostic cyclicity of the lower Eagle Ford can be related to coccolithophore productivity.

Spectral sizing of a coarse-spectral-resolution satellite sensor for XCO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;

Abstract. Verifying anthropogenic carbon dioxide (CO2) emissions globally is essential to inform about the progress of institutional efforts to mitigate anthropogenic climate forcing. To monitor localized emission sources, spectroscopic satellite sensors have been proposed that operate on the CO2 absorption bands in the shortwave-infrared (SWIR) spectral range with ground resolution as fine as a few tens of meters to about a hundred meters. When designing such sensors, fine ground resolution requires a trade-off towards coarse spectral resolution in order to achieve sufficient noise performance. Since fine ground resolution also implies limited ground coverage, such sensors are envisioned to fly in fleets of satellites, requiring low-cost and simple design, e.g., by restricting the spectrometer to a single spectral band. Here, we use measurements of the Greenhouse Gases Observing Satellite (GOSAT) to evaluate the spectral resolution and spectral band selection of a prospective satellite sensor with fine ground resolution. To this end, we degrade GOSAT SWIR spectra of the CO2 bands at 1.6 (SWIR-1) and 2.0 µm (SWIR-2) to coarse spectral resolution, without a further addition of noise, and we evaluate single-band retrievals of the column-averaged dry-air mole fractions of CO2 (XCO2) by comparison to ground truth provided by the Total Carbon Column Observing Network (TCCON) and by comparison to global “native” GOSAT retrievals with native spectral resolution and spectral band selection. Coarsening spectral resolution from GOSAT's native resolving power of &gt;20 000 to the range of 700 to a few thousand makes the scatter of differences between the SWIR-1 and SWIR-2 retrievals and TCCON increase moderately. For resolving powers of 1200 (SWIR-1) and 1600 (SWIR-2), the scatter increases from 2.4 (native) to 3.0 ppm for SWIR-1 and 3.3 ppm for SWIR-2. Coarser spectral resolution yields only marginally worse performance than the native GOSAT configuration in terms of station-to-station variability and geophysical parameter correlations for the GOSAT–TCCON differences. Comparing the SWIR-1 and SWIR-2 configurations to native GOSAT retrievals on the global scale, however, reveals that the coarse-resolution SWIR-1 and SWIR-2 configurations suffer from some spurious correlations with geophysical parameters that characterize the light-scattering properties of the scene such as particle amount, size, height and surface albedo. Overall, the SWIR-1 and SWIR-2 configurations with resolving powers of 1200 and 1600 show promising performance for future sensor design in terms of random error sources while residual errors induced by light scattering along the light path need to be investigated further. Due to the stronger CO2 absorption bands in SWIR-2 than in SWIR-1, the former has the advantage that measurement noise propagates less into the retrieved XCO2 and that some retrieval information on particle scattering properties is accessible.

How Do Secondary Minerals in Granite Help Distinguish Paleo- from Present-Day Permeable Fracture Zones? Joint Interpretation of SWIR Spectroscopy and Geophysical Logs in the Geothermal Wells of Northern Alsace

The investigation of permeable hydrothermally altered and fractured zones and their distribution is a key issue for the understanding of fluid circulation in granitic rocks, on which the success of geothermal projects relies. Based on the use of short-wave infrared (SWIR) spectroscopy applied to rock cuttings coupled with interpretation of geophysical logs, we propose an investigation of the clay signature of fault and fracture zones (FZ) inside the granitic basement. This methodology was applied to two geothermal wells: GRT-2 from the Rittershoffen and GPK-1 from the Soultz-sous-Forêts (Soultz) geothermal sites, both located in the Upper Rhine Graben (URG). A total of 1430 SWIR spectra were acquired and analysed. Variations in the 2200 nm absorption band area are correlated with hydrothermal alteration grades. The 2200 nm absorption band area is found to reflect the illite quantity and its variations in the granitic basement. Low, stable values are observed in the unaltered granite facies, showing good reproducibility of the method, whereas scattered high values are associated with high hydrothermal alteration and FZs. Variations in the 2200 nm absorption band area were correlated with the gamma ray and electrical resistivity logs. This procedure allowed us to confirm that illite mainly controls the resistivity response except inside the permeable FZs, where the resistivity response is controlled by the geothermal brine. Thus, the architecture of these permeable FZs was described precisely by using a combination of the 2200 nm absorption band area data and the electrical resistivity log. Moreover, by correlation with other geophysical logs (temperature (T), porosity, and density), paleo-permeable and currently permeable FZs inside the reservoir were distinguished. The correlation of SWIR spectroscopy with electrical resistivity logs appears to be a robust tool for geothermal exploration in granitic reservoirs in the URG.

Automatic determination of shoreline at maximum retreating

Coastal zones are the most influenced areas by global sea level rising. One of the important coastal zones in Egypt is the Red Sea. Red Sea coastal area is an attractive touristic area, which still under development. For the high value of such areas, the position of the set-back construction line is required for well planning and development. This position influenced greatly by the position of the shoreline at the maximum retreating. This paper introduces an automatic approach for determining the shoreline at the maximum retreating. This approach depends on three types of information; 1) the location of the current shoreline, 2) the mean higher high-water level (MHHWL), and 3) the topography of the coastal area.A study area North of Hurghada, is selected to apply the introduced approach. The shoreline is extracted out of Landsat-8 images based on a combination of three factors; a modified normal difference water index, the reflectance values of the water bodies in specific bands within the shortwave infra-red spectrum, and the relation of the reflectance values of water bodies in the Green, Red, NIR, and SWIR bands. The higher high-water level is determined from near tidal gauge station that has collected data each 30 min since 2008, and the topography of the coastal area is determined by the Kinematic GPS technique. The extracted shoreline was assessed by comparing it to actual shoreline traced on the ground. The results show that the accuracy of the extracted shoreline is within 15 m of the actual shoreline.

Sensing Ocean Plastics with an Airborne Hyperspectral Shortwave Infrared Imager.

Here, we present a proof-of-concept on remote sensing of ocean plastics using airborne shortwave infrared (SWIR) imagery. We captured red, green, and blue (RGB) and hyperspectral SWIR imagery with equipment mounted on a C-130 aircraft surveying the "Great Pacific Garbage Patch" at a height of 400 m and a speed of 140 knots. We recorded the position, size, color, and type (container, float, ghost net, rope, and unknown) of every plastic piece identified in the RGB mosaics. We then selected the top 30 largest items within each of our plastic type categories (0.6-6.8 m in length) to investigate SWIR spectral information obtained with a SASI-600 imager (950-2450 nm). Our analyses revealed unique SWIR spectral features common to plastics. The SWIR spectra obtained ( N = 118 items) were quite similar both in magnitude and shape. Nonetheless, some spectral variability was observed, likely influenced by differences in the object optical properties, the level of water submersion, and an intervening atmosphere. Our simulations confirmed that the ∼1215 and ∼1732 nm absorption features have potential applications in detecting ocean plastics from spectral information. We explored the potential of SWIR remote sensing technology for detecting and quantifying ocean plastics, thus provide relevant information to those developing better monitoring solutions for ocean plastic pollution.