Short-wave Infrared Wavelengths Research Articles

Remote Mapping of Bedrock for Future Cosmogenic Nuclide Exposure Dating Studies in Unvisited Areas of Antarctica

Cosmogenic nuclide exposure dating is an important technique for reconstructing glacial histories. Many of the most commonly applied cosmogenic nuclides are extracted from the mineral quartz, meaning sampling of felsic (silica-rich) rock is often preferred to sampling of mafic (silica-poor) rock for exposure dating studies. Fieldwork in remote regions such as Antarctica is subject to time constraints and considerable logistical challenges, making efficient sample recovery critical to successful research efforts. Remote sensing offers an effective way to map the geology of large areas prior to fieldwork and expedite the sampling process. In this study, we assess the viability of multispectral remote sensing to distinguish felsic from mafic rock outcrops at visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelengths using both the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and very high-resolution Worldview-3 (WV-3) imagery. We applied a combination of spectral mapping and ground truth from spectral measurements of 17 rock samples from Mount Murphy in the Amundsen Sea sector of West Antarctica. Using this approach, we identified four dominant rock types which we used as a basis for felsic–mafic differentiation: felsic granites and gneisses, and mafic basalts and fragmental hydrovolcanic rocks. Supervised classification results indicate WV-3 performs well at differentiating felsic and mafic rock types and that ASTER, while coarser, could also achieve satisfactory results and be used in concert with more targeted WV-3 image acquisitions. Finally, we present a revised felsic–mafic geological map for Mt Murphy. Overall, our results highlight the potential of spectral mapping for preliminary reconnaissance when planning future cosmogenic nuclide sampling campaigns in remote, unvisited areas of the polar regions.

Short-Wave Infrared Upconverting Nanoparticles.

Optical technologies enable real-time, noninvasive analysis of complex systems but are limited to discrete regions of the optical spectrum. While wavelengths in the short-wave infrared (SWIR) window (typically, 1700-3000 nm) should enable deep subsurface penetration and reduced photodamage, there are few luminescent probes that can be excited in this region. Here, we report the discovery of lanthanide-based upconverting nanoparticles (UCNPs) that efficiently convert 1740 or 1950 nm excitation to wavelengths compatible with conventional silicon detectors. Screening of Ln3+ ion combinations by differential rate equation modeling identifies Ho3+/Tm3+ or Tm3+ dopants with strong visible or NIR-I emission following SWIR excitation. Experimental upconverted photoluminescence excitation (U-PLE) spectra find that 10% Tm3+-doped NaYF4 core/shell UCNPs have the strongest 800 nm emission from SWIR wavelengths, while UCNPs with an added 2% or 10% Ho3+ show the strongest red emission when excited at 1740 or 1950 nm. Mechanistic modeling shows that addition of a low percentage of Ho3+ to Tm3+-doped UCNPs shifts their emission from 800 to 652 nm by acting as a hub of efficient SWIR energy acceptance and redistribution up to visible emission manifolds. Parallel experimental and computational analysis shows rate equation models are able to predict compositions for specific wavelengths of both excitation and emission. These SWIR-responsive probes open a new IR bioimaging window, and are responsive at wavelengths important for vision technologies.

A New Remote Sensing Index for Forest Dryness Monitoring Using Multi-Spectral Satellite Imagery

Canopy water content is a fundamental indicator for assessing the level of plant water stress. The correlation between changes in water content and the spectral reflectance of plant leaves at near-infrared (NIR) and short-wave infrared (SWIR) wavelengths forms the foundation for developing a new remote sensing index, the Infrared Canopy Dryness Index (ICDI), to monitor forest dryness that can be used to predict the consequences of water stress. This study introduces the index, that uses spectral reflectance analysis at near-infrared wavelengths, encapsulated by the Normalized Difference Infrared Index (NDII), in conjunction with specific canopy conditions as depicted by the widely recognized Normalized Difference Vegetation Index (NDVI). Development of the ICDI commenced with the construction of an NDII/NDVI feature space, inspired by a conceptual trapezoid model. This feature space was then parameterized, and the spatial region indicative of water stress conditions, referred to as the dry edge, was identified based on the analysis of 10,000 random observations. The ICDI was produced from the combination of the vertical distance (i.e., under consistent NDVI conditions) from an examined observation to the dry edge. Comparisons between data from drought-affected and non-drought-affected control plots in the Australian Northern Jarrah Forest affirmed that the ICDI effectively depicted forest dryness. Moreover, the index was able to detect incipient water stress several months before damage from an extended drought and heatwave. Using freely available satellite data, the index has potential for broad application in monitoring the onset of forest dryness. This will require validation of the ICDI in diverse forest systems to quantify the efficacy of the index.

Drone-Acquired Short-Wave Infrared (SWIR) Imagery in Landscape Archaeology: An Experimental Approach

Many rocks, minerals, and soil types reflect short-wave infrared (SWIR) imagery (900–2500 nm) in distinct ways, and geologists have long relied on this property to aid in the mapping of differing surface lithologies. Although surface archaeological features including artifacts, anthrosols, or structural remains also likely reflect SWIR wavelengths of light in unique ways, archaeological applications of SWIR imagery are rare, largely due to the low spatial resolution and high acquisition costs of these data. Fortunately, a new generation of compact, drone-deployable sensors now enables the collection of ultra-high-resolution (<10 cm), hyperspectral (>100 bands) SWIR imagery using a consumer-grade drone, while the analysis of these complex datasets is now facilitated by powerful imagery-processing software packages. This paper presents an experimental effort to develop a methodology that would allow archaeologists to collect SWIR imagery using a drone, locate surface artifacts in the resultant data, and identify different artifact types in the imagery based on their reflectance values across the 900–1700 nm spectrum. Our results illustrate both the potential of this novel approach to exploring the archaeological record, as we successfully locate and characterize many surface artifacts in our experimental study, while also highlighting challenges in successful data collection and analysis, largely related to current limitations in sensor and drone technology. These findings show that as underlying hardware sees continued improvements in the coming years, drone-acquired SWIR imagery can become a powerful tool for the discovery, documentation, and analysis of archaeological landscapes.

Characterizing stalagmite composition using hyperspectral imaging

Stalagmites offer nearly continuous records of past climate in continental settings at high temporal resolution. The climatic records preserved in stalagmites are commonly investigated by examining compositional characteristics such as mineralogy, organic content, and lamination patterns. These proxies provide valuable insights into the environmental conditions during stalagmite formation. However, the methods used to obtain information about these proxies are relatively destructive. This study uses hyperspectral imaging, a non-contact technique, to identify mineral composition, organic matter content, and laminations in stalagmites. It is the first wide spectrum imaging analysis in speleothem research, using both visible–near infrared and shortwave infrared wavelengths. Results obtained from hyperspectral imaging were compared by point spectral analysis using an ASD spectroradiometer and a grayscale profile along the growth axis of a stalagmite. Petrographic observation of thin sections and X-ray diffraction (XRD) analyses on selected stalagmite layers were performed to cross-validate the hyperspectral data. A travertine sample was also used to replicate the method on calcite. To automate mineral identification, a machine learning algorithm was developed to map spatial distribution and quantify relative proportions of minerals across the sample. Our findings are in good agreement with traditionally used methods for mineral identification, i.e. XRD and petrography, aiding in the interpretation of paleoclimate proxies, and offer a spatial guide for U–Th dating analyses. It also provides insight for future investigations of stalagmites using hyperspectral data and classification through machine learning algorithms.

Nonvolatile Switching of Large Nonreciprocal Optical Absorption at Shortwave Infrared Wavelengths.

We report large nonreciprocal optical absorption at shortwave infrared (SWIR) wavelengths in the magnetoelectric (ME) antiferromagnet (AFM) LiNiPO_{4}. The difference in absorption coefficients for light propagating in opposite directions, divided by the sum, reaches up to ∼40% at 1450nm. Moreover, the nonreciprocity is switched by a magnetic field in a nonvolatile manner. Using symmetry considerations, we reveal that the large nonreciprocal absorption is attributed to Ni^{2+} d-d transitions through the spin-orbit coupling. Furthermore, we propose that an even larger nonreciprocity can be achieved for a Ni-based ME AFM where electric dipoles of every NiO_{6} unit and Ni^{2+} spins are orthogonal and, respectively, form a collinear arrangement. This study provides a pathway toward nonvolatile switchable one-way transparency of SWIR light.

Non-Lambertian snow surface reflection models for simulated top-of-the-atmosphere radiances in the NIR and SWIR wavelengths

Accurately modeling snow reflectance is one way to improve satellite observations in the high-latitude regions. Snow surfaces are known to be challenging for atmospheric retrievals in the short-wave infrared (SWIR) wavelength regime due to their low reflectance. For example, current algorithms for satellite-based remote sensing of atmospheric carbon dioxide (CO2) do not take into account the unique reflective properties of snow surfaces. In this paper, we present a measurement-based snow surface reflectance model in the near-infrared (NIR; 755–775 nm) and SWIR (1590–1620 nm, 2040–2080 nm) bands used in remote sensing of atmospheric CO2. We study snow reflectance in detail using a novel atmospheric radiative transfer model (RTM) software and a measurement-based model for snow bi-directional reflectance distribution function (BRDF) to identify how the observations could be optimized in regards of observation geometry and wavelengths. The novel simulation software for NIR-SWIR atmospheric radiative transfer, Raysca, is presented and validated. Top-of-the-atmosphere radiance simulations show that forward-viewing geometries over snow-covered surfaces yield higher radiances than the traditional nadir-viewing geometries, which could indicate the preferability of forward-viewing observation modes in the retrieval of atmospheric CO2. Similarly, atmospheric observations in the 1.6 μm CO2 absorption band might be preferable to the 2.0 μm CO2 absorption band due to higher radiances in the 1.6 μm band.

Assessment of Mechanical Damage and Germinability in Flaxseeds Using Hyperspectral Imaging.

The high demand for flax as a nutritious edible oil source combined with increasingly restrictive import regulations for oilseeds mandates the exploration of novel quantity and quality assessment methods. One pervasive issue that compromises the viability of flaxseeds is the mechanical damage to the seeds during harvest and post-harvest handling. Currently, mechanical damage in flax is assessed via visual inspection, a time-consuming, subjective, and insufficiently precise process. This study explores the potential of hyperspectral imaging (HSI) combined with chemometrics as a novel, rapid, and non-destructive method to characterize mechanical damage in flaxseeds and assess how mechanical stresses impact the germination of seeds. Flaxseed samples at three different moisture contents (MCs) (6%, 8%, and 11.5%) were subjected to four levels of mechanical stresses (0 mJ (i.e., control), 2 mJ, 4 mJ, and 6 mJ), followed by germination tests. Herein, we acquired hyperspectral images across visible to near-infrared (Vis-NIR) (450-1100 nm) and short-wave infrared (SWIR) (1000-2500 nm) ranges and used principal component analysis (PCA) for data exploration. Subsequently, mean spectra from the samples were used to develop partial least squares-discriminant analysis (PLS-DA) models utilizing key wavelengths to classify flaxseeds based on the extent of mechanical damage. The models developed using Vis-NIR and SWIR wavelengths demonstrated promising performance, achieving precision and recall rates >85% and overall accuracies of 90.70% and 93.18%, respectively. Partial least squares regression (PLSR) models were developed to predict germinability, resulting in R2-values of 0.78 and 0.82 for Vis-NIR and SWIR ranges, respectively. The study showed that HSI could be a potential alternative to conventional methods for fast, non-destructive, and reliable assessment of mechanical damage in flaxseeds.

The quantum revolution in vehicle sensor technology

AbstractWith technology like lidar heralding a revolution in vehicle automation, SWIR (short‐wave infrared) imaging and sensing will become increasingly vital to the automotive sector. Quantum dots, nanoscale semiconductors tunable to SWIR wavelengths, could provide the answer to empowering SWIR and bringing enhanced capability to the mass market.

Evaluating potential of leaf reflectance spectra to monitor plant genetic variation

Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution (“hyperspectral”) spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation—information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata: wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400–2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations.

Molecular surface programming of rectifying junctions between InAs colloidal quantum dot solids

Heavy-metal-free III-V colloidal quantum dots (CQDs) show promise in optoelectronics: Recent advancements in the synthesis of large-diameter indium arsenide (InAs) CQDs provide access to short-wave infrared (IR) wavelengths for three-dimensional ranging and imaging. In early studies, however, we were unable to achieve a rectifying photodiode using CQDs and molybdenum oxide/polymer hole transport layers, as the shallow valence bandedge (5.0 eV) was misaligned with the ionization potentials of the widely used transport layers. This occurred when increasing CQD diameter to decrease the bandgap below 1.1 eV. Here, we develop a rectifying junction among InAs CQD layers, where we use molecular surface modifiers to tune the energy levels of InAs CQDs electrostatically. Previously developed bifunctional dithiol ligands, established for II-VI and IV-VI CQDs, exhibit slow reaction kinetics with III-V surfaces, causing the exchange to fail. We study carboxylate and thiolate binding groups, united with electron-donating free end groups, that shift upward the valence bandedge of InAs CQDs, producing valence band energies as shallow as 4.8 eV. Photophysical studies combined with density functional theory show that carboxylate-based passivants participate in strong bidentate bridging with both In and As on the CQD surface. The tuned CQD layer incorporated into a photodiode structure achieves improved performance with EQE (external quantum efficiency) of 35% (>1 μm) and dark current density < 400 nA cm-2, a >25% increase in EQE and >90% reduced dark current density compared to the reference device. This work represents an advance over previous III-V CQD short-wavelength IR photodetectors (EQE < 5%, dark current > 10,000 nA cm-2).

High-quality GeSn thin-film resonant cavities for short-wave infrared applications

High-quality infrared (IR) devices made of group IV materials are highly promising to replace traditional III–V semiconductor-based IR optoelectronics due primarily to their compatibility with mature silicon-based technologies and much lower costs. In this regard, germanium tin (GeSn) has emerged as the only direct bandgap material in the group IV family exhibiting superior electrical and optical characteristics. In the past years, GeSn IR optoelectronics including lasers and photodetectors have been realized, although novel device architectures are still needed to enhance their device performance. Here, we experimentally demonstrate high-performance, large-area (mm2) GeSn thin-film resonant cavities (film thickness resonance operating at short-wave IR wavelengths by employing membrane transfer techniques). The fabricated asymmetric air/GeSn/metal structures exhibit high absorptance (&amp;gt;90%) at designed resonance wavelengths, which are easily tuned by tailoring the GeSn layer thickness. The measured resonance absorption reveals excellent agreement with numerical simulations, which further elucidates the mode characteristics of the observed cavity resonances. The demonstrated thin-film device architectures could greatly facilitate the development of novel GeSn photonic devices with tunable wavelengths and enhanced performance enabled by strain engineering, and could allow for the integration of GeSn into many existing group IV-based devices for next-generation IR optoelectronics where high performance, small footprint, and low cost are all required.

Spectroscopic Microtomography in the Short-Wave Infrared Wavelength Range.

Spectroscopic microtomography provides the ability to perform 4D (3D structural and 1D chemical) imaging of a thick microscopic specimen. Here, we demonstrate spectroscopic microtomography in the short-wave infrared (SWIR) wavelength using digital holographic tomography, which captures both the absorption coefficient and refractive index. A broadband laser in tandem with a tunable optical filter allows us to scan the wavelength from 1100 to 1650 nm. Using the developed system, we measure human hair and sea urchin embryo samples. The resolution estimated with gold nanoparticles is 1.51 μm (transverse) and 1.57 μm (axial) for the field of view of 307 × 246 μm2. The developed technique will enable accurate and efficient analyses of microscopic specimens that have a distinctive absorption or refractive index contrast in the SWIR range.

72 Multi-Spectral SWIR Imaging in Humans Reveals Correlations with Distinct Skin Burn Depths

Abstract Introduction Burn depth determination is a critical aspect of burn patient care but currently lacks accuracy in clinical practice. We have shown first in animal models and then in a two-patient characterization that Short Wave Infrared (SWIR) imaging, which penetrates tissue better than visual or near-infrared light and is very sensitive to water content, can distinguish between superficial and deep tissue necrosis. Here we present the findings from our pilot study of 10 patients showing the use of multi-spectral SWIR imaging of human burn injury as a potential technique for burn depth determination. Methods Ten patients admitted for mixed depth thermal injuries between 5-40% TBSA were analyzed using our SWIR assessment tool. Prior to burn excision the SWIR system was used to image burn areas and normal skin at 4 different SWIR wavelengths (Figure 1A). Standard photographs from imaged areas were collected and presented for 5 independent, blinded, surgeons’ assessments. Using the visual and SWIR images, 5-15 regions of interest (ROI) were selected from each of the burned areas and normalized to adjacent normal skin and the reflected light intensity in each ROI was averaged at each wavelength. Statistics were done using Mann-Whitney U tests. Results Visual and SWIR images were collected from burn areas in 10 patients for a total of 273 burn ROIs. The ROIs were assessed by the surgeons with ROIs being agreed upon as being superficial or superficial partial thickness (SPT) (n=47), deep partial thickness (DPT) (n=97), or full thickness (FT) (n=129) burns by a majority (60% or above) consensus. As seen in Figure 1B, the reflectance intensities (RIs) from superficial and SPT burns were significantly different than the DPT burns at the 1940 nm wavelength (p=0.05) and significantly different than FT burns at 1200 nm (p=0.008) and 1940 nm (p=0.009). When DPT and FT burns were compared there was a significant difference a 1200 nm (p=0.003) and 2250 nm (p=0.01). Conclusions Here we present a 10 patient SWIR pilot study demonstrating distinct ROIs of SWIR wavelengths for different burn depths based on consensus surgeon assessments. Overall, as burn depth increases, the 1200 and 2250 nm wavelengths show increasing RIs and 1940 nm show decreasing RIs. These results motivate further studies of SWIR imaging including pending histological analysis in the hope to non-invasively and accurately identify operative versus non-operative burns. Applicability of Research to Practice This new SWIR technology may enable us to more objectively assess burn depth, leading to improved surgical decision making and better patient outcomes.

Links between light availability and spectral properties of forest floor in European forests

Remote sensing using spectral data has been commonly applied to retrieve tree layer properties while the monitoring of forest floor remains a less studied topic. We investigated the links between light availability at forest floor, and forest floor's spectral reflectance properties (350–2500 nm) and fractional cover across boreal and temperate Europe. We hypothesized that tree canopy structure (and thus, light availability at forest floor) is linked not only to the vegetation composition of forest floor, as has been shown previously, but also to forest floor's spectral reflectance properties, and that these relationships differ between forest biomes. Data were collected in situ from a total of 67 forest stands in southern boreal, hemiboreal, temperate floodplain, and temperate mountain sites. The variation of light availability at forest floor was linked to both the forest floor's composition and spectral reflectance properties. Each study site exhibited site-specific spectral features and a different mean reflectance spectrum. Openness in tree canopies was related to an increase in the fractional cover of vascular plants and to a decrease of plant litter, consequently enhancing the forest floors’ spectral absorptance features in the red and shortwave-infrared wavelengths, as well as reflectance in the near-infrared region. Also, the variations of normalized difference index values and red edge positions as functions light availability at forest floor and forest floor's composition were different for each site. Our results suggest that incorporating biome-specific relationships between tree canopy structure and forest floor reflectance properties would improve interpretation of optical remote sensing data. The measurement data are openly available.

Enhancing Soil Mapping with Hyperspectral Subsurface Images generated from soil lab Vis-SWIR spectra tested in southern Brazil

The Remote Sensing (RS) products involving the visible, near-infrared and short-wave infrared (Vis–NIR-SWIR, simply Vis-SWIR) wavelengths are applicable for spatial prediction of soil properties, usually in the process called Digital Soil Mapping (DSM). The multispectral optical orbital sensors lack spectral resolution, while the Proximal Soil Sensing (PSS) offers many application possibilities. The limited spectral resolution and soil cover are the main challenging aspects for DSM. The objective of the study was to combine the soil lab Vis-SWIR spectra within DSM technique to create new hyperspectral images to use as covariates in a novel method of soil mapping. The advantages of the method were demonstrated throughout the spatial prediction of the Total Carbon (TC) content on soils of the upper part of Itatiaia National Park (INP), (Rio de Janeiro and Minas Gerais States, southeastern Brazil). The covariates applied were multispectral orbital bands from Sentinel 2, relief with its terrain derivations, geology and geomorphology. Using uppermost soil horizon lab Vis-SWIR spectra information from 72 points in the upper part of INP, 130 hyperspectral images were generated, in other words, Hyperspectral Subsurface Image. The validation methods were 8-fold cross-validation (CV) and External Validation (EV) running alongside within Random Forest algorithm, where randomness was locked. The DSM reaches an average CV coefficient of determination (R2) of 0.39 and Root Mean Square Error (RMSE) of 4.6, while creating a new model with a set of hyperspectral images gave a CV R2 of 0.60 and RMSE of 4.06. The EV also presented an advantage for the second approach. The results support the method as the first strong integration between PSS and DSM, named by the authors as Hyperspectral Soil Mapping (HSM), which proved to be more efficient for mapping TC due to the pure soil lab Vis-SWIR spectra signal (free of atmospheric influence and vegetation cover). The method can be applied in dense or sparsely vegetated areas, for agricultural or conservation purposes, for various soil properties, with each and every wavelength.

Inter-Comparison of Landsat-8 and Landsat-9 during On-Orbit Initialization and Verification (OIV) Using Extended Pseudo Invariant Calibration Sites (EPICS): Advanced Methods

Three advanced methodologies were performed during Landsat-9 on orbit and initialization and verification (OIV): Extended Pseudo Invariant Calibration Sites Absolute Calibration Model Double Ratio (ExPAC Double Ratio) and Extended Pseudo Invariant Calibration Sites (EPICS)-based cross-calibration utilizing stable regions in Northern African desert sites (EPICS-NA) and a global scale (EPICS-Global). The development of these three techniques was described using uncertainties analysis. The ExPAC Double Ratio was derived based on the ratio between ExPAC model prediction and satellite measurements for Landsat-8 and Landsat-9. The ExPAC Double Ratio can be performed to determine differences between sensors ranging from visible, red edge, near-infrared, to short-wave infrared wavelengths. The ExPAC Double Ratio and EPICS-based inter-comparison ratio uncertainties were determined using the Monte Carlo Simulation. It was found that the uncertainty levels of 1–2% can be achieved. The EPICS-based cross-calibration results were derived using two targets: EPICS-NA and EPICS-Global, with uncertainties of 1–2.2% for all spectral bands. The inter-comparison results between Landsat-9 and Landsat-8 during the OIV period using the three advanced methods were well within 0.5% for all spectral bands except for the green band, which showed sub 1% agreement.

Application of Spectroscopic Characteristics of White Mica in Porphyry Tungsten Deposits: A Case Study Involving the Shimensi Deposit in Northern Jiangxi

Shortwave infrared (SWIR) technology is characterized by high efficiency and convenience and is widely used in the mineral exploration of porphyry, epithermal, and skarn types. However, studies on the SWIR spectral features of porphyry tungsten deposits are still lacking. The Dahutang tungsten deposit has reached an ultra large scale, characterized by the porphyry type. Based on the SWIR spectral features of white mica and its petrographic, geochemical, and Raman spectral features, this paper discusses the use of shortwave infrared and Raman spectral features and major and trace element contents in white mica for exploration of the Shimensi mine in Dahutang. The results showed that the SWIR wavelength of the single-frequency Al-O-H absorption peak position (Pos2200) of white micas in ore-bearing intrusions were over 2209 nm; the Raman shift of aluminium atom bridge-bonds (Al, O (br)) were mainly located between 410 and 420 cm−1. The contents of Si, Fe, and Mg were relatively high; the contents of Al, Na, and K were low; and the variation of the Nb/Ta value reflected the magmatic evolution degree. The shift of Pos2200 of white mica showed a correlation with the Raman spectral features and contents of Si, Al, and other elements. This study shows that the SWIR spectral features of white mica were useful for further exploration of the Shimensi area in Dahutang and provided a potential tool for the exploration of porphyry tungsten deposits.

Comparison of Recent Remote Sensing Data Using an Artificial Neural Network to Predict Soil Moisture by Focusing on Radiometric Indices

Remote sensing data is widely used as a common variable for digital soil mapping estimating models. The aim of this study, quite recently made available to researchers Operational Land Imager 2 (OLI–2) have structure Landsat 9 and Landsat 8 (OLI) and Sentinel 2A (MSI) to compare the performance of soil moisture estimation in multi-layer perceptron network (MLP) artificial intelligence algorithm of image data. The working area is 886.78 km2 and soil sampling was performed at 66 points for gravimetric soil moisture determination. In addition, after the satellite images were pre-processed, Soil Adjusted Vegetation Index (SAVI) and Normalized Difference Moisture Index (NDMI) were calculated. Landsat 9 (OLI-2) based SAVI and NDMI showed a moderately significant positive correlation relationship with gravimetric soil moisture (rSAVI-SM=0.62, rNMDI-SM=0.44). The relationship between Landsat 8 (OLI) (rSAVI-SM=0.57, rNDMI-SM=0.11) and Sentinel 2A (MSI) (rSAVI-SM=0.42, rNDMI-SM=0.27) based radiometric indices and soil moisture was lower than Landsat 9 (OLI-2). RMSE values of MLP models were found to be respectively 0.79, 1.16 and 1.17 for Landsat 9 (OLI-2), Landsat 8 (OLI) and Sentinel 2A (MSI). Our results showed that with an Operational Land Imager (OLI-2) and near and short-wave infrared wavelengths improvements to multispectral imaging have improved soil moisture estimation success.

Estimating snow cover from high-resolution satellite imagery by thresholding blue wavelengths

The extent and duration of snow cover, a critical component of the hydrologic cycle and the global climate system, is expected to shift dramatically under climate change. Therefore, developing high-resolution assessments of snow cover change is crucial for estimating the impact of changing snow cover on watershed and ecosystems processes in cold regions. Remote sensing tools provide a powerful method for mapping snow-covered area (SCA) across a landscape. The most common method for estimating SCA utilizes the normalized difference snow index (NDSI), which relies on spectral measurements in the shortwave-infrared wavelengths (SWIR). NDSI can effectively estimate catchment- to regional-scale SCA, but it cannot be used to assess fine-scale SCA because of current limitations on the spatial resolution of satellite-derived SWIR measurements. Here, we map SCA using a threshold of blue wavelengths and high-resolution satellite imagery. The thresholding method, which we call the Blue Snow Threshold algorithm (BST), has previously been used with digital camera imagery. We refine and automate the algorithm for use with cloud-free high-resolution satellite imagery and find that the BST can be used to assess fine-scale SCA. For validation, we compared BST-derived estimates of SCA to a) airborne lidar surveys, b) Landsat fractional SCA, and c) snow disappearance dates from Snow Telemetry (SNOTEL) stations. When compared to airborne lidar surveys of SCA, the BST predicted SCA had a range of F-scores between 0.81 and 0.94 in four study areas in California and Colorado. We also found general agreement between SCA and snow disappearance at multiple SNOTEL sites across the western United States. Given the relatively recent availability of high-resolution satellite imagery with spectral measurements in the visible wavelengths but lacking in SWIR, the BST offers a reliable and easy-to-apply tool for examining fine-scale snow-related processes.