Shortwave Infrared Bands Research Articles

Development of soil moisture indices from differences in water absorption between shortwave-infrared bands

Soil moisture (SM) controls the exchange of water and heat energy between the land surface and the atmosphere through evaporation and plant transpiration, and timely and accurate estimates of soil moisture are crucial for many studies. Although remote sensing provides many algorithms to obtain bare-soil moisture on large scales (e.g., Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) products), these algorithms are restricted to studies with low ground resolution. In the present study, we propose and evaluate three normalized shortwave-infrared (SWIR) difference bare soil moisture indices [NSDSI1 = (BSWIR1-BSWIR2)/BSWIR1, NSDSI2 = (BSWIR1 − BSWIR2)/BSWIR2, NSDSI3 = (BSWIR1 − BSWIR2)/(BSWIR1 + BSWIR2), where, SWIR1 = 1550–1750 nm, SWIR2 = 2100–2300 nm] to estimate the bare-soil moisture content and, based on the water absorption difference between shortwave-infrared bands, use them to map bare-soil moisture with high ground resolution Sentinel-2 MSI images. The three proposed bare-soil moisture indices are obtained by using the different water absorption in shortwave-infrared bands. Four traditional hyperspectral-based bare-soil moisture indices (such as the water index SOIL, or WISOIL, the normalized soil moisture index, or NSMI, etc.) were used as benchmark. The results show that (i) the differences in water absorption between shortwave-infrared bands is linear in soil moisture content; (ii) the combined use of two shortwave-infrared bands from four soils provides more accurate bare-soil moisture estimates than do single shortwave-infrared (SWIR) bands, and (iii) our proposed bare-soil moisture indices can be applied on broadband remote-sensing images (such as Landsat, Sentinel-2 MSI). The bare-soil moisture estimates obtained by using the proposed bare-soil moisture indices may help to obtain bare-soil moisture maps with high ground resolution.

Assessing the role of SWIR band in detecting agricultural crop stress: a case study of Raichur district, Karnataka, India.

The potential consequence of global climatic change caused the rise in temperature and precipitation decline, the aftermath of which has led to phenomenon like drought. As the agriculture is mainly dependent on the timely availability of water, delayed arrival of rainfall or decrease in the intensity of precipitation highly affects the growth of crops. If such situation persists for a longer period, the soil moisture content will be exploited completely and maturity of the crop will be stunted, finally adversely affecting the annual crop yield. In the present study, the capability of shortwave-infrared (SWIR) channels in detecting the agricultural crop stress in Raichur district of Karnataka state, India, using spectral vegetation indices namely Global Vegetation Moisture Index (GVMI) and Normalized Multi-band Drought Index (NMDI) has been analyzed using multi-temporal MODIS data. The vegetation health analysis by utilizing both indices were carried out from year 2002 to 2012 for the Kharif season, and it was seen that the year 2002 suffered major agricultural drought where the lower GVMI and NMDI values were covering the majority of the crop areas and in the year 2010 agricultural crop production was observed to be good. The average values of GVMI and NMDI for the years 2002 and 2010 were plotted with the average NDVI values of all months of both years and the results revealed that NDVI values were in concurrence with both NMDI and GVMI.

Twenty Years of ASTER Contributions to Lithologic Mapping and Mineral Exploration

The Advanced Spaceborne Thermal Emission and Reflection Radiometer is one of five instruments operating on the National Aeronautics and Space Administration (NASA) Terra platform. Launched in 1999, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has been acquiring optical data for 20 years. ASTER is a joint project between Japan’s Ministry of Economy, Trade and Industry; and U.S. National Aeronautics and Space Administration. Numerous reports of geologic mapping and mineral exploration applications of ASTER data attest to the unique capabilities of the instrument. Until 2000, Landsat was the instrument of choice to provide surface composition information. Its scanners had two broadband short wave infrared (SWIR) bands and a single thermal infrared band. A single SWIR band amalgamated all diagnostic absorption features in the 2–2.5 micron wavelength region into a single band, providing no information on mineral composition. Clays, carbonates, and sulfates could only be detected as a single group. The single thermal infrared (TIR) band provided no information on silicate composition (felsic vs. mafic igneous rocks; quartz content of sedimentary rocks). Since 2000, all of these mineralogical distinctions, and more, could be accomplished due to ASTER’s unique, high spatial resolution multispectral bands: six in the SWIR and five in the TIR. The data have sufficient information to provide good results using the simplest techniques, like band ratios, or more sophisticated analyses, like machine learning. A robust archive of images facilitated use of the data for global exploration and mapping.

Multi-sensor cloud and cloud shadow segmentation with a convolutional neural network

Cloud and cloud shadow segmentation is a crucial pre-processing step for any application that uses multi-spectral satellite images. In particular, disaster related applications (e.g., flood monitoring or rapid damage mapping), which are highly time- and data-critical, require methods that produce accurate cloud and cloud shadow masks in short time while being able to adapt to large variations in the target domain (induced by atmospheric conditions, different sensors, scene properties, etc.). In this study, we propose a data-driven approach to semantic segmentation of cloud and cloud shadow in single date images based on a modified U-Net convolutional neural network that aims to fulfil these requirements. We train the network on a global database of Landsat OLI images for the segmentation of five classes (“shadow”, “cloud”, “water”, “land” and “snow/ice”). We compare the results to state-of-the-art methods, proof the model's generalization ability across multiple satellite sensors (Landsat TM, Landsat ETM+, Landsat OLI and Sentinel-2) and show the influence of different training strategies and spectral band combinations on the performance of the segmentation. Our method consistently outperforms Fmask and a traditional Random Forest classifier on a globally distributed multi-sensor test dataset in terms of accuracy, Cohen's Kappa coefficient, Dice coefficient and inference speed. The results indicate that a reduced feature space composed solely of red, green, blue and near-infrared bands already produces good results for all tested sensors. If available, adding shortwave-infrared bands can increase the accuracy. Contrast and brightness augmentations of the training data further improve the segmentation performance. The best performing U-Net model achieves an accuracy of 0.89, Kappa of 0.82 and Dice coefficient of 0.85, while running the inference over 896 test image tiles with 44.8 s/megapixel (2.8 s/megapixel on GPU). The Random Forest classifier reaches an accuracy of 0.79, Kappa of 0.65 and Dice coefficient of 0.74 with 3.9 s/megapixel inference time (on CPU) on the same training and testing data. The rule-based Fmask method takes significantly longer (277.8 s/megapixel) and produces results with an accuracy of 0.75, Kappa of 0.60 and Dice coefficient of 0.72.

Replacing the Red Band with the Red-SWIR Band (0.74ρred+0.26ρswir) Can Reduce the Sensitivity of Vegetation Indices to Soil Background

Most vegetation indices (VIs) of remote sensing were designed based on the concept of soil-line, which represents a linear correlation between bare soil reflectance at the red and near-infrared (NIR) bands. Unfortunately, the soil-line can only suppress brightness variation, not color differences of bare soil. Consequently, soil variation has a considerable impact on vegetation indices, although significant efforts have been devoted to this issue. In this study, a new soil-line is established in a new feature space of the NIR band and a virtual band that combines the red and shortwave-infrared (SWIR) bands (0.74ρred+0.26ρswir). Then, plus versions of vegetation indices (VI+), i.e., normalized difference vegetation index plus (NDVI+), enhanced vegetation index plus (EVI+), soil-adjusted vegetation index plus (SAVI+), and modified soil-adjusted vegetation index plus (MSAVI+), are proposed based on the new soil-line, which replaces the red band with the red-SWIR band in the vegetation indices. Soil spectral data from several spectral libraries confirm that bare soil has much less variation for VI+ than the original VI. Simulation experiments show that VI+ correlates better with fractional vegetation coverage (FVC) and leaf area index (LAI) than original VI. Ground measured LAI data collected from BigFoot, VALERI, and other previous references also confirm that VI+ derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data correlates better with ground measured LAI than original VI. These data analyses suggest that replacing the red band with the red-SWIR band can reduce the sensitivity of VIs to soil background. We recommend employing the proposed NDVI+, EVI+, SAVI+, and MSAVI+ in applications of large area, sparse vegetation, or when soil color variation cannot be neglected, although sensitivity to soil moisture and clay content might cause slight side effects for the proposed VI+s.

Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data

Fractional cover of green vegetation (FCover) is a key variable when observing Arctic vegetation under a changing climate. Vegetation changes over large areas are traditionally monitored by linking plot-scale measurements to satellite data. However, integrating field and satellite data is not straightforward. Typically, the satellite data are at a much coarser scale in comparison to field measurements. Here, we studied how Unmanned Aerial Systems (UASs) can be used to bridge this gap. We covered three 250 m × 250 m sites in Fennoscandian tundra with varying productivity and FCover, ranging from barren vegetation to shrub tundra. The UAS sites were then used to train satellite data-based FCover models.First, we created a binary vegetation classification (absent, present) by using UAS-derived RGB-orthomosaics and logistic regression. Secondly, we used the classification to calculate FCover to Planet CubeSat (3 m), Sentinel-2A MSI (10 m, 20 m), and Landsat 8 OLI (30 m) grids, and examined how well FCover is explained by various spectral vegetation indices (VI) derived from satellite data.The overall classification accuracies for the UAS sites were ≥90%. The UAS-FCover were strongly related to the tested VIs (D2 89% at best). The explained deviance was generally higher for coarser resolution data, indicating that the effect of data resolution should be taken into account when comparing results from different sensors. VIs based on red-edge (at 740 nm, 783 nm), or near-infrared and shortwave infrared (SWIR) had the highest performance. We recommend wider inspection of red-edge and SWIR bands for future Arctic vegetation research.Our results demonstrate that UASs can be used for observing FCover at multiple scales. Individual UAS sites can serve as focus areas, which provide information at the finest resolution (e.g. individual plants), whereas a sample of several UAS sites can be used to train satellite data and examine vegetation over larger extents.

Apochromatic lens design in the short-wave infrared band using the Buchdahl dispersion model.

Chromatic aberration is an important artifact that influences image quality. Thus, it should be optimized during lens design. However, the typical combination and selection method of glass materials for the visible band can hardly satisfy the apochromatic requirements of short-wave infrared (SWIR) lenses. Therefore, we propose a method of glass selection for apochromatic lens design in the SWIR band through vector operation based on the Buchdahl model and third-order aberration theory. The proposed method overcomes limitations of traditional methods and unstable calculation results of special material properties. A design example is presented, which indicates that the proposed method can correct the secondary spectrum in the SWIR band and simplify the design using ordinary mass-produced glasses.

The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters

The OLI (Operational Land Imager) sensor on Landsat-8 has the potential to meet the requirements of remote sensing of water color. However, the optical properties of inland waters are more complex than those of oceanic waters, and inland atmospheric correction presents additional challenges. We examined the performance of atmospheric correction (AC) methods for remote sensing over three highly turbid or hypereutrophic inland waters in China: Lake Hongze, Lake Chaohu, and Lake Taihu. Four water-AC algorithms (SWIR (Short Wave Infrared), EXP (Exponential Extrapolation), DSF (Dark Spectrum Fitting), and MUMM (Management Unit Mathematics Models)) and three land-AC algorithms (FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes), 6SV (a version of Second Simulation of the Satellite Signal in the Solar Spectrum), and QUAC (Quick Atmospheric Correction)) were assessed using Landsat-8 OLI data and concurrent in situ data. The results showed that the EXP (and DSF) together with 6SV algorithms provided the best estimates of the remote sensing reflectance (Rrs) and band ratios in water-AC algorithms and land-AC algorithms, respectively. AC algorithms showed a discriminating accuracy for different water types (turbid waters, in-water algae waters, and floating bloom waters). For turbid waters, EXP gave the best Rrs in visible bands. For the in-water algae and floating bloom waters, however, all water-algorithms failed due to an inappropriate aerosol model and non-zero reflectance at 1609 nm. The results of the study show the improvements that can be achieved considering SWIR bands and using band ratios, and the need for further development of AC algorithms for complex aquatic and atmospheric conditions, typical of inland waters.

Mapping distribution of Sundarban mangroves using Sentinel-2 data and new spectral metric for detecting their health condition

The present study explores the suitability of Sentinel-2 images for mapping the physiological conditions of Sundarban mangroves and identifying suitable wavebands for the purpose. This work finds two shortwave infrared (SWIR) bands of Sentinel-2 appropriate and proposes a new spectral metric, namely Discriminant Normalized Vegetation Index (DNVI) based on these two bands. The spectral profile of mangroves is validated with the hyperspectral and high spatial resolution Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data. DNVI emphasized the importance of SWIR bands in segregating different health conditions of similar and dissimilar mangrove species assemblages. Classification using Support Vector Machine supervised learning model discriminated mangroves with an overall accuracy of 93.9%. The article highlighted the competence of high spatial resolution Sentinel-2 images and the proposed spectral metric can be exploited for mapping mangrove assemblages and their health at different eco-regions.

Tree Species Classification with Multi-Temporal Sentinel-2 Data

The Sentinel-2 program provides the opportunity to monitor terrestrial ecosystems with a high temporal and spectral resolution. In this study, a multi-temporal Sentinel-2 data set was used to classify common tree species over a mature forest in central Sweden. The tree species to be classified were Norway spruce (Picea abies), Scots pine (Pinus silvestris), Hybrid larch (Larix × marschlinsii), Birch (Betula sp.) and Pedunculate oak (Quercus robur). Four Sentinel-2 images from spring (7 April and 27 May), summer (9 July) and fall (19 October) of 2017 were used along with the Random Forest (RF) classifier. A variable selection approach was implemented to find fewer and uncorrelated bands resulting in the best model for tree species identification. The final model resulting in the highest overall accuracy (88.2%) came from using all bands from the four image dates. The single image that gave the most accurate classification result (80.5%) was the late spring image (27 May); the 27 May image was always included in subsequent image combinations that gave the highest overall accuracy. The five tree species were classified with a user’s accuracy ranging from 70.9% to 95.6%. Thirteen of the 40 bands were selected in a variable selection procedure and resulted in a model with only slightly lower accuracy (86.3%) than that using all bands. Among the highest ranked bands were the red edge bands 2 and 3 as well as the narrow NIR (near-infrared) band 8a, all from the 27 May image, and SWIR (short-wave infrared) bands from all four image dates. This study shows that the red-edge bands and SWIR bands from Sentinel-2 are of importance, and confirms that spring and/or fall images capturing phenological differences between the species are most useful to tree species classification.

Feasibility Study of Ore Classification Using Active Hyperspectral LiDAR

Recently, a major effort has been made to develop methods or tools for rock characterization and mineral content mapping. Light detection and ranging (LiDAR) is an efficient active remote sensing technique for collecting geometry information about rock surfaces. However, traditional LiDAR sensors work with a single-wavelength laser source, and it is unfeasible to obtain spectral information using one LiDAR sensor. The combination of hyperspectral imaging and LiDAR techniques is an emerging method for acquiring spatial and spectral information simultaneously that allows remote mapping of high-resolution mineral content and distributions and identifies subtle chemical variations. Unfortunately, spatial and spectral data registration, which introduces additional complicated data processing, is an inevitable and essential issue for this method. In this letter, first, we investigate the feasibility of ore classification applications with hyperspectral LiDAR (HSL). HSL consists of 17 spectral channels covering the visible–shortwave infrared (SWIR) spectral range. Spatial and spectral information about seven different ore samples is obtained under a controlled laboratory environment using HSL. The standard deviation of the distance measurements is less than 1.1 cm for different spectral channels, and the classification accuracy can reach 100% if all 17 spectral measurements are used. To optimize the system design with lower cost and system complexity, a spectral band selection criterion is built based on the feature contribution degree (FCD), which is calculated using the normalized variance of the reflectance values for different ore samples at each wavelength. Two different strategies of FCD selection are tested to generate vectors: ascending sequences and descending sequences. Feature vectors with descending sequences have better classification accuracy. In addition, the results show that the classification accuracy can reach 100% with the feature vector of the seven largest FCD values compared to 59.57% for the feature vector with the seven smallest FCD values. Moreover, we find that the channels with high FCD values are primarily centered in SWIR bands. This result could be a reference for optimizing the hardware design of HSL for ore classification or mineral identification.

High-sensitivity short-wave infrared technology for thermal imaging

Thermal imaging technology is widely used in various fields, such as security, defense, medicine and spaceborne applications in the Geo-synchronous Earth Orbit. Most thermal imaging systems operate in the long-wave infrared (LWIR) range or the medium-wave infrared (MWIR) range. However, the longer the wavelength is, the more difficult the imaging system is to provide high resolution due to diffraction limit. Compared to MWIR and LWIR imagers, a short-wave infrared (SWIR) imager achieves higher resolution owing to its higher diffraction limit. However, radiation from an object at 300 k in the SWIR band is weaker than that in the MWIR and LWIR bands. Therefore, the sensitivity is low. With the rapid development of detector technology, the sensitivity of SWIR detector is improved by low dark current and noise. It exhibits good performance in thermal imaging applications. In order to validate SWIR thermal imaging key performance, this study analyzes the thermal imaging capability of an SWIR imager compared to MWIR and LWIR. The noise equivalent temperature differences (NETDs) of SWIR, MWIR, and LWIR imagers for thermal imaging applications are calculated. A compact SWIR imager prototype covering a spectral range of 2.0–2.7 μm is designed and implemented. The facial thermal imaging experiment proves that the high-sensitivity SWIR imager can obtain high-quality thermal images with an NETD of 24 mK at a temperature of 310 K. In addition, in the continuous integration and multiple read-out (IMRO) imaging mode, the dynamic range of the system is extended by 20 dB. The theoretical analysis and experiment show that the SWIR imager can provide high sensitivity with long integration time. In addition, SWIR thermal imaging can provide higher resolution than MWIR and LWIR imagers. Therefore, SWIR imager has considerable potential for high-sensitivity thermal imaging applications in Geo-synchronous Earth Orbit.

Application of Mixture Tuned Matched Filtering on ASTER Data for Hydrothermal Alteration Mapping Related to Porphyry Cu Deposits in Jabal-Barez Ranges, Kerman Copper Belt, Iran

The study area is located in Jabal-Barez Ranges, southeastern part of Kerman Copper Belt (KCB), where the porphyry copper mineralization such as Kerver occurs. This article deals with the enhancement of hydrothermal alteration minerals for exploration of porphyry Cu mineralization, and differentiates intense hydrothermal alteration zones from those with low intensity. Shortwave Infrared (SWIR) bands of ASTER data were used for mineral enhancement. The spectra of representative alteration mineral assemblages consisting of sericite + illite, kaolinite + montmorillonite and epidote + chlorite were extracted from SWIR bands of ASTER. The spectra were applied for mineral discrimination using Mixture Tuned Matched Filtering (MTMF) technique. The image processing results were verified using field observation, spectral measurements, thin section and X-ray diffraction studies of the samples. Findings exhibit ASTER capability to discriminate different alteration zones. Findings also significantly show the role of MTMF algorithm as a useful technique for mineral exploration.

Atmospheric correction of geostationary Himawari-8 satellite data for Total Suspended Sediment mapping: A case study in the Coastal Waters of Western Australia

Recent studies in the application of geostationary satellite sensors, such as the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the European Union’s meteorological satellite, and the Geostationary Ocean Color Imager (GOCI) from South Korea, to monitor the Total Suspended Sediment (TSS) in coastal waters have shown that the higher temporal dynamics in the coastal processes can be better achieved with high temporal resolution geostationary satellite sensors. The availability of the geostationary satellite, Himawari-8 carrying the Advanced Himawari Imager (AHI) sensor with visible, Near InfraRed (NIR) and Shortwave InfraRed (SWIR) bands over the coastal waters of Australia has prompted this study to test the feasibility of AHI in mapping TSS concentration. In this study, we present an atmospheric correction method for AHI data using two different aerosol correction methods, a combination of two SWIR bands, and a combination of one NIR and one SWIR band. The outcome from this study indicated that the atmospherically corrected AHI data at 10 min temporal resolution can be used to discern TSS concentrations greater than ∼0.15 mg/L when solar zenith angles (θ0) are less than 60°. The result of the cross-validation of AHI derived TSS concentration, with MODIS-Aqua and Lansat-8 Operational Land Imager (OLI) derived TSS concentration showed good correlation, with correlation coefficients r = 0.71 and r = 0.91, respectively.

Determination of the TROPOMI-SWIR instrument spectral response function

Abstract. The Tropospheric Monitoring Instrument (TROPOMI) is the single instrument on board the ESA Copernicus Sentinel-5 Precursor satellite. TROPOMI is a nadir-viewing imaging spectrometer with bands in the ultraviolet and visible, the near infrared and the shortwave infrared (SWIR). An accurate instrument spectral response function (ISRF) is required in the SWIR band where absorption lines of CO, methane and water vapor overlap. In this paper, we report on the determination of the TROPOMI-SWIR ISRF during an extensive on-ground calibration campaign. Measurements are taken with a monochromatic light source scanning the whole detector, using the spectrometer itself to determine the light intensity and wavelength. The accuracy of the resulting ISRF calibration key data is well within the requirement for trace-gas retrievals. Long-term in-flight monitoring of SWIR ISRF is achieved using five on-board diode lasers.

Guide star catalog generation for short-wave infrared (SWIR) All-Time star sensor.

As an important part of All-Time star sensor design, the generation of the short-wave infrared (SWIR) guide star catalog is crucial to the system performance. The generation process needs estimation of the instrument magnitude and the guide star selection. Different from the commonly used star sensors, since the SWIR band is far away from the visual band and the detectable magnitude limit of the All-Time star sensor is dynamically changing as the observation conditions vary, the current methods of estimating the instrument magnitude cannot be directly applied and the catalog obtained through the current reduction methods that mainly aimed at improving the distribution uniformity cannot ensure enough stars measured in the field of view under strong sky background radiation. To solve the problems, we propose a method for guide star catalog generation for the All-Time star sensor. First, through the specific analysis of the spectral response curves of the SWIR detector and 2MASS detection bandpasses, the method of estimating instrument magnitudes for the All-Time sensor is determined. Subsequently, dynamic detectable magnitude limits are determined through the signal-to-noise model and the atmospheric background radiation intensity analysis. Based on the dynamic detectable magnitude limits, a reduction method is proposed. The simulation experiment results indicate that the RMS error of the estimation of instrument magnitude is 0.075. Compared to the magnitude filtering method, the guide star catalog obtained through our method can guarantee the completeness, besides, the global distribution uniformity increases by 2.2 times and the local distribution uniformity increases by 10.7 times.

Enhancement of Image Classification for Forest Encroachment Mapping With Destriped SWIR Band in the Wavelet Domain

Commonly used destriping techniques in remote sensing applications have underlying Fourier transform process which tends to alter heavily the digital number values of surface features; thus, impacting its subsequent use in any image classification scheme. In this paper, we develop a novel destriping technique in the wavelet domain where statistical measures namely, standard deviation, signal-to-noise ratio, and entropy of the image and stripes are integrated in the wavelet filter design. Wavelet transform essentially keeps spatial information of the stripes in the transformed domain; thus, allowing a wavelet to filter stripes at different scales with minimal effect on image pixels relating to the surface features. We further propose a novel s for an environmental application, where a destriped band in the wavelet domain is included in image classification for forest encroachment mapping. The technique is implemented on IRS-1D LISS-III data for the mapping of forest encroachment in the Andaman and Nicobar Island, India, wherein the short wave infrared (SWIR) band is destriped and included in the image classification based on the linear support vector machine method. Statistical accuracy in terms of Kappa values for the resultant classified images by 1) including striped SWIR, 2) excluding SWIR, and 3) using destriped SWIR band are 0.97, 0.98, and 1, respectively. Z -tests indicate that the thematic map with destriped SWIR band is statistically significantly better than the other two maps.

Solution-Synthesized High-Mobility Tellurium Nanoflakes for Short-Wave Infrared Photodetectors.

Two-dimensional (2D) materials, particularly black phosphorus (bP), have demonstrated themselves to be excellent candidates for high-performance infrared photodetectors and transistors. However, high-quality bP can be obtained only via mechanical exfoliation from high-temperature- and high-pressure-grown bulk crystals and degrades rapidly when exposed to ambient conditions. Here, we report solution-synthesized and air-stable quasi-2D tellurium (Te) nanoflakes for short-wave infrared (SWIR) photodetectors. We perform comprehensive optical characterization via polarization-resolved transmission and reflection measurements and report the absorbance and complex refractive index of Te crystals. It is found that this material is an indirect semiconductor with a band gap of 0.31 eV. From temperature-dependent electrical measurements, we confirm this band-gap value and find that 12 nm thick Te nanoflakes show high hole mobilities of 450 and 1430 cm2 V-1 s-1 at 300 and 77 K, respectively. Finally, we demonstrate that despite its indirect band gap, Te can be utilized for high-performance SWIR photodetectors by employing optical cavity substrates consisting of Au/Al2O3 to dramatically increase the absorption in the semiconductor. By changing the thickness of the Al2O3 cavity, the peak responsivity of Te photoconductors can be tuned from 1.4 μm (13 A/W) to 2.4 μm (8 A/W) with a cutoff wavelength of 3.4 μm, fully capturing the SWIR band. An optimized room-temperature specific detectivity ( D*) of 2 × 109 cm Hz1/2 W-1 is obtained at a wavelength of 1.7 μm.

Sentinel-MSI VNIR and SWIR Bands Sensitivity Analysis for Soil Salinity Discrimination in an Arid Landscape

Depending on the band position on the electromagnetic spectrum, optical and electronic characteristics, sensors collect the reflected energy by the Earth’s surface and the atmosphere. Currently, the availability of the new generation of medium resolution, such as the Multi-Spectral Instrument (MSI) on board the Sentinel-2 satellite, offers new opportunities for long-term high-temporal frequency for Earth’s surfaces observation and monitoring. This paper focuses on the analysis and the comparison of the visible, the near-infrared (VNIR), and the shortwave infrared (SWIR) spectral bands of the MSI for soil salinity discrimination in an arid landscape. To achieve these, a field campaign was organized, and 160 soil samples were collected with various degrees of soil salinity, including non-saline soil samples. The bidirectional reflectance factor was measured above each soil sample in a goniometric laboratory using an ASD (Analytical Spectral Devices) spectroradiometer. In the laboratory work, pHs, electrical conductivity (EC-Lab), and the major soluble cations (Na+, K+, Ca2++, and Mg2+) and anions (CO32−, HCO3−, Cl−, and SO42−) were measured using extraction from a saturated soil paste, and the sodium adsorption ratio (SAR) was calculated using a standard procedure. These parameters, in addition to the field observations, were used to interpret and investigate the spectroradiometric measurements and their relevant transformations using the continuum removed reflectance spectrum (CRRS) and the first derivative (FD). Moreover, the acquired spectra over all the soil samples were resampled and convolved in the solar-reflective spectral bands using the Canadian Modified Herman transfer radiative code (CAM5S) and the relative spectral response profiles characterizing the Sentinel-MSI band filters. The statistical analyses conducted were based on the second-order polynomial regression (p < 0.05) between the measured EC-Lab and the reflectances in the MSI convolved spectral bands. The results obtained indicate the limitation of VNIR bands and the potential of SWIR domain for soil salinity classes’ discrimination. The CRRS and the FD analyses highlighted a serious spectral-signal confusion between the salt and the soil optical properties (i.e., color and brightness) in the VNIR bands. Likewise, the results stressed the independence of the SWIR domain vis-a-vis these soil artifacts and its capability to differentiate significantly among several soil salinity classes. Moreover, the statistical fit between each MSI individual spectral band and EC-Lab corroborates this trend, which revealed that only the SWIR bands were correlated significantly (R2 of 50% and 64%, for SWIR-1 and SWIR-2, respectively), while the R2 between the VNIR bands and EC-Lab remains less than 9%. According to the convergence of these four independent analysis methods, it is concluded that the Sentinel-MSI SWIR bands are excellent candidates for an integration in soil salinity modeling and monitoring at local, regional, and global scales.

SHARPENDING OF THE VNIR AND SWIR BANDS OF THE WIDE BAND SPECTRAL IMAGER ONBOARD TIANGONG-II IMAGERY USING THE SELECTED BANDS

Abstract. The Tiangong-II space lab was launched at the Jiuquan Satellite Launch Center of China on September 15, 2016. The Wide Band Spectral Imager (WBSI) onboard the Tiangong-II has 14 visible and near-infrared (VNIR) spectral bands covering the range from 403–990 nm and two shortwave infrared (SWIR) bands covering the range from 1230–1250 nm and 1628–1652 nm respectively. In this paper the selected bands are proposed which aims at considering the closest spectral similarities between the VNIR with 100 m spatial resolution and SWIR bands with 200 m spatial resolution. The evaluation of Gram-Schmidt transform (GS) sharpening techniques embedded in ENVI software is presented based on four types of the different low resolution pan band. The experimental results indicated that the VNIR band with higher CC value with the raw SWIR Band was selected, more texture information was injected the corresponding sharpened SWIR band image, and at that time another sharpened SWIR band image preserve the similar spectral and texture characteristics to the raw SWIR band image.