Explore the vast range of titles available.

Aboveground biomass (AGB) plays a critical functional role in coastal wetland ecosystem stability, with high biomass vegetation contributing to organic matter production, sediment accretion potential, and the surface elevation’s ability to keep pace with relative sea level rise. Many remote sensing studies have employed either imaging spectrometer or synthetic aperture radar (SAR) for AGB estimation in various environments for assessing ecosystem health and carbon storage. This study leverages airborne data from NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) to assess their unique capabilities in combination to estimate AGB in coastal deltaic wetlands. Here we develop AGB models for emergent herbaceous and forested wetland vegetation in coastal Louisiana. In addition to horizontally emitted, vertically received (HV) backscatter, SAR parameters are expressed by the Freeman–Durden polarimetric decomposition components representing volume and double-bounce scattering. The imaging spectrometer parameters include normalized difference vegetation index (NDVI), reflectance from 290 visible-shortwave infrared (VSWIR) bands, the first derivatives from those bands, or partial least squares (PLS) x-scores derived from those data. Model metrics and cross-validation indicate that the integrated models using the Freeman-Durden components and PLS x-scores improve AGB estimates for both wetland vegetation types. In our study domain over Louisiana’s Wax Lake Delta (WLD), we estimated a mean herbaceous wetland AGB of 3.58 Megagrams/hectare (Mg/ha) and a total of 3551.31 Mg over 9.92 km2, and a mean forested wetland AGB of 294.78 Mg/ha and a total of 27,499.14 Mg over 0.93 km2. While the addition of SAR-derived values to imaging spectrometer data provides a nominal error decrease for herbaceous wetland AGB, this combination significantly improves forested wetland AGB prediction. This integrative approach is particularly effective in forested wetlands as canopy-level biochemical characteristics are captured by the imaging spectrometer in addition to the variable structural information measured by the SAR.

The Emirates Mars Mission Emirates Mars Infrared Spectrometer (EMIRS) will provide remote measurements of the martian surface and lower atmosphere in order to better characterize the geographic and diurnal variability of key constituents (water ice, water vapor, and dust) along with temperature profiles on sub-seasonal timescales. EMIRS is a FTIR spectrometer covering the range from 6.0-100+ μm (1666-100 cm −1 ) with a spectral sampling as high as 5 cm −1 and a 5.4-mrad IFOV and a 32.5×32.5 mrad FOV. The EMIRS optical path includes a flat 45° pointing mirror to enable one degree of freedom and has a +/- 60° clear aperture around the nadir position which is fed to a 17.78-cm diameter Cassegrain telescope. The collected light is then fed to a flat-plate based Michelson moving mirror mounted on a dual linear voice-coil motor assembly. An array of deuterated L-alanine doped triglycine sulfate (DLaTGS) pyroelectric detectors are used to sample the interferogram every 2 or 4 seconds (depending on the spectral sampling selected). A single 0.846 μm laser diode is used in a metrology interferometer to provide interferometer positional control, sampled at 40 kHz (controlled at 5 kHz) and infrared signal sampled at 625 Hz. The EMIRS beamsplitter is a 60-mm diameter, 1-mm thick 1-arcsecond wedged chemical vapor deposited diamond with an antireflection microstructure to minimize first surface reflection. EMIRS relies on an instrumented internal v-groove blackbody target for a full-aperture radiometric calibration. The radiometric precision of a single spectrum (in 5 cm −1 mode) is <3.0×10 −8 W cm −2  sr −1 /cm −1 between 300 and 1350 cm −1 over instrument operational temperatures (<∼0.5 K NE Δ T @ 250 K). The absolute integrated radiance error is < 2% for scene temperatures ranging from 200-340 K. The overall EMIRS envelope size is 52.9×37.5×34.6 cm and the mass is 14.72 kg including the interface adapter plate. The average operational power consumption is 22.2 W, and the standby power consumption is 18.6 W with a 5.7 W thermostatically limited, always-on operational heater. EMIRS was developed by Arizona State University and Northern Arizona University in collaboration with the Mohammed bin Rashid Space Centre with Arizona Space Technologies developing the electronics. EMIRS was integrated, tested and radiometrically calibrated at Arizona State University, Tempe, AZ.

The OSIRIS-REx Visible and Infrared Spectrometer (OVIRS) is a point spectrometer covering the spectral range of 0.4 to 4.3 microns (25,000-2300 cm−1). Its primary purpose is to map the surface composition of the asteroid Bennu, the target asteroid of the OSIRIS-REx asteroid sample return mission. The information it returns will help guide the selection of the sample site. It will also provide global context for the sample and high spatial resolution spectra that can be related to spatially unresolved terrestrial observations of asteroids. It is a compact, low-mass (17.8 kg), power efficient (8.8 W average), and robust instrument with the sensitivity needed to detect a 5% spectral absorption feature on a very dark surface (3% reflectance) in the inner solar system (0.89-1.35 AU). It, in combination with the other instruments on the OSIRIS-REx Mission, will provide an unprecedented view of an asteroid's surface.

Fire severity represents fire-induced environmental changes and is an important variable for modeling fire emissions and planning post-fire rehabilitation. Remotely sensed fire severity is traditionally evaluated using the differenced normalized burn ratio (dNBR) derived from multispectral imagery. This spectral index is based on bi-temporal differenced reflectance changes caused by fires in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions. Our study aims to evaluate the spectral sensitivity of the dNBR using hyperspectral imagery by identifying the optimal bi-spectral NIR SWIR combination. This assessment made use of a rare opportunity arising from the pre- and post-fire airborne image acquisitions over the 2013 Rim and 2014 King fires in California with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. The 224 contiguous bands of this sensor allow for 5760 unique combinations of the dNBR at a high spatial resolution of approximately 15 m. The performance of the hyperspectral dNBR was assessed by comparison against field data and the spectral optimality statistic. The field data is composed of 83 in situ measurements of fire severity using the Geometrically structured Composite Burn Index (GeoCBI) protocol. The optimality statistic ranges between zero and one, with one denoting an optimal measurement of the fire-induced spectral change. We also combined the field and optimality assessments into a combined score. The hyperspectral dNBR combinations demonstrated strong relationships with GeoCBI field data. The best performance of the dNBR combination was derived from bands 63, centered at 0.962 µm, and 218, centered at 2.382 µm. This bi-spectral combination yielded a strong relationship with GeoCBI field data of R2 = 0.70 based on a saturated growth model and a median spectral index optimality statistic of 0.31. Our hyperspectral sensitivity analysis revealed optimal NIR and SWIR bands for the composition of the dNBR that are outside the ranges of the NIR and SWIR bands of the Landsat 8 and Sentinel-2 sensors. With the launch of the Precursore Iperspettrale Della Missione Applicativa (PRISMA) in 2019 and several planned spaceborne hyperspectral missions, such as the Environmental Mapping and Analysis Program (EnMAP) and Surface Biology and Geology (SBG), our study provides a timely assessment of the potential and sensitivity of hyperspectral data for assessing fire severity.

This work proposes a novel approach to developing a core component for a near-infrared (NIR) spectrometer with wavelength tunability, which is based on a liquid crystal (LC)-in-cavity structure as a hybrid photonic crystal (PC). By electrically altering the tilt angle of the LC molecules under applied voltage, the proposed PC/LC photonic structure consisting of an LC layer sandwiched between two multilayer films generates transmitted photons at specific wavelengths as defect modes within the photonic bandgap (PBG). The relationship between the number of defect-mode peaks and the cell thickness is investigated using a simulated approach based on the 4 × 4 Berreman numerical method. Furthermore, the defect-mode wavelength shifts driven by various applied voltages are studied experimentally. To minimize the power consumption of the optical module for spectrometric application, cells of different thicknesses are explored for the wavelength-tunability performance of the defect modes scanning through the entire free spectral ranges to the wavelengths of their next higher orders at null voltage. A 7.9 μm thick PC/LC cell is verified to attain the low operating voltage of merely 2.5 Vrms required to successfully cover the entire NIR spectral range between 1250 and 1650 nm. The proposed PBG structure is thus an excellent candidate for application in monochromator or spectrometer development.

The use of a portable near-infrared (NIR) spectrometer for detecting sea buckthorn juice SSC has not been explored. In this study, spectral data of 180 juice samples were collected using a portable NIR spectrometer. An SSC prediction model based on a mixture of experts convolutional neural network (MoE-CNN) was proposed. The MoE-CNN model was compared with traditional chemometric models in terms of prediction performance and feature extraction capability. The results showed that detecting the SSC of sea buckthorn juice using a portable NIR spectrometer combined with the MoE-CNN model is feasible. The optimal chemometric model, CARS-PLS, achieved RMSEP and RPD values of 1.42% and 2.67, respectively. The MoE-CNN model outperformed chemometric models and the CNN model, achieving an RMSEP of 1.26% and RPD of 3.02. Compared with CARS-PLSR, MoE-CNN adaptively weighted spectral features through MoE and feature fusion modules, effectively suppressing spectral noise and improving detailed feature extraction. These findings demonstrate that combining a portable NIR spectrometer with MoE-CNN is effective for rapid SSC detection in sea buckthorn juice. This study provides a new approach for the rapid detection of sea buckthorn juice SSC.

The thermal Infrared Spectrometer (TIS) is the thermal infrared (TIR) sensor on-board the first Sustainable Development Goals (SDG-1) satellite. The TIS data can potentially be used to support improved monitoring of ground conditions with high-spatial resolutions, so accurate radiometric calibration is required. A meticulous radiometric calibration was conducted on the prototype of TIS to test its ability to convert a raw digital number (DN) to at-aperture radiance. The initial maximum radiometric error was 2.19 K at 300 K for Band 1(B1) and the minimum radiometric error was 0.25 K at 300 K rooted in Band 3 (B3). The R-Squared (R2) was over 0.99 for each band. The methodology was refined to divide the channel detectable temperature range into three sub-ranges and then the maximum radiometric errors were reduced to less than 1 K at 300 K for three bands. Subsequently, the Generalized Split-Window (SW) algorithm was preformed to estimate the ability of TIS on land surface temperature (LST) retrieval. In order to take advantage of its high-spatial resolution and make full use of TIR data, three-channel SW algorithm was also performed for intercomparison. Results showed that the SW algorithm can obtain LST with root-mean-square error (RMSE) less than 1K. Compared with two-channel algorithm with RMSE = 0.94 K, three-channel algorithm achieves better results in retrieving LST with RMSE = 0.82 K. For different land surface types, water samples achieved the minimum RMSE, and for different atmospheric column water vapor (CWV), dry atmospheres obtained better results. The sensitivity analysis of SW algorithm was considered along with noise-equivalent differential temperature (NEΔT), uncertainty of land surface emissivity (LSE) and input land surface temperature (Ts). Generally, three-channel algorithm was more stable to LSE uncertainties, and the error changes were within 40%. But when NEΔT and Ts uncertainties were included, the error percentage of three-channel SW method increases more, which means three-channel SW method is more sensitive to those two factors. All in all, the methodology and results used for radiometric calibration and LST retrieval in this study provide valuable guidance for the flight model of TIS and post-launch applications.

Soil-available nitrogen is a vital index related to the growth and development of crops. The real-time and nondestructive detection of the soil-available nitrogen content based on near-infrared (NIR) spectroscopy could improve the accurate management of crop nutrients. In this manuscript, soil NIR spectroscopy and available nitrogen data are used in a stacked framework to develop a reliable and accurate soil-available nitrogen model. The spectral reflectance of the soil samples was collected in the 900 to 1700 nm band with nine pre-processing methods using a handheld micronear-infrared spectrometer. The stacking framework of this manuscript has two layers. Extreme gradient boosting (XGBoost), categorical boosting (CatBoost), a light gradient boosting machine (LightGBM), and a random forest, which are tree-based algorithms, are stacked as base models in the first layer. In the second layer, linear regression is employed in a meta-model to identify the unique learning pattern of the base model. The results show that the range and characteristics of the spectra can be used to make relevant predictions , and the micro-NIR spectra are variable under different pre-treatments. In addition, the stacked model achieves the best performance of all the models tested. Notably, the coefficient of determination (R 2 ) is 0.942, and the relative percent difference is 4.192 with Savitzky–Golay and multiplicative scatter correction. This manuscript presents an efficient method for predicting soil-available nitrogen levels with a handheld micronear-infrared spectrometer.

The moisture content of black tea is an important factor affecting its suitability for processing and forming the unique flavor. At present, the research on the moisture content of black tea mainly focuses on a single withering step, but the research on the rapid detection method of moisture content of black tea applicable to the entire processing stage is ignored. This study is based on a miniaturized near-infrared spectrometer(micro−NIRS) and establishes the prediction models for black tea moisture content through machine learning algorithms. We use micro−NIRS for spectroscopic data acquisition of samples. Linear partial least squares (PLS) and nonlinear support vector regression (SVR) were combined with four spectral pre−processing methods, and principal component analysis (PCA) was applied to establish the predictive models. In addition, we combine the gray wolf optimization algorithm (GWO) with SVR for the prediction of moisture content, aiming to establish the best prediction model of black tea moisture content by optimizing the selection of key parameters (c and g) of the kernel function in SVR. The results show that SNV, as a method to correct the error of the spectrum due to scattering, can effectively extract spectral features after combining with PCA and is better than other pre−processing methods. In contrast, the nonlinear SVR model outperforms the PLS model, and the established mixed model SNV−PCA−GWO−SVR achieves the best prediction effect. The correlation coefficient of the prediction set and the root mean square error of the prediction set are 0.9892 and 0.0362, respectively, and the relative deviation is 6.5001. Experimental data show that the moisture content of black tea can be accurately and effectively determined by micro-near-infrared spectroscopy.

Until 29 May 2020, the Visible and Near-Infrared Imaging Spectrometer (VNIS) onboard the Yutu-2 Rover of the Chang’e-4 (CE-4) has acquired 96 high-resolution surface in-situ imaging spectra. These spectra were acquired under different illumination conditions, thus photometric normalization should be conducted to correct the introduced albedo differences before deriving the quantitative mineralogy for accurate geologic interpretations. In this study, a Lommel–Seeliger (LS) model and Hapke radiative transfer (Hapke) model were used and empirical phase functions of the LS model were derived. The values of these derived phase functions exhibit declining trends with the increase in phase angles and the opposition effect and phase reddening effect were observed. Then, we discovered from in-situ and laboratory measurements that the shadows caused by surface roughness have significant impacts on reflectance spectra and proper corrections were introduced. The validations of different phase functions showed that the maximum discrepancy at 1500 nm of spectra corrected by the LS model was less (~3.7%) than that by the Hapke model (~7.4%). This is the first time that empirical phase functions have been derived for a wavelength from 450 to 2395 nm using in-situ visible and near-infrared spectral datasets. Generally, photometrically normalized spectra exhibit smaller spectral slopes, lower FeO contents and larger optical maturity parameter (OMAT) than spectra without correction. In addition, the band centers of the 1 and 2 μm absorption features of spectra after photometric normalization exhibit a more concentrated distribution, indicating the compositional homogeneity of soils at the CE-4 landing site.