Spectral Source Research Articles

SCEC/USGS Community Stress-Drop Validation Study: How Spectral Fitting Approaches Influence Measured Source Parameters

ABSTRACT Spectral source parameters used to estimate an earthquake’s stress drop (Δσ) can vary significantly across measurement approaches. The Statewide California Earthquake Center/U.S. Geological Survey Community Stress-Drop Validation Study was initiated to compare source parameter estimates, focusing initially on a dataset from the 2019 Ridgecrest earthquake sequence. As part of that validation effort, here we focus on one potential source of uncertainty: whether spectral fitting approaches alone, applied to a common set of spectra from the 2019 Ridgecrest sequence result in different source parameter estimates. By using a common set of benchmark spectra analyzed across a consistent frequency band of 1–40 Hz, we eliminate many sources of variability. A subgroup of validation study participants volunteered to estimate the low-frequency displacement (Ω0) and corner frequency (fc) by fitting a smooth function to benchmark displacement spectra. Participants used linear- or log-sampled spectra, assumed a Brune or Boatwright spectral model, and applied different misfit criteria. We compare 17 approaches used to estimate Ω0, fc, and Δσ for 54 earthquake spectra. Our results reveal that 35% of events have Δσ estimates within a factor of two, whereas others exhibit variations exceeding an order of magnitude. The variability in Ω0 and fc can largely be attributed to whether a spectrum is consistent with the smooth function of an idealized simple crack model. The trade-off between Ω0 and fc may be more pronounced when using linearly sampled spectra, as higher frequency spectral bumps control the fits. As expected, methods that assumed a Boatwright model tended to have lower Ω0 and somewhat higher fc compared to those assuming a Brune model, although resulting Δσ estimates are similar. When compared to the overall validation study results, the fitting approach alone may account for between 5% and 90% (25% on average) of the total variability in spectral Δσ.

Underwater radiated noise characteristics and source spectrum model of typical ships in Yangtze River

To address the ecological challenge posed by the underwater radiated noise (URN) from ships in the Yangtze River, it is crucial to thoroughly understand its characteristics and establish a comprehensive source spectrum model. In this study, the spectral characteristics and transmission loss (TL) law of URN from ships in the Yangtze River were investigated by the on-site monitoring. A spectral source level (SSL) for a "reference" ship was constructed, and the URN source spectrum model was optimized. The results indicated that the URN frequencies predominantly ranged from 25 to 2500 Hz, with source levels (SL) for each one-third octave (1/3-octave) band distributed between 140 and 175 dB (re μPa). The hydroacoustic TL coefficient increased with frequency, indicating greater attenuation at higher frequency. The absolute error of the source spectrum models of typical ships in the Yangtze River was within ±10 dB (re μPa), showing strong agreement with the measurement values. These findings provide theoretical support for the coordinated development of shipping activities and ecological protection in the Yangtze River.

Precision crop mapping: within plant canopy discrimination of crop and soil using multi-sensor hyperspectral imagery

Leveraging diverse optomechanical and imaging technologies for precision agriculture applications is gaining attention in emerging economies. The precise spatial detection of plant objects in farms is crucial for optimizing plant-level nutrition and managing pests and diseases. High-resolution remote sensors mounted on drones have been increasingly deployed for large-scale crop mapping and field variability characterization. While field-level crop identification and crop-soil discrimination have been studied extensively, within-plant canopy discrimination of crop and soil has not been explored in real agricultural farms. The objectives of this study are: (i) adoption and assessment of spectral unmixing for discriminating crop and soil at within-plant canopy level, and (ii) generation of benchmark terrestrial and drone-based hyperspectral datasets for plant or sub-plant level discrimination using various spectral mixture modelling approaches and sources of endmembers. We acquired hyperspectral imagery of vegetable crops using a frame-based sensor mounted on a drone flying at different heights. Further, several linear, non-linear, and sparse-based spectral unmixing methods were used to discriminate plant and soil based on spectral signatures (endmembers) extracted from different spectral libraries prepared using in situ or field, ground-based, and drone-based hyperspectral imagery. The results, validated against pixel-to-pixel ground truth data, indicate an overall crop-soil discrimination accuracy of 99–100%, subject to a combination of endmember source and flying height. The influences of different endmember sources, spatial resolution as indicated by flying height, and inversion algorithms on the quality of estimated abundances are assessed from a verifiable and functionally relevant perspective. The generated hyperspectral datasets and ground truth data can be used for developing and testing new methods for sub-canopy level soil-crop discrimination in various agricultural applications of remote sensing.

Seismic moment of local earthquakes in the central part of the Baikal rift by the coda envelope inversion

The article presents an estimation of scalar seismic moment by the coda envelope inversion of local earthquakes recordings in the central part of the Baikal rift zone. In addition to the earthquake source parameters, the method allows us to simultaneously estimate seismic energy loss and site amplification factors for frequency bands from 0.53 to 34 Hz. Because of the compromise between spectral source energy and site responses, we estimated amplification factors for used stations relative to the "UlanUde" (UUDB) reference site located in crystalline rock. In order to estimate intrinsic and scattering attenuation we used events recorded at three broadband seismic stations and epicentral distances between 40 and 120 km. The result suggests that intrinsic absorption is dominant over scattering attenuation in the central part of the Baikal rift zone for most frequency bands, but the seismic albedo B0, expressing the contribution of scattering to the total attenuation, showed variations from 0.23 to 0.6 for frequencies below 1 Hz, with mean B0 value 0.33. Attenuation and siteamplification factors were used to estimate the seismic moment and moment magnitudes Mw of local earthquakes. The resultant moment magnitudes exhibit a good agreement with routinely reported local magnitude (ML) estimates for the study area. The coda inversion estimates of seismic moment provide stable, unbiased moment magnitudes for events that are too small to be seen at teleseismic distances.

The Chandra Source Catalog Release 2 Series

The Chandra Source Catalog (CSC) is a virtual X-ray astrophysics facility that enables both detailed individual source studies and statistical studies of large samples of X-ray sources detected in Advanced CCD Imaging Spectrometer and High Resolution Camera-I imaging observations obtained by the Chandra X-ray Observatory. The catalog provides carefully curated, high-quality, and uniformly calibrated and analyzed tabulated positional, spatial, photometric, spectral, and temporal source properties, as well as science-ready X-ray data products. The latter includes multiple types of source- and field-based FITS format products that can be used as a basis for further research, significantly simplifying follow-up analysis of scientifically meaningful source samples. We discuss in detail the algorithms used for the CSC Release 2 Series, including CSC 2.0, which includes 317,167 unique X-ray sources on the sky identified in observations released publicly through the end of 2014, and CSC 2.1, which adds Chandra data released through the end of 2021 and expands the catalog to 407,806 sources. Besides adding more recent observations, the CSC Release 2 Series includes multiple algorithmic enhancements that provide significant improvements over earlier releases. The compact source sensitivity limit for most observations is ∼5 photons over most of the field of view, which is ∼2× fainter than Release 1, achieved by coadding observations and using an optimized source detection approach. A Bayesian X-ray aperture photometry code produces robust fluxes even in crowded fields and for low-count sources. The current release, CSC 2.1, is tied to the Gaia-CRF3 astrometric reference frame for the best sky positions for catalog sources.

Local Modeling by Adapting Source Calibration Models to Analyte Shifted Target Domain Samples Without Reference Values.

Spectral multivariate calibration aims to derive models characterizing mathematical relationships between sample analyte amounts and corresponding spectral responses. These models are effective at predicting target domain sample analyte amounts when target samples are within the analyte and spectral calibration source domain. Models fail when target samples shift (analyte amounts and/or spectra) from the original calibration domain model. A total recalibration solution requires acquisition of new sample reference values and spectra. However, obtaining enough reference values to distinguish the target domain may be challenging or expensive. A simpler approach adapts the original model to the target domain using target sample spectra without analyte reference values (unlabeled). Analytical chemists have developed several machine learning algorithms using unlabeled regression domain adaptation processes. Unfortunately, prediction accuracy declines for these methods depending on how much the target domain analyte distribution has shifted from the calibration distribution, and regression transfer learning methods are instead needed. Regression domain adaptation and transfer learning are often referred to as model updating in analytical chemistry, but regression domain adaptation only applies to spectral shifts. The regression transfer learning method presented in this paper named null augmentation regression constant analyte (NARCA) leverages unlabeled repeat spectra of a single target sample to update an original calibration model to the shifted target domain sample. With sample repeat spectra, the analyte amount can be assumed constant or nearly constant for NARCA and because models are formed for one sample, NARCA operates as a local modeling method. The performance of NARCA as a regression transfer learning method is evaluated using five near-infrared data sets.

Proprioception enhancement for robot assisted neural rehabilitation: a dynamic electrical stimulation based method and preliminary results from EEG analysis

Objective.In recent years, the robot assisted (RA) rehabilitation training has been widely used to counteract defects of the manual one provided by physiotherapists. However, since the proprioception feedback provided by the robotic assistance or the manual methods is relatively weak for the paralyzed patients, their rehabilitation efficiency is still limited. In this study, a dynamic electrical stimulation (DES) based proprioception enhancement and the associated quantitative analysis methods have been proposed to overcome the limitation mentioned above.Approach.Firstly, the DES based proprioception enhancement method was proposed for the RA neural rehabilitation. In the method, the relationship between the surface electromyogram (sEMG) envelope of the specified muscle and the associated joint angles was constructed, and the electrical stimulation (ES) pulses for the certain joint angles were designed by consideration of the corresponding sEMG envelope, based on which the ES can be dynamically regulated during the rehabilitation training. Secondly, power spectral density, source estimation, and event-related desynchronization of electroencephalogram, were combinedly used to quantitatively analyze the proprioception from multiple perspectives, based on which more comprehensive and reliable analysis results can be obtained. Thirdly, four modes of rehabilitation training tasks, namely active, RA, DES-RA, and ES-only training, were designed for the comparison experiment and validation of the proposed DES based proprioception enhancement method.Main results.The results indicated that the activation of the sensorimotor cortex was significantly enhanced when the DES was added, and the cortex activation for the DES-RA training was similar to that for the active training. Meanwhile, relatively consistent results from the multiple perspectives were obtained, which validates the effectiveness and robustness of the proposed proprioception analysis method.Significance.The proposed methods have the potential to be applied in the practical rehabilitation training to improve the rehabilitation efficiency.

Nonlinear Inversion for a Multilayer Seismic S‐Wave Attenuation Model Using Radiative Transfer Theory

AbstractWe numerically solve the acoustic radiative transfer equation for seismic S‐waves via Monte Carlo simulation. By assuming a von Kármán‐type random medium with anisotropic scattering, we are able to simulate a realistic medium and determine its attenuation properties. In this study, we present an improved method, called QEST, to determine the frequency‐dependent intrinsic and scattering attenuation by nonlinear envelope inversion for a 1‐D multilayer model. Additionally, the spectral source energy of earthquakes and the energy site amplification of stations are determined. The code was applied to real data from the northern and southern Leipzig‐Regensburg fault zone (LRZ), Germany, as well as fluid‐induced earthquakes at the Insheim geothermal reservoir, Germany. The attenuation was analyzed in several frequency bands between 4.2 and 33.9 Hz and between 6.0 and 67.9 Hz, respectively. The inversion results reveal that the crystalline crustal subsurface along the LRZ shows little to no depth dependence, but there are differences in attenuation between the north and south. At Insheim, the near‐surface sedimentary basin exhibits significantly greater absorption and scattering than the crystalline basement. The inversion also shows that isotropic scattering can be an oversimplification and thus underestimate attenuation.

Wide Dynamic Range, High Uniformity Spectral Irradiance Source for Calibration of Scientific-Grade, Large-Size Space Detectors

In order to meet the high uniformity calibration requirements for scientific-grade, large-size space detectors used in the CHES Extrasolar Planet Exploration Mission, this paper presents the design of a wide dynamic range, high uniformity spectral irradiance source (WHUIS). Utilizing a cascade integrating sphere design, and optimizing the overlapping area radiant flux adjustment structure and illumination light path, we achieve a wide dynamic range and high uniformity irradiance output. We established an irradiance transmission model based on the new assumption and analyzed the influence of factors such as illumination distance, stray light, and non-uniform radiance on the uniformity of irradiance output. The model is then validated by building experimental equipment. The findings show that in a circular area of 40 mm, the irradiance uniformity of our light source system exceeds 99.9%, and constant color temperature is adjustable within six orders of magnitude, consistent with the uniformity level predicted by the model.

A Wrist-Worn Internet of Things Sensor Node for Wearable Equivalent Daylight Illuminance Monitoring.

Light exposure is a vital regulator of physiology and behavior in humans. However, monitoring of light exposure is not included in current wearable Internet of Things (IoT) devices, and only recently have international standards defined [Formula: see text] -optic equivalent daylight illuminance (EDI) measures for how the eye responds to light. This article reports a wearable light sensor node that can be incorporated into the IoT to provide monitoring of EDI exposure in real-world settings. We present the system design, electronic performance testing, and accuracy of EDI measurements when compared to a calibrated spectral source. This includes consideration of the directional response of the sensor, and a comparison of performance when placed on different parts of the body, and a demonstration of practical use over 7 days. Our device operates for 3.5 days between charges, with a sampling period of 30 s. It has 10 channels of measurement, over the range 415-910 nm, balancing accuracy and cost considerations. Measured [Formula: see text]-opic EDI results for 13 devices show a mean absolute error of less than 0.07 log lx, and a minimum between device correlation of 0.99. These findings demonstrate that accurate light sensing is feasible, including at wrist worn locations. We provide an experimental platform for use in future investigations in real-world light exposure monitoring and IoT-based lighting control.

Speckle Noise Reduction via Linewidth Broadening for Planetary Laser Reflectance Spectrometers

The low obliquity of the Moon leads to challenging solar illumination conditions at the poles, especially for passive reflectance measurements aimed at determining the presence and extent of surface volatiles. A nascent alternate method is to use active laser illumination sources in either a multispectral or hyperspectral design. With a laser spectral source, however, the achievable reflectance precision may be limited by speckle noise resulting from the interference effects of a coherent beam interacting with a rough surface. Here, we have experimentally tested the use of laser linewidth broadening to reduce speckle noise and, thus, increase reflectance precision. We performed a series of speckle imaging tests with near-infrared laser sources of varying coherence, compared them to both theory and speckle pattern simulations, and measured the reflectance precision using calibrated targets. By increasing the laser linewidth, we observed a reduction in speckle contrast and the corresponding increase in reflectance precision, which was 80% of the theoretical improvement. Finally, we discuss methods of laser linewidth broadening and spectral resolution requirements for planetary laser reflectance spectrometers.

Facility for spectral radiance calibration at low light level

The spectral radiance measurement at daytime level can be realized with high accuracy, while it’s difficult when the spectral radiance is at nighttime level. We design a spectral radiance calibration facility which has the characteristics of completely unchanged spectrum over 3 orders of magnitude and approximately unchanged spectrum for about 6 orders of magnitude. It combines a spectral radiance light source, a precision aperture and a white diffuser together, make it easy to reproduce the spectral radiance at 380 nm from 4 × 10−9 W/(m2·sr·nm) to 4 × 10−3 W/(m2·sr·nm). The facility can be easily used to calibrate a spectroradiometer at nighttime level. When the spectral radiance from 380 nm to 780 nm is around 1 × 10−7W/(m2·sr·nm), the calibration uncertainty of the spectroradiometer is 0.87%∼1.0% (k = 1).

Using clustering as pre-processing in the framework of signal unmixing for exhaustive exploration of archaeological artefacts in Raman imaging

Analytical chemistry on archaeological material is an essential part of modern archaeological investigations and from year to year, instrumental improvement has made it possible to generate data at a high spatial and temporal frequency. In particular, Raman spectral imaging can be successfully applied in archaeological research by its simplicity of implementation to study past human societies through the analysis of their material remains. This technique makes it possible to simultaneously obtain spatial and spectral information by preserving sample integrity. However, because of the inherent complexity of the samples in Archaeology (e.g. seniority, fragility, lack or full absence of any information about its composition), chemical interpretation can be difficult at first glance. Indeed, specific problems of spectral selectivity related to unexpected chemical compounds could appear due to their state of conservation. Furthermore, detecting minor compounds becomes challenging as major components impose their contributions in the acquired spectra. Therefore, a relevant chemometric approach has been introduced in this context to characterize distinct spectral sources in a Raman imaging dataset of an archaeological specimen – a mosaic fragment. The fragment was unearthed during the Ruscino archaeological dig on the outskirts of Perpignan, France. It dates back to the oppidum period. The aim is to extract selective spectral information from pixel clustering analysis in order to enhance the initial optimisation step within the Multivariate Curve Resolution and Alternating Least-Squares (MCR-ALS) algorithm, a well-known signal unmixing technique. The underlying principle of the MCR-ALS is that the acquired spectra can be expressed as linear combinations of pure spectra of all individual components present in the chemical system under study. Sometimes it can be difficult to obtain the desired results through the algorithm, particularly if initial estimates of spectral or concentration profiles are inaccurate due to complex signals, noise or lack of selectivity, resulting in rank deficiency (i.e. a poor estimation of the total number of pure signals). For this reason, an innovative threshold-based clustering algorithm, combined with multiple Orthogonal Projection Approaches (OPA), has been developed to improve matrix rank investigation and thus the initialisation step of the MCR-ALS approach before optimisation. The effective analysis of Raman imaging data for an archaeological mosaic played a crucial role in uncovering significant chemical information about a particular biogenic material. This insight sheds light on the origins of mortar manufacture during the oppidum period.

Mathematical model to optimize solar spectrum allocation for a Stirling engine-based concentrated photovoltaic thermal system

Incorporating a Stirling engine into a concentrated photovoltaic thermal system (CPVT) with spectral beam splitting converts wasted photovoltaic panel energy into electricity, boosting efficiency. However, complex Stirling engine development slows system performance estimation, which explains the scarce studies on the Stirling engine-based CPVT spectrum allocation range. Furthermore, the literature shows inconsistent remarks on low or high-energy photon allocation to a thermal system and uncertainty of solar irradiance variation effect on spectrum allocation. A generic methodology is presented for determining optimal spectrum allocation for a Stirling engine-based CPVT system with a dichroic filter. The analysis in this work shows that considering non-absorption and thermalization losses improves performance by 13.2 % compared to solely accounting for non-absorption loss in beam splitting. Additionally, a shift of up to 40 nm in the optimal spectrum allocation range is observed with a daily spectral irradiance source compared to the AM1.5D source. The PV mathematical model is validated with an average error of 8.9 %, and the Stirling engine model with an error of 5.2 %. Through this study, future researchers can determine an optimal spectrum allocation range for Stirling engine-based CPVT that maximizes PV performance based on a specified location with the proposed generalized CPVT model.

High-power flat mid-infrared supercontinuum spectral light source (Newsletter)

High-power flat mid-infrared supercontinuum spectral light source (Newsletter)

Deductive machine learning models for product identification.

Deductive solution strategies are required in prediction scenarios that are under determined, when contradictory information is available, or more generally wherever one-to-many non-functional mappings occur. In contrast, most contemporary machine learning (ML) in the chemical sciences is inductive learning from example, with a fixed set of features. Chemical workflows are replete with situations requiring deduction, including many aspects of lab automation and spectral interpretation. Here, a general strategy is described for designing and training machine learning models capable of deduction that consists of combining individual inductive models into a larger deductive network. The training and testing of these models is demonstrated on the task of deducing reaction products from a mixture of spectral sources. The resulting models can distinguish between intended and unintended reaction outcomes and identify starting material based on a mixture of spectral sources. The models also perform well on tasks that they were not directly trained on, like performing structural inference using real rather than simulated spectral inputs, predicting minor products from named organic chemistry reactions, identifying reagents and isomers as plausible impurities, and handling missing or conflicting information. A new dataset of 1 124 043 simulated spectra that were generated to train these models is also distributed with this work. These findings demonstrate that deductive bottlenecks for chemical problems are not fundamentally insuperable for ML models.

Comparing ground below-canopy and satellite spectral data for an improved and integrated forest phenology monitoring system

Phenology monitoring allows a better understanding of forest functioning and climate impacts. Satellite indicators are used to upscale ground phenological observations, but often differential responses are observed, and data availability can be limited. In view of climate impacts, new tools capable to detect rapid phenological changes and to work at single species level are needed. This research compares indices derived by the TreeTalker© (TT + ) below canopy upward-looking spectral data and Sentinel 2 satellite data, used to assess the phenological behavior and changepoints in several European beech forests. Overall, a mismatch between the information derived by the two sensor types is evidenced, with main differences in: start/end and length of season and phenology changepoints; larger variability captured by TT + with respect to Sentinel 2 especially in the leaf on period; mixed signal response from multiple vegetation layers in Sentinel 2 data. The complementarity of satellite and TT + indices allow exploring the phenological responses from different vegetation layers. TT + higher temporal resolution demonstrates precision in capturing the phenological changepoints in beech forests, especially if satellite image availability is limited by cloud cover and leads to miss critical phenological dates. The best settings for TT + data collection and the advantages to have two spectral data sources for improved forest phenology monitoring are also commented. The TT+, collecting additional tree parameters, can be a valuable tool for an integrated monitoring system based on spectral signals from above and below the canopy, at high temporal frequency and high spatial resolution.

Observation and Modelling on the Shipping Noise in Shallow Waters with Complex Islands and Reefs of the East China Sea

The impact of the noise radiated from merchant ships on marine life has become an active area of research. In this paper, a methodology integrating observation at a single location and modelling the whole noise field in shallow waters is presented. Specifically, underwater radiated noise data of opportunistic merchant ships in the waters of Zhoushan Archipelago were collected at least one day in each month from January 2015 to November 2016. The noise data were analyzed and a modified empirical spectral source level (SSL) model of merchant ships was proposed inspired by the RANDI-3 model (Research Ambient Noise Directionality) methodology. Then combining the modified model with the realistic geoacoustic parameters and AIS data of observed merchant ships, the noise mappings in this area were performed with N×2D of Normal Mode calculations, in which the SSL of each ship was estimated using the modified model. The sound propagation at different receiving positions is different due to the shielding effect of islands and bottom topography. The methodology proposed in this paper may provide a reference for modelling shipping noise in shallow waters with islands and reefs.

Multiparametric analysis and authentication of Argentinian vinegars from spectral sources

Ultraviolet-visible (UV-Vis) and near infrared (NIR) spectroscopies allied to chemometrics were investigated for quality control and authentication of Argentinean wine and balsamic vinegars. First, a multiparametric approach was conducted to acquire predictive models by using partial least squares regression (PLS) to quantify total acidity, volatile acidity, fixed acidity, pH and total polyphenols that are the main quality parameters used to control products. Individual UV-Vis and NIR sensors as well as merged data were assessed. Reliability models with correlation coefficients higher than 0.99 and prediction error lesser than 2.2 were acquired for the UV-Vis data. Furthermore, a classification approach was performed on wine vinegar samples to classify them according to their acetification process. At first, the data provided by each individual sensor (UV-Vis and NIR) were separately analyzed by PLS-discriminant analysis. Then, datasets were jointly analyzed by applying sequential and orthogonalized PLS coupled with linear discriminant analysis (SO-PLS-LDA). The overall accuracy of the fused model reached an optimal performance with a value of 0.92 in the prediction stage. Finally, according to the analysis proposed, this work reveals when it is proper to conduct a data fusion methodology.

Spectral aerosol optical depth from SI-traceable spectral solar irradiance measurements

Abstract. Spectroradiometric measurements of direct solar irradiance traceable to the SI were performed by three spectroradiometer systems during a 3-week campaign in September 2022 at the Izaña Atmospheric Observatory (IZO) located on the island of Tenerife, Canary Islands, Spain. The spectroradiometers provided direct spectral irradiance measurements in the spectral ranges 300 to 550 nm (QASUME), 550 to 1700 nm (QASUME-IR), 300 to 2150 nm (BiTec Sensor, BTS), and 316 to 1030 nm (Precision Solar Spectroradiometer, PSR), with relative standard uncertainties of 0.7 %, 0.9 %, and 1 % for QASUME/QASUME-IR, the PSR, and the BTS respectively. The calibration of QASUME and QASUME-IR was validated prior to this campaign at Physikalisch-Technische Bundesanstalt (PTB) by measuring the spectral irradiance from two spectral irradiance sources, the high-temperature blackbody BB3200pg as a national primary standard and the tuneable laser facility TULIP. The top-of-atmosphere (ToA) solar irradiance spectra from the spectroradiometers were retrieved from direct solar irradiance measurements using zero-air-mass extrapolation during cloud-free conditions, which were then compared to the TSIS-1 Hybrid Solar Reference Spectrum (HSRS). These ToA solar spectra agreed to within 1 % for spectral ranges longer than 400 nm (for QASUME also at shorter wavelengths) in the spectral regions free of significant trace gas absorption and were well within the combined uncertainties over the full investigated spectral range. Using the results from the comparison with QASUME, the relative standard uncertainty of the TSIS-1 HSRS ToA solar spectrum in the spectral range 308 to 400 nm could be reduced from its nominal 1.3 % to 0.8 %, representing the relative standard uncertainty of the QASUME ToA solar spectrum in this spectral range. The spectral aerosol optical depth (AOD) retrieved from the solar irradiance measurements of these spectroradiometers using the TSIS-1 HSRS as the reference ToA solar spectrum agreed to within 0.01 in optical depth in nearly all common spectral channels of two narrowband filter radiometers belonging to the Global Atmosphere Watch Precision Filter Radiometer (GAW-PFR) network and Aerosol Robotic Network (AERONET). This study shows that it is now possible to retrieve spectral AOD over the extended spectral range from 300 to 1700 nm using solar irradiance measurements traceable to the SI using laboratory-calibrated spectroradiometers with similar quality to that from traditional Langley-based calibrated instruments. The main improvement to previous investigations is the recent availability of the high-spectral-resolution TSIS-1 HSRS with very low uncertainties, which provides the top-of-atmosphere reference for the spectral atmospheric transmission measurements obtained from ground-based solar irradiance measurements.