Fine Spectral Resolution Research Articles

Quantifying the effects of spectral and directional distribution of radiation on its propagation in saline water

The interactions of radiation with saline water facilitate various energy-related applications, such as radiative evaporation at the air–water interface, radiation-driven underwater vapor generation, and underwater photovoltaic systems. However, these applications require a comprehensive understanding of radiation propagation through saline water, considering both its spectral and directional characteristics, which are often inadequately explored. This study introduces a three-dimensional Monte Carlo radiative transfer model equipped with fine spectral resolution and detailed angular considerations. The model simulates the transfer of radiation from the air to the air–water interface and throughout the saline water body to thoroughly examine the effects of spectral and directional properties of incident radiation on its propagation across different depths of saline water. The findings reveal that within the solar spectrum, radiation entering the water at a 62.7-degree angle of incidence and completely diffuse radiation exhibit similar absorption effects in water layers less than 2 meters deep. In addition, the incident angle has little impact on the absorption rate of both the water surface and the water body when the angle is below 62.7°. Spectrally, radiation wavelengths longer than 1.4μm, 1.14μm, and 1μm are fully absorbed within the first 1, 8, and 50 centimeters of saline water, respectively, representing approximately 20%, 30%, and 50% of incident solar radiation. Additionally, radiation from blackbody sources below 1300 Kelvin is absorbed entirely within the top 1 centimeter of saline water. Empirical correlations are then developed to easily estimate the absorption rate based on the depth of the water and the temperature of the blackbody heat source. The findings elucidate the influence of the spectral and directional characteristics of incident radiation on its underwater propagation, offering essential guidance for the design and performance evaluation of various energy-centric applications.

Multispectral imaging through metasurface with quasi-bound states in the continuum

By controlling light fields in subwavelength scales, metasurfaces enable novel ways for miniaturization and integration of spectral imaging system. Metasurfaces supporting quasi bound states in the continuum (quasi-BICs) can control the quality factor and spectral response by changing structural parameters. In this work, we present an ultra-compact multispectral imaging device, whereby spectral modulation is achieved by meta-atoms arrays supporting quasi-BICs. The designed meta-atom array can serve as filters over a wide range of wavelengths, which enables the device capable of a large operating range and high-fidelity spectral reconstruction with a fine spectral resolution. The microspectrometers composed of BIC metasurfaces also can work as imaging pixels to achieve computational imaging spectroscopy through periodic arrangement, which successfully resolves images with spatial aliasing in different channels. This spectrometer device can meet the market demand for miniaturization for rapidly object recognition and appropriate spatial spectral resolution at low cost.

Nasal/Oral Vowel Perception in French-Speaking Children With Cochlear Implants and Children With Typical Hearing.

The present study investigates the perception of vowel nasality in French-speaking children with cochlear implants (CIs; CI group) and children with typical hearing (TH; TH group) aged 4-12 years. By investigating the vocalic nasality feature in French, the study aims to document more broadly the effects of the acoustic limitations of CI in processing segments characterized by acoustic cues that require optimal spectral resolution. The impact of various factors related to children's characteristics, such as chronological/auditory age, age of implantation, and exposure to cued speech, has been studied on performance, and the acoustic characteristics of the stimuli in perceptual tasks have also been investigated. Identification and discrimination tasks involving French nasal and oral vowels were administered to two groups of children: 13 children with CIs (CI group) and 25 children with TH (TH group) divided into three age groups (4-6 years, 7-9 years, and 10-12 years). French nasal vowels were paired with their oral phonological counterpart (phonological pairing) as well as to the closest oral vowel in terms of phonetic proximity (phonetic pairing). Post hoc acoustic analyses of the stimuli were linked to the performance in perception. The results indicate an effect of the auditory status on the performance in the two tasks, with the CI group performing at a lower level than the TH group. However, the scores of the children in the CI group are well above chance level, exceeding 80%. The most common errors in identification were substitutions between nasal vowels and phonetically close oral vowels as well as confusions between the phoneme /u/ and other oral vowels. Phonetic pairs showed lower discrimination performance in the CI group with great variability in the results. Age effects were observed only in TH children for nasal vowel identification, whereas in children with CIs, a positive impact of cued speech practice and early implantation was found. Differential links between performance and acoustic characteristics were found within our groups, suggesting that in children with CIs, selective use of certain acoustic features, presumed to be better transmitted by the implant, leads to better perceptual performance. The study's results reveal specific challenges in children with CIs when processing segments characterized by fine spectral resolution cues. However, the CI children in our study appear to effectively compensate for these difficulties by utilizing various acoustic cues assumed to be well transmitted by the implant, such as cues related to the temporal resolution of stimuli. https://doi.org/10.23641/asha.25328704.

Detecting Multiple Mixed Bacteria Using Dual-Mode Hyperspectral Imaging and Deep Neural Networks

Identifying and analyzing mixed pathogenic bacteria is important for clinical diagnosis and antibiotic therapy of multiple bacterial infection. In this paper, a dual-mode hyperspectral microscopic detection technology with hybrid deep neural networks (DNNs) was proposed for simultaneous quantitative analysis of four kinds of pathogenic bacteria in mixed samples. To acquire both transmission and fluorescence spectra regarding the mixed pathogens, we developed a dual-mode hyperspectral detection system with fine spectral resolution and wide wavelength range, which can also generate spatial images that can be used to calculate the total amount of mixed bacteria. The dual-mode spectra were regarded as mixed proportion characteristics and the input of the neural network for predicting the proportion of each bacterium present in the mixture. To better analyze the dual-mode spectral data, we customized a mixed bacteria measurement network (MB-Net) with hybrid DNNs architectures based on spectral feature fusion. Using the fusion strategy, two DNNs frameworks applied for transmission/fluorescence spectral feature processing were stacked to form the MB-Net that processes these features simultaneously, and the achieved average coefficient of determination (R2) and RMSE of validation set are 0.96 and 0.03, respectively. To the best of our knowledge, it is the first time of simultaneously detecting four types of mixed pathogenic bacteria using spectral detection technology, showing excellent potential in clinical practice.

Multispectral near-infrared imaging for wetness estimation.

Estimation of the wetness of objects is an important technique for recognizing states in the real world. In this paper, we propose a non-contact method for estimating the wetness of objects using multispectral near-infrared (NIR) imaging. In contrast with a previous method that requires hyperspectral (110-band) images taken with fine spectral resolution (5 nm intervals) to estimate the degree of wetness, our method enables accurate wetness estimation using few-band NIR images with coarse spectral resolution (40 nm intervals). In general, water absorbs a substantial amount of incident light at wavelengths around 1000 nm and a smaller amount at wavelengths around 900 nm. This phenomenon indicates that the light absorption coefficient of water particularly varies over the NIR spectral band. These differences in the light absorption coefficients of water in the NIR bands are exploited in the model we derived for the appearance of a wet object surface, facilitating accurate wetness estimation. The effectiveness of the proposed method is demonstrated experimentally.

Detection of Na and K in the Atmosphere of the Hot Jupiter HAT-P-1b with P200/DBSP

We present a new optical transmission spectrum of the hot Jupiter HAT-P-1b based on two transits observed with the Double Spectrograph (DBSP) on the Palomar 200-inch (P200) telescope. The DBSP transmission spectrum, covering a wavelength range from 3250 to 10007 Å, is consistent with that observed with the Hubble Space Telescope (HST), but the former has a finer spectral resolution. The DBSP spectrum alone reveals the presence of a pressure-broadened line wing for Na, the line cores for both Na and K, and tentative evidence for H2O. We obtain consistent results from the spectral retrieval analyses performed on the DBSP-only data set and the DBSP, HST, and Spitzer combined data set. Our retrievals suggest a mostly clear atmosphere for HAT-P-1b, with a cloud coverage of % that is dominated by enhanced haze. We derive subsolar abundances for Na, K, and C, and subsolar to solar for O. Future observations with James Webb Space Telescope and ground-based high-resolution spectrographs should be able to not only confirm the presence of these species but also stringently constrain the formation and migration pathways for HAT-P-1b.

Mapping Plant Species in a Former Industrial Site Using Airborne Hyperspectral and Time Series of Sentinel-2 Data Sets

Industrial activities induce various impacts on ecosystems that influence species richness and distribution. An effective way to assess the resulting impacts on biodiversity lies in vegetation mapping. Species classification achieved through supervised machine learning algorithms at the pixel level has shown promising results using hyperspectral images and multispectral, multitemporal images. This study aims to determine whether airborne hyperspectral images with a high spatial resolution or phenological information obtained by spaceborne multispectral time series (Sentinel-2) are suitable to discriminate species and assess biodiversity in a complex impacted context. The industrial heritage of the study site has indeed induced high spatial heterogeneity in terms of stressors and species over a reduced scale. First, vegetation indices, derivative spectra, continuum removed spectra, and components provided by three feature extraction techniques, namely, Principal Component Analysis, Minimal Noise Fraction, and Independent Component Analysis, were calculated from reflectance spectra. These features were then analyzed through Sequential Floating Feature Selection. Supervised classification was finally performed using various machine learning algorithms (Random Forest, Support Vector Machines, and Regularized Logistic Regression) considering a probability-based rejection approach. Biodiversity metrics were derived from resulted maps and analyzed considering the impacts. Average Overall Accuracy (AOA) reached up to 94% using the hyperspectral image and Regularized Logistic Regression algorithm, whereas the time series of multispectral images never exceeded 72% AOA. From all tested spectral transformations, only vegetation indices applied to the time series of multispectral images increased the performance. The results obtained with the hyperspectral image degraded to the specifications of Sentinel-2 emphasize the importance of fine spatial and spectral resolutions to achieve accurate mapping in this complex context. While no significant difference was found between impacted and reference sites through biodiversity metrics, vegetation mapping highlighted some differences in species distribution.

State-of-the-Art Review of Assessing Water Quality from Space

Water acts as the medium which helps supply seafood and freshwater food for human consumption and drinking water. It is thus imperative that such a precious resource should be well monitored to safeguard human health and survival. Conventionally water quality can be monitored through manual laboratory-based methods which are time-consuming. Ground-based sensors are helping in expediting this task, though it involves the use of multiple sensors at various locations and requires regular maintenance and replacement. Satellite technology provides a good alternative method as it can scan large areas at a relatively low cost. Measurements of parameters such as sea surface temperature, total suspended solids (turbidity), primary food production (chlorophyll A), abnormal movement of aquatic animal, disease occurrence, water oxygen deficiency, growth monitoring of aquatic life and many others have been successfully reported in the literature. With the advent of micro-satellites that can transmit higher resolution images with a finer spectral resolution, greater advancements can be made for the detection of a greater number of critical water quality parameters. This paper reviewed the existing status of the water quality monitoring from space technology and covered published research done in various parts of the globe, including the problems, solutions, algorithm used, the advantages of the study, research region, water bodies, water quality variable, and satellites data.

Anisotropic dynamics of two-photon ionization: An attosecond movie of photoemission.

Imaging in real time the complete dynamics of a process as fundamental as photoemission has long been out of reach because of the difficulty of combining attosecond temporal resolution with fine spectral and angular resolutions. Here, we achieve full decoding of the intricate angle-dependent dynamics of a photoemission process in helium, spectrally and anisotropically structured by two-photon transitions through intermediate bound states. Using spectrally and angularly resolved attosecond electron interferometry, we characterize the complex-valued transition probability amplitude toward the photoelectron quantum state. This allows reconstructing in space, time, and energy the complete formation of the photoionized wave packet.

Deep Unsupervised Blind Hyperspectral and Multispectral Data Fusion

Hyperspectral images (HSIs) usually have finer spectral resolution but coarser spatial resolution than multispectral images (MSIs). To obtain a desired HSI with higher spatial resolution, great research attention has been paid to achieving hyperspectral super-resolution by fusing the observed HSI with an auxiliary MSI of the same scene. However, most of the existing HSI-MSI fusion methods rely either on prior knowledge of the degradation model or on sufficient training data, hindering their practicality and interpretability. In this letter, we propose a novel unsupervised HSI-MSI fusion network with the ability of degradation adaptive learning, namely, UDALN. Specifically, we propose three modules to straightly encode the spatial and spectral transformations across resolutions, i.e., SpaDnet, SpeUnet, and SpeDnet. Through an elaborately designed three-stage unsupervised training strategy, the estimated network parameters can exhibit clear physical meanings of degradation processes and therefore help guarantee a faithful reconstruction of the desired HSI. The experimental results on two widely used hyperspectral datasets demonstrate the effectiveness of our method in comparison to the state-of-the-art HSI-MSI fusion models. (Code available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JiaxinLiCAS/UDALN_GRSL</uri> .)

A New Approach to Identify On-Ground Lamp Types from Night-Time ISS Images

Artificial night-time light (NTL), emitted by various on-ground human activities, has become intensive in many regions worldwide. Its adverse effects on human and ecosystem health crucially depend on the light spectrum, making the remote discrimination between different lamp types a highly important task. However, such studies remain extremely limited, and none of them exploit freely available satellite imagery. In the present analysis, the possibility to remotely assess the relative contribution of different lamp types into outdoor lighting is tested. For this sake, we match two data sources: (i) the radiometrically calibrated RGB image provided by the ISS (coarse spectral resolution data), and (ii) a set of in situ measurements with detailed spectral signatures conducted by ourselves (fine spectral resolution data). First, we analyze the fine spectral resolution data: using spectral signatures of standard lamp types from the LICA UCM library as endmembers, we perform an unmixing analysis upon NTL in situ measurements; by this, we obtain the estimates for relative contributions of the standard lamp types in each examined in situ measurement. Afterward, we focus on the coarse spectral resolution data: by using various types of statistical models, we predict the estimated relative contributions of each lamp type via RGB characteristics of spatially corresponding pixels of the ISS image. The built models predict sufficiently well (with R2 reaching ~0.87) the contributions of two standard lamp types: high-pressure sodium (HPS) and metal-halide (MH) lamps, the most widespread lamp types in the study area (Haifa, Israel). The restored map for HPS allocation demonstrates high concordance with the network of municipal roads, while that for MH shows notable coincidence with the industrial facilities and the airport.

Experimental realization of a Fresnel hologram as a super spectral resolution optical element

A highly dispersive, diffractive optical element is designed and realized for an extremely high spectral resolution spectroscopy for exoplanet telescope application. Our design uses an annular Fresnel hologram to transform incident starlight directly into a spectrogram. The recording of the hologram is accomplished using two spherical waves of different radius of curvature. The resultant hologram consists of an annular grating structure with a gradually shrinking period as a function of increasing radius. The variable period not only could bring the incoming star-light into focus, but also exhibits a large on-axis chromatic behavior. We demonstrate a dispersion of wavelength 430–700 nm over 190 mm on-axis distance, leading to a super fine spectral resolution 0.0266 nm at wavelength 515 nm for a detector size of 20 µm.

Surface water estimation at regional scale using hybrid techniques in GEE environment-A case study on Punjab State of India

Monitoring surface water bodies accurately is one of the most crucial applications of remote sensing. It is a global challenge in the times of climate change, since surface water is the most important component of the hydrological cycle. Modified Normalized Difference Water Index (MNDWI) method based on spectral index is calculated from the Green and Shortwave-Infrared (SWIR) bands, for monitoring surface water. This index helps to map surface water accurately based on the threshold using Otsu method for achieving higher level of accuracy. Earth observing satellite by ESA, Sentinel-2 provides multispectral images at a fine spatial and spectral resolution. This dataset has high potentiality for significantly mapping regional surface water, due to its frequent revisiting capabilities and spectral capabilities. Google Earth Engine (GEE) platform can be used for processing the huge datasets easily in the most efficient way for fast computation, storing and displaying the huge datasets. A pilot study has been carried out in Northwest part of India to demonstrate the effectiveness of mapping mean surface water for the temporal window June 2016 to March 2020 from Sentinel-2 using GEE so as to understand the patterns of surface water inundation at a regional scale.

Integrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images

Sentinel-3 is a newly launched satellite implemented by the European Space Agency (ESA) for global observation. The Ocean and Land Colour Imager (OLCI) sensor onboard Sentinel-3 provides 21 band images with a fine spectral resolution and is of great value for ocean, land and atmospheric monitoring. The two platforms (Sentinel-3A and -3B) can provide OLCI images at an almost daily temporal resolution. The coarse spatial resolution of the 21 band OLCI images (i.e., 300 m), however, limits greatly their utility for local, precise monitoring. Sentinel-2, another satellite provided by ESA, carries the Multispectral Imager (MSI) sensor which can supply much finer spatial resolution (e.g., 10 m and 20 m) images. This paper introduces a new fusion framework integrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images, which has two parts. Based on bands with similar wavelengths (i.e., bands 2, 3, 4 and 8a for Sentinel-2 and bands Oa4, Oa6, Oa8 and Oa17 for Sentinel-3), the four Sentinel-3 bands are first downscaled to the spatial resolution of Sentinel-2 images by applying spatio-temporal fusion to Sentinel-2 MSI and Sentinel-3 OLCI images. Then, to take full advantage of all 21 available OLCI bands of the Sentinel-3 images, the extended image pair-based spatio-spectral fusion (EIPSSF) method is proposed in this paper to downscale the other 17 bands. EIPSSF is performed based on the new concept of the extended image pair (EIP) and by exploiting existing spatio-temporal fusion approaches. The framework consisting of spatio-temporal and spatio-spectral fusion is entirely general, which provides a practical solution for comprehensive downscaling of Sentinel-3 OLCI images for fine spatial, temporal and spectral resolution monitoring.

FEATURE EXTRACTION OF DESIS AND PRISMA HYPERSPECTRAL REMOTE SENSING DATASETS FOR GEOLOGICAL APPLICATIONS

Abstract. With the recent launch of advanced hyperspectral satellites with global coverage, including DESIS and PRISMA, a massive volume of high spectral resolution data is available. This work is focused on the spectral analysis and implementation of feature extraction or data dimensionality reduction techniques on both newly available datasets for geological interpretation. Three of the best feature extraction algorithms, Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA), were tested for lithological mapping for the Rajasthan state of India. The present work demonstrates the advantage of the feature extraction algorithm in geological mapping and interpretability as it shows the excellent performance for these datasets. The narrowband ratios for the clay minerals, dolomite, kaolinite, amphiboles, and Al-OH are generated using the PCA and MNF components. All of these band ratios were compared with the Lithological Map available. It is concluded that PCA is the first choice for feature-based lithological classification of hyperspectral remote sensing data. ICA is giving some impressive results which can be explored further. DESIS and PRISMA have a 30 km swath, finer spectral resolution, and high signal-to-noise ratio, which shows much potential in lithological mapping over the parts of northern India. It is suggested to use advanced feature extraction algorithms with recently launched hyperspectral data for accurate and updated mineral mapping over a sizeable geological importance area.

Remote sensing of coastal hydro-environment with portable unmanned aerial vehicles (pUAVs) a state-of-the-art review

Urbanization together with climate change is imposing growing stresses on the sustainable development of major coastal cities around the world, particularly in developing countries. Thus, the need for regular monitoring of coastal water quality is becoming increasingly vital, and remote sensing is deemed to be a viable approach particularly due to its cost-effectiveness. Over the past two decades, remote sensing using satellite imaging has already been adopted in various tasks related to the monitoring of coastal hydro-enviro nment, including the detection of oil spills, identification of turbid plumes, mapping of marine ecosystems, etc. Despite their usefulness, space-borne sensors are subjected to constraints such as low spatial resolution, clouds and atmospheric interference, sparse frequency, and inflexible scheduling. Recently, remote sensing with portable Unmanned Aerial Vehicles (pUAVs) is emerging as a promising alternative for high spatial-resolution monitoring of coastal hydro-environment, with the key advantages that they can minimize the effects of atmospheric disruptions and be implemented with a frequent schedule to perform on-demand monitoring. In addition, with the technological advances in the sensor's design, UAVs are now capable of capturing data with fine spectral resolutions, ranging from several (multispectral) to hundreds (hyperspectral) of spectral bands. Data analytic is also improving rapidly with the advancement in artificial intelligence (AI) and deep learning algorithms. In this paper, we present a systematic review of the recent developments in the utilization of UAV-based remote sensing for the sustainable development of the coastal hydro-environment, particularly with pUAVs. The key technical issues, including the design of UAV systems and the processing of data, are examined for specific applications. Future prospects for implementation are also discussed.

A faint companion around CrA-9: protoplanet or obscured binary?

ABSTRACT Understanding how giant planets form requires observational input from directly imaged protoplanets. We used VLT/NACO and VLT/SPHERE to search for companions in the transition disc of 2MASS J19005804-3645048 (hereafter CrA-9), an accreting M0.75 dwarf with an estimated age of 1–2 Myr. We found a faint point source at ∼0.7-arcsec separation from CrA-9 (∼108 au projected separation). Our 3-epoch astrometry rejects a fixed background star with a 5σ significance. The near-IR absolute magnitudes of the object point towards a planetary-mass companion. However, our analysis of the 1.0–3.8$\,\mu$m spectrum extracted for the companion suggests it is a young M5.5 dwarf, based on both the 1.13-μm Na index and comparison with templates of the Montreal Spectral Library. The observed spectrum is best reproduced with high effective temperature ($3057^{+119}_{-36}$K) BT-DUSTY and BT-SETTL models, but the corresponding photometric radius required to match the measured flux is only $0.60^{+0.01}_{-0.04}$ Jovian radius. We discuss possible explanations to reconcile our measurements, including an M-dwarf companion obscured by an edge-on circum-secondary disc or the shock-heated part of the photosphere of an accreting protoplanet. Follow-up observations covering a larger wavelength range and/or at finer spectral resolution are required to discriminate these two scenarios.

Fast Hyper-Spectral Radiative Transfer Model Based on the Double Cluster Low-Streams Regression Method

Fast radiative transfer models (RTMs) are required to process a great amount of satellite-based atmospheric composition data. Specifically designed acceleration techniques can be incorporated in RTMs to simulate the reflected radiances with a fine spectral resolution, avoiding time-consuming computations on a fine resolution grid. In particular, in the cluster low-streams regression (CLSR) method, the computations on a fine resolution grid are performed by using the fast two-stream RTM, and then the spectra are corrected by using regression models between the two-stream and multi-stream RTMs. The performance enhancement due to such a scheme can be of about two orders of magnitude. In this paper, we consider a modification of the CLSR method (which is referred to as the double CLSR method), in which the single-scattering approximation is used for the computations on a fine resolution grid, while the two-stream spectra are computed by using the regression model between the two-stream RTM and the single-scattering approximation. Once the two-stream spectra are known, the CLSR method is applied the second time to restore the multi-stream spectra. Through a numerical analysis, it is shown that the double CLSR method yields an acceleration factor of about three orders of magnitude as compared to the reference multi-stream fine-resolution computations. The error of such an approach is below 0.05%. In addition, it is analysed how the CLSR method can be adopted for efficient computations for atmospheric scenarios containing aerosols. In particular, it is discussed how the precomputed data for clear sky conditions can be reused for computing the aerosol spectra in the framework of the CLSR method. The simulations are performed for the Hartley–Huggins, O2 A-, water vapour and CO2 weak absorption bands and five aerosol models from the optical properties of aerosols and clouds (OPAC) database.

The Wide-field VLBA Calibrator Survey: WFCS

Abstract This paper presents the results of the largest very long baseline interferometry (VLBI) absolute astrometry campaign to date of 13,645 radio source observations with the Very Long Baseline Array. Of these, 7220 have been detected, including 6755 target sources that have never been observed with VLBI before. This makes the present VLBI catalog the largest ever published. The positions of the target sources have been determined with the median uncertainty of 1.7 mas, and 15,542 images of 7171 sources have been generated. Unlike previous absolute radio astrometry campaigns, observations were made at 4.3 and 7.6 GHz simultaneously using a single wide-band receiver. Because of the fine spectral and time resolutions, the field of view was 4′–8′—much greater than the 10″–20″ in previous surveys. This made possible the use of input catalogs with low position accuracy and the detection of a compact component in extended sources. Unlike previous absolute astrometry campaigns, both steep- and flat-spectrum sources were observed. The observations were scheduled in the so-called filler mode to fill the gaps between other high-priority programs. This was achieved by the development of the totally automatic scheduling procedure.

Mid-infrared dual frequency comb spectroscopy for combustion analysis from 2.8 to 5 µm

We demonstrate the application of mode-locked mid-infrared dual frequency comb spectroscopy for combustion analysis. With two settings of the same dual-comb system, the measurement spans 1500 cm−1 from 2.8 to 5 µm with 0.0067 cm−1 (200 MHz) point spacing, or almost a quarter-million discrete comb modes. Using this broadband spectrometer, we quantify the pyrolysis and smoldering combustion of wood samples. Specifically, we measure 20-second time-resolved mole fractions of CH4, H2O, two isotopologues of CO2 (12C16O2, 13C16O2), two isotopologues of CO (12C16O, 13C16O), ethane, formaldehyde, methanol, formic acid, as well as gas temperature directly above radiatively heated wood samples. The combination of the fine spectral resolution and the broad bandwidth of the dual-comb spectrometer allows for precise separation of absorption signatures of individual molecules from the congested spectra.