Hyperspectral Observations Research Articles

Research on the Earth Reflected Solar Spectral Radiation Observation System Based on the Lagrange L1 Point of the Earth–Moon System

We propose an observation system based on the Lagrange L1 point of the Earth–Moon system to observe solar spectral radiation reflected from the Earth, enabling continuous hyperspectral observation of the Earth’s hemisphere. The system can observe the solar spectral radiation reflected by the Moon, with its data applicable to on-orbit spectral radiation calibration. In this paper, the spectral irradiance at the entrance pupil of the Earth spectral radiation observation system (ESROS) is analyzed, and the optical design of the ESROS is introduced. An off-axis two-mirror telescope system, a coupling system of a microlens array and a fiber bundle, and an optical splitting system based on concave grating are used to achieve the full field of view hyperspectral splitting and miniaturization of the instrument. Finally, the stray radiation suppression of the instrument is introduced. The results show that the spectral resolution of the system is better than 5 nm in the 380–1000 nm band, and the spectral resolution is better than 10 nm in the 1000–1700 nm band. When observing the Earth, the signal-to-noise ratio is greater than 200. The external stray radiation suppression reaches the order of 10−9. The ESROS will provide crucial data support for researching global energy balance, climate change, and the spectral characteristics of exoplanets, facilitating planetary science and the exploration of extraterrestrial life.

Impact of atmospheric vertical profile on hyperspectral simulations over bright desert pseudo-invariant calibration site

ABSTRACT This paper investigates how atmospheric vertical profiles of pressure, temperature, and concentration affect molecular absorption calculation. This sensitivity analysis is performed in preparation to the upcoming TRUTHS mission, anticipated to provide hyperspectral TOA BRF records with a radiometric accuracy better than 1%. Two methods for characterizing the atmospheric vertical profile are compared: rescaling the H2O and O3 concentrations of an AFGL U.S. Standard vertical profile, and using customized profiles based on CAMS data. The study investigates the effects of those methods on multi-spectral observations in key spectral regions affected, respectively, by water vapour, ozone and methane, as well as on hyperspectral observations covering the visible to SWIR region. Results show that when molecular transmittance exceeds 97%, the choice of method has minimal impact, with less than 1% uncertainty. When the molecular transmittance decreases from 97% to 75%, the corresponding uncertainty on the TOA BRF simulation increases from 1% up to 5%. For transmittance below 75%, using CAMS data for vertical profile characterization is recommended. The study also highlights how pressure and temperature profiles influence Rayleigh optical thickness estimation, particularly affecting TOA BRF in the blue spectral region.

Early Radiometric Assessment of NOAA-21 Visible Infrared Imaging Radiometer Suite Reflective Solar Bands Using Vicarious Techniques

The VIIRS instrument on the JPSS-2 (renamed NOAA-21 upon reaching orbit) spacecraft was launched in November 2022, making it the third sensor in the VIIRS series, following those onboard the SNPP and NOAA-20 spacecrafts, which are operating nominally. As a multi-disciplinary instrument, the VIIRS provides the worldwide user community with high-quality imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans. This study provides an early assessment of the calibration stability and radiometric consistency of the NOAA-21 VIIRS RSBs using the latest NASA SIPS C2 L1B products. Vicarious approaches are employed, relying on reflectance data obtained from the Libya-4 desert and Dome C sites, deep convective clouds, and simultaneous nadir overpasses, as well as intercomparison with the first two VIIRS instruments using MODIS as a transfer radiometer. The impact of existing band spectral differences on sensor-to-sensor comparison is corrected using scene-specific a priori hyperspectral observations from the SCIAMACHY sensor onboard the ENVISAT platform. The results indicate that the overall radiometric performance of the newly launched NOAA-21 VIIRS is quantitatively comparable to the NOAA-20 for the VIS and NIR bands. For some SWIR bands, the measured reflectances are lower by more than 2%. An upward adjustment of 6.1% in the gain of band M11 (2.25 µm), based on lunar intercomparison results, generates more consistent results with the NOAA-20 VIIRS.

Validation of aerosol chemical composition and optical properties provided by Copernicus Atmosphere Monitoring Service (CAMS) using ground-based global data

Monitoring particulate matter (PM) air pollution in terms of both concentration and composition, is very important due to its effects on human health and climate. In the PRIMARY project we aim at retrieving the aerosol composition from space using the hyperspectral observations from the Italian Space Agency's PRISMA mission. To this end, we are developing a machine learning algorithm trained with synthetic top-of-atmosphere reflectances and underlying aerosol fields. As part of this process, we plan to use the global forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) as the core to generate this synthetic dataset. However, to proceed in this direction, a preliminary assessment of the reliability of this model-based dataset when compared to observations is necessary, also to bias correct the output if needed. With this aim, we assess the representation of the aerosol chemical composition and the related optical properties at selected globally distributed sites in CAMS, comparing the simulations with near-surface aerosol chemical analyses from the SPARTAN network and column sun-photometer observations from the AERONET network. We found that CAMS forecasts skills changed over time due to updates in the modelling system, with the latter two version cycles (46 and 47) being similar. Generally, they reproduce the aerosol composition within a factor of 2. We found a substantial overestimation of organic matter (OM) by a factor of 3. Applying a correcting factor to OM (constant at the global level) warrants a much more realistic representation of PM2.5 total mass and relative fraction of single species in CAMS. From the so derived CAMS aerosol-speciated profiles, we calculate aerosol optical properties, needed for subsequent use in a radiative transfer model. Comparison against AERONET indeed shows that OM bias correction resulted in improvements in Extinction Ångstrom Exponent (α440nm870nm). Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA) and Asymmetry Parameter (g) simulations resulted slightly degraded, confirming the possibility of using CAMS as the base for a synthetic retrieval training dataset.

Hyperspectral Leaf Area Index and Chlorophyll Retrieval over Forest and Row-Structured Vineyard Canopies

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m−2, NRMSD = 64–84%, bias = −0.17–0.89 g m−2). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m−2, NRMSD = 52–73%, bias = 0.03–0.42 g m−2) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation.

Radiometric Calibration of the Near-Infrared Bands of GF-5-02/DPC for Water Vapor Retrieval

The GaoFen (GF)-5-02 satellite is one of the new generations of hyperspectral observation satellites launched by China in 2021. The directional polarimetric camera (DPC) is an optical sensor onboard the GF-5-02 satellite. The precipitable water vapor (PWV) is a key detection parameter of DPC. However, the existing PWV data developed using DPC data have significant errors due to the lack of the timely calibration of the two bands (865, 910 nm) of DPC used for PWV retrieval. In order to acquire DPC PWV data with smaller errors, a calibration method is developed for these two bands. The method consists of two parts: (1) calibrate the 865 nm band of the DPC using the cross-calibration method, (2) calibrate the 910 nm band of the DPC according to the calibrated 865 nm band of the DPC. This method effectively addresses the issue of the absence of a calibration method for the water vapor absorption band (910 nm) of the DPC. Regardless of whether AERONET PWV data or SuomiNET PWV data are used as the reference data, the accuracy of the DPC PWV data developed using calibrated DPC data is significantly superior to that of the DPC PWV data retrieved using data before recalibration. This means that the calibration method for the NIR bands of the DPC can effectively enhance the quality of DPC PWV data.

Impact of Channel Selection with Different Bandwidths on Retrieval at 50–60 GHz

Microwave hyperspectral instruments represent one of the main atmospheric sounders of China’s next-generation Fengyun meteorological satellites. In order to better apply microwave hyperspectral observations in the fields of atmospheric parameter retrieval and data assimilation, this paper analyzes the sensitivity of trace gases to five selected bandwidth channels using a radiative transfer model based on the simulated data of microwave hyperspectral radiances at 50–60 GHz. This method uses information entropy and a weighting function to select channels and analyze the impact of this on the retrieval accuracy of atmospheric profiles before and after channel selection. The experimental results show that channel selection can reduce the number of channels by approximately 74.05% while maintaining a large amount of information content, and this retrieval effect is significantly better than that of MWTS-III. After channel selection, the 10 MHz, 30 MHz, and 50 MHz bandwidths have the best retrieval results in the stratosphere, whole atmosphere, and troposphere, respectively. When considering the number of channels, computational scale, and retrieval results comprehensively, the channel selection method is effective.

Drivers of deciduous forest near-infrared reflectance: A 3D radiative transfer modeling exercise based on ground lidar

The fraction of near infrared (NIR) solar energy reflected by forest canopies has been shown to correlate with leaf nitrogen availability and photosynthesis. But the canopy properties behind this relation are unclear, partly because whole canopy reflectance and albedo results from multiple complex interactions between photons and tree parts, and also because modeling these interactions has been challenging due to difficulties in representing forest canopies with sufficient detail. Ground lidar technology has been applied to this problem in recent years; it is used here to provide highly detailed forest canopy representations as inputs into a ray tracing radiative transfer model. These structural representations are coupled with the best available field data on leaf optical properties to produce simulated images of canopy reflectance in the NIR, as well as the detailed 3D mapping of NIR energy absorption within the canopy. The simulations performed for two deciduous forests in the northeastern USA were first compared to airborne hyperspectral observations to assess their accuracy. The radiative transfer model was then used to quantify the sensitivity of whole canopy reflectance and vertical absorption of NIR to changes in model parametrization in an attempt to identify the main drivers of canopy reflectance. The results point to the arrangement of leaves in the topmost canopy layers, both in terms of leaf angles and leaf clumping within shoots, and to the leaf-level absorptance in all canopy layers as the dominant factors affecting canopy reflectance and albedo. These findings emphasize the need for improving our understanding of leaf arrangement in canopy tops – including what it covaries with – and for including measurements of leaf transmission when measuring leaf spectral properties in the field.

Atmospheric Temperature Measurements Using Microwave Hyper-Spectrum from Geostationary Satellite: Band Design, Weighting Functions and Information Content

A passive microwave instrument will be carried by China’s geostationary microwave satellite. A microwave hyper-spectral band included by the instrument ranges from 52.6 to 57.3 GHz, and totally has 89 channels in this spectral domain. The design of the hyper-spectral band is described from the aspects of scientific objectives and specifications. The weighting functions for each channel are calculated utilizing radiative transfer simulations under clear sky conditions. Then, the information content as well as the degree of freedom for signal are computed and analyzed to characterize this hyper-spectral sounding for atmospheric temperature profiling. Both the vertical distribution of the weighting functions and the width of retrieval averaging kernels indicate that the hyper-spectral band can provide more denser sampling for atmospheric temperature. The information content for the hyper-spectral band is approximately 46% higher than that of the ATMS-type channel 3 to 15, indicating that hyper-spectral measurement can improve the accuracy of retrieval. The most informative channels mainly locate near 57 GHz, having good consistency with the existing channels. The height range where the retrieval using the hyper-spectral observations is sensitive to the true profile, begins from about 800 to 1 hPa. Some channels can be considered as alternatives to each other since they have very similar information content and weighting functions. These results are expected to provide a valuable reference for future applications of the microwave hyper-spectral measurements.

Quantifying particulate matter optical properties and flow rate in industrial stack plumes from the PRISMA hyperspectral imager

Abstract. Industrial activities such as metallurgy, coal and oil combustion, cement production, and petrochemistry release aerosol particles into the atmosphere. We propose analyzing the aerosol composition of plumes emitted by different industrial stacks using PRISMA (PRecursore IperSpettrale della Missione Applicativa) satellite hyperspectral observations. Three industrial sites have been observed: a coal-fired power plant in Matla, South Africa (imaged on 25 September 2021); a steel plant in Wuhan, China (24 March 2021); and gas flaring at an oil extraction site in Hassi Messaoud, Algeria (9 July 2021). Below-plume surface reflectances are constrained using a combination of PRISMA and Sentinel-2/MSI images. Radiative transfer simulations are performed for each scene including the surface, background atmosphere, and plume optical properties. The plume aerosol optical thickness (AOT), particle radius, volume of coarse-mode aerosol, and soot are then retrieved within the plumes following an optimal estimation framework. The mean plume retrieved AOT at 500 nm ranges between 0.27 and 1.27 and the median radius between 0.10 and 0.12 µm. We found a volume fraction of soot of 3.6 % and 10.4 % in the sinter plant and coal-fired plant plumes, respectively. The mass flow rate of particulate matter at a point source estimated by an integrated mass enhancement method varies from 840 ± 155 g s−1 for the flaring emission to 1348 ± 570 g s−1 at the coal-fired plant.

Multi-mission, multi-sensor study of the Shackleton Crater constrained for volatiles with emphasis on albedo distribution of the Lunar South Pole

Shackleton Crater on the Moon is named in honor of a pivotal figure of the Heroic Age of Antarctic Exploration Sir Ernest Henry Shackleton. The Crater was formed about 3.6 billion years ago at the Lunar South Pole. Due to the low (1.5°) tilt of the lunar axis of rotation, the interiors of the crater receive almost no direct sunlight and observations by passive hyperspectral sensors are only possible due to reflected light. Active sensors are however able to illuminate the interior surface of the crater and make observations. Here we present the results of study of the crater using data from multiple sensors onboard two missions of Indian Space Research Organization (ISRO) and National Aeronautics and Space Administration (NASA). Hyperspectral observations from Moon Mineralogy Mapper (M3) on-board Chandrayaan-1, temperature from Diviner Lunar Radiometer Experiment (DLRE), albedo from Lunar Orbiter Laser Altimeter (LOLA) and Off/On band ratios from Lyman-Alpha Mapping Project (LAMP) on board Lunar Reconnaissance Orbiter (LRO) have been used. Permanently Shaded Regions (PSRs) are identified and Shackleton has been found, as reported, to be a PSR. Emphasis has been laid on LOLA albedo analysis of lunar south pole (80°-90° South). It is observed that the Shackleton Crater exhibits significantly higher albedo compared to its surrounding regions. Some regions exhibit albedo even higher than the Shackleton Crater in the lunar south pole region. The findings of high albedo, coupled with high Off/On band ratio of LAMP, extremely low DLRE temperatures in the permanently shaded regions and detection of hydration features in the M3 hyperspectral data suggests higher probability of hydration in the Shackleton Crater.

Response of soil oxides in complex terrain region to environment revealed by hyperspectral observations

Soil oxides are important diagnostic indicators for soil quality assessment, and their content has a positive effect on adsorption of heavy metals, remediation of pollution, and enhancement of organic matter enrichment. Moreover, soil oxides are affected by multiple environmental factors such as soil pH, geomorphological, and elevation, and their distribution is highly regional. In this study, 421 ground soil samples and 2 hyperspectral satellite data were collected. The content distribution of 8 main oxides in soil, including SiO2, Al2O3, Fe2O3, MgO, CaO, Na2O, K2O, and Corg. were obtained according to the process of data preprocessing, spectral transformation, feature extraction, band combination, and model establishment. The results indicated that the homogenization spectral transformation method is the most suitable; The calculation accuracy of complex spectral characteristic parameters is inferior to that of characteristic bands; The difference model is more suitable for the extraction of soil oxide content in this study area; The average content of SiO2 and CaO shows a decreasing trend with the elevation increasing; The average content of SiO2 and Fe2O3 shows an upward trend with the increase of slope; East is the main geomorphic direction of soil oxide enrichment. SiO2, Al2O3, and MgO are the three soil oxides with the highest correlation. The response relationship between soil oxides, topography and pH is discussed, and the distribution rule of soil oxides and environmental determinants are obtained, which provides a set of technical schemes for digital soil research. This study can extract the content of soil components in a large area, analyze the causes, and provide a new technical scheme for soil investigation and treatment.

The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model Projections

AbstractEarth system models (ESMs) typically simplify the representation of land surface spectral albedo to two values, which correspond to the photosynthetically active radiation (PAR, 400–700 nm) and the near infrared (NIR, 700–2,500 nm) spectral bands. However, the availability of hyperspectral observations now allows for a more direct retrieval of ecological parameters and reduction of uncertainty in surface reflectance. To investigate sensitivity and quantify biases of incorporating hyperspectral albedo information into ESMs, we examine how shortwave soil albedo affects surface radiative forcing and simulations of the carbon and water cycles. Results reveal that the use of two broadband values to represent soil albedo can introduce systematic radiative‐forcing differences compared to a hyperspectral representation. Specifically, we estimate soil albedo biases of ±0.2 over desert areas, which can result in spectrally integrated radiative forcing divergences of up to 30 W m−2, primarily due to discrepancies in the blue (404–504 nm) and far‐red (702–747 nm) regions. Furthermore, coupled land‐atmosphere simulations indicate a significant difference in net solar flux at the top of the atmosphere (>3.3 W m−2), which can impact global energy fluxes, rainfall, temperature, and photosynthesis. Finally, simulations show that considering the hyperspectrally resolved soil reflectance leads to increased maximum daily temperatures under current and future CO2 concentrations.

The Mars Atmosphere Water Ice Aerosol Climatology by MRO/CRISM: 5 Mars Years of Observations

AbstractWe use near‐infrared spectra returned from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) aboard the Mars Reconnaissance Orbiter (MRO) to provide retrievals of the column‐integrated optical depth of water ice aerosols for more than 5 Martian years between Mars Year (MY) 28 at Ls = 112° (27 September 2006) and MY 33 at Ls = 213° (30 August 2016). We have developed a radiative transfer model for this purpose that retrieves aerosol opacity from the water ice aerosol feature near 3.3 μm using CRISM hyperspectral observations for the first time. The resulting retrievals well depict the main features in the water ice aerosol climatology with repeatable patterns every Martian year. The aphelion cloud belt (ACB) is observed between 10°S and 30°N, with peak optical depth around Ls = 90°. Apart from the global dust storm during MY 28, modest interannual variability in the water ice aerosol optical depth is observed in the ACB. The north polar hood is observed at latitudes poleward of 45°N throughout northern spring and summer seasons and reappears in northern winter. The south polar hood is observed in early to mid‐southern autumn, reaching latitudes around 60°S, and re‐emerging during southern winter between 45°S and 60°S. A lack of water ice aerosols is repeatedly seen in the southern hemisphere throughout southern spring and summer. High optical depth in the ACB is observed over the volcanoes of Olympus Mons, Elysium, and the Tharsis bulge, as well as over Hellas Basin.

Research on the Effects of Detector Shape on Spectral Calibration of Infrared Hyperspectral Interferometer Based on Simulation Data

ABSTRACT Infrared hyperspectral instrument is a complex optical instrument. The requirement for the application of the infrared hyperspectral interferometer is fine calibration accuracy. The accuracy of the spectral frequency of the infrared hyperspectral interferometer is a key influencing factor of the radiometric calibration accuracy. In order to meet the quantitative application, the spectral calibration accuracy is generally within 10ppm. For infrared hyperspectral instruments in geostationary orbit, the focal plane has the characteristics of large array and small detector size. When the detector size of an infrared hyperspectral interferometer is in the geostationary orbit whether the detector as a point detector will affect the spectral calibration accuracy. In this paper, we simulate and analyze the influence of the shape of the detector (square and point detector) on the spectral calibration accuracy. This paper first builds an infrared hyperspectral observation and calibration simulation model. The brightness temperature deviation between the calibration spectrum and the input spectrum is close to 0 K, which shows the rationality of building the simulation model. Then, in the process of spectral calibration, two methods, square detectors and point detectors, are adopted, respectively. The spectral calibration of the square detector needs to calculate the line shape function of the instrument according to the detector position. Regarding point detector spectral calibration, this paper proposes a spectral calibration parameter optimization algorithm based on the optimization algorithm, which does not depend on the physical parameters of the instrument, but only uses the observed simulated spectrum and the reference spectrum. The results show that the spectral calibration accuracy of the point detector based on the optimization method can be close to 0ppm, which is consistent with the spectral calibration accuracy of the square detector.

Surface oxides characterization based on hyperspectral observations

During steel sheets manufacturing, the addition of alloying elements to the steel to improve its mechanical performance can cause the formation of layers of selective oxides at the surface. These layers can significantly impact the surface properties of the steel sheets, which makes their detection an essential task. In this article, we present a novel methodology based on hyperspectral measurements to detect the presence of surface layers of selective oxides on steel sheets. Hyperspectral sensors have the ability to measure the reflectance of the steel surface over a large spectral band of the electromagnetic spectrum, usually in the infrared range. However, due to their limited spatial resolution, the observed reflectance spectra usually mix the contribution of several elementary oxides. A first step toward surface characterization is therefore to identify the elementary oxides contributing to the hyperspectral observations, a task referred to as hyperspectral unmixing in the literature. In our case, a major difficulty is that the optical model describing the formation of the electromagnetic wave coming onto the sensors is strongly nonlinear. To alleviate this difficulty, we rely on an approach that formulates the hyperspectral unmixing task as a sparse regression problem. This approach works by pre-computing the optical reflectance associated to reference layers of heterogeneous oxides that can form at the surface to construct a dictionary of reflectance spectra and by seeking to recover the hyperspectral observations as a linear combination of a small number of elements taken from this dictionary. To perform the recovery, we propose and compare two approaches based on the path and group Lasso algorithms. We illustrate our methodology on numerical experiments, which demonstrate that our approach is able to estimate the presence of selective oxides with high recall and precision. These numerical experiments also highlight the main limitations of the proposed approach. We finally apply our estimation method to characterize the surface chemistry of experimental samples. Our characterization approach is then compared to observations conducted on the samples through transmission electron microscopy and XPS analysis to demonstrate its validity.

First Retrievals of Surface and Atmospheric Properties Using EnMAP Measurements over Antarctica

The paper presents the first retrievals of clean snow properties using spaceborne hyperspectral observations via the Environmental Mapping and Analysis Program (EnMAP). The location close to the Concordia station at the Dome C Plateau (Antarctica) was selected. At this location, the atmospheric effects (except molecular light scattering and absorption) are weak, and the simplified atmospheric correction scheme could be applied. The ice grain size, snow specific surface area, and snow spectral and broadband albedos were retrieved using single-view EnMAP measurements. In addition, we propose a technique to retrieve trace gas concentrations (e.g., water vapor and ozone) from EnMAP observations over the snow surfaces. A close correspondence of satellite and ground-measured parameters was found.

Characterization of the layered SIF distribution through hyperspectral observation and SCOPE modeling for a subtropical evergreen forest

Solar-induced chlorophyll fluorescence (SIF) has been widely used as a prospective proxy of plant photosynthesis to accurately detect the response of vegetation to climate change and study terrestrial ecosystem carbon cycle. However, the challenge of estimating vegetation gross primary product (GPP) based on SIF observations remains unresolved due to the confounding effects of leaf physiology and canopy structure, especially the vertical heterogeneity of plant attributes. Radiative transfer models are ideally suited to investigate the variability in radiance signals including fluorescence, but previous studies lack vertically layered spectra to validate the model. In this study, we developed a vertically layered hyperspectral system and proposed an innovative framework for assessing vertical characteristics of SIF and improving the estimation of GPP based on SIF in a subtropical evergreen forest. The global sensitivity analysis on Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) model with vertical profiles showed that chlorophyll content (Cab), leaf inclination distribution (LIDFa), leaf area index (LAI), and senescent material (Cs) dominate the vertical variations in reflectance (Ref) and SIF. We found that the vertical characteristics of SIF were mainly impacted by these parameters of the adjacent vertical layer, except for the effects of LAI across the vertical layers on the observed understory SIFU, which were approximately equivalent. 7.8 ± 1.7% of the SIFU was transferred to the top of canopy, contributing up to 17% of the SIF observed on the top of canopy (SIFTOC) during the year. Furthermore, our findings suggest that substituting the observed SIFTOC with the total emitted SIF, particularly from the simulated overstory and understory SIF, could enhance the correlation between GPP and SIF with an increased R2 (ΔR2 = 0.09). This study highlights the importance of accounting for the contribution of the layered SIF in the quantification of the total SIF emission and can benefit the GPP estimation based on SIF signals in subtropical evergreen forests with high canopy density.

Deep Retrieval Architecture of Temperature and Humidity Profiles from Ground-Based Infrared Hyperspectral Spectrometer

Temperature and humidity profiles in the atmospheric boundary layer are essential for climate studies. The ground-based infrared hyperspectral spectrometer has the advantage of measuring radiances emitted from the atmosphere at a high temporal and moderate vertical resolution. In this article, the retrieval of temperature and humidity profiles from ground-based infrared hyperspectral observations is exploited. Although existing inversion algorithms based on physical models or statistical learning have made some progress, they still suffer from high computational complexity or poor performance. Motivated by the strength of the deep learning, we present a deep retrieval architecture (DReA) by skillfully designing a light-weight one-dimensional convolution neural network (CNN) to retrieve the temperature and humidity profiles. Experiments were conducted using atmospheric emitted radiance interferometer (AERI) and radiosonde data to demonstrate the superiority of the proposed DReA. The validation of the DReA with the radiosonde, using 802 profiles with 37 layers below 3 km, presents an excellent retrieval ability with a root mean square error (RMSE) of 0.87 K for the temperature and 1.06 g/kg for the water vapor mixing ratio. Furthermore, a thorough comparison with commonly used inversion methods such as the traditional back propagation (BP) and the eigenvector (EV) regression method, shows that our proposed DReA method obtains a leading solution in retrieving temperature and humidity profiles.

Winter Water Quality Modeling in Xiong'an New Area Supported by Hyperspectral Observation.

Xiong'an New Area is defined as the future city of China, and the regulation of water resources is an important part of the scientific development of the city. Baiyang Lake, the main supplying water for the city, is selected as the study area, and the water quality extraction of four typical river sections is taken as the research objective. The GaiaSky-mini2-VN hyperspectral imaging system was executed on the UAV to obtain the river hyperspectral data for four winter periods. Synchronously, water samples of COD, PI, AN, TP, and TN were collected on the ground, and the in situ data under the same coordinate were obtained. A total of 2 algorithms of band difference and band ratio are established, and the relatively optimal model is obtained based on 18 spectral transformations. The conclusion of the strength of water quality parameters' content along the four regions is obtained. This study revealed four types of river self-purification, namely, uniform type, enhanced type, jitter type, and weakened type, which provided the scientific basis for water source traceability evaluation, water pollution source area analysis, and water environment comprehensive treatment.