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NASA NTRSDate not supplied

CLARREO Pathfinder Mission Overview and its Intercalibration Capabilities

NASA's Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) mission will deploy an Earth-observing reflected solar (RS) spectrometer, designed to measure Earth-reflected solar radiation from the International Space Station with a remarkable SI-traceable radiometric uncertainty of 0.3% (k=1). This spectrometer, known as the Hyperspectral Imager for Climate Science (HySICS), will provide measurements within a spectral range of 350-2300 nm with 3-nm spectral intervals. Covering a nadir swath of 70 km, HySICS captures 480 discrete measurement pixels that provide spectrally-resolved Earth-reflected radiances. The CPF mission encompasses two principal objectives. The first objective is to demonstrate on-orbit calibration methodologies that achieve and uphold an unprecedented level of accuracy while maintaining traceability to SI standards. The second objective is to showcase an innovative on-orbit intercalibration approach, which involves the intercalibration of two other RS sensors—namely, the shortwave (SW) channel of the Clouds and the Earth’s Radiant Energy System (CERES) and the Reflective Solar (RS) bands of the Visible Infrared Imager Radiometer Suite (VIIRS)—against CPF benchmark measurements. The targeted intercalibration methodology uncertainty for these target instruments is 0.3% (k=1). Empowered by the CPF payload's two-axis pointing capability, moderate spatial sampling of 0.5 km, and wide spectral coverage, the CPF instrument will capture near-simultaneous temporal, spatial, angular, and spectrally matched observations with intercalibration targets. The CPF intercalibration science development team has devised novel methods to address spatial, spectral, polarization, and angular differences between CPF and the target instruments' intercalibration footprints to achieve the stringent 0.3% intercalibration methodology uncertainty. Comprehensive details of these methods and their validation will be elaborated upon during the conference presentation.

Hyperspectral↗
NASA NTRSDate not supplied

Updates in Developing a Prototype Science Pipeline and Full-Volume, Global Hyperspectral Synthetic Data Sets for NASA’s Earth System Observatory’s Upcoming Surface, Biology and Geology Mission

The Surface Biology and Geology (SBG) mission recently passed mission confirmation review and has entered phase A – design and development. SBG will acquire high resolution solar-reflected spectroscopy and thermal infrared observations at a data rate of ~2.5 TB/day and generate products at ~40 TB/day. Given that the per-day volume is greater than NASA’s total extant airborne hyperspectral data collection, collecting, processing, disseminating, and exploiting the SBG data present new challenges. To meet these challenges, we have developed a prototype science pipeline and a full-volume global hyperspectral synthetic data set to help prepare for SBG’s flight (see poster GC42D-0730). Our science pipeline is based on the science processing technology developed for NASA’s Kepler and TESS planet-hunting missions. The pipeline infrastructure, Ziggy, provides a scalable architecture for robust, repeatable, and replicable science and application products that can be run on a range of systems from a laptop to the cloud or a supercomputer. Ziggy is compliant with NASA Procedural Requirement (NPR) 7150.2C, is at a technical readiness level (TRL) of 7 and has been released to github.com/nasa/ziggy. We integrated Ziggy with EO-1/Hyperion workflows to build a prototype pipeline and ingested the 17-year mission archive that provides globally sampled visible through shortwave infrared spectra that are representative of SBG data types and volumes. We fully implemented the first stage and processed the entire 55 TB Hyperion data set from the raw data (Level 0) to top-of-the-atmosphere radiance (Level 1R). We are currently evaluating the ISOFIT atmospheric correction module to convert the L1R data to surface reflectance (Level 2) before reprocessing the full data set to L2. Crosschecks are being performed with RadCalNet as well as with coincident observations by AVIRIS. We are also investigating modern methods for georectifying the Hyperion scenes. Finally, we describe an analysis of the cost to conduct forward processing and reprocessing campaigns for SBG on HECC with dedicated compute and storage resources using the resurrected Hyperion pipeline as a proxy for full-volume SBG data. The analysis demonstrates that SBG L0 data can be processed to L2 on HECC with full reprocessing campaigns every two years for ~$2.6M over a 7-year lifespan. Moreover, 69% of the system capacity would be available for other activities, possibly enabling future open-source science activities, including algorithm development, L3+ processing, .etc.

ESD↗
DOE OSTI2026

Hyperspectral traits (TSWIFT) UC Davis Populus trichocarpa Common Garden

This dataset provides tower-based hyperspectral remote sensing measurements of individualPopulustrees collected with the TSWIFT system to support genetic analyses of canopy photosynthetic traits over time under drought. From 2022-08-18 to 2022-10-18, spectra were repeatedly acquired from the same targeted canopy area of each tree using fixed pointing coordinates. The dataset includes hyperspectral measurements from 400–900 nm and ultraspectral measurements from 730–780 nm. These spectra enable calculation of reflectance-based vegetation indices and other spectral traits, including solar-induced fluorescence (SIF) retrievals from the ultraspectral region. Because measurements were collected exclusively over a drought treatment plot, derived phenotypes are intended for drought-context genetic association and prediction analyses.

09 BIOMASS FUELS↗
NASA NTRS2017

Hyperspectral Radiative Transfer Modeling to Explore the Combined Retrieval of Biophysical Parameters and Canopy Fluorescence from FLEX - Sentinel-3 Tandem Mission Multi-Sensor Data

The FLuorescence EXplorer (FLEX) satellite mission, selected as ESA's 8th Earth Explorer, has been designed forthe measurement of sun-induced fluorescence (F) spectra emitted by plants. This will be accomplished through amulti-sensor approach by placing it in a common orbit in tandem with the Sentinel-3 (S3) mission, which willhave two optical sensors on board, OLCI (Ocean and Land Colour Instrument) and SLSTR (Sea and Land SurfaceTemperature Radiometer) to complement FLEX. These S3 instruments will be used in combination with theimaging spectrometers on board FLEX to provide data useful for atmospheric correction of FLEX data. However,a fully synergetic approach, i.e. by exploiting the spectral and directional information from all tandem missioninstruments together, is an attractive alternative which is explored in this paper. By employing all combined topof-atmosphere (TOA) spectral radiance data, one can (i) characterize the relevant optical properties of the atmosphere,(ii) retrieve biophysical canopy properties including the associated reflectance anisotropy, and (iii)retrieve a more accurate and consistent canopy F.Regarding retrieval methods, Fraunhofer Line Depth (FLD) and Spectral Fitting (SF) are well-known techniquesapplied to hyperspectral data. Both methods depend on a high spectral resolution and assume aLambertian (isotropic) canopy reflectance. However, most vegetation canopies are non-Lambertian. This impliesthat, in particular when ignoring the anisotropic surface reflection, substantial retrieval errors can occur due tothe interaction between atmospheric absorption bands and surface reflectance anisotropy. In this paper, a novelmethod based on spectral radiative transfer (RT) modeling is proposed, in which coupled RT models are used tosimulate TOA radiance spectra. These are then matched with ‘measured' spectra in order to retrieve surfacefluorescence, along with a suite of biophysical parameters, by model inversion through optimization. By applyingcoupled RT models of the soil-leaf-canopy and the surface-atmosphere systems, TOA radiance spectra canbe simulated for all optical sensors of this tandem mission. In this way, complex effects due to surface reflectanceanisotropy and the spectral sampling by the various instruments, which are difficult to compensate for in the endproducts, are properly taken into account by their incorporation in the forward modeling. Next, by model inversionof TOA radiance data via optimization, the most accurate F retrievals can be achieved in a consistentmanner, along with important canopy level biophysical parameters that may help interpret the F spectrum, suchas chlorophyll content and leaf area index (LAI). The potential of this approach has been explored in a numericalexperiment, and the results are presented in this paper. We find that, with the assumed well-characterized andplausible FLEX/S3 instrument performances, the simultaneous retrieval of biophysical canopy parameters and Fspectra would be possible with a remarkable accuracy, provided the correct atmospheric characterization isavailable.

Verhoef, Wouter↗
NASA NTRS2017

Determining the Optimal Spectral Sampling Frequency and Uncertainty Thresholds for Hyperspectral Remote Sensing of Ocean Color

Using a modified geostatistical technique, empirical variograms were constructed from the first derivative of several diverse remote sensing reflectance and phytoplankton absorbance spectra to describe how data points are correlated with distance across the spectra. The maximum rate of information gain is measured as a function of the kurtosis associated with the Gaussian structure of the output, and is determined for discrete segments of spectra obtained from a variety of water types (turbid river filaments, coastal waters, shelf waters, a dense Microcystis bloom, and oligotrophic waters), as well as individual and mixed phytoplankton functional types (PFTs; diatoms, chlorophytes, cyanobacteria, coccolithophores). Results show that a continuous spectrum of 5 to 7 nm spectral resolution is optimal to resolve the variability across mixed reflectance and absorbance spectra. In addition, the impact of uncertainty on subsequent derivative analysis is assessed, showing that a limit of 3 Gaussian noise (SNR 66) is tolerated without smoothing the spectrum, and 13 (SNR 15) noise is tolerated with smoothing.

Noise in imaging systems↗
DOE OSTI2024

Beyond the visible: Accounting for ultraviolet and far‐red radiation in vegetation productivity and surface energy budgets

Photosynthetically active radiation (PAR) is typically defined as light with a wavelength within 400–700 nm. However, ultra-violet (UV) radiation within 280–400 nm and far-red (FR) radiation within 700–750 nm can also excite photosystems, though not as efficiently as PAR. Vegetation and land surface models (LSMs) typically do not explicitly account for UV's contribution to energy budgets or photosynthesis, nor FR's contribution to photosynthesis. However, whether neglecting UV and FR has significant impacts remains unknown. Here, we explored how canopy radiative transfer (RT) and photosynthesis are impacted when explicitly implementing UV in the canopy RT model and accounting for UV and FR in the photosynthesis models within a next-generation LSM that can simulate hyperspectral canopy RT. We validated our improvements using photosynthesis measurements from plants under different light sources and intensities and surface reflection from an eddy-covariance tower. Our model simulations suggested that at the whole plant level, after accounting for UV and FR explicitly, chlorophyll content, leaf area index (LAI), clumping index, and solar radiation all impact the modeling of gross primary productivity (GPP). At the global scale, mean annual GPP within a grid would increase by up to 7.3% and the increase is proportional to LAI; globally integrated GPP increases by 4.6 PgC year −1 (3.8% of the GPP without accounting for UV + FR). Further, using PAR to proxy UV could overestimate surface albedo by more than 0.1, particularly in the boreal forests. Our results highlight the importance of improving UV and FR in canopy RT and photosynthesis modeling and the necessity to implement hyperspectral or multispectral canopy RT schemes in future vegetation and LSMs.

energy budget↗
NASA NTRS2007

Assessment of Vegetation Stress Using Reflectance or Fluorescence Measurements

Current methods for large-scale vegetation monitoring rely on multispectral remote sensing, which has serious limitation for the detection of vegetation stress. To contribute to the establishment of a generalized spectral approach for vegetation stress detection, this study compares the ability of high-spectral resolution reflectance (R) and fluorescence (F) foliar measurements to detect vegetation changes associated with common environmental factors affecting plant growth and productivity. To obtain a spectral dataset from a broad range of species and stress conditions, plant material from three experiments was examined, including (i) corn, nitrogen (N) deficiency/excess; (ii) soybean, elevated carbon dioxide, and ozone levels; and (iii) red maple, augmented ultraviolet irradiation. Fluorescence and R spectra (400-800 nm) were measured on the same foliar samples in conjunction with photosynthetic pigments, carbon, and N content For separation of a wide range of treatment levels, hyperspectral (5-10 nm) R indices were superior compared with F or broadband R indices, with the derivative parameters optimal results. For the detection of changes in vegetation physiology, hyperspectral indices can provide a significant improvement over broadband indices. The relationship of treatment levels to R was linear, whereas that to F was curvilinear. Using reflectance measurements, it was not possible to identify the unstressed vegetation condition, which was accomplished in all three experiments using F indices. Large-scale monitoring of vegetation condition and the detection of vegetation stress could be improved by using hyperspectral R and F information, a possible strategy for future remote sensing missions.

Campbell, P. K. E.↗
NASA NTRS2004

Estimating Surface Soil Moisture in Simulated AVIRIS Spectra

Soil albedo is influenced by many physical and chemical constituents, with moisture being the most influential on the spectra general shape and albedo (Stoner and Baumgardner, 1981). Without moisture, the intrinsic or matrix reflectance of dissimilar soils varies widely due to differences in surface roughness, particle and aggregate sizes, mineral types, including salts, and organic matter contents. The influence of moisture on soil reflectance can be isolated by comparing similar soils in a study of the effects that small differences in moisture content have on reflectance. However, without prior knowledge of the soil physical and chemical constituents within every pixel, it is nearly impossible to accurately attribute the reflectance variability in an image to moisture or to differences in the physical and chemical constituents in the soil. The effect of moisture on the spectra must be eliminated to use hyperspectral imagery for determining minerals and organic matter abundances of bare agricultural soils. Accurate soil mineral and organic matter abundance maps from air- and space-borne imagery can improve GIS models for precision farming prescription, and managing irrigation and salinity. Better models of soil moisture and reflectance will also improve the selection of soil endmembers for spectral mixture analysis.

Whiting, Michael L.↗
DOE OSTI2024

Quantum Frequency Combs with Path Identity for Quantum Remote Sensing

Quantum sensing promises to revolutionize sensing applications by employing quantum states of light or matter as sensing probes. Photons are the clear choice as quantum probes for remote sensing because they can travel to and interact with a distant target. Existing schemes are mainly based on the quantum illumination framework, which requires quantum memory to store a single photon of an initially entangled pair until its twin reflects off a target and returns for final correlation measurements. Existing demonstrations are limited to tabletop experiments, and expanding the sensing range faces various roadblocks, including long-time quantum storage and photon loss and noise when transmitting quantum signals over long distances. We propose a novel quantum sensing framework that addresses these challenges using quantum frequency combs with path identity for remote sensing of signatures (“qCOMBPASS”). The combination of one key quantum phenomenon and two quantum resources—namely, quantum-induced coherence by path identity, quantum frequency combs, and two-mode squeezed light—allows for quantum remote sensing without requiring quantum memory. The proposed scheme is akin to a quantum radar based on entangled frequency-comb pairs that uses path identity to detect, range, or sense a remote target of interest by measuring pulses of one comb in the pair that never traveled to the target but that contains target information “teleported” by quantum-induced coherence by path identity from the other comb in the pair that traveled to the target but is not detected. We develop the basic qCOMBPASS theory, analyze the properties of the qCOMBPASS transceiver, and introduce the qCOMBPASS equation—a quantum analog of the well-known LIDAR equation in classical remote sensing. We also describe an experimental scheme to demonstrate the concept using two-mode squeezed quantum combs. qCOMBPASS can strongly impact various applications in remote quantum sensing, imaging, metrology, and communications. These applications include detection and ranging of low-reflectivity objects, measurement of small displacements of a remote target with precision beyond the standard quantum limit (SQL), standoff hyperspectral quantum imaging, discreet surveillance from space with low detection probability (detect without being detected), very-long-baseline interferometry, quantum Doppler sensing, quantum clock synchronization, and networks of distributed quantum sensors. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2012

Canopy Level Chlorophyll Fluorescence and the PRI in a Cornfield

Two bio-indicators, the Photochemical Reflectance Index (PRI) and solar-induced red and far-red Chlorophyll Fluorescence (SIF), were derived from directional hyperspectral observations and studied in a cornfield on two contrasting days in the growing season. Both red and far-red SIF exhibited higher values on the day when the canopy in the early senescent stage, but only the far-red SIF showed sensitivity to viewing geometry. Consequently, the red/far-red SIF ratio varied greatly among azimuth positions while the largest values were obtained for the "hotspot" at both growth stages. This ratio was lower (approx.0.88 +/- 0.4) in early July than in August when the ratio approached equivalence (near approx.1). In concert, the PRI exhibited stronger responses to both zenith and azimuth angles and different values on the two growth stages. The potential of using these indices to monitor photosynthetic activities needs further investigation

Middleton, Elizabeth M.↗
NASA NTRS2002

Spectral Data Reduction via Wavelet Decomposition

The greatest advantage gained from hyperspectral imagery is that narrow spectral features can be used to give more information about materials than was previously possible with broad-band multispectral imagery. For many applications, the new larger data volumes from such hyperspectral sensors, however, present a challenge for traditional processing techniques. For example, the actual identification of each ground surface pixel by its corresponding reflecting spectral signature is still one of the most difficult challenges in the exploitation of this advanced technology, because of the immense volume of data collected. Therefore, conventional classification methods require a preprocessing step of dimension reduction to conquer the so-called "curse of dimensionality." Spectral data reduction using wavelet decomposition could be useful, as it does not only reduce the data volume, but also preserves the distinctions between spectral signatures. This characteristic is related to the intrinsic property of wavelet transforms that preserves high- and low-frequency features during the signal decomposition, therefore preserving peaks and valleys found in typical spectra. When comparing to the most widespread dimension reduction technique, the Principal Component Analysis (PCA), and looking at the same level of compression rate, we show that Wavelet Reduction yields better classification accuracy, for hyperspectral data processed with a conventional supervised classification such as a maximum likelihood method.

Kaewpijit, S.↗
NASA NTRS2003

Advancing Glaciological Applications of Remote Sensing with EO-1: (1) Mapping Snow Grain Size and Albedo on the Greenland Ice Sheet Using an Imaging Spectrometer, and (2) ALI Evaluation for Subtle Surface Topographic Mapping via Shape-from Shading

The Hyperion sensor, onboard NASA's Earth Observing-1 (EO-1) satellite,is an imaging spectroradiometer with 220 spectral bands over the spectral range from 0.4 - 2.5 microns. Over the course of summer 2001, the instrument acquired numerous images over the Greenland ice sheet. Our main motivation is to develop an accurate and robust approach for measuring the broadband albedo of snow from satellites. Satellite-derived estimates of broadband have typically been plagued with three problems: errors resulting from inaccurate atmospheric correction, particularly in the visible wavelengths from the conversion of reflectance to albedo (accounting for snow BRDE); and errors resulting from regression-based approaches used to convert narrowband albedo to broadband albedo. A typerspectral method has been developed that substantially reduces these three main sources of error and produces highly accurate estimates of snow albedo. This technique uses hyperspectral data from 0.98 - 1.06 microns, spanning a spectral absorption feature centered at 1.03 microns. A key aspect of this work is that this spectral range is within an atmospheric transmission window and reflectances are largely unaffected by atmospheric aerosols, water vapor, or ozone. In this investigation, we make broadband albedo measurements at four sites on the Greenland ice sheet: Summit, a high altitude station in central Greenland; the ETH/CU camp, a camp on the equilibrium line in western Greenland; Crawford Point, a site located between Summit and the ETH/CU camp; and Tunu, a site located in northeastern Greenland at 2000 m. altitude. Each of these sites has an automated weather station (AWS) that continually measures broadband albedo thereby providing validation data.

Source record↗
NASA NTRS2016

Evaluation of GLAMR-Based Calibration for SI-Traceable Field Reflectance Retrievals

The reflected solar instrument that is part of the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission is being formulated with a goal of providing SI-traceable measurement of radiance that is an order of magnitude more accurate than the current imaging sensors. The Goddard Laser for Absolute Measurement of Radiance (GLAMR) is a key element to reaching such accuracy along with transferring the laboratory calibration to on-orbit measurements. Results from field reflectance retrievals using three separate instruments all of which have been calibrated using GLAMR are shown. The instruments include a commercial field spectrometer and a portable version of CLARREO's calibration demonstration system. The third instrument is NASA Goddard's Lidar, Hyperspectral and Thermal Imager (G-LiHT) which is an airborne system. All three were operated during a March 2013 measurement campaign at Red Lake Playa, Arizona as part of the on-orbit commissioning phase of Landsat 8. Reflectance is derived from near-coincident measurements by the three sensors for a small area of the playa. The retrieved results are SI-traceable and demonstrate the ability to transfer the GLAMR calibration to the field. Use of the G-LiHT data in the calibration of Landsat-7 and -8 sensors permits them both to be placed on the GLAMR-scale as well.

Angal, Amit↗
DOE OSTI2020

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the ‘Osprey’) using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Global Monitoring of Terrestrial Chlorophyll Fluorescence from Moderate-Spectral-Resolution Near-Infrared Satellite Measurements: Methodology, Simulations, and Application to GOME-2

Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. Previous satellite retrievals of fluorescence have relied solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near-global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data with a simplified radiative transfer model to disentangle the spectral signatures of three basic components: atmospheric absorption, surface reflectance, and fluorescence radiance. An empirically based principal component analysis approach is employed, primarily using cloudy data over ocean, to model and solve for the atmospheric absorption. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate-spectral-resolution measurements with a relatively high signal-to-noise ratio can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with those from a simpler technique applied to the Greenhouse gases Observing SATellite (GOSAT). GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. Near-global coverage is provided within a few days. We are able to show clearly for the first time physically plausible variations in fluorescence over the course of a single month at a spatial resolution of 0.5 0.5. We also show some significant differences between fluorescence and coincident normalized difference vegetation indices (NDVI) retrievals.

Fluorescence↗
NASA NTRS2013

Global Monitoring of Terrestrial Chlorophyll Fluorescence from Moderate-spectral-resolution Near-infrared Satellite Measurements: Methodology, Simulations, and Application to GOME-2

Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. Previous satellite retrievals of fluorescence have relied solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near-global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data with a simplified radiative transfer model to disentangle the spectral signatures of three basic components: atmospheric absorption, surface reflectance, and fluorescence radiance. An empirically based principal component analysis approach is employed, primarily using cloudy data over ocean, to model and solve for the atmospheric absorption. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate-spectral-resolution measurements with a relatively high signal-to-noise ratio can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with those from a simpler technique applied to the Greenhouse gases Observing SATellite (GOSAT). GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. Near-global coverage is provided within a few days. We are able to show clearly for the first time physically plausible variations in fluorescence over the course of a single month at a spatial resolution of 0.5 deg × 0.5 deg. We also show some significant differences between fluorescence and coincident normalized difference vegetation indices (NDVI) retrievals.

remote sensing↗
NASA NTRS2018

Enhancements to the Open Access Spectral Band Adjustment Factor Online Calculation Tool for Visible Channels

With close to 40 years of satellite observations, from which, cloud, land-use, and aerosol parameters can be measured, inter-consistent calibrations are needed to normalize retrievals across satellite records. Various visible-sensor inter-calibration techniques have been developed that utilize radiometrically stable Earth targets, e.g., deep convective clouds and desert/polar ice pseudo-invariant calibration sites. Other equally effective, direct techniques for intercalibration between satellite imagers are simultaneous nadir overpass comparisons and ray-matched radiance pairs. Combining independent calibration results from such varied techniques yields robust calibration coefficients, and is a form of self-validation. One potential source of significant error when cross-calibrating satellite sensors, however, are the often small but substantial spectral discrepancies between comparable bands, which must be accounted for. As such, visible calibration methods rely on a Spectral Band Adjustment Factor (SBAF) to account for the spectral-response function- induced radiance differences between analogous imagers. The SBAF is unique to each calibration method as it is a function of the Earth-reflected spectra. In recent years, NASA Langley pioneered the use of SCIAMACHY-, GOME-2-, and Hyperion-retrieved Earth spectra to compute SBAFs. By carefully selecting hyperspectral footprints that best represent the conditions inherent to an inter-calibration technique, the uncertainty in the SBAF is greatly reduced. NASA Langley initially provided the Global Space-based Inter-calibration System processing and research centers with online SBAF tools, with which users select conditions to best match their calibration criteria. This article highlights expanded SBAF tool capabilities for visible wavelengths, with emphasis on the use of the spectral range filtering for the purpose of separating scene conditions for the channel that the SBAF is needed based on the reflectance values of other bands. In other words, spectral filtering will enable better scene-type selection for bands where scene determination is difficult without information from other channels, which should prove valuable to users in the calibration community.

Scarino, Benjamin↗