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NASA NTRS2015

Principle Component Analysis of AIRS and CrIS Data

Synthetic Eigen Vectors (EV) used for the statistical analysis of the PC reconstruction residual of large ensembles of data are a novel tool for the analysis of data from hyperspectral infrared sounders like the Atmospheric Infrared Sounder (AIRS) on the EOS Aqua and the Cross-track Infrared Sounder (CrIS) on the SUOMI polar orbiting satellites. Unlike empirical EV, which are derived from the observed spectra, the synthetic EV are derived from a large ensemble of spectra which are calculated assuming that, given a state of the atmosphere, the spectra created by the instrument can be accurately calculated. The synthetic EV are then used to reconstruct the observed spectra. The analysis of the differences between the observed spectra and the reconstructed spectra for Simultaneous Nadir Overpasses of tropical oceans reveals unexpected differences at the more than 200 mK level under relatively clear conditions, particularly in the mid-wave water vapor channels of CrIS. The repeatability of these differences using independently trained SEV and results from different years appears to rule out inconsistencies in the radiative transfer algorithm or the data simulation. The reasons for these discrepancies are under evaluation.

infrared↗

NASA NTRS2008

Using Hyperspectral Imagery to Identify Turfgrass Stresses

The use of a form of remote sensing to aid in the management of large turfgrass fields (e.g. golf courses) has been proposed. A turfgrass field of interest would be surveyed in sunlight by use of an airborne hyperspectral imaging system, then the raw observational data would be preprocessed into hyperspectral reflectance image data. These data would be further processed to identify turfgrass stresses, to determine the spatial distributions of those stresses, and to generate maps showing the spatial distributions. Until now, chemicals and water have often been applied, variously, (1) indiscriminately to an entire turfgrass field without regard to localization of specific stresses or (2) to visible and possibly localized signs of stress for example, browning, damage from traffic, or conspicuous growth of weeds. Indiscriminate application is uneconomical and environmentally unsound; the amounts of water and chemicals consumed could be insufficient in some areas and excessive in most areas, and excess chemicals can leak into the environment. In cases in which developing stresses do not show visible signs at first, it could be more economical and effective to take corrective action before visible signs appear. By enabling early identification of specific stresses and their locations, the proposed method would provide guidance for planning more effective, more economical, and more environmentally sound turfgrass-management practices, including application of chemicals and water, aeration, and mowing. The underlying concept of using hyperspectral imagery to generate stress maps as guides to efficient management of vegetation in large fields is not new; it has been applied in the growth of crops to be harvested. What is new here is the effort to develop an algorithm that processes hyperspectral reflectance data into spectral indices specific to stresses in turfgrass. The development effort has included a study in which small turfgrass plots that were, variously, healthy or subjected to a variety of controlled stresses were observed by use of a hand-held spectroradiometer. The spectroradiometer readings in the wavelength range from 350 to 1,000 nm were processed to extract hyperspectral reflectance data, which, in turn, were analyzed to find correlations with the controlled stresses. Several indices were found to be correlated with drought stress and to be potentially useful for identifying drought stress before visible symptoms appear.

Hutto, Kendall↗

NASA NTRSDate not supplied

Some Aeronautical Communications Experiments

Classically there has existed an asymmetry between the computing and communicating sides of aerospace systems. Over the past few decades, this asymmetry has shifted to favoring communication link technologies, meaning that advancements in available central processing units (CPUs), storage devices, and internal data buses have stagnated. Indeed, the increased emphasis placed on refining subsystem performance such as with antenna bandwidth in phased arrays, amplifier power efficiency, software defined radio (SDR) flexibility and encoding for data compression and error correction has given rise to successful debuts of multi-gigabit-per-second data return from long space-link distances. These accomplishments are easily quantifiable through link budgets and illustrate what is possible, but also reveal the deficiencies in overall communications capabilities. In particular, the ever-accelerating presence of aerospace vehicles gives rise to newer and larger classes of challenges to address the needs of 21st century systems. Furthermore remote sensing and imaging capabilities have far outpaced our ability to transmit their products to the ground, so we are increasingly dependent on pre-processing and downselection to contend with the communications bottleneck. No longer may we depend upon the constrained logistics in delivering end-to-end data delivery through manual reconfigurations, static event scheduling and execution on a per-vehicle basis, for these methods do not scale and therefore must give way to dynamic, networked approaches with an overall systems view in mind. Emerging mission requirements exhibit a trend toward multiple smaller-scale vehicles working together to perform dissimilar observations. Such operations necessitate sensor fusion across a constellation, and where data processing may be distributed throughout a fairly disconnected network whose topology changes over time in non-deterministic manners. Individual communications link performance is still very relevant to deploying an effective communications system, but now must be embedded within a greater architecture of capability to optimally utilize the bandwidth available from each link to generate an ultimate end-to-end quality of service. The deleterious effects of timing uncertainty across the arrangement presents a challenge to measurement synchronization and delivery, so a successful deployed system needs to be tolerant to the delays inherent in time-of-light between elements and digital processing latencies existing at each node. In this presentation we share the flight test results from a high performance Gbps laser communications terminal evaluated with a suite of store and forward capabilities called High-rate Delay Tolerant Networking (HDTN). The communications payload is operated over Lake Erie across a range of configurations including several convergence layers, and is evaluated to determine recovery time after link disruptions, information loss, efficiency and speed. The effectiveness of utilizing a flying laboratory to increase the Technology Readiness Level (TRL) of an integrated system in relevant environments is discussed, as well as the value of conducting aeronautics experiments to retire risk for technology infusion into space missions. Upcoming flight campaigns will be presented, including opportunities to demonstrate secure command and control, data intensive hyperspectral imaging, quantum link characterization, 4k High Definition (HD) video streaming and internetworked space-ground-aero relay operations. These experiments will pave the way for future missions which will depend upon interoperability across disparate government and privately owned networks, involve contention with uncertain and dynamic timing, and require agility to autonomously configure optimal parameters across networks of ever-increasing size and complexity to ensure data delivery. https://www1.grc.nasa.gov/space/scan/acs/tech-studies/dtn/

Daniel Raible↗

NASA NTRS2001

A Multi-Band Analytical Algorithm for Deriving Absorption and Backscattering Coefficients from Remote-Sensing Reflectance of Optically Deep Waters

A multi-band analytical (MBA) algorithm is developed to retrieve absorption and backscattering coefficients for optically deep waters, which can be applied to data from past and current satellite sensors, as well as data from hyperspectral sensors. This MBA algorithm applies a remote-sensing reflectance model derived from the Radiative Transfer Equation, and values of absorption and backscattering coefficients are analytically calculated from values of remote-sensing reflectance. There are only limited empirical relationships involved in the algorithm, which implies that this MBA algorithm could be applied to a wide dynamic range of waters. Applying the algorithm to a simulated non-"Case 1" data set, which has no relation to the development of the algorithm, the percentage error for the total absorption coefficient at 440 nm a (sub 440) is approximately 12% for a range of 0.012 - 2.1 per meter (approximately 6% for a (sub 440) less than approximately 0.3 per meter), while a traditional band-ratio approach returns a percentage error of approximately 30%. Applying it to a field data set ranging from 0.025 to 2.0 per meter, the result for a (sub 440) is very close to that using a full spectrum optimization technique (9.6% difference). Compared to the optimization approach, the MBA algorithm cuts the computation time dramatically with only a small sacrifice in accuracy, making it suitable for processing large data sets such as satellite images. Significant improvements over empirical algorithms have also been achieved in retrieving the optical properties of optically deep waters.

Lee, Zhong-Ping↗

NASA NTRS2013

Arctic Tundra Vegetation Functional Types Based on Photosynthetic Physiology and Optical Properties

Non-vascular plants (lichens and mosses) are significant components of tundra landscapes and may respond to climate change differently from vascular plants affecting ecosystem carbon balance. Remote sensing provides critical tools for monitoring plant cover types, as optical signals provide a way to scale from plot measurements to regional estimates of biophysical properties, for which spatial-temporal patterns may be analyzed. Gas exchange measurements were collected for pure patches of key vegetation functional types (lichens, mosses, and vascular plants) in sedge tundra at Barrow, AK. These functional types were found to have three significantly different values of light use efficiency (LUE) with values of 0.013 plus or minus 0.0002, 0.0018 plus or minus 0.0002, and 0.0012 plus or minus 0.0001 mol C mol (exp -1) absorbed quanta for vascular plants, mosses and lichens, respectively. Discriminant analysis of the spectra reflectance of these patches identified five spectral bands that separated each of these vegetation functional types as well as nongreen material (bare soil, standing water, and dead leaves). These results were tested along a 100 m transect where midsummer spectral reflectance and vegetation coverage were measured at one meter intervals. Along the transect, area-averaged canopy LUE estimated from coverage fractions of the three functional types varied widely, even over short distances. The patch-level statistical discriminant functions applied to in situ hyperspectral reflectance data collected along the transect successfully unmixed cover fractions of the vegetation functional types. The unmixing functions, developed from the transect data, were applied to 30 m spatial resolution Earth Observing-1 Hyperion imaging spectrometer data to examine variability in distribution of the vegetation functional types for an area near Barrow, AK. Spatial variability of LUE was derived from the observed functional type distributions. Across this landscape, a fivefold variation in tundra LUE was observed. LUE calculated from the functional type cover fractions was also correlated to a spectral vegetation index developed to detect vegetation chlorophyll content. The concurrence of these alternate methods suggest that hyperspectral remote sensing can distinguish functionally distinct vegetation types and can be used to develop regional estimates of photosynthetic LUE in tundra landscapes.

VEGETATION↗

NASA NTRS2010

NASA Tech Briefs, February 2010

Topics covered include: Insulation-Testing Cryostat With Lifting Mechanism; Optical Testing of Retroreflectors for Cryogenic Applications; Measuring Cyclic Error in Laser Heterodyne Interferometers; Self-Referencing Hartmann Test for Large-Aperture Telescopes; Measuring a Fiber-Optic Delay Line Using a Mode-Locked Laser; Reconfigurable Hardware for Compressing Hyperspectral Image Data; Spatio-Temporal Equalizer for a Receiving-Antenna Feed Array; High-Speed Ring Bus; Nanoionics-Based Switches for Radio-Frequency Applications; Lunar Dust-Tolerant Electrical Connector; Compact, Reliable EEPROM Controller; Quad-Chip Double-Balanced Frequency Tripler; Ka-Band Waveguide Two-Way Hybrid Combiner for MMIC Amplifiers; Radiation-Hardened Solid-State Drive; Use of Nanofibers to Strengthen Hydrogels of Silica, Other Oxides, and Aerogels; Two Concepts for Deployable Trusses; Concentric Nested Toroidal Inflatable Structures; Investigating Dynamics of Eccentricity in Turbomachines; Improved Low-Temperature Performance of Li-Ion Cells Using New Electrolytes; Integrity Monitoring of Mercury Discharge Lamps; White-Light Phase-Conjugate Mirrors as Distortion Correctors; Biasable, Balanced, Fundamental Submillimeter Monolithic Membrane Mixer; ICER-3D Hyperspectral Image Compression Software; and Context Modeler for Wavelet Compression of Spectral Hyperspectral Images.

Source record↗

NASA NTRS1999

Hyperspectral Remote Sensing and Ecological Modeling Research and Education at Mid America Remote Sensing Center (MARC): Field and Laboratory Enhancement

The purpose of this project was to establish a new hyperspectral remote sensing laboratory at the Mid-America Remote sensing Center (MARC), dedicated to in situ and laboratory measurements of environmental samples and to the manipulation, analysis, and storage of remotely sensed data for environmental monitoring and research in ecological modeling using hyperspectral remote sensing at MARC, one of three research facilities of the Center of Reservoir Research at Murray State University (MSU), a Kentucky Commonwealth Center of Excellence. The equipment purchased, a FieldSpec FR portable spectroradiometer and peripherals, and ENVI hyperspectral data processing software, allowed MARC to provide hands-on experience, education, and training for the students of the Department of Geosciences in quantitative remote sensing using hyperspectral data, Geographic Information System (GIS), digital image processing (DIP), computer, geological and geophysical mapping; to provide field support to the researchers and students collecting in situ and laboratory measurements of environmental data; to create a spectral library of the cover types and to establish a World Wide Web server to provide the spectral library to other academic, state and Federal institutions. Much of the research will soon be published in scientific journals. A World Wide Web page has been created at the web site of MARC. Results of this project are grouped in two categories, education and research accomplishments. The Principal Investigator (PI) modified remote sensing and DIP courses to introduce students to ii situ field spectra and laboratory remote sensing studies for environmental monitoring in the region by using the new equipment in the courses. The PI collected in situ measurements using the spectroradiometer for the ER-2 mission to Puerto Rico project for the Moderate Resolution Imaging Spectrometer (MODIS) Airborne Simulator (MAS). Currently MARC is mapping water quality in Kentucky Lake and vegetation in the Land-Between-the Lakes (LBL) using Landsat-TM data. A Landsat-TM scene of the same day was obtained to relate ground measurements to the satellite data. A spectral library has been created for overstory species in LBL. Some of the methods, such as NPDF and IDFD techniques for spectral unmixing and reduction of effects of shadows in classifications- comparison of hyperspectral classification techniques, and spectral nonlinear and linear unmixing techniques, are being tested using the laboratory.

Cetin, Haluk↗

NASA NTRS1998

Monitoring of Volcanogenic CO2-Induced Tree Kills with AVIRIS Image Data at Mammoth Mountain, California

Elevated cold CO2 emissions from the flank of Mammoth Mountain volcano on the southwest rim of the Long Valley Caldera, eastern California, have been the cause of over 100 acres of dead trees in that area since 1990. The source of the CO2 gas is thought to be from one or more magmatic intrusion(s) beneath Mammoth Mountain and is probably related to a period of seismic unrest that began in 1989. The gas rises to the surface probably from depths of a few kilometers, along faults and fracture zones. The gas is at ambient temperature and diffuses from the soil rather than discharging from distinct vents. Typically, soil gas concentrations in tree-kill areas range from 10% to over 90% CO2 by volume, as compared to normal background of < 1% in healthy forest. The gas composition is predominantly CO2 mixed with air (sulfur gases are not elevated), and C and He isotopic ratios are consistent with a magmatic origin for the gas. The total CO2 emission has been estimated at 1200 tons/day, comparable to the emissions at Kilauea. Some of the dead trees are as old as 250 years, suggesting that similar anomalous gas discharge has not occurred over the previous few hundred years. The delta C-13/12 ratio in the Mammoth Mountain CO2 emission averages about -4.5 (PDB standard). This is consistent with a mantle source for the carbon. However, the large volume of the emission suggests that not all of the CO2 is necessarily being generated from the 1989 intrusion. The voluminous gas could be leaking from a vapor-rich zone, capped by an impermeable layer, that was supplied CO2 from degassing of many small magma bodies that intruded beneath the mountain over a period of decades or centuries. Earthquakes in 1989 could have fractured the capping layer and provided pathways for the escape Of CO2 to the surface. Alternatively, some of the CO2 could be derived from contact metamorphism of carbonate rocks intruded by magma. Carbonate-bearing Paleozoic roof pendents crop out in close proximity to Mammoth Mountain. It is possible that similar rocks could occur at depth beneath Mammoth Mountain, and could have contributed CO2 from thermal decomposition caused from recent intrusions. We hope to determine the C-13/12 ratio of a suite of samples to demonstrate if the carbonate rocks could be the source of at least part of the 1990-97 CO2 emission. To better understand the behavior of the CO2 gas, we have used hyperspectral imagery data of Mammoth Mountain acquired from the Airborne Visual/Infrared Imaging Spectrometer (AVIRIS) to map out areas of dead trees. The areas of tree kill have increased in size from about 50 acres in 1994 to about 100 acres in 1997. Tree kill is the major surface manifestation of the carbon dioxide flux at Mammoth Mountain, is widely dispersed, and has been cursorily mapped by regular field mapping techniques in the area. Initial investigations using airborne digital imagery from the Thematic Mapper Simulator (NS001) and AVIRIS instruments have shown extremely encouraging results for complete delineation of the vegetation anomalies. The most successful maps (when compared with ground truth) were developed using AVIRIS data with spectral angle mapper and matched filter algorithms with a data set that was reduced to maximum variance via the minimum noise fraction transformation. The result of this work is a series of maps that show the tree kill areas occurring in an halo-pattern surrounding the base of Mammoth Mountain. We are applying these same techniques to earlier AVIRIS images of Mammoth Mountain to examine the progression of the tree kill areas over time. Temporal maps of the tree kill areas may assist in constructing a picture of the structure beneath Mammoth Mountain.

Hausback, Brian P.↗

NASA NTRS1985

NASA image-based geological expert system development project for hyperspectral image analysis

The NASA image-based geological expert system was applied to analyze remotely sensed hyperspectral image data. The major objective is for geologists to identify the earth surface mineral properties directly from the airborne and spaceborne imaging spectrometer data. With certain constraints, it is shown that the system can identify correctly different classes of mineral. It has the built-in learning paradigm to enhance the confidence factor of mineral identification. A very powerful natural language system was incorporated as the user-friendly front end, and the concurrent processing efficiency of the frame-based knowledge representation in the hypercube microsupercomputer simulation was tested.

Chiou, W. C., Sr.↗

NASA NTRS1999

Enhancement of Capabilities in Hyperspectral and Radar Remote Sensing for Environmental Assessment and Monitoring

The University of Utah, Department of Geography has developed a research and instructional program in satellite remote sensing and image processing. The University requested funds for the purchase of software licenses, mass storage for massive hyperspectral imager data sets, upgrades for the central data server to handle the additional storage capacity, a spectroradiometer for field data collection. These purchases have been made. This equipment will support research in one of the newest and most rapidly expanding areas of remote sensing.

Hepner, George F.↗

NASA NTRS2009

Progressive Band Selection

Progressive band selection (PBS) reduces spectral redundancy without significant loss of information, thereby reducing hyperspectral image data volume and processing time. Used onboard a spacecraft, it can also reduce image downlink time. PBS prioritizes an image's spectral bands according to priority scores that measure their significance to a specific application. Then it uses one of three methods to select an appropriate number of the most useful bands. Key challenges for PBS include selecting an appropriate criterion to generate band priority scores, and determining how many bands should be retained in the reduced image. The image's Virtual Dimensionality (VD), once computed, is a reasonable estimate of the latter. We describe the major design details of PBS and test PBS in a land classification experiment.

Fisher, Kevin↗

NASA NTRS2015

Ultrafast High Accuracy PCRTM_SOLAR Model for Cloudy Atmosphere

An ultrafast high accuracy PCRTM_SOLAR model is developed based on PCA compression and principal component-based radiative transfer model (PCRTM). A fast algorithm for simulation of multi-scattering properties of cloud and/or aerosols is integrated into the fast infrared PCRTM. We completed radiance simulation and training for instruments, such as IASI, AIRS, CrIS, NASTI and SHIS, under diverse conditions. The new model is 5 orders faster than 52-stream DISORT with very high accuracy for cloudy sky radiative transfer simulation. It is suitable for hyperspectral remote data assimilation and cloudy sky retrievals.

Yang, Qiguang↗

NASA NTRS2016

New Methods for Retrieval of Chlorophyll Red Fluorescence from Hyperspectral Satellite Instruments: Simulations and Application to GOME-2 and SCIAMACHY

Global satellite measurements of solar-induced fluorescence (SIF) from chlorophyll over land and ocean have proven useful for a number of different applications related to physiology, phenology, and productivity of plants and phytoplankton. Terrestrial chlorophyll fluorescence is emitted throughout the red and far-red spectrum, producing two broad peaks near 683 and 736nm. From ocean surfaces, phytoplankton fluorescence emissions are entirely from the red region (683nm peak). Studies using satellite-derived SIF over land have focused almost exclusively on measurements in the far red (wavelengths greater than 712nm), since those are the most easily obtained with existing instrumentation. Here, we examine new ways to use existing hyperspectral satellite data sets to retrieve red SIF (wavelengths less than 712nm) over both land and ocean. Red SIF is thought to provide complementary information to that from the far red for terrestrial vegetation. The satellite instruments that we use were designed to make atmospheric trace-gas measurements and are therefore not optimal for observing SIF; they have coarse spatial resolution and only moderate spectral resolution (0.5nm). Nevertheless, these instruments, the Global Ozone Monitoring Instrument 2 (GOME-2) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), offer a unique opportunity to compare red and far-red terrestrial SIF at regional spatial scales. Terrestrial SIF has been estimated with ground-, aircraft-, or satellite-based instruments by measuring the filling-in of atmospheric andor solar absorption spectral features by SIF. Our approach makes use of the oxygen (O2) gamma band that is not affected by SIF. The SIF-free O2 gamma band helps to estimate absorption within the spectrally variable O2 B band, which is filled in by red SIF. SIF also fills in the spectrally stable solar Fraunhofer lines (SFLs) at wavelengths both inside and just outside the O2 B band, which further helps to estimate red SIF emission. Our approach is then an extension of previous approaches applied to satellite data that utilized only the filling-in of SFLs by red SIF. We conducted retrievals of red SIF using an extensive database of simulated radiances covering a wide range of conditions. Our new algorithm produces good agreement between the simulated truth and retrievals and shows the potential of the O2 bands for noise reduction in red SIF retrievals as compared with approaches that rely solely on SFL filling. Biases seen with existing satellite data, most likely due to instrumental artifacts that vary in time, space, and with instrument, must be addressed in order to obtain reasonable results. Our 8-year record of red SIF observations over land with the GOME-2 allows for the first time reliable global mapping of monthly anomalies. These anomalies are shown to have similar spatiotemporal structure as those in the far red, particularly for drought-prone regions. There is a somewhat larger percentage response in the red as compared with the far red for these areas that are drought sensitive. We also demonstrate that good-quality ocean fluorescence line height retrievals can be achieved with GOME-2, SCIAMACHY, and similar instruments by utilizing the full complement of radiance measurements that span the red SIF emission feature.

spatiotemporal structure↗

NASA NTRSDate not supplied

Assessing Carbon Properties in Coastal Waters with a New Observing System Testbed

Large rainfall events over land can lead to a substantial flux of carbon and nutrients to estuaries and the coastal ocean. In the mid-Atlantic on the east coast of North America (35° - 42° N), these events often happen due to tropical storm activity as well as less predictable anomalously large midlatitude storms or abrupt spring snow melt runoff. Storms can directly impact the coastal carbon cycle via export of carbon from land to sea, while also stimulating phytoplankton production due to the influx of nutrients to the coastal ocean. To assess the impact of high precipitation and river flow events on the coastal carbon cycle, we have integrated multiple observing platforms in an analytical framework to dynamically observe carbon-related properties. The new observing system testbed (NOS-T) allows for an estimate of phytoplankton and organic carbon stocks in the surface ocean, with a goal of providing near real time analytical capability. A case study of high river discharge in the mid-Atlantic in the summer and fall of 2018 and 2021 were used to examine how riverine carbon manifests along the land-estuary-ocean continuum particularly in Chesapeake Bay. Chesapeake Bay was chosen as the study site because of long term monitoring by the Chesapeake Bay Program and a robust and well-developed regional biogeochemical modeling system, the Chesapeake Bay Environmental Forecast System (CBEFS), that is publicly available. There is also new capability in Chesapeake Bay to combine hyperspectral radiometric data from the Aerosol Robotic Network site that is online in 2022 with in-water observations of optical properties. Precipitation data from the Global Precipitation Measurement IMERG data set was used to establish triggering criteria for storm carbon flux observation. USGS and in-water carbon data were used to establish statistical models to estimate the mass flux of organic carbon into Chesapeake Bay using measured river discharge. The year 2018 was examined as there was record rainfall and near record river flow in the late summer. The CBEFS modeling was compared with in-water estimates of dissolved organic carbon (DOC) to assess the model’s ability to capture storm fluxes of carbon to the ecosystem. Multiple satellite platforms were also used to assess how remote sensing using passive Earth orbiting sensors can be used to observe carbon in this complex coastal region. A set of recommendations have been established to improve sensing capability to measure aquatic carbon during storm events. A unique challenge in these dynamic inland waters is how rapidly carbon fluxes can evolve in space and time, with many sources contributing to the water leaving reflectance that satellites can observe. To improve our ability to quantify carbon stocks and fluxes in near real time, a suite of satellite sensors and high-resolution modeling capability is needed, all supplemented by in situ monitoring. Future inclusion of in-water observations that would be deployed when the system reaches triggering criteria, as well as taskable orbital instruments, will improve estimates of ocean carbon properties and provide the ability to calculate major carbon stocks and fluxes in near real time following episodic storm events.

land-ocean continuum↗

NASA NTRSDate not supplied

PACE UV-VIS Polarimetric Remote Sensing of Atmosphere-Ocean Systems: Sensitivity to Variations in Brown Carbon

The NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission, scheduled for launch in 2024, will carry two instruments that measure ultraviolet (UV) radiance emerging from the top of the atmosphere: the Ocean Color Instrument (OCI) and the Spectro-Polarimeter for Planetary Exploration (SPEXone) instrument. OCI will provide single-view, hyperspectral radiometric data, whereas SPEXone will provide multi-angle, multispectral polarimetric data. Together, these instruments will be the most advanced in NASA’s history for the combined observation of ocean color and atmospheric aerosols in the UV, and they will therefore provide unprecedented research opportunities for the ocean color and atmospheric aerosol communities. In particular, UV observations are best suited to retrieve properties of Colored Dissolved Organic Matter (CDOM) in the ocean, and of Brown Carbon (BrC) aerosols in the atmosphere. However, both CDOM and BrC exhibit very similar absorption spectra in the UV and the visible (VIS) spectrum; hence, care must be taken in accurately modeling these spectra for analyses of OCI and SPEXone measurements. In this presentation, we summarize a simple but highly accurate three-parameter model for complex refractive index spectra of BrC in the UV and VIS. We apply this model to simulations of PACE-like total and polarized multiangle radiance in the UV, and demonstrate that the impact of BrC and CDOM variations can be similar in total radiance, but is easy to distinguish in polarized radiance.

PACE↗

NASA NTRS2009

Improving 3D Wavelet-Based Compression of Hyperspectral Images

Two methods of increasing the effectiveness of three-dimensional (3D) wavelet-based compression of hyperspectral images have been developed. (As used here, images signifies both images and digital data representing images.) The methods are oriented toward reducing or eliminating detrimental effects of a phenomenon, referred to as spectral ringing, that is described below. In 3D wavelet-based compression, an image is represented by a multiresolution wavelet decomposition consisting of several subbands obtained by applying wavelet transforms in the two spatial dimensions corresponding to the two spatial coordinate axes of the image plane, and by applying wavelet transforms in the spectral dimension. Spectral ringing is named after the more familiar spatial ringing (spurious spatial oscillations) that can be seen parallel to and near edges in ordinary images reconstructed from compressed data. These ringing phenomena are attributable to effects of quantization. In hyperspectral data, the individual spectral bands play the role of edges, causing spurious oscillations to occur in the spectral dimension. In the absence of such corrective measures as the present two methods, spectral ringing can manifest itself as systematic biases in some reconstructed spectral bands and can reduce the effectiveness of compression of spatially-low-pass subbands. One of the two methods is denoted mean subtraction. The basic idea of this method is to subtract mean values from spatial planes of spatially low-pass subbands prior to encoding, because (a) such spatial planes often have mean values that are far from zero and (b) zero-mean data are better suited for compression by methods that are effective for subbands of two-dimensional (2D) images. In this method, after the 3D wavelet decomposition is performed, mean values are computed for and subtracted from each spatial plane of each spatially-low-pass subband. The resulting data are converted to sign-magnitude form and compressed in a manner similar to that of a baseline hyperspectral- image-compression method. The mean values are encoded in the compressed bit stream and added back to the data at the appropriate decompression step. The overhead incurred by encoding the mean values only a few bits per spectral band is negligible with respect to the huge size of a typical hyperspectral data set. The other method is denoted modified decomposition. This method is so named because it involves a modified version of a commonly used multiresolution wavelet decomposition, known in the art as the 3D Mallat decomposition, in which (a) the first of multiple stages of a 3D wavelet transform is applied to the entire dataset and (b) subsequent stages are applied only to the horizontally-, vertically-, and spectrally-low-pass subband from the preceding stage. In the modified decomposition, in stages after the first, not only is the spatially-low-pass, spectrally-low-pass subband further decomposed, but also spatially-low-pass, spectrally-high-pass subbands are further decomposed spatially. Either method can be used alone to improve the quality of a reconstructed image (see figure). Alternatively, the two methods can be combined by first performing modified decomposition, then subtracting the mean values from spatial planes of spatially-low-pass subbands.

Klimesh, Matthew↗

NASA NTRS2006

Novel Hyperspectral Sun Photometer for Satellite Remote Sensing Data Radiometeic Calibration and Atmospheric Aerosol Studies

A simple and cost-effective, hyperspectral sun photometer for radiometric vicarious remote sensing system calibration, air quality monitoring, and potentially in-situ planetary climatological studies, was developed. The device was constructed solely from off the shelf components and was designed to be easily deployable for support of short-term verification and validation data collects. This sun photometer not only provides the same data products as existing multi-band sun photometers but also the potential of hyperspectral optical depth and diffuse-to-global products. As compared to traditional sun photometers, this device requires a simpler setup, less data acquisition time and allows for a more direct calibration approach. Fielding this instrument has also enabled Stennis Space Center (SSC) Applied Sciences Directorate personnel to cross-calibrate existing sun photometers. This innovative research will position SSC personnel to perform air quality assessments in support of the NASA Applied Sciences Program's National Applications program element as well as to develop techniques to evaluate aerosols in a Martian or other planetary atmosphere.

Pagnutti, Mary↗

NASA NTRS2022

Using airborne and DESIS imaging spectroscopy to map plant diversity across the largest contiguous tract of tallgrass prairie on earth

Grassland ecosystems are under threat globally, primarily due to land-use and land-cover changes that have adversely affected their biodiversity. Given the negative ecological impacts of biodiversity loss in grasslands, there is an urgent need for developing an operational biodiversity monitoring system that functions in these ecosystems. In this paper, we assessed the capability of airborne and spaceborne imaging spectroscopy (also known as hyperspectral imaging) to capture plant α-diversity in a large naturally-assembled grassland while considering the impact of common management practices, specifically prescribed fire. We collected a robust in-situ plant diversity data set, including species composition and percent cover from 2500 sampling points with different burn ages, from recently-burned to transitional and pre-prescribed fire at the Joseph H. Williams Tallgrass Prairie Preserve in Oklahoma, USA. We expressed in-situ plant α-diversity using the first three Hill numbers, including species richness (number of observed species in a plant community), exponential Shannon entropy index (hereafter Shannon diversity; effective number of common species, where species are weighed proportional to their percent cover), and inverse Simpson concentration index (hereafter Simpson diversity; effective number of dominant species, where more weight is given to dominant species) at four different plot sizes, including 60 m × 60 m, 120 m × 120 m, 180 m × 180 m, and 240 m × 240 m. We collected full-range airborne hyperspectral data with fine spatial resolution (1 m) and visible and near-infrared spaceborne hyperspectral data from DESIS sensor with coarse spatial resolution (30 m), and used the spectral diversity hypothesis—i.e., that the variability in spectral data is largely driven by plant diversity—to estimate α-diversity remotely. In recently-burned plots and those at the transitional stage, both airborne and spaceborne data were capable of capturing Simpson diversity—a metric that calculates the effective number of dominant species by emphasizing abundant species and discounting rare species—but not species richness or Shannon diversity. Further, neither airborne nor spaceborne hyperspectral data sets were capable of capturing plant α-diversity of 60 m × 60 m or 120 m × 120 m plots. Based on these results, three main findings emerged: (1) management practices influence grassland biodiversity patterns that can be remotely detected, (2) both fine- and coarse-resolution remotely-sensed data can detect the effective number of dominant species (e.g., Simpson diversity), and (3) attention should be given to site-specific plant diversity field data collection to appropriately interpret remote sensing results. Findings of this study indicate the feasibility of estimating Simpson diversity in naturally-assembled grasslands using forthcoming spaceborne imagers such as National Aeronautics and Space Administration's Surface Biology and Geology mission.

Hamed Gholizadeh↗