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NASA NTRS2012

The International Space Station: New Capabilities for Disaster Response and Humanitarian Aid

The International Space Station (ISS) has been acquiring Earth imagery since 2000, primarily in the form of astronaut photography using hand-held film and digital cameras. Recent additions of more sophisticated multispectral and hyperspectral sensor systems have expanded both the capabilities and relevance of the ISS to basic research, applied Earth science, and development of new sensor technologies. Funding opportunities established within NASA, the US National Laboratories and the international partner organizations have generated instrument proposals that will further enhance these capabilities. With both internal and external sensor location options, and the availability of both automated and human-tended operational environments, the ISS is a unique platform within the constellation of Earth-observing satellites currently in orbit. Current progress and challenges associated with development of ISS terrestrial remote sensing capabilities in the area of disaster response and support of relief efforts will be presented. The ISS orbit allows for imaging of the Earth's surface at varying times of day and night, providing opportunities for data collection over approximately 95% of the populated regions. These opportunities are distinct from--yet augment--the data collection windows for the majority of sensors on polar-orbiting satellites. In addition to this potential for "being in the right place at the right time" to collect critical information on an evolving disaster, the presence of a human crew also allows for immediate recognition of an event from orbit, notification of relevant organizations on the ground, and re-tasking of available remote sensing resources to support humanitarian response and relief efforts. Challenges to establishing an integrated response capability are both technical (coordination of sensor targeting and data collection, rapid downlink and posting of data to a central accessible hub, timely generation and distribution of relevant data products) and operational (notification and engagement of sensor support teams, international partner agency sanction of astronaut support activities). To better collaborate on common issues and strengthen applications, including using the data to support disaster response, we established an ISS Program Science Forum Working Group for Earth Observations comprised of representatives from the international partner agencies. This international forum welcomes input and support from relevant United Nations task groups regarding our disaster response and humanitarian aid to enable development of the ISS capabilities in this area for greatest value to the international community.

Stefanov, William↗
NASA NTRS2012

Cross-calibration of Imaging Sensors using Model-based, SI-traceable Predictions of At-sensor Radiance

Many inter-consistency efforts force empirical agreement between sensors viewing a source nearly coincident in time and geometry that ensures consistency between sensors rather than obtain an SI-traceable calibration with documented error budgets. The method described here provides interconsistency via absolute radiometric calibration with defensible error budget avoiding systematic errors through prediction of at-sensor radiance for a site viewed by multiple sensors but not necessarily viewed at coincident times. The method predicts spectral radiance over a given surface site for arbitrary view and illumination angles and for any date dominated by clear-sky conditions. The foundation is a model-based, SI-traceable prediction of at-sensor radiance over selected sites based on physical understanding of the surface and atmosphere. The calibration of the ground site will include spatial, spectral, and sunview geometric effects based on satellite and ground-based data. The result is an interconsistency of hyperspectral and multispectral sensors spanning spatial resolutions from meters to kilometers all relative to the surface site rather than a single sensor. The source-centric philosophy of calibrating the site inherently accounts for footprint size mismatch, spectral band mismatch, and temporal and spatial sampling effects. The method for characterizing the test site allows its use for SI-traceable calibration of any sensor that can view the calibrated test site. Interconsistency is obtained through the traceability and error budget rather than coincident views. Such an approach to inter-consistency provides better understanding of biases between sensors as well producing more accurate results with documented SI-traceability that reduces the need for overlapping data sets.

Thome, Kurtis↗
NASA NTRS2020

Radiometric Performance Characterization of the CubeSat Infrared Atmospheric Sounder (CIRAS) High Operating Temperature-Barrier Infrared Detectors (HOT-BIRD))

The CubeSat Infrared Atmospheric Sounder (CIRAS) is a remote sensing instrument under development for thedemonstration and technology maturation of hyperspectral infrared sounding in a 6U CubeSat. The CIRASinstrument utilizes a 2D Focal Plane Array (FPA) of High Operating Temperature-Barrier Infrared Detectors(HOT-BIRD). The HOT-BIRD material provides improved uniformity, higher allowable operating temperatures,and lower 1/f noise. The performance of the HOT-BIRD FPA was tested inside a Integrated Dewarand Cryocooler Assembly (IDCA), allowing the operating temperature of the FPA to reach low temperatures.Experimentation involved using an external Blackbody Calibrator Target (BCT) to generate numerous imagesof the BCT object at varying scene temperatures. The data acquired was compared to a radiometric model tocharacterize the responsivity of the detectors. In addition, a solid angle correction was developed to improve theaccuracy of the modeling. Real and synthetic images are presented in the comparative analysis to validate theexpected responsivity of the detectors through calculation of the quantum efficency.

Rafol, Sir B.↗
NASA NTRS2014

NASA Tech Briefs, June 2014

Topics include: Real-Time Minimization of Tracking Error for Aircraft Systems; Detecting an Extreme Minority Class in Hyperspectral Data Using Machine Learning; KSC Spaceport Weather Data Archive; Visualizing Acquisition, Processing, and Network Statistics Through Database Queries; Simulating Data Flow via Multiple Secure Connections; Systems and Services for Near-Real-Time Web Access to NPP Data; CCSDS Telemetry Decoder VHDL Core; Thermal Response of a High-Power Switch to Short Pulses; Solar Panel and System Design to Reduce Heating and Optimize Corridors for Lower-Risk Planetary Aerobraking; Low-Cost, Very Large Diamond-Turned Metal Mirror; Very-High-Load-Capacity Air Bearing Spindle for Large Diamond Turning Machines; Elevated-Temperature, Highly Emissive Coating for Energy Dissipation of Large Surfaces; Catalyst for Treatment and Control of Post-Combustion Emissions; Thermally Activated Crack Healing Mechanism for Metallic Materials; Subsurface Imaging of Nanocomposites; Self-Healing Glass Sealants for Solid Oxide Fuel Cells and Electrolyzer Cells; Micromachined Thermopile Arrays with Novel Thermo - electric Materials; Low-Cost, High-Performance MMOD Shielding; Head-Mounted Display Latency Measurement Rig; Workspace-Safe Operation of a Force- or Impedance-Controlled Robot; Cryogenic Mixing Pump with No Moving Parts; Seal Design Feature for Redundancy Verification; Dexterous Humanoid Robot; Tethered Vehicle Control and Tracking System; Lunar Organic Waste Reformer; Digital Laser Frequency Stabilization via Cavity Locking Employing Low-Frequency Direct Modulation; Deep UV Discharge Lamps in Capillary Quartz Tubes with Light Output Coupled to an Optical Fiber; Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems, Version II; Advanced Sensor Technology for Algal Biotechnology; High-Speed Spectral Mapper; "Ascent - Commemorating Shuttle" - A NASA Film and Multimedia Project DVD; High-Pressure, Reduced-Kinetics Mechanism for N-Hexadecane Oxidation; Method of Error Floor Mitigation in Low-Density Parity-Check Codes; X-Ray Flaw Size Parameter for POD Studies; Large Eddy Simulation Composition Equations for Two-Phase Fully Multicomponent Turbulent Flows; Scheduling Targeted and Mapping Observations with State, Resource, and Timing Constraints;

Source record↗
NASA NTRS1994

Model for the Interpretation of Hyperspectral Remote-Sensing Reflectance

Remote-sensing reflectance is easier to interpret for the open ocean than for coastal regions because the optical signals are highly coupled to the phytoplankton (e.g., chlorophyll) concentrations. For estuarine or coastal waters, variable terrigenous colored dissolved organic matter (CDOM), suspended sediments, and bottom reflectance, all factors that do not covary with the pigment concentration, confound data interpretation. In this research, remote-sensing reflectance models are suggested for coastal waters, to which contributions that are due to bottom reflectance, CDOM fluorescence, and water Raman scattering are included. Through the use of two parameters to model the combination of the backscattering coefficient and the Q factor, excellent agreement was achieved between the measured and modeled remote-sensing reflectance for waters from the West Florida Shelf to the Mississippi River plume. These waters cover a range of chlorophyll of 0.2-40 mg/cu m and gelbstoff absorption at 440 nm from 0.02-0.4/m. Data with a spectral resolution of 10 nm or better, which is consistent with that provided by the airborne visible and infrared imaging spectrometer (AVIRIS) and spacecraft spectrometers, were used in the model evaluation.

Lee, Zhongping↗
NASA NTRS2011

Hyperspectral Mapping of the Invasive Species Pepperweed and the Development of a Habitat Suitability Model

Mapping and predicting the spatial distribution of invasive plant species is central to habitat management, however difficult to implement at landscape and regional scales. Remote sensing techniques can reduce the impact field campaigns have on these ecologically sensitive areas and can provide a regional and multi-temporal view of invasive species spread. Invasive perennial pepperweed (Lepidium latifolium) is now widespread in fragmented estuaries of the South San Francisco Bay, and is shown to degrade native vegetation in estuaries and adjacent habitats, thereby reducing forage and shelter for wildlife. The purpose of this study is to map the present distribution of pepperweed in estuarine areas of the South San Francisco Bay Salt Pond Restoration Project (Alviso, CA), and create a habitat suitability model to predict future spread. Pepperweed reflectance data were collected in-situ with a GER 1500 spectroradiometer along with 88 corresponding pepperweed presence and absence points used for building the statistical models. The spectral angle mapper (SAM) classification algorithm was used to distinguish the reflectance spectrum of pepperweed and map its distribution using an image from EO-1 Hyperion. To map pepperweed, we performed a supervised classification on an ASTER image with a resulting classification accuracy of 71.8%. We generated a weighted overlay analysis model within a geographic information system (GIS) framework to predict areas in the study site most susceptible to pepperweed colonization. Variables for the model included propensity for disturbance, status of pond restoration, proximity to water channels, and terrain curvature. A Generalized Additive Model (GAM) was also used to generate a probability map and investigate the statistical probability that each variable contributed to predict pepperweed spread. Results from the GAM revealed distance to channels, distance to ponds and curvature were statistically significant (p < 0.01) in determining the locations of suitable pepperweed habitats.

Pepperweed↗
NASA NTRS2007

Mosaic-Detector-Based Fluorescence Spectral Imager

A battery-powered, pen-sized, portable instrument for measuring molecular fluorescence spectra of chemical and biological samples in the field has been proposed. Molecular fluorescence spectroscopy is among the techniques used most frequently in laboratories to analyze compositions of chemical and biological samples. Heretofore, it has been possible to measure fluorescence spectra of molecular species at relative concentrations as low as parts per billion (ppb), with a few nm spectral resolution. The proposed instrument would include a planar array (mosaic) of detectors, onto which a fluorescence spectrum would be spatially mapped. Unlike in the larger laboratory-type molecular fluorescence spectrometers, mapping of wavelengths to spatial positions would be accomplished without use of relatively bulky optical parts. The proposed instrument is expected to be sensitive enough to enable measurement of spectra of chemical species at relative concentrations <1 ppb, with spectral resolution that could be tailored by design to be comparable to a laboratory molecular fluorescence spectrometer. The proposed instrument (see figure) would include a button-cell battery and a laser diode, which would generate the monochromatic ultraviolet light needed to excite fluorescence in a sample. The sample would be held in a cell bounded by far-ultraviolet-transparent quartz or optical glass. The detector array would be, more specifically, a complementary metal oxide/ semiconductor or charge-coupled- device imaging photodetector array, the photodetectors of which would be tailored to respond to light in the wavelength range of the fluorescence spectrum to be measured. The light-input face of the photodetector array would be covered with a matching checkerboard array of multilayer thin film interference filters, such that each pixel in the array would be sensitive only to light in a spectral band narrow enough so as not to overlap significantly with the band of an adjacent pixel. The wavelength interval between adjacent pixels (and, thus, the spectral resolution) would typically be chosen by design to be approximately equal to the width of the total fluorescence wavelength range of interest divided by the number of pixels. The unitary structure comprising the photodetector array overlaid with the matching filter array would be denoted a hyperspectral mosaic detector (HMD) array.

Son, Kyung-Ah↗
NASA NTRSDate not supplied

Mission Overview and Status IGARSS Conference

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Scheduled for launch in January 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records [1]. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s, building on the exceptional heritages of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), and the Visible Infrared Imaging Radiometer Suite (VIIRS). The PACE project office at NASA’s GSFC is responsible for the satellite development, launch and operations. The NASA Headquarters PACE Program Science office is responsible for supporting the science data processing system and assembling competed community science teams, which will include field-based vicarious calibration and data product validation efforts to support the PACE Project Science team. The mission is planned for launch into a Sun synchronous polar orbit at 676.5 km with an inclination of 98 degrees and a 1 pm local ascending node crossing time. The PACE observatory is comprised of three instruments, an Ocean Color Instrument (OCI) and two polarimeters, the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone). The OCI is the primary instrument on the observatory and is being developed at Goddard Space Flight Center (GSFC). The OCI is a hyperspectral scanning radiometer designed to measure spectral radiances from the ultraviolet to shortwave infrared (SWIR) to enable advanced ocean color and heritage cloud and atmospheric aerosol science [2]. The HARP2 and SPEXone are complimentary instruments on the PACE observatory, acquired outside of GSFC. The HARP2 is multi-spectral, wide swath (supporting atmospheric correction of OCI), and hyper-angular, with capabilities for cloud science that exceed what is capable from OCI alone. The SPEXone is narrow swath, multi-angular, and hyperspectral, with capabilities for atmospheric aerosol science that exceed what can be accomplished with OCI. NASA Headquarters directed the mission development to be guided by a Design-to-Cost (DTC) process. All elements of the mission, other than the cost, are in the DTC trade space. At the heart of the DTC process are the mission studies, performed across all the mission elements. The mission studies were used to define appropriate approaches within and across elements while maximizing science capabilities at a high cost confidence. Mission baseline requirements development is also embedded within the DTC process, as these requirements were not established at the onset of the mission concept development. Baseline mission requirements are a product of the mission studies and are defined by the project office as part of the DTC process. At the time of this writing, OCI, HARP2 and SPEXone are integrated onto the PACE spacecraft, creating the PACE observatory. The instruments and spacecraft subsystems are undergoing combined functional performance testing in preparation for observatory-level environmental testing prior to shipment to the launch site. Here, we will provide an update on science capabilities and observatory readiness for its early 2024 launch.

remote sensing↗
NASA NTRS2003

BENCAL Cruise Report

This report documents the scientific activities on board the South African Fisheries Research Ship (FRS) Africana during an ocean color calibration and validation cruise in the Benguela upwelling ecosystem (BEN-CAL), 4-17 October 2002. The cruise, denoted Afncana voyage 170, was staged in the southern Benguela between Cape Town and the Orange River within the region 14-18.5 deg E,29-34 deg S, with 15 scientists participat- ing from seven different international organizations. Uniquely in October 2002, four high-precision ocean color sensors were operational, and these included the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Aqua and Terra spacecraft, the Medium Resolution Imaging Spectrometer (MERIS), and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). SeaWiFS imagery was transmitted daily to the ship to assist in choosing the vessel's course and selecting stations for bio-optical deployments. There were four primary objectives of the cruise. The first was to conduct bio-optical measurements with above- and in-water optical instruments to vicariously calibrate the satellite sensors. The second was to interrelate diverse measurements of the apparent optical properties (AOPs) at satellite sensor wavelengths with inherent optical properties (IOPs) and bio-optically active constituents of seawater such as particles, pigments, and dissolved compounds. The third was to determine the interrelationships between optical properties, phytoplankton pigment composition, photosynthetic rates, and primary production, while the fourth objective was to collect samples for a second pigment round-robin intercalibration experiment. Weather conditions were generally very favorable, and a range of hyperspectral and fixed wavelength AOP instruments were deployed during daylight hours. Various IOP instruments were used to determine the absorption, attenuation, scattering, and backscattering properties of particulate matter and dissolved substances, while a Fast Repetition Rate Fluorometer (FRRF) was deployed to acquire data on phytoplankton photosynthetic activity. Hydrographic profiling was conducted routinely during the cruise, and seawater samples were collected for measurements of salinity, oxygen, inorganic nutrients, pigments, particulate organic carbon, suspended particulate material, and primary production. Location of stations and times of optical deployments were selected to coincide with satellite overpasses whenever possible, and to cover a large range in trophic conditions.

Hooker, Stanford B.↗
NASA NTRS2016

Utilization of O4 Slant Column Density to Derive Aerosol Layer Height from a Space-Borne UV-Visible Hyperspectral Sensor: Sensitivity and Case Study

The sensitivities of oxygen-dimer (O4) slant column densities (SCDs) to changes in aerosol layer height are investigated using the simulated radiances by a radiative transfer model, the linearized pseudo-spherical vector discrete ordinate radiative transfer (VLIDORT), and the differential optical absorption spectroscopy (DOAS) technique. The sensitivities of the O4 index (O4I), which is defined as dividing O4 SCD by 10(sup 40) molecules (sup 2) per centimeters(sup -5), to aerosol types and optical properties are also evaluated and compared. Among the O4 absorption bands at 340, 360, 380, and 477 nanometers, the O4 absorption band at 477 nanometers is found to be the most suitable to retrieve the aerosol effective height. However, the O4I at 477 nanometers is significantly influenced not only by the aerosol layer effective height but also by aerosol vertical profiles, optical properties including single scattering albedo (SSA), aerosol optical depth (AOD), particle size, and surface albedo. Overall, the error of the retrieved aerosol effective height is estimated to be 1276, 846, and 739 meters for dust, non-absorbing, and absorbing aerosol, respectively, assuming knowledge on the aerosol vertical distribution shape. Using radiance data from the Ozone Monitoring Instrument (OMI), a new algorithm is developed to derive the aerosol effective height over East Asia after the determination of the aerosol type and AOD from the MODerate resolution Imaging Spectroradiometer (MODIS). About 80 percent of retrieved aerosol effective heights are within the error range of 1 kilometer compared to those obtained from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements on thick aerosol layer cases.

Air Pollution↗
NASA NTRS2001

Modeling and Prediction of Wildfire Hazard in Southern California, Integration of Models with Imaging Spectrometry

Large urban wildfires throughout southern California have caused billions of dollars of damage and significant loss of life over the last few decades. Rapid urban growth along the wildland interface, high fuel loads and a potential increase in the frequency of large fires due to climatic change suggest that the problem will worsen in the future. Improved fire spread prediction and reduced uncertainty in assessing fire hazard would be significant, both economically and socially. Current problems in the modeling of fire spread include the role of plant community differences, spatial heterogeneity in fuels and spatio-temporal changes in fuels. In this research, we evaluated the potential of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data for providing improved maps of wildfire fuel properties. Analysis concentrated in two areas of Southern California, the Santa Monica Mountains and Santa Barbara Front Range. Wildfire fuel information can be divided into four basic categories: fuel type, fuel load (live green and woody biomass), fuel moisture and fuel condition (live vs senesced fuels). To map fuel type, AVIRIS data were used to map vegetation species using Multiple Endmember Spectral Mixture Analysis (MESMA) and Binary Decision Trees. Green live biomass and canopy moisture were mapped using AVIRIS through analysis of the 980 nm liquid water absorption feature and compared to alternate measures of moisture and field measurements. Woody biomass was mapped using L and P band cross polarimetric data acquired in 1998 and 1999. Fuel condition was mapped using spectral mixture analysis to map green vegetation (green leaves), nonphotosynthetic vegetation (NPV; stems, wood and litter), shade and soil. Summaries describing the potential of hyperspectral and SAR data for fuel mapping are provided by Roberts et al. and Dennison et al. To utilize remotely sensed data to assess fire hazard, fuel-type maps were translated into standard fuel models accessible to the FARSITE fire spread simulator. The FARSITE model and BEHAVE are considered industry standards for fire behavior analysis. Anderson level fuels map, generated using a binary decision tree classifier are available for multiple dates in the Santa Monica Mountains and at least one date for Santa Barbara. Fuel maps that will fill in the areas between Santa Barbara and the Santa Monica Mountains study sites are in progress, as part of a NASA Regional Earth Science Application Center, the Southern California Wildfire Hazard Center. Species-level maps, were supplied to fire managing agencies (Los Angeles County Fire, California Department of Forestry). Research results were published extensively in the refereed and non-refereed literature. Educational outreach included funding of several graduate students, undergraduate intern training and an article featured in the California Alliance for Minorities Program (CAMP) Quarterly Journal.

Roberts, Dar A.↗
NASA NTRS2023

Modeling Global Indices for Estimating Non-Photosynthetic Vegetation Cover

Non-photosynthetic vegetation (NPV) includes plant litter, senesced leaves, and crop residues. NPV plays an essential role in terrestrial ecosystem processes, and is an important indicator of drought severity, ecosystem disturbance, agricultural resilience, and wildfire danger. Current moderate spatial resolution multispectral satellite systems (e.g., Landsat and Sentinel-2) have only a single band in the 2000–2500 nm shortwave infrared “SWIR2” range where non-pigment biochemical constituents of NPV, including cellulose and lignin, have important spectral absorption features. Thus, these current systems have suboptimal capabilities for characterizing NPV cover. This research used simulated spectral mixtures accounting for variability among NPV and soils to evaluate globally-appropriate hyperspectral and multispectral indices for estimation of fractional NPV cover. The Continuum Interpolated NPV Depth Index (CINDI), a weighted ratio index measuring lignocellulose absorption near 2100 nm, was found to produce the lowest error in estimating NPV cover. CINDI was less sensitive to variability in soil spectra and green vegetation cover than competing indices. While CINDI was sensitive to the relative water content of soil and NPV, this sensitivity allowed for correcting error in estimated NPV cover as water content increased. CINDI bands were less capable than Dual Absorption NPV Index (DANI) bands for maintaining continuity with the heritage Landsat SWIR2 band, but combining multiple CINDI bands demonstrated adequate continuity. Three SWIR2 bands with band centers at 2038, 2108, and 2211 nm can provide superior capabilities for future moderate resolution multispectral/superspectral systems targeting NPV monitoring, including the next generation Landsat mission (Landsat Next). These bands and the associated CINDI index provide potential for global NPV monitoring using a constellation of future superspectral sensors and imaging spectrometers, with applications including improving soil management, preventing land degradation, evaluating impacts of drought, mapping ecosystem disturbance, and assessing wildfire danger.

Index optimization↗
NASA NTRS2015

Improving AIRS Radiance Spectra in High Contrast Scenes Using MODIS

The Atmospheric Infrared Sounder (AIRS) on the EOS Aqua Spacecraft was launched on May 4, 2002. AIRS acquires hyperspectral infrared radiances in 2378 channels ranging in wavelength from 3.7-15.4 microns with spectral resolution of better than 1200, and spatial resolution of 13.5 km with global daily coverage. The AIRS is designed to measure temperature and water vapor profiles for improvement in weather forecast accuracy and improved understanding of climate processes. As with most instruments, the AIRS Point Spread Functions (PSFs) are not the same for all detectors. When viewing a non-uniform scene, this causes a significant radiometric error in some channels that is scene dependent and cannot be removed without knowledge of the underlying scene. The magnitude of the error depends on the combination of non-uniformity of the AIRS spatial response for a given channel and the non-uniformity of the scene, but is typically only noticeable in about 1% of the scenes and about 10% of the channels. The current solution is to avoid those channels when performing geophysical retrievals. In this effort we use data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument to provide information on the scene uniformity that is used to correct the AIRS data. For the vast majority of channels and footprints the technique works extremely well when compared to a Principal Component (PC) reconstruction of the AIRS channels. In some cases where the scene has high inhomogeneity in an irregular pattern, and in some channels, the method can actually degrade the spectrum. Most of the degraded channels appear to be slightly affected by random noise introduced in the process, but those with larger degradation may be affected by alignment errors in the AIRS relative to MODIS or uncertainties in the PSF. Despite these errors, the methodology shows the ability to correct AIRS radiances in non-uniform scenes under some of the worst case conditions and improves the ability to match AIRS and MODIS radiances in non-uniform scenes.

spatial↗
NASA NTRS2014

NASA Tech Briefs, January 2014

Topics include: Multi-Source Autonomous Response for Targeting and Monitoring of Volcanic Activity; Software Suite to Support In-Flight Characterization of Remote Sensing Systems; Visual Image Sensor Organ Replacement; Ultra-Wideband, Dual-Polarized, Beam-Steering P-Band Array Antenna; Centering a DDR Strobe in the Middle of a Data Packet; Using a Commercial Ethernet PHY Device in a Radiation Environment; Submerged AUV Charging Station; Habitat Demonstration Unit (HDU) Vertical Cylinder Habitat; Origami-Inspired Folding of Thick, Rigid Panels; A Novel Protocol for Decoating and Permeabilizing Bacterial Spores for Epifluorescent Microscopy; Method and Apparatus for Automated Isolation of Nucleic Acids from Small Cell Samples; Enabling Microliquid Chromatography by Microbead Packing of Microchannels; On-Command Force and Torque Impeding Devices (OC-FTID) Using ERF; Deployable Fresnel Rings; Transition-Edge Hot-Electron Microbolometers for Millimeter and Submillimeter Astrophysics; Spacecraft Trajectory Analysis and Mission Planning Simulation (STAMPS) Software; Cross Support Transfer Service (CSTS) Framework Library; Arbitrary Shape Deformation in CFD Design; Range Safety Flight Elevation Limit Calculation Software; Frequency-Modulated, Continuous-Wave Laser Ranging Using Photon-Counting Detectors; Calculation of Operations Efficiency Factors for Mars Surface Missions; GPU Lossless Hyperspectral Data Compression System; Robust, Optimal Subsonic Airfoil Shapes; Protograph-Based Raptor-Like Codes; Fuzzy Neuron: Method and Hardware Realization; Kalman Filter Input Processor for Boresight Calibration; Organizing Compression of Hyperspectral Imagery to Allow Efficient Parallel Decompression; and Temperature Dependences of Mechanisms Responsible for the Water-Vapor Continuum Absorption.

Source record↗
NASA NTRS2001

Mapping Arid Vegetation Species Distributions in the White Mountains, Eastern California, Using AVIRIS, Topography, and Geology

Our challenge is to model plant species distributions in complex montane environments using disparate sources of data, including topography, geology, and hyperspectral data. From an ecologist's point of view, species distributions are determined by local environment and disturbance history, while spectral data are 'ancillary.' However, a remote sensor's perspective says that spectral data provide picture of what vegetation is there, topographic and geologic data are ancillary. In order to bridge the gap, all available data should be used to get the best possible prediction of species distributions using complex multivariate techniques implemented on a GIS. Vegetation reflects local climatic and nutrient conditions, both of which can be modeled, allowing predictive mapping of vegetation distributions. Geologic substrate strongly affects chemical, thermal, and physical properties of soils, while climatic conditions are determined by local topography. As elevation increases, precipitation increases and temperature decreases. Aspect, slope, and surrounding topography determine potential insolation, so that south-facing slopes are warmer and north-facing slopes cooler at a given elevation. Topographic position (ridge, slope, canyon, or meadow) and slope angle affect sediment accumulation and soil depth. These factors combine as complex environmental gradients, and underlie many features of plant distributions. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, digital elevation models, digitized geologic maps, and 378 ground control points were used to predictively map species distributions in the central and southern White Mountains, along the western boundary of the Basin and Range province. Minimum Noise Fraction (MNF) bands were calculated from the visible and near-infrared AVIRIS bands, and combined with digitized geologic maps and topographic variables using Canonical Correspondence Analysis (CCA). CCA allows for modeling species 'envelopes' in multidimensional environmental space, which can then be projected across entire landscapes.

VandeVen, C.↗
NASA NTRS1996

Algal Accessory Pigment Detection Using AVIRIS Image-Derived Spectral Radiance Data

Visual and derivative analyses of AVIRIS spectral data can be used to detect algal accessory pigments in aquatic communities. This capability extends the use of remote sensing for the study of aquatic ecosystems by allowing detection of taxonomically significant pigment signatures which yield information about the type of algae present. Such information allows remote sensing-based assessment of aquatic ecosystem health, as in the detection of nuisance blooms of cyanobacteria or toxic blooms of dinoflagellates. Remote sensing of aquatic systems has traditionally focused on quantification of chlorophyll a, a photoreactive (and light-harvesting) pigment which is common to all algae as well as cyanobacteria (bluegreen algae). Due to the ubiquitousness of this pigment within algae, chl a is routinely measured to estimate algal biomass both during ground-truthing and using various airborne or satellite based sensors, including AVIRIS. Within the remote sensing and aquatic sciences communities, ongoing research has been performed to detect algal accessory pigments for assessment of algal population composition. This research is based on the fact that many algal accessory pigments are taxonomically significant, and all are spectrally unique. Aquatic scientists have been refining pigment analysis techniques, primarily high performance liquid chromatography, or HPLC, to detect specific pigments as a time-saving alternative to individual algal cell identifications and counts. Remote sensing scientists are investigating the use of pigment signatures to construct pigment libraries analogous to mineral spectral libraries used in geological remote sensing applications. The accessory pigment approach has been used successfully in remote sensing using data from the Thematic Mapper, low-altitude, multiple channel scanners, field spectroradiometers and the AVIRIS hyperspectral scanner. Due to spectral and spatial resolution capabilities, AVIRIS is the sensor of choice for such studies. We present here our results on detection of algal accessory pigments using AVIRIS data.

Richardson, Laurie L.↗
NASA NTRS1998

Diurnal Reflectance Changes in Vegetation Observed with AVIRIS

Among the most important short-term dynamic biological processes are diurnal changes in canopy water relations. Plant regulation of water transport through stomatal openings affects other gaseous transport processes, often dramatically decreasing photosynthetic fixation of carbon dioxide during periods of water stress. Water stress reduces stomatal conductance of water vapor through the leaf surface and alters the diurnal timing of stomatal opening. Under non-water stressed conditions, stomates typically open soon after dawn and transpire water vapor throughout the daylight period. During stress periods, stomates may close for part of the day, generally near mid-day. Under prolonged stress conditions, stomatal closure shifts to earlier times during the day; stomates may close by mid-morning and remain closed until the following morning - or remain closed entirely. Under these conditions the relationship between canopy greenness (e.g., measured with a vegetation index or by spectral mixture analysis) and photosynthetic fixation of carbon is lost and the remotely sensed vegetation metric is a poor predictor of gas exchange. Prediction of stomatal regulation and exchange of water and trace gases is critical for ecosystem and climate models to correctly estimate budgets of these gases and understand or predict other processes like gross and net ecosystem primary production. Plant gas exchange has been extensively studied by physiologists at the leaf and whole plant level and by biometeorologists at somewhat larger scales. While these energy driven processes follow a predictable if somewhat asymmetric diurnal cycle dependent on soil water availability and the constraints imposed by the solar energy budget, they are nonetheless difficult to measure at the tree and stand levels using conventional methods. Ecologists have long been interested in the potential of remote sensing for monitoring physiological changes using multi-temporal images. Much of this research has focused on day-to-day changes in water use, especially for agricultural applications. Ustin et al. showed seasonal changes in canopy water content in chaparral shrub could be estimated using optical methods. Vanderbilt et al. followed asymmetric diurnal changes in the reflectance of a walnut orchard, but could not attribute specific reflectance changes to specific changes in canopy architecture or physiology. Forests and shrub lands in California experience prolonged periods of drought, sometimes extending six months without precipitation. The conifer and evergreen chaparral communities common to the foothill region around the central valley of California retain their foliage throughout the summer and have low transpiration rates despite high net radiation and temperature conditions. In contrast, grasslands and drought resistant deciduous species in the same habitat are seasonally dormant in summer. Because of differences in the mechanisms of drought tolerance, rooting depth and physiology between different plant communities in the region, it is likely that they display differences in diurnal water relations. The presence of diverse plant communities provides an opportunity to investigate possible diurnal landscape patterns in water relations that could be observed by an airborne hyperspectral scanner. This investigation of AVIRIS data collected over forest and shrub land represents the continuation of a prior investigation involving spectral mixture analysis of diurnal effects in the same AVIRIS data set.

Vanderbilt, V. C.↗
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↗