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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Commercial Systems Validation Overview

The Commercial Systems Validation Laboratory (CSVL) is composed of the 1) Virtual Product Laboratory (VPL), 2) Commercial Instrument Validation Laboratory (CIVL), and 3) In-Flight Verification & Validation Network (IVVN). All three laboratories are described in this viewgraph presentation, which covers the characterization of CCD cameras, hyperspectral sensors, ground reference equipment, and the Multispectral Thermal Imager (MTI). The presentation also includes sections on thermal modeling, measurements taken from a float, and the Scientific Data Purchase (SDP) Program. The section of the SDP profiles four data providers: 1) EarthSat; 2) Intermap; 3) Positive Systems; 4) Space Imaging.

Ryan, Robert↗
NASA NTRS2016

Inter-Comparison of S-NPP VIIRS and Aqua MODIS Thermal Emissive Bands Using Hyperspectral Infrared Sounder Measurements as a Transfer Reference

This paper compares the calibration consistency of the spectrally-matched thermal emissive bands (TEB) between the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), using observations from their simultaneous nadir overpasses (SNO). Nearly-simultaneous hyperspectral measurements from the Aqua Atmospheric Infrared Sounder(AIRS) and the S-NPP Cross-track Infrared Sounder (CrIS) are used to account for existing spectral response differences between MODIS and VIIRS TEB. The comparison uses VIIRS Sensor Data Records (SDR) in MODIS five-minute granule format provided by the NASA Land Product and Evaluation and Test Element (PEATE) and Aqua MODIS Collection 6 Level 1 B (L1B) products. Each AIRS footprint of 13.5 km (or CrIS field of view of 14 km) is co-located with multiple MODIS (or VIIRS) pixels. The corresponding AIRS- and CrIS-simulated MODIS and VIIRS radiances are derived by convolutions based on sensor-dependent relative spectral response (RSR) functions. The VIIRS and MODIS TEB calibration consistency is evaluated and the two sensors agreed within 0.2 K in brightness temperature.Additional factors affecting the comparison such as geolocation and atmospheric water vapor content are also discussed in this paper.

Li, Yonghong↗
NASA NTRSDate not supplied

The NASA TEMPO Mission: Hourly Daytime Air Pollution Observations for Enhanced Health and Air Quality Applications

The Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission was launched into Geostationary Earth Orbit (GEO) to 91°W longitude on April 7, 2023, and had a successful 3-day First Light period starting July 31 with TEMPO making its first hourly daytime scans across the TEMPO Field of Regard (FoR) covering greater North America on August 2. TEMPO will provide hourly daytime observations of aerosols and trace gases, including criteria pollutants of nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3), at high spatial resolution (e.g., 2.0 x 4.75 km2) across the FoR. The hyperspectral ultraviolet-to-visible measurements from the TEMPO imaging grating spectrometer will enable an O3 profile product capable of monitoring the daytime diurnal evolution of O3 pollution in the planetary boundary layer and assessing related health impacts. The non-standard or special scan operations of TEMPO at sub-hourly frequency (e.g., 2-10 minutes) over selected slices of the FoR will further enhance air quality monitoring capabilities during disasters (e.g., wildfires, volcanic eruptions, dust storms, industrial accidents) and other episodic events for better understanding emission sources and the evolution of harmful air pollutants. A large diversity of stakeholders and end-users have been engaged in the TEMPO Early Adopters Program, supported by the NASA Applied Sciences Program, which aims to enhance health and air quality applications and maximize the societal benefit of TEMPO data. Key outcomes from the Early Adopters Program include enhanced knowledge and preparation for using TEMPO data, tailoring the data dissemination and visualization tools for TEMPO based on user needs, and broadening the health and air quality applications facet of the mission. This presentation will provide the latest TEMPO mission and Early Adopters Program updates, insight into the health and air quality applications enabled through TEMPO data, and showcase the First Light NO2 imagery and provisional data from the mission.

Remote Sensing↗
NASA NTRS1989

Simple models for complex natural surfaces - A strategy for the hyperspectral era of remote sensing

A two-step strategy for analyzing multispectral images is described. In the first step, the analyst decomposes the signal from each pixel (as expressed by the radiance or reflectance values in each channel) into components that are contributed by spectrally distinct materials on the ground, and those that are due to atmospheric effects, instrumental effects, and other factors, such as illumination. In the second step, the isolated signals from the materials on the ground are selectively edited, and recombined to form various unit maps that are interpretable within the framework of field units. The approach has been tested on multispectral images of a variety of natural land surfaces ranging from hyperarid deserts to tropical rain forests. Data were analyzed from Landsat MSS (multispectral scanner) and TM (Thematic Mapper), the airborne NS001 TM simulator, Viking Lander and Orbiter, AIS, and AVRIS (Airborne Visible and Infrared Imaging Spectrometer).

Adams, John B.↗
NASA NTRSDate not supplied

Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder Intercalibration Data Analysis Strategy

One of the prime science objectives of NASA’s Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) mission is to acquire unprecedentedly accurate Système Internationale (SI)-traceable Earth-view measurements that can be used as reference for intercalibrating the Clouds and the Earth’s Radiant Energy System (CERES) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments onboard NOAA-20 satellite. The hyperspectral nature of CPF measurements will significantly reduce spectrally induced biases when intercalibrating multiband or broadband satellite instruments with CPF. This advancement eliminates the requirement for spectral band adjustment factors, representing a substantial improvement in sensor intercalibration studies. The CPF intercalibration team is aiming to achieve a maximum intercalibration methodology uncertainty of 0.3 % (k=1). Our studies have revealed that the most significant contribution to the targeted uncertainty budget originates from the combined effects of spatial and temporal matching errors. Spatial matching error arises from discrepancies in CPF and target instrument pixel resolution and geolocation uncertainty, while temporal matching error is caused by changes in scene radiances over time, occurring between when the target and reference instruments observe the same scenes. To estimate the maximum expected uncertainty contribution from these sources, spatial and temporal matching noise analyses were conducted using algorithmically filtered Landsat 9 Operational Land Imager (OLI) and Geostationary Operational Environmental Satellite (GOES)-16 ABI CONUS scan data as proxies for CPF and target instruments. In the upcoming conference presentation, we will elaborate on the methodology employed in these experiments, provide details of the data filtering algorithms, and present results of the spatial and temporal matching uncertainty analyses.

Intercalibration↗
NASA NTRS2002

Combining S-space and image spatial information for characterization of land-cover elements using AVIRIS data

Imaging spectroscopy data provides information that is both spatially and spectrally rich. However,hyperspectral classification and identification procedures are generally based solely on the spectral data. The S-space concept treats the spectrum as a 'spatial' dimension thereby facilitating the exploration of a relationship between the spectral and spatial image domains. This paper presents the ideas that will be researched as well as some preliminary results.

AVIRIS image spectroscopy↗
NASA NTRS1992

Hyperspectral modeling for extracting aerosols from aircraft/satellite data

Sensitivity studies using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) bands are currently under investigation. The objective of this research is to develop improved algorithms for the determination of aerosol type (e.g., presently described by S(sub 12). Preliminary computer calculations were made using LOWTRAN 7, which is the most advanced atmospheric code of the Air Force Geophysics Laboratory. LOWTRAN 7 permits the calculation of atmospheric transmission and absorption caused by aerosols and molecules along a non-homogeneous path for a variety of aerosol models. The calculations used the Navy Aerosol Model (NAM), which was constructed for the marine environment using a three component size distribution model. Calculations of the total transmittance (L(sub T)), the path radiance (L(sub P)), and the reflected radiance (L(sub r)) were made as a function of wavelength (lambda) from 0.4 to 2.4 microns for each of the 224 10 nm wide AVIRIS bands. These calculations were made for various types of atmospheres for the troposphere (0-2 km).

Hickman, G. Daniel↗
NASA NTRS2003

High Spatial Resolution Thermal Satellite Technologies

This document in the form of viewslides, reviews various low-cost alternatives to high spatial resolution thermal satellite technologies. There exists no follow-on to Landsat 7 or ASTER high spatial resolution thermal systems. This document reviews the results of the investigation in to the use of new technologies to create a low-cost useful alternative. Three suggested technologies are examined. 1. Conventional microbolometer pushbroom modes offers potential for low cost Landsat Data Continuity Mission (LDCM) thermal or ASTER capability with at least 60-120 ground sampling distance (GSD). 2. Backscanning could produce MultiSpectral Thermal Imager performance without cooled detectors. 3. Cooled detector could produce hyperspectral thermal class system or extremely high spatial resolution class instrument.

Ryan, Robert↗
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 NTRS2013

Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion-EO-1 Data

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy approx. 70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using approx. 20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was approx. 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or "the curse of high dimensionality") in hyperspectral data for a particular application (e.g., biophysical characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI. Index Terms-Hyperion, field reflectance, imaging spectroscopy, HyspIRI, biophysical parameters, hyperspectral vegetation indices, hyperspectral narrowbands, broadbands.

Vegetation↗
NASA NTRS2018

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

satellite data↗
NASA NTRS2018

NASA NeMO-Net

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low- resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

NASA↗
NASA NTRS1996

Evolution of Imaging Spectrometry: Past, Present and Future

An overview of the physical principals of imaging spectrometry for detailed characterization of remote objects and of gas vapors is given. The terms Multi-spectral, Hyperspectral, and Ultra-spectral are defined within the framework of applications and instrument system design approaches.

imaging↗
NASA NTRS2023

Development of the Ames Global Hyperspectral Synthetic Data Set: Surface Bidirectional Reflectance Distribution Function

This study introduces the Ames Global Hyperspectral Synthetic Data set (AGHSD), in particular the surface bidirectional reflectance distribution function (BRDF) product, to support the NASA Surface Biology and Geology (SBG) mission development. The data set is generated based on the corresponding multispectral BRDF products from NASA's MODIS satellite sensor. Based on theories of radiative transfer in vegetation canopies, we derive a simple but robust relationship that indicates that the hyperspectral surface BRDF can be accurately approximated as a weighted sum of the soil surface reflectance, the leaf single albedo, and the canopy scattering coefficient, where the weights or coefficients are spectrally invariant and thus readily estimated from the multispectral MODIS products. We validate the algorithm with simulations by a Monte Carlo Ray Tracing model and find the results highly consistent with the theoretic derivation. Using reflectance spectra of soil and vegetation derived from existing spectral libraries, we apply the algorithm to generate the AGHSD BRDF product at 1 km and 8-day resolutions for the year of 2019. The data set is biogeochemically and biogeophysically coherent and consistent, and serves the goal to support the SBG community in developing sciences and applications for the future global imaging spectroscopy mission.

Hyperspectral Remote Sensing↗
NASA NTRS2018

NASA Fluid Lensing & MiDAR: Next-Generation Remote Sensing Technologies for Aquatic Remote Sensing

We present two recent instrument technology developments at NASA, Fluid Lensing and MiDAR, and their application to remote sensing of Earth's aquatic systems. Fluid Lensing is the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions. MiDAR is a next-generation active hyperspectral remote sensing and optical communications instrument capable of active fluid lensing. Fluid Lensing has been used to provide 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) to enable a new method for remote sensing of living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an objectâ€"TM"s non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Previously, MiDAR was developed into a TRL 2 laboratory instrument capable of imaging in thirty-two narrowband channels across the VNIR spectrum (400-950nm). Recently, MiDAR UV was raised to TRL4 and expanded to include five ultraviolet bands from 280-400nm, permitting UV remote sensing capabilities in UV A, B, and C bands and enabling mineral identification and stimulated fluorescence measurements of organic proteins and compounds, such as green fluorescent proteins in terrestrial and aquatic organics.

fluid lensing↗
NASA NTRS2013

NASA Tech Briefs, September 2013

Topics include: ISS Ammonia Leak Detection Through X-Ray Fluorescence; A System for Measuring the Sway of the Vehicle Assembly Building; Fast, High-Precision Readout Circuit for Detector Arrays; Victim Simulator for Victim Detection Radar; Hydrometeor Size Distribution Measurements by Imaging the Attenuation of a Laser Spot; Quasi-Linear Circuit; High-Speed, High-Resolution Time-to-Digital Conversion; Li-Ion Battery and Supercapacitor Hybrid Design for Long Extravehicular Activities; Ultrasonic Low-Friction Containment Plate for Thermal and Ultrasonic Stir Weld Processes; High-Powered, Ultrasonically Assisted Thermal Stir Welding; Next-Generation MKIII Lightweight HUT/Hatch Assembly; Centrifugal Sieve for Gravity-Level-Independent Size; Segregation of Granular Materials; Ion Exchange Technology Development in Support of the Urine Processor Assembly; Nickel-Graphite Composite Compliant Interface and/or Hot Shoe Material; UltraSail CubeSat Solar Sail Flight Experiment; Mechanism for Deploying a Long, Thin-Film Antenna From a Rover; Counterflow Regolith Heat Exchanger; Acquisition and Retaining Granular Samples via a Rotating Coring Bit; Very-Low-Cost, Rugged Vacuum System; Medicine Delivery Device With Integrated Sterilization and Detection; FRET-Aptamer Assays for Bone Marker Assessment, C-Telopeptide, Creatinine, and Vitamin D; Multimode Directional Coupler for Utilization of Harmonic Frequencies from TWTAs; Dual-Polarization, Multi-Frequency Antenna Array for use with Hurricane Imaging Radiometer; Complementary Barrier Infrared Detector (CBIRD) Contact Methods; Autonomous Control of Space Nuclear Reactors; High-Power, High-Speed Electro-Optic Pockels Cell Modulator; Covariance Analysis Tool (G-CAT) for Computing Ascent, Descent, and Landing Errors; Enigma Version 12; Micrometeoroid and Orbital Debris (MMOD) Shield Ballistic Limit Analysis Program; Spitzer Telemetry Processing System; Planetary Protection Bioburden Analysis Program; Wing Leading Edge RCC Rapid Response Damage Prediction Tool (IMPACT2); ISSM: Ice Sheet System Model; Automated Loads Analysis System (ATLAS); Integrated Main Propulsion System Performance Reconstruction Process/Models. Phoenix Telemetry Processor; Contact Graph Routing Enhancements Developed in ION for DTN; GFEChutes Lo-Fi; Advanced Strategic and Tactical Relay Request Management for the Mars Relay Operations Service; Software for Generating Troposphere Corrections for InSAR Using GPS and Weather Model Data; Ionospheric Specifications for SAR Interferometry (ISSI); Implementation of a Wavefront-Sensing Algorithm; Sally Ride EarthKAM - Automated Image Geo-Referencing Using Google Earth Web Plug-In; Trade Space Specification Tool (TSST) for Rapid Mission Architecture (Version 1.2); Acoustic Emission Analysis Applet (AEAA) Software; Memory-Efficient Onboard Rock Segmentation; Advanced Multimission Operations System (ATMO); Robot Sequencing and Visualization Program (RSVP); Automating Hyperspectral Data for Rapid Response in Volcanic Emergencies; Raster-Based Approach to Solar Pressure Modeling; Space Images for NASA JPL Android Version; Kinect Engineering with Learning (KEWL); Spacecraft 3D Augmented Reality Mobile App; MPST Software: grl_pef_check; Real-Time Multimission Event Notification System for Mars Relay; SIM_EXPLORE: Software for Directed Exploration of Complex Systems; Mobile Timekeeping Application Built on Reverse-Engineered JPL Infrastructure; Advanced Query and Data Mining Capabilities for MaROS; Jettison Engineering Trajectory Tool; MPST Software: grl_suppdoc; PredGuid+A: Orion Entry Guidance Modified for Aerocapture; Planning Coverage Campaigns for Mission Design and Analysis: CLASP for DESDynl; and Space Place Prime.

Source record↗
NASA NTRS1994

High Altitude Hyperspectral Remote Sensing with AVIRIS

None given. From Intro: This paper describes AVIRIS (the Airborne Visible and InfraRed Imaging Spectrometer) as a remote sensing instrument that is supported, calibrated, and maintained by JPL.

AVIRIS detector fiberoptics remote sensing spectro↗