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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Stratigraphy of the Apollo 17 Landslide Core 73002 From FMR Maturity and VNIR and Mössbauer Spectroscopy

A suite of samples from the Apollo missions to the Moon (1969-1973) were set aside and stored under controlled conditions to have unexamined lunar samples available decades later for analyses that take advantage of evolved sample handling techniques, maturation of then existing instrumentation, and development of new analytical techniques and instrumentation [e.g., 1]. One preserved sample is the Apollo 17 double drive tube core (73001/2) that was driven into the lunar surface on the landslide deposit at Station 3 on the South Massif in the Taurus-Littrow valley [2]. The deeper section (73001) was stored frozen in a Core Sample Vacuum Container (CSVC) to maximize preservation of lunar volatiles. We report here, as a part of the Apollo Next Generation Sample Analysis (ANGSA) Initiative [1,3], stratigraphy for the upper core section (73002) with respect to maturity (ferromagnetic resonance (FMR) maturity index Is/FeO [4-6]), visible-near-IR (VNIR) spectroscopy, and, for representative samples, grain-size analysis and Mössbauer spectroscopy. Stratigraphy provides data to model the dynamics of lunar landslide deposits and their post-emplacement evolution in comparison to impact-driven mixing on an airless body and stratigraphic context for volatile and (if any) organic behavior. Reported by [7] are preliminary results for multispectral imaging and hyperspectral scanning of the first dissection pass of 73002.

R. V. Morris↗
DOE OSTI2026

ClassNMSW- a real-time classification approach for non-recycled municipal solid waste using hyperspectral imaging

Real-time classification of non-recycled municipal solid waste (NMSW) is essential for efficient valorization. This study introduces ClassNMSW, a comprehensive framework for classifying 22 NMSW subclasses under industrial constraints by using hyperspectral imaging (HSI). A primary innovation of this work is the development of a variance-controlled spectral extraction algorithm. Unlike traditional methods that rely on simple averaging, this approach systematically investigates the extent of pixel extraction to minimize the loss of critical chemical information while maximizing data reduction thus ensuring high spectral fidelity with low computational cost. The approach developed in this work integrates automated, computer-vision-based background removal, eliminating the need for the manual thresholding common in current literature. To resolve ambiguities among chemically similar subclasses, a tiered classification and multi-camera fusion strategy (NIR17 and NIR22) is implemented. Results demonstrate that ClassNMSW achieves an object-wise weighted accuracy of 98.70% for single-sensor configurations and 100% under sensor fusion. A novel rolling-window strategy satisfies desired end-to-end latency of <2 s, satisfying the strict deterministic requirements of high-speed industrial sorting environments. The ClassNMSW framework provides a scalable foundation for advancing circularity and resource recovery in large-scale waste valorization operations.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Source:sink imbalance detected with leaf and canopy-level spectroscopy in a field-grown crop

The finely-tuned balance between sources and sinks determines plant resource partitioning and regulates growth and development. Understanding and measuring metabolic indicators of source or sink limitation forms a vital part of global efforts to increase crop yield for future food security. Here, we measured metabolic profiles of Cucurbita pepo (zucchini) grown in the field under carbon sink limitation and control conditions. We demonstrate that these profiles can be measured non-destructively using hyperspectral reflectance at both leaf and canopy scales. Total non-structural carbohydrates (TNC) increased 82% in sink-limited plants; leaf mass per unit area (LMA) increased 38%; free amino acids increased 22%. Partial least-squares regression models link these measured functional traits with reflectance data, enabling high-throughput estimation of traits comprising the sink limitation response. Leaf- and canopy-scale models for TNC had R 2 values of 0.93 and 0.64 and %RMSE of 13% and 38% respectively. For LMA, R 2 values were 0.91 and 0.60 and %RMSE 7% and 14%; for free amino acids, R 2 was 0.53 and 0.21 with %RMSE 20% and 26%. Remote sensing can enable accurate, rapid detection of sink limitation in the field at the leaf and canopy scale, greatly expanding our ability to understand and measure metabolic responses to stress.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

NASA Tech Briefs, April 2004

Topics covered include: Analysis of SSEM Sensor Data Using BEAM; Hairlike Percutaneous Photochemical Sensors; Video Guidance Sensors Using Remotely Activated Targets; Simulating Remote Sensing Systems; EHW Approach to Temperature Compensation of Electronics; Polymorphic Electronic Circuits; Micro-Tubular Fuel Cells; Whispering-Gallery-Mode Tunable Narrow-Band-Pass Filter; PVM Wrapper; Simulation of Hyperspectral Images; Algorithm for Controlling a Centrifugal Compressor; Hybrid Inflatable Pressure Vessel; Double-Acting, Locking Carabiners; Position Sensor Integral with a Linear Actuator; Improved Electromagnetic Brake; Flow Straightener for a Rotating-Drum Liquid Separator; Sensory-Feedback Exoskeletal Arm Controller; Active Suppression of Instabilities in Engine Combustors; Fabrication of Robust, Flat, Thinned, UV-Imaging CCDs; Chemical Thinning Process for Fabricating UV-Imaging CCDs; Pseudoslit Spectrometer; Waste-Heat-Driven Cooling Using Complex Compound Sorbents; Improved Refractometer for Measuring Temperatures of Drops; Semiconductor Lasers Containing Quantum Wells in Junctions; Phytoplankton-Fluorescence-Lifetime Vertical Profiler; Hexagonal Pixels and Indexing Scheme for Binary Images; Finding Minimum-Power Broadcast Trees for Wireless Networks; and Automation of Design Engineering Processes.

Source record↗
NASA NTRS2012

Retrieving Atmospheric Temperature and Moisture Profiles from NPP CRIS/ATMS Sensors Using Crimss EDR Algorithm

The Joint Polar Satellite System (JPSS) is a U.S. National Oceanic and Atmospheric Administration (NOAA) mission in collaboration with the U.S. National Aeronautical Space Administration (NASA) and international partners. The NPP Cross-track Infrared Microwave Sounding Suite (CrIMSS) consists of the infrared (IR) Crosstrack Infrared Sounder (CrIS) and the microwave (MW) Advanced Technology Microwave Sounder (ATMS). The CrIS instrument is hyperspectral interferometer, which measures high spectral and spatial resolution upwelling infrared radiances. The ATMS is a 22-channel radiometer similar to Advanced Microwave Sounding Units (AMSU) A and B. It measures top of atmosphere MW upwelling radiation and provides capability of sounding below clouds. The CrIMSS Environmental Data Record (EDR) algorithm provides three EDRs, namely the atmospheric vertical temperature, moisture and pressure profiles (AVTP, AVMP and AVPP, respectively), with the lower tropospheric AVTP and the AVMP being JPSS Key Performance Parameters (KPPs). The operational CrIMSS EDR an algorithm was originally designed to run on large IBM computers with dedicated data management subsystem (DMS). We have ported the operational code to simple Linux systems by replacing DMS with appropriate interfaces. We also changed the interface of the operational code so that we can read data from both the CrIMSS science code and the operational code and be able to compare lookup tables, parameter files, and output results. The detail of the CrIMSS EDR algorithm is described in reference [1]. We will present results of testing the CrIMSS EDR operational algorithm using proxy data generated from the Infrared Atmospheric Sounding Interferometer (IASI) satellite data and from the NPP CrIS/ATMS data.

Liu, X.↗
NASA NTRS2011

Miniaturized Airborne Imaging Central Server System

In recent years, some remote-sensing applications require advanced airborne multi-sensor systems to provide high performance reflective and emissive spectral imaging measurement rapidly over large areas. The key or unique problem of characteristics is associated with a black box back-end system that operates a suite of cutting-edge imaging sensors to collect simultaneously the high throughput reflective and emissive spectral imaging data with precision georeference. This back-end system needs to be portable, easy-to-use, and reliable with advanced onboard processing. The innovation of the black box backend is a miniaturized airborne imaging central server system (MAICSS). MAICSS integrates a complex embedded system of systems with dedicated power and signal electronic circuits inside to serve a suite of configurable cutting-edge electro- optical (EO), long-wave infrared (LWIR), and medium-wave infrared (MWIR) cameras, a hyperspectral imaging scanner, and a GPS and inertial measurement unit (IMU) for atmospheric and surface remote sensing. Its compatible sensor packages include NASA s 1,024 1,024 pixel LWIR quantum well infrared photodetector (QWIP) imager; a 60.5 megapixel BuckEye EO camera; and a fast (e.g. 200+ scanlines/s) and wide swath-width (e.g., 1,920+ pixels) CCD/InGaAs imager-based visible/near infrared reflectance (VNIR) and shortwave infrared (SWIR) imaging spectrometer. MAICSS records continuous precision georeferenced and time-tagged multisensor throughputs to mass storage devices at a high aggregate rate, typically 60 MB/s for its LWIR/EO payload. MAICSS is a complete stand-alone imaging server instrument with an easy-to-use software package for either autonomous data collection or interactive airborne operation. Advanced multisensor data acquisition and onboard processing software features have been implemented for MAICSS. With the onboard processing for real time image development, correction, histogram-equalization, compression, georeference, and data organization, fast aerial imaging applications, including the real time LWIR image mosaic for Google Earth, have been realized for NASA fs LWIR QWIP instrument. MAICSS is a significant improvement and miniaturization of current multisensor technologies. Structurally, it has a complete modular and solid-state design. Without rotating hard drives and other moving parts, it is operational at high altitudes and survivable in high-vibration environments. It is assembled from a suite of miniaturized, precision-machined, standardized, and stackable interchangeable embedded instrument modules. These stackable modules can be bolted together with the interconnection wires inside for the maximal simplicity and portability. Multiple modules are electronically interconnected as stacked. Alternatively, these dedicated modules can be flexibly distributed to fit the space constraints of a flying vehicle. As a flexibly configurable system, MAICSS can be tailored to interface a variety of multisensor packages. For example, with a 1,024x1,024 pixel LWIR and a 8,984x6,732 pixel EO payload, the complete MAICSS volume is approximately 7x9x11 in. (=18x23x28 cm), with a weight of 25 lb (=11.4 kg).

Sun, Xiuhong↗
DOE OSTI2023

A review on recent machine learning applications for imaging mass spectrometry studies

Imaging mass spectrometry (IMS) is a powerful analytical technique widely used in biology, chemistry, and materials science fields that continue to expand. IMS provides a qualitative compositional analysis and spatial mapping with high chemical specificity. The spatial mapping information can be 2D or 3D depending on the analysis technique employed. Due to the combination of complex mass spectra coupled with spatial information, large high-dimensional datasets (hyperspectral) are often produced. Therefore, the use of automated computational methods for an exploratory analysis is highly beneficial. The fast-paced development of artificial intelligence (AI) and machine learning (ML) tools has received significant attention in recent years. These tools, in principle, can enable the unification of data collection and analysis into a single pipeline to make sampling and analysis decisions on the go. There are various ML approaches that have been applied to IMS data over the last decade. Here, in this review, we discuss recent examples of the common unsupervised (principal component analysis, non-negative matrix factorization, k-means clustering, uniform manifold approximation and projection), supervised (random forest, logistic regression, XGboost, support vector machine), and other methods applied to various IMS datasets in the past five years. The information from this review will be useful for specialists from both IMS and ML fields since it summarizes current and representative studies of computational ML-based exploratory methods for IMS.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Programs and Code for Geothermal Exploration Artificial Intelligence

The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including: - Land Surface Temperature K-Means classifier - Labeling AI using Self Organizing Maps (SOM) - Post-processing for Permanent Scatterer InSAR (PSInSAR) analysis with SOM - Mineral marker summarizing - Artificial Intelligence (AI) Data splitting: creates data set from a single raster file - Artificial Intelligence Model: creates AI from a single data set, after splitting in Train, Validation and Test subsets - AI Mapper: creates a classification map based on a raster file

15 GEOTHERMAL ENERGY↗
NASA NTRS2018

The New CCSDS Standard for Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression

This paper describes the emerging Issue 2 of the CCSDS-123.0-B standard for low-complexity compression of multispectral and hyperspectral imagery, focusing on its new features and capabilities. Most significantly, this new issue incorporates a closed-loop quantization scheme to provide near-lossless compression capability while still supporting lossless compression, and introduces a new entropy coding option that provides better compression of low-entropy data.

Kiely, Aaron B.↗
NASA NTRS2010

Spinoff 2010

Topics covered include: Burnishing Techniques Strengthen Hip Implants; Signal Processing Methods Monitor Cranial Pressure; Ultraviolet-Blocking Lenses Protect, Enhance Vision; Hyperspectral Systems Increase Imaging Capabilities; Programs Model the Future of Air Traffic Management; Tail Rotor Airfoils Stabilize Helicopters, Reduce Noise; Personal Aircraft Point to the Future of Transportation; Ducted Fan Designs Lead to Potential New Vehicles; Winglets Save Billions of Dollars in Fuel Costs; Sensor Systems Collect Critical Aerodynamics Data; Coatings Extend Life of Engines and Infrastructure; Radiometers Optimize Local Weather Prediction; Energy-Efficient Systems Eliminate Icing Danger for UAVs; Rocket-Powered Parachutes Rescue Entire Planes; Technologies Advance UAVs for Science, Military; Inflatable Antennas Support Emergency Communication; Smart Sensors Assess Structural Health; Hand-Held Devices Detect Explosives and Chemical Agents; Terahertz Tools Advance Imaging for Security, Industry; LED Systems Target Plant Growth; Aerogels Insulate Against Extreme Temperatures; Image Sensors Enhance Camera Technologies; Lightweight Material Patches Allow for Quick Repairs; Nanomaterials Transform Hairstyling Tools; Do-It-Yourself Additives Recharge Auto Air Conditioning; Systems Analyze Water Quality in Real Time; Compact Radiometers Expand Climate Knowledge; Energy Servers Deliver Clean, Affordable Power; Solutions Remediate Contaminated Groundwater; Bacteria Provide Cleanup of Oil Spills, Wastewater; Reflective Coatings Protect People and Animals; Innovative Techniques Simplify Vibration Analysis; Modeling Tools Predict Flow in Fluid Dynamics; Verification Tools Secure Online Shopping, Banking; Toolsets Maintain Health of Complex Systems; Framework Resources Multiply Computing Power; Tools Automate Spacecraft Testing, Operation; GPS Software Packages Deliver Positioning Solutions; Solid-State Recorders Enhance Scientific Data Collection; Computer Models Simulate Fine Particle Dispersion; Composite Sandwich Technologies Lighten Components; Cameras Reveal Elements in the Short Wave Infrared; Deformable Mirrors Correct Optical Distortions; Stitching Techniques Advance Optics Manufacturing; Compact, Robust Chips Integrate Optical Functions; Fuel Cell Stations Automate Processes, Catalyst Testing; Onboard Systems Record Unique Videos of Space Missions; Space Research Results Purify Semiconductor Materials; and Toolkits Control Motion of Complex Robotics.

Source record↗
NASA NTRS2019

Improving the longest SI traceable hyperspectral infrared record from space from the AIRS

The Atmospheric Infrared Sounder (AIRS) is a “facility” instrument developed by NASA as an experimental demonstration of advanced technology for remote sensing and the benefits of high resolution infrared spectra to weather forecasting and science investigations . It was launched into a 1:30 am/pm polar orbit on May 4, 2002 on the EOS Aqua Spacecraft, and is expected to provide highly calibrated data beyond 2024. AIRS has 2378 infrared channels ranging from 3.7 um to 15.4 um and a 13.5 km footprint. The AIRS data are used for weather forecasting, climate process studies and validating climate models. AIRS is a vital IR reference sensor for GSICS and is used by JMA for comparison to Himawari AHI , KMA for comparison to COMS , NOAA for comparison to CrIS and GOES , and EUMETSAT for comparison to IASI .

Strow, Larrabee↗
NASA NTRS2011

Filling-In of Broad Far-Red Solar Lines by Terrestrial Fluorescence and Atmospheric Raman Scattering as Detected by SCIAMACHY Satellite Measurements

Global mapping of terrestrial vegetation fluorescence from space has recently been accomplished with high spectral resolution measurements from the Japanese Greenhouse gases Observing SATellite (GOSAT). These data are of interest because they can potentially provide global information on the functional status of vegetation including light use efficiency and global primary productivity that can be used for global carbon cycle modeling. Quantifying the impact of fluorescence on the O2-A band is important as this band is used for cloud- and aerosol-characterization for other trace-gas retrievals including CO2. Here, we demonstrate that fluorescence information can be derived from space using potentially lower-cost hyperspectral instrumentation, i.e., more than an order of magnitude less spectral resolution than GOSAT, with a relatively simple algorithm. As a demonstration, we use the filling-in of one of the few wide and deep solar Fraunhofer lines in the red and far-red chlorophylla fluorescence bands, the calcium II line near 866 nm, to retrieve fluorescence with the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) satellite instrument. Although the signal from vegetation fluorescence is extremely weak at 866 nm, our results suggest that useful information may be obtained after adjustments are made to the observed spectra to correct for instrumental artifacts. We compare fluorescence from SCIAMACHY with that retrieved at 758 and 770 nm from similarly-corrected GOSAT data as well with the Enhanced Vegetation Index (EVI) from the MODerate-resolution Imaging Spectroradiometer (MODIS). We also show that filling-in occurs at 866 nm over barren areas, possibly originating from luminescent minerals in rock and soil.

Joiner, J.↗
DOE OSTI2022

A Roadmap for Edge Computing Enabled Automated Multidimensional Transmission Electron Microscopy

The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine. However, many microscopists find such experiments daunting since analysis, collection, long-term storage, and networking of such datasets remain challenging. Some common issues are their large and unwieldy size that often are several gigabytes, non-standardized data analysis routines, and a lack of clarity about the computing and network resources needed to utilize the electron microscope. The existing computing and networking bottlenecks introduce significant penalties in each step of these experiments, and thus, real-time analysis-driven automated experimentation for multidimensional TEM is challenging. One solution is to integrate microscopy with edge computing, where moderately powerful computational hardware performs the preliminary analysis before handing off the heavier computation to high-performance computing (HPC) systems. In this work, we trace the roots of computation in modern electron microscopy, demonstrate deep learning experiments running on an edge system, and discuss the networking requirements for tying together microscopes, edge computers, and HPC systems.

47 OTHER INSTRUMENTATION↗
NASA NTRS2022

Impact of Assimilating Adaptively Thinned AIRS Cloud-Cleared Radiances on the Analysis of Polar Lows and Mediterranean Sea Tropical-Like Cyclone in a Global Modeling and Data Assimilation Framework

Polar lows, and mesoscale convective cyclones bearing resemblance to tropical cyclones but originating outside of the tropics, are storms that are challenging to represent accurately in global analyses and models because of their small size, rapid growth at subsynoptic scales, occurrence in data poor oceanic regions, and difficulties in objectively validating them in analysis. Building on previous positive results obtained with respect to the representation of tropical cyclones (TCs) in a global model, a set of observing system experiments (OSEs) performed using the NASA Goddard Earth Observing System (GEOS, version 5) are investigated, focusing on three case studies—a polar low in the Sea of Okhotsk, a polar low in the Southern Ocean, and a Mediterranean Sea tropical-like cyclone that occurred during the boreal fall season of 2014. Experiments assimilating adaptively thinned cloud-cleared hyperspectral infrared radiances from the Atmospheric Infrared Sounder (AIRS) instrument on board the NASA Aqua satellite, with higher density in the vicinity of each storm and its pre-cyclogenesis environment, and lower density elsewhere, demonstrate a positive impact on the analyzed representation of each storm. The adaptive thinning experiments improve the storm intensity and structure, including vertical alignment, depth, symmetry, strength, and compactness of warm core compared to the reference experiments. The results suggest that jet-level processes associated with extremely strong horizontal velocity gradients as represented in the model analysis can be useful to locate dynamically active regions of the extratropical atmosphere where denser data coverage is likely to improve the analyzed representation of polar lows and other similar marine mesoscale convective cyclones.

global modeling↗
DOE OSTI2020

Custom NEON AOP reflectance mosaics and maps of shade masks, canopy water content

This mosaic of visible to shortwave infrared (VSWIR) data was derived from the assignable asset NEON AOP radiance data that was collected by LBNL’s Watershed Function SFA during the summer of 2018 (DOI: 10.15485/1617204). This atmospheric correction was completed to take into account site-specific terrain variability in the 334 km2 survey area centered around Crested Butte, CO. The atmospheric correction was completed using ACORN atmospheric correction software executed on 200 x 200 pixel kernels rather than line by line in order to capture local flight altitude conditions. Manual cloud delineation removed any small shaded areas that occurred within the data collection areas. Mosaics were developed based on preference for days that were in close timing proximity to the coincident ground campaign and with mosaicing criteria that minimized the angle between the sun and the sensor to retrieve the most consistent reflectance (min_phase_refl_tiled). Finally, we generated shade masks based on the geometry between the sun angle at time of flight, the ground surface, and the sensor. Here we provide the orthomosaiced estimated reflectance data (min_phase_refl_tiled.tif), canopy water content (wtrl), estimated atmospheric water vapor (wtrv), the observational data (obs) for this particular mosaic, the estimated visibility (vis), shade masks derived from the digital surface elevation model (shade) and the digital terrain model (shade_tch) added to the canopy height model (DOI: 10.15485/1617203), and a wavelength metadata file. Each image file is provided as a GeoTiff with internal tiling and LZW compression. These data can also be found on Google Earth Engine for extraction, download, and analysis of smaller extents (Google Earth Engine data is linked in external link table).

2018 NEON and 2025 CHESS Campaigns↗
NASA NTRS1996

Spectral imaging applications: Remote sensing, environmental monitoring, medicine, military operations, factory automation and manufacturing

This paper reviews the activities at OKSI related to imaging spectroscopy presenting current and future applications of the technology. The authors discuss the development of several systems including hardware, signal processing, data classification algorithms and benchmarking techniques to determine algorithm performance. Signal processing for each application is tailored by incorporating the phenomenology appropriate to the process, into the algorithms. Pixel signatures are classified using techniques such as principal component analyses, generalized eigenvalue analysis and novel very fast neural network methods. The major hyperspectral imaging systems developed at OKSI include the Intelligent Missile Seeker (IMS) demonstration project for real-time target/decoy discrimination, and the Thermal InfraRed Imaging Spectrometer (TIRIS) for detection and tracking of toxic plumes and gases. In addition, systems for applications in medical photodiagnosis, manufacturing technology, and for crop monitoring are also under development.

National Defense↗
NASA NTRSDate not supplied

Spectrally Dependent Radiometric Measurement Errors Due to Ccd Serial Pixel-to-Pixel Readout Interference in the Ocean Color Instrument of the NASA PACE Mission

The Ocean Color Instrument on NASA’s PACE mission is a 322 887 nm hyperspectral imager with 1 km x 1 km nadir spatial resolution and 5 nm spectral resolution utilizing charge-coupled devices (CCDs) operating in Time Delay Integration (TDI) mode where each TDI column represents a different wavelength in 0.625 nm increments. After TDI, the charge is moved into serial output pixels and read out. The spatial resolution requires an 8.5 MHz readout rate. This only allows 59 ns for the CCD reset and video to be asserted and settled before sampling. The response exhibits serial pixel-to-pixel readout interference due to the lack of full settling. Each serial pixel value has a dependence on the value of the preceding pixel value. This leads to a spectrally dependent radiometric measurement error of up to 0.3 %. We explain the operation of the detection system, the behavior of the interference, and show the resulting measurement error based on data from both ground testing and on-orbit characterization.

Remote Sensing↗
NASA NTRS2024

Spectrally Dependent Radiometric Measurement Errors Due to CCD Serial Pixel-to-Pixel Readout Interference in the Ocean Color Instrument of the NASA PACE Mission

The Ocean Color Instrument on NASA’s PACE mission is a 322 887 nm hyperspectral imager with 1 km x 1 km nadir spatial resolution and 5 nm spectral resolution utilizing charge-coupled devices (CCDs) operating in Time Delay Integration (TDI) mode where each TDI column represents a different wavelength in 0.625 nm increments. After TDI, the charge is moved into serial output pixels and read out. The spatial resolution requires an 8.5 MHz readout rate. This only allows 59 ns for the CCD reset and video to be asserted and settled before sampling. The response exhibits serial pixel-to-pixel readout interference due to the lack of full settling. Each serial pixel value has a dependence on the value of the preceding pixel value. This leads to a spectrally dependent radiometric measurement error of up to 0.3 %. We explain the operation of the detection system, the behavior of the interference, and show the resulting measurement error based on data from both ground testing and on-orbit characterization.

Remote Sensing↗