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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2026

Satellite Optical Remote Sensing of Clouds and Aerosols: From Particle Single-Scattering and Gaseous Absorption Through Radiative Transfer to Retrieval Products

Clouds and aerosols are fundamental regulators of Earth’s radiation budget and climate system, influencing both solar and terrestrial radiation through scattering, absorption, and emission processes. Accurate characterization of their physical and radiative properties from space requires a rigorous understanding of particle single-scattering, gaseous absorption, and radiative transfer in the atmosphere, as well as reliable inversion methods. This review synthesizes the physical foundations and algorithmic implementations of satellite-based passive optical remote sensing of clouds and aerosols, spanning the ultraviolet to thermal infrared spectral range. Beginning with electromagnetic scattering theory and state-of-the-art methods for computing single-scattering by nonspherical particles and computationally efficient methods for accounting for atmospheric absorption, we discuss the radiative transfer framework underpinning cloud and aerosol retrievals. The connection between single-scattering and multiple-scattering is rigorously formulated. We then summarize operational and research-grade retrieval techniques, including cloud masking and thermodynamic phase determination, CO₂ slicing for cloud-top pressure, the Nakajima-King shortwave bi-spectral, and infrared split-window approaches for cloud optical thickness and effective particle size, inversion algorithms for determining aerosol properties from multi-spectral and/or multi-angle radiometric and polarimetric measurements, and active-passive sensing synergy. Examples of the global cloud and aerosol climatologies are illustrated using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging SpectroRadiometer (MISR). Furthermore, the unique strengths of active remote sensing techniques based on spaceborne lidar observations are briefly elaborated in the context of studying ice clouds composed of randomly and horizontally oriented ice crystals, which is a significant challenge for conventional passive remote sensing techniques. By connecting physical theory to practical retrievals, this review highlights both the maturity of current methodologies and the remaining challenges in reducing uncertainties in particle morphology, vertical structure, absorption, and aerosol-cloud interactions. Furthermore, the impact of artificial intelligence (AI) on atmospheric remote sensing is briefly addressed.

Aerosols↗
DOE OSTI2025

Hyper Spectral Anomaly Detection

Anomaly detection is a common machine learning (ML) task with growing importance in the fields of imaging, quality assurance, and multiple security related disciplines. Anomaly detection is more difficult than traditional machine learning methods due to the inherent unlabeled nature of the datasets. Existing anomaly detection architectures commonly face challenges with explainability, retaining information related to the relational structure of the data, and false positive rates. Hyperspectral Imaging Anomaly Detection (HSI) is a statistical model that employs vertex and edge weighted graphs to preserve the data’s relationships on different topographical scales. The model is able to generalize from anomaly detection in 2D images to novel datasets related to cyber-security. Furthermore, the use of multi-spectral and other filtering methods results in fewer false positives and increases the explainability of model predictions. When applying HSI to cyber-security datasets, we are able to successfully detect malicious activity with a relatively high degree of accuracy.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Integrating AI and Multi-sensor Technology for better Bioenergy Preprocessing

Integrating multi-sensor identification with machine learning significantly enhances bioenergy preprocessing by using non-destructive, multi-spectral sensors to improve feedstock purity and quality. This approach not only reduces preprocessing costs but also ensures a more efficient and sustainable bioenergy production process.

09 - BIOMASS FUELS↗
NASA NTRS2002

Multi-band quantum well infrared photodetectors

The GaAs/AlGaAs based Quantum Well Infrared Photodetectors (QWIPs) afford greater flexibility than the usual extrinsically doped semiconductor IR detectors because the wavelength of the peak response and cutoff can be continuously tailored over any wavelength between 6-20 mu m.

IR quantum well thermal imaging multi-spectral↗
NASA NTRS1997

Battlefield De-Confliction Sensor And Information System

A battlefield de-confliction and assessment sensor system architecture is described that uses a hyper-spectral imaging spectomerer instrument onboard a robitc aerial vehicle (UAV) to identify, characterize,and track missiles and aircraft.

wavelength.↗
NASA NTRS2004

Megapixel Multi-band QWIP Focal Plane Arrays

A mid-wavelength 1024x1024 pixel quantum well infrared photodetector (QW) focal plane array has been demonstrated with excellent imagery. Noise equivalent differential temperature (NETD) of 19 mK was achieved at 95K operating temperature with f/2.5 optics at 300K background. This focal plane array has shown background limited performance (BLIP) at 90K operating temperature with the same optics and background conditions. In this paper, we will discuss its performance in quantum efficiency, NETD, uniformity, and operability.

focal plane arrays↗
NASA NTRS2001

Cancer Detection Using Neural Computing Methodology

This paper describes a novel learning methodology used to analyze bio-materials. The premise of this research is to help pathologists quickly identify anomalous cells in a cost efficient method. Skilled pathologists must methodically, efficiently and carefully analyze manually histopathologic materials for the presence, amount and degree of malignancy and/or other disease states. The prolonged attention required to accomplish this task induces fatigue that may result in a higher rate of diagnostic errors. In addition, automated image analysis systems to date lack a sufficiently intelligent means of identifying even the most general regions of interest in tissue based studies and this shortfall greatly limits their utility. An intelligent data understanding system that could quickly and accurately identify diseased tissues and/or could choose regions of interest would be expected to increase the accuracy of diagnosis and usher in truly automated tissue based image analysis.

liquid crystal tunable filters↗
NASA NTRS2011

High Speed, Multi-Channel, Thermal Instrument Development in Support of HyspIRI-TIR

The Jet Propulsion Laboratory is currently developing an end-to-end instrument which will provide a proof of concept prototype vehicle for a high data rate, multi-channel, thermal instrument in support of the Hyperspectral Infrared Imager (HyspIRI)-Thermal Infrared (TIR) space mission. HyspIRI mission was recommended by the National Research Council Decadal Survey (DS). The HyspIRI mission includes a visible shortwave infrared (SWIR) pushboom spectrometer and a multispectral whiskbroom thermal infrared (TIR) imager. The prototype testbed instrument addressed in this effort will only support the TIR. Data from the HyspIRI mission will be used to address key science questions related to the Solid Earth and Carbon Cycle and Ecosystems focus areas of the NASA Science Mission Directorate. Current designs for the HyspIRI-TIR space borne imager utilize eight spectral bands delineated with filters. The system will have 60m ground resolution, 200mK NEDT, 0.5C absolute temperature resolution with a 5-day repeat from LEO orbit. The prototype instrument will use mercury cadmium telluride (MCT) technology at the focal plane array in time delay integration mode. A custom read out integrated circuit (ROIC) will provide the high speed readout hence high data rates needed for the 5 day repeat. The current HyspIRI requirements dictate a ground knowledge measurement of 30m, so the prototype instrument will tackle this problem with a newly developed interferometeric metrology system. This will provide an absolute measurement of the scanning mirror to an order of magnitude better than conventional optical encoders. This will minimize the reliance on ground control points hence minimizing postprocessing (e.g. geo-rectification computations).

Multi-spectral↗