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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 55 records · Page 3
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NASA NTRSDate not supplied

Principal Component and Machine Learning Approach to Gap Fill Hyperspectral Ocean Color Satellite Retrievals

Retrievals of ocean color properties from space are important for monitoring the health of the ocean ecosystem but such retrievals can be limited spatially due to conditions such as clouds, aerosols, and sun glint. Gap filling of ocean color retrievals is typically performed by combining retrievals from multiple satellites or temporally averaging multiple days of retrievals. Despite these techniques large gaps still exist posing challenges for near real time monitoring of events like harmful algae blooms. To address these limitations, we developed a spatial gap filling approach applying machine learning approach to hyperspectral instruments to learn how to perform an atmospheric correction under challenging retrieval conditions. In this approach a principal component analysis is used to decompose the hyperspectral measurements into spectral components that describe the scattering and absorption of the atmosphere mixed with the surface spectral signatures. The coefficients of the principal components are used to train a neural network to predict ocean color properties derived from a standard MODIS ocean color algorithm. We apply the approach to two hyperspectral UV/VIS sensors, the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can be used to estimate ocean color properties such as chlorophyll, remote sensing reflectance, and fluorescence line height. This method could be used as a gap-filling technique for the future Ocean Color Instrument (OCI) onboard upcoming NASA's Plankton, Aerosol Cloud, ocean Ecosystem (PACE) satellite to provide additional information for monitoring the health of our global oceans. Additionally, it could be applied to the first NASA and Smithsonian geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) spectrometer to better understand diurnal variability in inland and coastal ocean ecology.

Zachary Fasnacht↗
NASA NTRSDate not supplied

A Principal Component and Machine Learning Approach to Spatially Gap Fill Hyperspectral Ocean Color Satellite Retrievals

Retrievals of ocean color properties from space are important for monitoring the health of the ocean ecosystem but such retrievals tend to be limited in spatial coverage due to conditions such as clouds, aerosols, and sun glint. Gap filling of ocean color retrievals is typically performed by combining retrievals from multiple satellites or temporally averaging multiple days of retrievals but despite these techniques large gaps still exist posing challenges for near real time monitoring of events like harmful algae blooms. To address these limitations, we propose a spatial gap filling approach using machine learning to learn how to perform an atmospheric correction under challenging retrieval conditions. In this approach a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from a standard ocean color algorithm such as the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can be used to estimate ocean color properties such as chlorophyll, remote sensing reflectance, and fluorescence line height. This method could be used as a gap-filling technique for the future Ocean Color Instrument (OCI) which will be onboard NASA's Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite to provide additional information for monitoring the health of our global oceans. Additionally, it could be applied to the geostationary satellite Tropospheric Emissions: Monitoring of Pollution (TEMPO) to better understand diurnal variability in ocean ecology.

MODIS atmospheric correction algorithm↗
NASA NTRS2024

Noise Reduction for Solar-Induced Fluorescence Retrievals Using Machine Learning and Principal Component Analysis: Simulations and Applications to GOME-2 Satellite Retrievals

We use a spectral-based approach that employs principal component analysis along with a relatively shallow artificial neural network (NN) to substantially reduce noise and other artifacts in terrestrial chlorophyll solar-induced fluorescence (SIF) retrievals. SIF is a very small emission at red and far-red wavelengths that is difficult to measure and is highly sensitive to random errors and systematic artifacts. Our approach relies upon an assumption that a trained NN can effectively reconstruct the total SIF signal from a relatively small number of leading principal components of the satellite-observed far-red radiance spectra without using information from the trailing modes that contain most of the random errors. We test the approach with simulated reflectance spectra produced with a full atmospheric and surface radiative transfer model using different observing and geophysical parameters and various noise levels. Resulting noisy and noise-reduced retrieved SIF values are compared with true values to assess performance. We then apply our noise reduction approach to SIF derived from two different satellite spectrometers. For evaluation, since the truth in this case is unknown, we compare SIF retrievals from two independent sensors with each other. We also compare the noise-reduced SIF temporal variations with those from an independent gross primary product (GPP) product that should display similar variations. Results show that our noise reduction approach improves capture of SIF seasonal and inter-annual variability. Our approach should be applicable to many noisy data products derived from spectral measurements. Our methodology does not replace the original retrieval algorithms; rather the original noisy retrievals are needed as the target for the NN training process.

noise reduction, solar-induced fluorescence, SIF, ↗
NASA NTRS2006

Spatiotemporal Filtering Using Principal Component Analysis and Karhunen-Loeve Expansion Approaches for Regional GPS Network Analysis

Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals. The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases. A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis (PCA) and the Karhunen-Loeve expansion (KLE) both decompose network time series into a set of temporally varying modes and their spatial responses. Therefore they provide a mathematical framework to perform spatiotemporal filtering.We apply the combination of PCA and KLE to daily station coordinate time series of the Southern California Integrated GPS Network (SCIGN) for the period 2000 to 2004. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components, which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California. Furthermore, the common mode errors exhibit temporally nonrandom patterns.

displacement↗
NASA NTRS1973

Principal sources and dispersal patterns of suspended particulate matter in nearshore surface waters of the northeast Pacific Ocean and the Hawaiian Islands

The author has identified the following significant results. ERTS-1 multispectral scanner imagery of the nearshore surface waters of the Northeast Pacific Ocean is proving to be a useful tool for determining source and dispersal of suspended particulate matter. The principal sources of the turbid water, seen best on the green and red bands, are river and stream effluents and actively eroding coastlines; secondary sources are waste effluents and production of planktonic organisms, but these may sometimes be masked by the very turbid plumes of suspended sediment being discharged into the nearshore zone during times of high river discharge. The configuration and distribution of the plumes of turbid water also can be used to infer near-surface current directions. Comparison of imagery of the nearshore water off the northern California coast from October 1972 and January 1973 shows a reversal of the near-surface currents, from predominantly south-setting in the fall (California Current) to north-setting in the winter (Davidson Current).

Carlson, P. R.↗
NASA NTRS1974

Perturbation of the calcium principal series by metastable calcium atoms

The absorption of the principal series of calcium is observed with about 4% of the atoms placed in the 4s4p 3P level by selective laser excitation, and is compared with the absorption with all atoms in the ground state. We observe a broadening of the absorption lines accompanied by a shift to longer wavelengths and the occurrence of satellite lines at shorter wavelengths.

Mcilrath, T. J.↗
NASA NTRS1975

Enhancement of LANDSAT imagery by combination of multispectral classification and principal component analysis

Digital enhancement of LANDSAT imagery was obtained by application of principal component analysis separately on each of the classes previously determined in a multispectral classification step. Each part of the image is thus enhanced whatever its spectral signature may be. A document was obtained which is a synthesis between a conventional image and an ordinary computerized classification. The interpreter can, at the same time, take into account not only the classification but also other features such as context and structure. An example is discussed with the help of geological interpretation.

Fontanel, A.↗
NASA NTRS1977

Cygnus A at 99 GHz: Observations of the three principal components and interpretation of the central source

The three principal emission components of Cygnus A were observed at 99 GHz, the highest frequency at which radio measurements of this source have been accomplished. The observations show no definite indication of a high-frequency cutoff in the spectrum of the compact central component, which perhaps may be attributed to an optically thin synchrotron source that peaks at a frequency of several hundred GHz.

Hobbs, R. W.↗
NASA NTRS1978

A bibliography of planetary geology principal investigators and their associates, 1976-1978

This bibliography cites publications submitted by 484 principal investigators and their associates who were supported through NASA's Office of Space Sciences Planetary Geology Program. Subject classifications include: solar system formation, comets, and asteroids; planetary satellites, planetary interiors, geological and geochemical constraints on planetary evolution; impact crater studies, volcanism, eolian studies, fluvian studies, Mars geological mapping; Mercury geological mapping; planetary cartography; and instrument development and techniques. An author/editor index is provided.

Source record↗
NASA NTRS1978

Cygnus A at 99 GHz - Observations of the three principal components and interpretation of the central source

The three principal emission components of Cygnus A have been observed at 99 GHz, the highest frequency at which radio measurements of this source have been accomplished. The observations show no definite indication of a high-frequency cutoff in the spectrum of the compact central component, which perhaps may be attributed to an optically thin synchrotron source that peaks at a frequency of several hundred GHz.

Hobbs, R. W.↗
NASA NTRS1979

The viscoelastic behavior of the principal compliance matrix of a unidirectional graphite/epoxy composite

The time-temperature response of the principal compliances of a unidirectional graphite/epoxy composite was determined. It is shown that two components of the compliance matrix are time and temperature independent and that the compliance matrix is symmetric for the viscoelastic composite. The time-temperature superposition principle is used to determine shift factors which are independent of fiber orientation, for fiber angles that vary from 10 D to 90 D with respect to the load direction.

Morris, D. H.↗