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All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Water color and circulation southern Chesapeake Bay, part 1

Satellite imagery from two EREP passes over the Rappahannock Estuary of the Chesapeake region is analyzed to chart colored water types, to delineate color boundaries and define circulatory patterns. Surface observations from boats and helicopters concurrent with Skylab overpass define the distributions of suspended sediment, transparency, temperature, salinity, phytoplankton, color of suspended material and optical ratio. Important features recorded by the imagery are a large-scale turbidity maximum and massive red tide blooms. Water movement is revealed by small-scale mixing patterns and tidal plumes of apparent sediment-laden water. The color patterns broadly reflect the bottom topography and the seaward gradient of suspended material between the river and the bay. Analyses of red, green and natural color photos by microdensitometry demonstrate the utility of charting water color types of potential use for managing estuarine water quality. The Skylab imagery is superior to aerial photography and surface observations for charting water color.

Nichols, M. M.↗
NASA NTRS1977

Computability of five-color maps

Aside from the mathematical question as to whether any planar map can be colored with four or five colors so that no adjacent regions are assigned the same color, there is a computational problem of finding a coloration for a given map. This report presents an algorithm for the five-color problem which has an asymptotic computing time which is proportional to the square of the number of regions, at worst. The algorithm can be modified to produce six-color maps in linear time. The algorithm has been implemented for practical use in an image processing system and can be used in practical cases to produce four-color maps.

Mclemore, B. D.↗
NASA NTRS1986

On the colors of Jupiter's satellite Io - Irradiation of solid sulfur at 77 K

The colors of seven sulfur allotropes at 77 K have been investigated both before and after irradiation by a high-pressure mercury lamp. S8 is white at 77 K but turns intense yellow within a few minutes on irradiation through quartz or DURAN. On warming, the yellow species (presumably chain-like sulfur diradicals characterized by an absorption at 430 nm) decomposes near 260 K and polymeric sulfur (S-infinity) is formed; the sample color changes gradually to the very similar color of an S8-S-infinity mixture, resulting in no visually detectable color change with temperatures from 77 K to 260 K. This is consistent with the lack of posteclipse brightening on Io. Other sulfur forms also turn intense yellow (S7, S10), grayish yellow (S12, S20, S-infinity), or brownish yellow (S6) on irradiation at 77 K, and these colors also change to the normal colors of the starting materials on warming to 298 K. It is suggested that the yellowish features on Io are caused by the effect of solar radiation on elemental sulfur. The yellow color thus produced may subsequently be modified by deposits of SO2 frosts and volcanic ash.

Steudel, R.↗
NASA NTRS1988

Running head: What color is it

Color vision provides low-resolution spectrophotometric information about candidate materials for planetary surfaces that is comparable in precision to wideband photoelectric photometry, and considerably superior to Voyager TV data. Briefly explained are the basic concepts, teminology, and notation of color science. Also shown is how to convert a reflectance spectrum into a color specification. An Appendix lists a simple computer subroutine to convert spectral reflectance into CIE coordinates, and the text explains how to convert these to a surface color in a standard color atlas. Target and printed Solar System colors are compared to show how accurate are the printed colors.

Young, Andrew T.↗
NASA NTRS1989

IRAS colors of carbon stars - An optical spectroscopic test

Optical spectra are obtained of 57 photographic counterparts to IRAS sources not previously studied spectroscopically, and expected on the basis of their IRAS colors to be M or C type stars. Confirmed carbon stars are found only in a restricted range of 12-25 index, and constitute a striking 'vertical' sequence in the 12-25-60 micron color-color diagram. This sequence is in accord with evolutionary models for AGB stars that convert M into C stars by dredge-up, and follow loops in the color-color plane. Optically visible and optically invisible carbon stars occupy different color-color locations consistent with their representations of different evolutionary states in the life of relatively low-mass stars.

Cohen, Martin↗
NASA NTRS1994

Seed viability detection using computerized false-color radiographic image enhancement

Seed radiographs are divided into density zones which are related to seed germination. The seeds which germinate have densities relating to false-color red. In turn, a seed sorter may be designed which rejects those seeds not having sufficient red to activate a gate along a moving belt containing the seed source. This results in separating only seeds with the preselected densities representing biological viability lending to germination. These selected seeds demand a higher market value. Actual false-coloring isn't required for a computer to distinguish the significant gray-zone range. This range can be predetermined and screened without the necessity of red imaging. Applying false-color enhancement is a means of emphasizing differences in densities of gray within any subject from photographic, radiographic, or video imaging. Within the 0-255 range of gray levels, colors can be assigned to any single level or group of gray levels. Densitometric values then become easily recognized colors which relate to the image density. Choosing a color to identify any given density allows separation by morphology or composition (form or function). Additionally, relative areas of each color are readily available for determining distribution of that density by comparison with other densities within the image.

Vozzo, J. A.↗
NASA NTRS2001

Ocean Color Optical Property Data Derived from OCTS and POLDER: A Comparison Study

We describe our efforts in studying and comparing the ocean color data derived from the Japanese Ocean Color and Temperature Scanner (OCTS) and the French Polarization and Directionality of the Earth's Reflectances (POLDER). OCTS and POLDER were both on board Japan's Sun-synchronous Advanced Earth Observing Satellite (ADEOS-1) from August 1996 to June 1997, collecting about 10 months of global ocean color data. This provides a unique opportunity for developing methods and strategies for the merging of ocean color data from multiple ocean color sensors. In this paper, we describe our approach in developing consistent data processing algorithms for both OCTS and POLDER and using a common in situ data set to vicariously calibrate the two sensors. Therefore, the OCTS and POLDER-measured radiances are effectively bridged through common in situ measurements. With this approach in processing data from two different sensors, the only differences in the derived products from OCTS and POLDER are the differences inherited from the instrument characteristics. Results show that there are no obvious bias differences between the OCTS and POLDER-derived ocean color products, whereas the differences due to noise, which stem from variations in sensor characteristics, are difficult to correct. It is possible, however, to reduce noise differences with some data averaging schemes. The ocean color data from OCTS and POLDER can therefore be compared and merged in the sense that there is no significant bias between two.

Wang, Menghua↗
NASA NTRS2004

Tholins as Coloring Agents on Solar System Bodies

Pre-biotic organic materials appear to be common on many small bodies in the outer Solar System, as evidenced by the color properties of these objects. We report on our continuing study of color properties in connection with the presence of complex organic solids (tholins) among the planets and their satellites, the asteroids, and the trans- Neptunian objects (Kuiper Belt objects). Most small, icy bodies in the Solar System, whether they have high or low surface reflectance (albedo), show a pronounced downward slope in reflectance at wavelengths shorter than approx. 1 micron. This increasing absorption of sunlight toward shorter wavelengths is characteristic of pi-bonds in hydrocarbons having chains or rings of conjugated C atoms. Tholins, which contain polycyclic aromatic and aliphatic hydrocarbons, exhibit these color properties. Using the complex refractive indices of tholins in models of the reflectance spectra of icy bodies in the Solar System, we find that these complex organic materials satisfactorily account for the coloration so widely observed. The new results presented here show that the wide variety of colors of Kuiper Belt objects can be fit very well with tholins, as can the colors of Pluto and Triton. The implications of these fits of Kuiper Belt objects is that complex organic material is created on their surfaces by energetic particle bombardment of native ices, and also may be accreted from external sources. In the cases of Pluto and Triton, photochemistry of their weak N2 + CH4 + CO atmospheres produces complex organic molecules that precipitate to the surface, providing local color.

Cruikshank, D. P.↗
NASA NTRS1999

Effect of color coding and subtraction on the accuracy of contrast echocardiography

BACKGROUND: Contrast echocardiography may be used to assess myocardial perfusion. However, gray scale assessment of myocardial contrast echocardiography (MCE) is difficult because of variations in regional backscatter intensity, difficulties in distinguishing varying shades of gray, and artifacts or attenuation. We sought to determine whether the assessment of rest myocardial perfusion by MCE could be improved with subtraction and color coding. METHODS AND RESULTS: MCE was performed in 31 patients with previous myocardial infarction with a 2nd generation agent (NC100100, Nycomed AS), using harmonic triggered or continuous imaging and gain settings were kept constant throughout the study. Digitized images were post processed by subtraction of baseline from contrast data and colorized to reflect the intensity of myocardial contrast. Gray scale MCE alone, MCE images combined with baseline and subtracted colorized images were scored independently using a 16 segment model. The presence and severity of myocardial contrast abnormalities were compared with perfusion defined by rest MIBI-SPECT. Segments that were not visualized by continuous (17%) or triggered imaging (14%) after color processing were excluded from further analysis. The specificity of gray scale MCE alone (56%) or MCE combined with baseline 2D (47%) was significantly enhanced by subtraction and color coding (76%, p<0.001) of triggered images. The accuracy of the gray scale approaches (respectively 52% and 47%) was increased to 70% (p<0.001). Similarly, for continuous images, the specificity of gray scale MCE with and without baseline comparison was 23% and 42% respectively, compared with 60% after post processing (p<0.001). The accuracy of colorized images (59%) was also significantly greater than gray scale MCE (43% and 29%, p<0.001). The sensitivity of MCE for both acquisitions was not altered by subtraction. CONCLUSION: Post-processing with subtraction and color coding significantly improves the accuracy and specificity of MCE for detection of perfusion defects.

Multicenter Study↗
NASA NTRS2004

Real Data and Rapid Results: Ocean Color Data Analysis with Giovanni (GES DISC Interactive Online Visualization and ANalysis Infrastructure)

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has taken a major step addressing the challenge of using archived Earth Observing System (EOS) data for regional or global studies by developing an infrastructure with a World Wide Web interface which allows online, interactive, data analysis: the GES DISC Interactive Online Visualization and ANalysis Infrastructure, or "Giovanni." Giovanni provides a data analysis environment that is largely independent of underlying data file format. The Ocean Color Time-Series Project has created an initial implementation of Giovanni using monthly Standard Mapped Image (SMI) data products from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission. Giovanni users select geophysical parameters, and the geographical region and time period of interest. The system rapidly generates a graphical or ASCII numerical data output. Currently available output options are: Area plot (averaged or accumulated over any available data period for any rectangular area); Time plot (time series averaged over any rectangular area); Hovmeller plots (image view of any longitude-time and latitude-time cross sections); ASCII output for all plot types; and area plot animations. Future plans include correlation plots, output formats compatible with Geographical Information Systems (GIs), and higher temporal resolution data. The Ocean Color Time-Series Project will produce sensor-independent ocean color data beginning with the Coastal Zone Color Scanner (CZCS) mission and extending through SeaWiFS and Moderate Resolution Imaging Spectroradiometer (MODIS) data sets, and will enable incorporation of Visible/lnfrared Imaging Radiometer Suite (VIIRS) data, which will be added to Giovanni. The first phase of Giovanni will also include tutorials demonstrating the use of Giovanni and collaborative assistance in the development of research projects using the SeaWiFS and Ocean Color Time-Series Project data in the online Laboratory for Ocean Color Users (LOCUS). The synergy of Giovanni with high-quality ocean color data provides users with the ability to investigate a variety of important oceanic phenomena, such as coastal primary productivity related to pelagic fisheries, seasonal patterns and interannual variability, interdependence of atmospheric dust aerosols and harmful algal blooms, and the potential effects of climate change on oceanic productivity.

Acker, J. G.↗
NASA NTRS2012

Water Detection Based on Color Variation

This software has been designed to detect water bodies that are out in the open on cross-country terrain at close range (out to 30 meters), using imagery acquired from a stereo pair of color cameras mounted on a terrestrial, unmanned ground vehicle (UGV). This detector exploits the fact that the color variation across water bodies is generally larger and more uniform than that of other naturally occurring types of terrain, such as soil and vegetation. Non-traversable water bodies, such as large puddles, ponds, and lakes, are detected based on color variation, image intensity variance, image intensity gradient, size, and shape. At ranges beyond 20 meters, water bodies out in the open can be indirectly detected by detecting reflections of the sky below the horizon in color imagery. But at closer range, the color coming out of a water body dominates sky reflections, and the water cue from sky reflections is of marginal use. Since there may be times during UGV autonomous navigation when a water body does not come into a perception system s field of view until it is at close range, the ability to detect water bodies at close range is critical. Factors that influence the perceived color of a water body at close range are the amount and type of sediment in the water, the water s depth, and the angle of incidence to the water body. Developing a single model of the mixture ratio of light reflected off the water surface (to the camera) to light coming out of the water body (to the camera) for all water bodies would be fairly difficult. Instead, this software detects close water bodies based on local terrain features and the natural, uniform change in color that occurs across the surface from the leading edge to the trailing edge.

Rankin, Arturo L.↗
NASA NTRS2018

Optical Coronagraphic Spectroscopy of AU Mic: Evidence of Time Variable Colors?

We present coronagraphic long slit spectra of AU Mic's debris disk taken with the STIS instrument aboard the Hubble Space Telescope. Our spectra are the first spatially-resolved, scattered light spectra of the system's disk, which we detect at projected distances between approximately 10 and 45 au. Our spectra cover a wavelength range between 5200 and 10200 Angstrom. We find that the color of AU Mic's debris disk is bluest at small (12-17 au) projected separations. These results both confirm and quantify the findings qualitatively noted by Krist et al. and are different than IR observations that suggested a uniform blue or gray color as a function of projected separation in this region of the disk. Unlike previous literature, which reported that the color of AU Mic's disk became increasingly more blue as a function of projected separation beyond approx. 30 au, we find the disk's optical color between 35 and 45 au to be uniformly blue on the southeast side of the disk and decreasingly blue on the northwest side. We note that this apparent change in disk color at larger projected separations coincides with several fast, outward moving ''features'' that are passing through this region of the southeast side of the disk. We speculate that these phenomenon might be related and that the fast moving features could be changing the localized distribution of sub-micron-sized grains as they pass by, thereby reducing the blue color of the disk in the process. We encourage follow-up optical spectroscopic observations of AU Mic to both confirm this result and search for further modifications of the disk color caused by additional fast moving features propagating through the disk.

Lomax, Jamie R.↗
NASA NTRS2019

Going Beyond Standard Ocean Color Observations: Lidar and Polarimetry

Passive ocean color images have provided a sustained synoptic view of the distribution of ocean optical properties and color and biogeochemical parameters for the past 20-plus years. These images have revolutionized our view of the ocean. Remote sensing of ocean color has relied on measurements of the radiance emerging at the top of the atmosphere, thus neglecting the polarization and the vertical components. Ocean color remote sensing utilizes the intensity and spectral variation of visible light scattered upward from beneath the ocean surface to derive concentrations of biogeochemical constituents and inherent optical properties within the ocean surface layer. However, these measurements have some limitations. Specifically, the measured property is a weighted-integrated value over a relatively shallow depth, it provides no information during the night and retrievals are compromised by clouds, absorbing aerosols, and low Sun zenithal angles. In addition, ocean color data provide limited information on the morphology and size distribution of marine particles. Major advances in our understanding of global ocean ecosystems will require measurements from new technologies, specifically lidar and polarimetry. These new techniques have been widely used for atmospheric applications but have not had as much as interest from the ocean color community. This is due to many factors including limited access to in-situ instruments and/or space-borne sensors and lack of attention in university courses and ocean science summer schools curricula. However, lidar and polarimetry technology will complement standard ocean color products by providing depth-resolved values of attenuation and scattering parameters and additional information about particle morphology and chemical composition. This review aims at presenting the basics of these techniques, examples of applications and at advocating for the development of in-situ and space-borne sensors. Recommendations are provided on actions that would foster the embrace of lidar and polarimetry as powerful remote sensing tools by the ocean science community.

Jamet, Cedric↗
NASA NTRSDate not supplied

Use of TEMPO as a Proxy for Hyperspectral Geostationary Ocean Color Measurements from the GeoXO OCX Instrument: Harnessing Machine Learning and Principal Component Techniques for Atmospheric and Glint Correction

Retrievals of ocean color from space are important for better understanding the ocean ecosystem. The launch of atmospheric geostationary hyperspectral sensors such as TEMPO, provides a unique opportunity to examine the diurnal variability in ocean ecology. While TEMPO does not have as high spatial resolution or full spectral coverage as planned coastal ocean sensors such as the Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) or GeoXO Ocean Color instrument (OCX), its hourly measurements provide coverage of regions such as Lake Erie and the Gulf of Mexico at spatial scales of approximately 5 km. These data can be useful for testing new algorithms. We will apply our newly developed machine learning based atmospheric correction approach for ocean color retrievals to TEMPO data. Our approach begins by decomposing measured radiances from hyperspectral sensors into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface reflectance. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from collocated MODIS/VIIRS physically-based retrievals. This machine learning approach does not rely on radiative transfer modeling, and the use of MODIS/VIIRS data for training accounts for possible calibration b in hyperspectral data. Previously, we applied our approach using blue and UV wavelengths with the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can estimate ocean color properties in less-than-ideal conditions such as lightly to moderately clouded conditions as well as sun glint and thus improve the spatial coverage of ocean color measurements. TEMPO provides an opportunity to improve on this approach since it will provide collocated measurements at green and red wavelengths that were not available from OMI and TROPOMI and are important particularly for coastal waters. Additionally, our technique can be applied early in the mission and has potential to demonstrate the value of near real time ocean color products that are important for monitoring of harmful algae blooms and other oceanic phenomena.

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↗
DOE OSTI2023

A theory for colors of strongly correlated electronic systems

Many strongly correlated transition metal insulators are colored, even though they have band gaps much larger than the highest energy photons from the visible light. An adequate explanation for the color requires a theoretical approach able to compute subgap excitons in periodic crystals, reliably and without free parameters—a formidable challenge. The literature often fails to disentangle two important factors: what makes excitons form and what makes them optically bright. We pick two archetypal cases as examples: NiO with green color and MnF 2 with pink color, and employ two kinds of ab initio many body Green’s function theories; the first, a perturbative theory based on low-order extensions of the $GW$ approximation, is able to explain the color in NiO, while the same theory is unable to explain why MnF 2 is pink. We show its color originates from higher order spin-flip transitions that modify the optical response, which is contained in dynamical mean-field theory (DMFT). We show that symmetry lowering mechanisms may determine how ‘bright’ these excitons are, but they are not fundamental to their existence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2013

Colors of Jupiter's large anticyclones and the interaction of a Tropical Red Oval with the Great Red Spot in 2008

The nature and mechanisms producing the chromophore agents that provide color to the upper clouds and hazes of the atmospheres of the giant planets are largely unknown. In recent times, the changes in red coloration that have occurred in large- and medium-scale Jovian anticyclones have been particularly interesting. In late June and early July 2008, a particularly color-intense red tropical oval interacted with the Great Red Spot (GRS) leading to the destruction of the red tropical oval and cloud dispersion. We present a detailed study of the tropical vortices, usually white but sometimes red, and a characterization of their color spectral signatures and dynamics. From the spectral reflectivity in methane bands we study their vertical cloud structure compared to that of the GRS and BA. Using two spectral indices we found a near-correlation between anticyclones cloud top altitudes and red color. We present detailed observations of the interaction of the red oval with the GRS and model simulations of the phenomena that allow us to constrain the relative vertical extent of the vortices. We conclude that the vertical cloud structure, vertical extent and dynamics of Jovian anticyclones are not the causes of their coloration. We propose that the red chromophore forms when background material (a compound or particles) is entrained by the vortex, transforming into red once inside the vortex due to internal conditions, exposure to ultraviolet radiation or to the mixing of two chemical compounds that react inside the vortex, confined by a potential vorticity ring barrier.

red color↗
DOE OSTI2023

A guide to diagnosing colored resonances at hadron colliders

We present a comprehensive study on how to distinguish the properties of heavy dijet resonances at hadron colliders. A variety of spins, chiral couplings, charges, and QCD color representations are considered. Distinguishing the different color representations is particularly difficult at hadron colliders. To determine the QCD color structure, we consider a third jet radiated in a resonant dijet event. We show that the relative rates of three-jet versus two-jet processes are sensitive to the color representation of the resonance. We also show analytically that the antennae radiation pattern of soft radiation depends on the color structure of dijet events and develops an observable that is sensitive to the antennae patterns. Finally, we exploit a Convolutional Neural Network with Machine Learning techniques to differentiate the radiation patterns from different colored resonances and find encouraging results to discriminate them. We demonstrate our results numerically at a 14 TeV LHC, and the methodology presented here should be applicable to other future hadron colliders.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗