SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Ultraviolet video imaging system, spectral distribution of UV stimulated luminescence, and measurement of UV reflectance
Calibration of the on-orbit gain changes of the narrow bandwidth reflective solar bands (RSB) of Terra and Aqua MODIS is usually based on the band center wavelength. The relative spectral response (RSR) of each band is assumed to be constant on orbit and the time dependence of an overall gain factor is calculated. Any on-orbit changes to the RSR of the MODIS bands will introduce some error into the calibration and may also have an impact on the Earth scene radiance retrieval. We consider two different ways to track how the RSR of the MODIS RSB may be changing on orbit, and the effects that these changes will have on the calibration. First, we study the broadband degradation of the MODIS scan mirror and how it may be changing the effective out-of-band response of the RSB. Second, we examine in-band RSR measurements from the spectro-radiometric calibration assembly (SRCA) carried on-board both MODIS instruments. We find that RSR changes have a relatively small effect on the radiance calibrated using the on-board solar diffuser, generally less than 0.5% for all RSB at any time in the missions, though the effect may be slightly larger for some scan angles. The impact of RSR changes on the Earth scene radiance retrieval is highly dependent on the spectral properties of the scene and could be significantly larger. The bands showing the largest impacts are Terra bands 1, 4, 8, 9, 17, and 19 and Aqua bands 8 and 9.
NASA’s Earth Polychromatic Imaging Camera (EPIC) onboard NOAA’s Deep Space Climate Observatory (DSCOVR) satellite observes the entire sunlit Earth every 65 to 110 min from the Sun–Earth Lagrangian L1 point. This paper presents initial EPIC shortwave spectral observations of the sunlit Earth reflectance and analyses of its diurnal and seasonal variations. The results show that the reflectance depends mostly on (1) the ratio between land and ocean areas exposed to the Sun and (2) cloud spatial and temporal distributions over the sunlit side of Earth. In particular, the paper shows that (a) diurnal variations of the Earth’s reflectance are determined mostly by periodic changes in the land–ocean fraction of its the sunlit side; (b) the daily reflectance displays clear seasonal variations that are significant even without including the contributions from snow and ice in the polar regions (which can enhance daily mean reflectances by up to 2 to 6% in winter and up to 1 to 4% in summer); (c) the seasonal variations of the sunlit Earth reflectance are mostly determined by the latitudinal distribution of oceanic clouds.
Supraglacial meltwater lakes on the western Greenland Ice Sheet (GrIS) are critical components of its surface hydrology and surface mass balance, and they also affect its ice dynamics. Estimates of lake volume, however, are limited by the availability of in situ measurements of water depth,which in turn also limits the assessment of remotely sensed lake depths. Given the logistical difficulty of collecting physical bathymetric measurements, methods relying upon in situ data are generally restricted to small areas and thus their application to largescale studies is difficult to validate. Here, we produce and validate spaceborne estimates of supraglacial lake volumes across a relatively large area (1250 km(exp 2) of west Greenland's ablation region using data acquired by the WorldView-2 (WV-2) sensor, making use of both its stereo-imaging capability and its meter-scale resolution. We employ spectrally-derived depth retrieval models, which are either based on absolute reflectance (single-channel model) or a ratio of spectral reflectances in two bands (dual-channel model). These models are calibrated by usingWV-2multispectral imagery acquired early in the melt season and depth measurements from a high resolutionWV-2 DEM over the same lake basins when devoid of water. The calibrated models are then validated with different lakes in the area, for which we determined depths. Lake depth estimates based on measurements recorded in WV-2's blue (450-510 nm), green (510-580 nm), and red (630-690 nm) bands and dual-channel modes (blue/green, blue/red, and green/red band combinations) had near-zero bias, an average root-mean-squared deviation of 0.4 m (relative to post-drainage DEMs), and an average volumetric error of b1%. The approach outlined in this study - image-based calibration of depth-retrieval models - significantly improves spaceborne supraglacial bathymetry retrievals, which are completely independent from in situ measurements.
Spectral hemispherical-conical reflectances of a nadir looking sensor were taken throughout the day for a lodgepole pine and two grass canopies. Mathematical simulations of both spectral hemispherical-conical and bi-hemispherical reflectances were performed for two theoretical canopies of contrasting geometric structure. These results and comparisons with literature studies showed a great amount of variability of vegetation canopy reflectances as a function of solar zenith angle. Explanations for this variability are discussed and recommendations for further measurements are proposed.
The changes are presented in spectral transmittance, and reflectance due to exposure of various optical materials to the exhaust plume of a 5-pound thrust bipropellant rocket. The engine was fired in a pulsed mode for a total exposure of 223.7 second. Spectral optical properties were measured in air before and after exposure to the exhaust plume in vacuum. The contaminating layer resulted in both absorption and scattering effects which caused changes as large as 30-50% for transmitting elements and 15% for mirrors in the near ultraviolet wavelengths. The changes in spectral properties of materials exposed to the exhaust plume for 44 and 223.7 seconds are compared and found to be similar.
An analytical method for quantifying the characteristics (phase abundances, phase composition, and grain size) of an olivine-orthopyroxene mixture from reflectance spectra is described. The spectral parameters related to reflectance, wavelength position, and albedo are investigated; the absorption bands for the olivine-orthopyroxene mixture is also studied. Primary calibrations useful for the determination of the olivine-orthopyroxene mixture characteristics are examined.
A flux formulation for a planar slab of molecular gas radiation bounded by diffuse reflecting walls is developed. While this formulation is limited to the planar geometry, it is useful for studying approximations necessary in modeling nongray radiative heat transfer. The governing equations are derived by considering the history of multiple reflections between the walls. Accurate solutions are obtained by explicitly accounting for a finite number of reflections and approximating the spectral effects of the remaining reflections. Four approximate methods are presented and compared using a single absorption band of H2O. All four methods reduce to an identical zeroth-order formulation, which accounts for all reflections approximately but does handle nonreflected radiation correctly. A single absorption band of CO2 is also considered using the best-behaved approximation for higher orders. A zeroth-order formulation is sufficient to predict the radiative transfer accurately for many cases considered. For highly reflecting walls, higher order solutions are necessary for better accuracy. Including all the important bands of H2O, the radiative source distributions are also obtained for two different temperature and concentration profiles.
Since launch, the Terra and Aqua moderate resolution imaging spectroradiometer (MODIS) instruments have successfully operated on-orbit for more than 9 and 6.5 years, respectively. Iv1ODIS, a key instrument for the NASA's Earth Observing System (EOS) missions, was designed to make continuous observations for studies of Earth's land, ocean, and atmospheric properties and to extend existing data records from heritage earth-observing sensors. In addition to frequent global coverage, MODIS observations are made in 36 spectral bands, covering both solar reflective and thermal emissive spectral regions. Nearly 40 data products are routinely generated from MODIS' observations and publicly distributed for a broad range of applications. Both instruments have produced an unprecedented amount of data in support of the science community. As a general reference for understanding sensor operation and calibration, and thus science data quality, we ;provide an overview of the MODIS instruments and their pre-launch calibration and characterization, and describe their on-orbit calibration algorithms and performance. On-orbit results from both Terra and Aqua MODIS radiometric, spectral, and "spatial calibration are discussed. Currently, both instruments, including their on-board calibration devices, are healthy and are expected to continue operation for several }rears to come.
This paper describes the atmospheric correction (AC) component of the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) which introduces a new way to compute parameters of the Ross-Thick Li-Sparse (RTLS) Bi-directional reflectance distribution function (BRDF), spectral surface albedo and bidirectional reflectance factors (BRF) from satellite measurements obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS). MAIAC uses a time series and spatial analysis for cloud detection, aerosol retrievals and atmospheric correction. It implements a moving window of up to 16 days of MODIS data gridded to 1 km resolution in a selected projection. The RTLS parameters are computed directly by fitting the cloud-free MODIS top of atmosphere (TOA) reflectance data stored in the processing queue. The RTLS retrieval is applied when the land surface is stable or changes slowly. In case of rapid or large magnitude change (as for instance caused by disturbance), MAIAC follows the MODIS operational BRDF/albedo algorithm and uses a scaling approach where the BRDF shape is assumed stable but its magnitude is adjusted based on the latest single measurement. To assess the stability of the surface, MAIAC features a change detection algorithm which analyzes relative change of reflectance in the Red and NIR bands during the accumulation period. To adjust for the reflectance variability with the sun-observer geometry and allow comparison among different days (view geometries), the BRFs are normalized to the fixed view geometry using the RTLS model. An empirical analysis of MODIS data suggests that the RTLS inversion remains robust when the relative change of geometry-normalized reflectance stays below 15%. This first of two papers introduces the algorithm, a second, companion paper illustrates its potential by analyzing MODIS data over a tropical rainforest and assessing errors and uncertainties of MAIAC compared to conventional MODIS products.
A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GePCO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately. The compiled data are available at doi:10.1594PANGAEA.854832 (Valente et al., 2015).
Ocean color measured from satellites provides daily global, synoptic views of spectral water-leaving reflectances that can be used to generate estimates of marine inherent optical properties (IOPs). These reflectances, namely the ratio of spectral upwelled radiances to spectral downwelled irradiances, describe the light exiting a water mass that defines its color. IOPs are the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents. Because of their dependence on the concentration and composition of marine constituents, IOPs can be used to describe the contents of the upper ocean mixed layer. This information is critical to further our scientific understanding of biogeochemical oceanic processes, such as organic carbon production and export, phytoplankton dynamics, and responses to climatic disturbances. Given their importance, the international ocean color community has invested significant effort in improving the quality of satellite-derived IOP products, both regionally and globally. Recognizing the current influx of data products into the community and the need to improve current algorithms in anticipation of new satellite instruments (e.g., the global, hyperspectral spectroradiometer of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission), we present a synopsis of the current state of the art in the retrieval of these core optical properties. Contemporary approaches for obtaining IOPs from satellite ocean color are reviewed and, for clarity, separated based their inversion methodology or the type of IOPs sought. Summaries of known uncertainties associated with each approach are provided, as well as common performance metrics used to evaluate them. We discuss current knowledge gaps and make recommendations for future investment for upcoming missions whose instrument characteristics diverge sufficiently from heritage and existing sensors to warrant reassessing current approaches.
A method for satellite remote sensing of spectral UVB radiation incident at the earth's surface for snow and/or ice free areas has been developed. Measurements of total ozone and UV reflectively from the Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) instrument have been applied to this technique. Comparison of satellite estimates with ground based measurements of spectral UVB irradiance show differences which are comparable to differences between near simultaneous measurements made with two or more ground based co-located instruments.
A fast and accurate principal component-based radiative transfer model in the solar spectral region (PCRTMSOLAR) has been developed. The algorithm is capable of simulating reflected solar spectra in both clear sky and cloudy atmospheric conditions. Multiple scattering of the solar beam by the multilayer clouds and aerosols are calculated using a discrete ordinate radiative transfer scheme. The PCRTM-SOLAR model can be trained to simulate top-of-atmosphere radiance or reflectance spectra with spectral resolution ranging from 1 cm(exp −1) resolution to a few nanometers. Broadband radiances or reflectance can also be calculated if desired. The current version of the PCRTM-SOLAR covers a spectral range from 300 to 2500 nm. The model is valid for solar zenith angles ranging from 0 to 80 deg, the instrument view zenith angles ranging from 0 to 70 deg, and the relative azimuthal angles ranging from 0 to 360 deg. Depending on the number of spectral channels, the speed of the current version of PCRTM-SOLAR is a few hundred to over one thousand times faster than the medium speed correlated-k option MODTRAN5. The absolute RMS error in channel radiance is smaller than 10(exp −3) mW∕cm)exp 2)∕sr∕cm(exp −1) and the relative error is typically less than 0.2%.
An understanding of the behavior of vegetation canopy reflectance as a function of solar zenith angle is important to several remote sensing applications. Spectral hemispherical-conical reflectances of a nadir looking sensor were taken throughout the day of a lodgepole pine and two grass canopies. Mathematical simulations of both a spectral hemispherical-conical reflectance factor and a spectral bi-hemispherical reflectance were performed for two theoretical canopies of contrasting geometric structure. These results and results from literature studies showed a great amount of variability of vegetation canopy reflectances as a function of solar zenith angle. Explanations for this variability are discussed and recommendations for future measurements are proposed.
Canopy radiative transfer is the primary mechanism by which models relate vegetation composition and state to the surface energy balance, which is important to light- and temperature-sensitive plant processes as well as understanding land–atmosphere feedbacks. In addition, certain parameters (e.g., specific leaf area, SLA) that have an outsized influence on vegetation model behavior can be constrained by observations of shortwave reflectance, thus reducing model predictive uncertainty. Importantly, calibrating against radiative transfer outputs allows models to directly use remote sensing reflectance products without relying on highly derived products (such as MODIS leaf area index) whose assumptions may be incompatible with the target vegetation model and whose uncertainties are usually not well quantified. Here, we created the EDR model by coupling the two-stream representation of canopy radiative transfer in the Ecosystem Demography model version 2 (ED2) with a leaf radiative transfer model (PROSPECT-5) and a simple soil reflectance model to predict full-range, high-spectral-resolution surface reflectance that is dependent on the underlying ED2 model state. We then calibrated this model against estimates of hemispherical reflectance (corrected for directional effects) from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States. The calibration significantly reduced uncertainty in model parameters related to leaf biochemistry and morphology and canopy structure for five plant functional types.
Canopy radiative transfer is the primary mechanism by which models relate vegetation composition and state to the surface energy balance, which is important to light- and temperature-sensitive plant processes as well as understanding land–atmosphere feedbacks. In addition, certain parameters (e.g., specific leaf area, SLA) that have an outsized influence on vegetation model behavior can be constrained by observations of shortwave reflectance, thus reducing model predictive uncertainty. Importantly, calibrating against radiative transfer outputs allows models to directly use remote sensing reflectance products without relying on highly derived products (such as MODIS leaf area index) whose assumptions may be incompatible with the target vegetation model and whose uncertainties are usually not well quantified. Here, we created the EDR model by coupling the two-stream representation of canopy radiative transfer in the Ecosystem Demography model version 2 (ED2) with a leaf radiative transfer model (PROSPECT-5) and a simple soil reflectance model to predict full-range, high-spectral-resolution surface reflectance that is dependent on the underlying ED2 model state. We then calibrated this model against estimates of hemispherical reflectance (corrected for directional effects) from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States. The calibration significantly reduced uncertainty in model parameters related to leaf biochemistry and morphology and canopy structure for five plant functional types. Using a single common set of parameters across all sites, the calibrated model was able to accurately reproduce surface reflectance for sites with highly varied forest composition and structure. However, the calibrated model's predictions of leaf area index (LAI) were less robust, capturing only 46 % of the variability in the observations. Comparing the ED2 radiative transfer model with another two-stream soil–leaf–canopy radiative transfer model commonly used in remote sensing studies (PRO4SAIL) illustrated structural errors in the ED2 representation of direct radiation backscatter that resulted in systematic underestimation of reflectance. In addition, we also highlight that, to directly compare with a two-stream radiative transfer model like EDR, we had to perform an additional processing step to convert the directional reflectance estimates of AVIRIS to hemispherical reflectance (also known as “albedo”). In future work, we recommend that vegetation models add the capability to predict directional reflectance, to allow them to more directly assimilate a wide range of airborne and satellite reflectance products. We ultimately conclude that despite these challenges, using dynamic vegetation models to predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data.
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).