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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Correcting Satellite Image Derived Surface Model for Atmospheric Effects

This project was a continuation of the project entitled "Resolution Earth Surface Features from Repeat Moderate Resolution Satellite Imagery". In the previous study, a Bayesian Maximum Posterior Estimate (BMPE) algorithm was used to obtain a composite series of repeat imagery from the Advanced Very High Resolution Radiometer (AVHRR). The spatial resolution of the resulting composite was significantly greater than the 1 km resolution of the individual AVHRR images. The BMPE algorithm utilized a simple, no-atmosphere geometrical model for the short-wave radiation budget at the Earth's surface. A necessary assumption of the algorithm is that all non geometrical parameters remain static over the compositing period. This assumption is of course violated by temporal variations in both the surface albedo and the atmospheric medium. The effect of the albedo variations is expected to be minimal since the variations are on a fairly long time scale compared to the compositing period, however, the atmospheric variability occurs on a relatively short time scale and can be expected to cause significant errors in the surface reconstruction. The current project proposed to incorporate an atmospheric correction into the BMPE algorithm for the purpose of investigating the effects of a variable atmosphere on the surface reconstructions. Once the atmospheric effects were determined, the investigation could be extended to include corrections various cloud effects, including short wave radiation through thin cirrus clouds. The original proposal was written for a three year project, funded one year at a time. The first year of the project focused on developing an understanding of atmospheric corrections and choosing an appropriate correction model. Several models were considered and the list was narrowed to the two best suited. These were the 5S and 6S shortwave radiation models developed at NASA/GODDARD and tested extensively with data from the AVHRR instrument. Although the 6S model was a successor to the 5S and slightly more advanced, the 5S was selected because outputs from the individual components comprising the short-wave radiation budget were more easily separated. The separation was necessary since both the 5S and 6S did not include geometrical corrections for terrain, a fundamental constituent of the BMPE algorithm. The 5S correction code was incorporated into the BMPE algorithm and many sensitivity studies were performed.

Emery, William↗
NASA NTRS2021

Sky-Scanning Sun-Tracking Airborne Radiometer (3STAR): Instrument Design, Flight Testing, and Tracking Performance

The Sky-Scanning, Sun-Tracking Airborne Radiometer (3STAR) adapts commercial radiometer technology developed for the ocean color research community to airborne measurement of spectrally resolved solar irradiance and sky radiance. These atmospheric observations are used to derive aerosol optical depth (AOD), supporting localized AOD inputs for atmospheric correction of satellite and airborne data over terrestrial and aquatic (including optically dark) targets. The ability to regionally “tune” atmospheric correction schemes with relevant spatial AOD supports constraining atmospheric correction of remote sensing reflectance. Very wide dynamic range has been achieved for multi-channel band-pass-filter-radiometers originally designed for deployment into the water column. By actively tracking and directly pointing to the Sun, the light attenuation by aerosol particles in the atmospheric column can be quantified. These measurements improve knowledge of atmospheric constituents and the atmospheric corrections required to improve remote sensing capabilities for interpreting reflectance measurements from the Earth surface. 3STAR incorporates a custom Sun-tracking/sky-scanning pointing head, a Sun-tracking camera, and a commercially available, cylindrical, 19-channel radiometer tube assembly. An accurate and responsive mount and tracking system has been developed and certified to NASA and Naval Air Systems Command (NAVAIR) airworthiness standards for deployment into the aircraft slipstream. Ground and flight testing indicate typical tracking errors of less than 0.1 degrees, well within the field of view of the radiometer as required to minimize measurement uncertainty from alignment error. Preliminary AOD measurements compare to within 0.013 with 15 measurements from an Aerosol Robotic Network (AERONET) Cimel instrument at 500 nm wavelength and low solar angle.

Atmosphere↗
NASA NTRS1974

Real-time correction of atmospherically degraded telescope images through image sharpening

We present a new technique for the correction of atmospheric distortion in telescope images. Most of this distortion arises from a random phase variation of the incoming light across the telescope aperture. This variation limits the resolving power of even large telescopes to about one arc second. If the sharpness of the images is defined in a suitable way, this sharpness is maximized only when the phase distortion of the incoming light is zero. We present computer simulations of a simple feedback system in which active optical elements, set to maximize the sharpness, correct most of the atmospheric distortion. Photon statistics set the limiting magnitude of the object for which a practical feedback system can work. Details in a sixth magnitude object smaller than 0.1 sec of arc should be resolvable. The system can be conveniently employed within existing telescopes.

Muller, R. A.↗
NASA NTRS1992

Atmospheric refraction correction for Ka-band blind pointing on the DSS-13 beam waveguide antenna

An analysis of the atmospheric refraction corrections at the DSS-13 34-m diameter beam waveguide (BWG) antenna for the period Jul. - Dec. 1990 is presented. The current Deep Space Network (DSN) atmospheric refraction model and its sensitivity with respect to sensor accuracy are reviewed. Refraction corrections based on actual atmospheric parameters are compared with the DSS-13 station default corrections for the six-month period. Average blind-pointing improvement during the worst month would have amounted to 5 mdeg at 10 deg elevation using actual surface weather values. This would have resulted in an average gain improvement of 1.1 dB.

Perez-Borroto, I. M.↗
NASA NTRS2022

Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint

Retrievals of ocean color from space are important for better understanding of the ocean ecosystem but can be limited under conditions such as clouds, aerosols, and sunglint. Many ocean color algorithms use a few selected spectral bands to perform an atmospheric correction and then derive the upwelling radiance from the ocean. The limitations in the atmospheric correction under certain conditions lead to many gaps in daily spatial coverage of ocean color retrievals. To address these limitations, we introduce a new approach that uses machine learning to estimate ocean color from top of atmosphere radiances or reflectance measurements. 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 and the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from 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 the TROPOspheric Monitoring Instrument (TROPOMI), using measurements from 320–500 nm to show that it can be used to reproduce ocean color properties in less-than-ideal conditions. This machine learning approach complements the current atmospheric correction ocean color retrievals by filling in the gaps resulting from cloud, aerosol, and sunglint contamination. This method can be applied to the future hyperspectral Ocean Color Instrument (OCI), which will be onboard NASA’s Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite set to launch in 2024.

Ocean color↗
NASA NTRS1978

Parameterization of surface temperature retrieval for remote sensing experiments

The paper describes a parameterization procedure for determining the actual surface temperature from the effective brightness temperature measured radiometrically in the 11-micron window region. The algorithm permits computation of the atmospheric correction without performing detailed radiative transfer calculations, and corrections for atmospheric water vapor and for variations of surface emittance are explained. The parameterization is based on model calculations performed with a line-by-line radiative transfer program, and the sensitivity of the retrieved surface temperature to uncertainties concerning the water vapor and the surface emittance is examined.

Tiwari, S. N.↗
NASA NTRS2001

Nutrient Stress Detection in Corn, Using Neural Networks and AVIRIS Hyperspectral Imagery

The US Department of Agriculture (USDA) Agricultural Research Service (ARS) Variable Rate (VRAT) Nitrogen Application site in Shelton, Nebraska, represents a well-documented, corn-growing quarter section. The USDA VRAT site is used to systematically study nutrient stress in corn by varying sub-plot application of fertilizer. The field has four replicates of five blocks that vary by nitrogen treatment from 0-kg/ha to 200-kg/ha in 50-kg/ha increments. The treatment blocks are set out in a randomized, complete block design. Typically, the VRAT is planted in a ridge till, monoculture corn and is watered by a central pivot irrigation system on a three-day period. Since water stress can increase spectral reflectance from corn leaves, it is important that the N-application plots be adequately watered so that only nutrient-related stress will predominate. A figure shows imagery of the USDA VRAT site with the fertilizer amounts for each block shown. Low-altitude Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imagery was acquired over the Shelton, Nebraska, VRAT site on July 22, 1999. The overflight produced 3-meter pixels with 224 spectral bands. Ground personnel supported the mission with measurements at the time of the overflight. The image data was pre-processed at JPL before being sent out to an investigator. The data arrived radiometrically corrected, allowing ready application of an atmospheric correction procedure. The Atmosphere Removal Program (ATREM) was used to perform an atmospheric correction. The AVIRIS imagery after ATREM correction was output as relative reflectance. This relative reflectance file was scaled by an empirical line procedure to provide reflectances that matched closely those measured in the field.

Estep, Lee↗
NASA NTRS2019

Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency's (ESA's) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration's (NASA's) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from theArgo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS.We also identify the impact of the corrections for atmospheric effectsrecently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.

SSS↗
NASA NTRSDate not supplied

Generation of Land Surface Reflectance with Combined Geo-KOMPSAT-2A AMI and Himawari 8 AHI Observations

The latest generation of geostationary satellites has opened a new era of Earth observations with unprecedented spatiotemporal resolution and spectral range. Together with GOES 16/17 ABI, FY4-A AGRI, and Himawari-8 AHI, a new Korean geostationary satellite (Geo-KOMPSAT-2A AMI) has operationally collected a full-disk image in 16 channels every ten minutes since July 2019, allowing diurnal land surface monitoring over a large proportion of Asia and all of Oceania. Retrieving accurate surface reflectance (SR) over land from GK-2A/AMI is a challenging but high priority objective. One of the challenges is the absence of a spectral band in the 2.2 m SWIR range from AMI, which is required by many atmospheric correction algorithms to retrieve atmospheric aerosol properties. To remedy this issue, we adopt a strategy that combines concurrent GK-2A/AMI and Himawari 8/AHI observations in order to derive AMI SR. We have adapted the NASA Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to process the data stream from Himawari 8/AHI. The advantages of the MAIAC algorithm is its capability to exploit the high temporal frequency and varying illumination geometry of the geostationary data for advanced cloud/snow detection, aerosol retrieval, and characterization of surface bidirectional reflectance properties. Leveraging the similarities of spectral bands and the sun-target-sensor geometry between AMI and AHI, we are able to create denser time series of observations and enhanced BRDF samples over most of the spatial coverage of AMI (and AHI). The combined stereo-type observations not only help derive SR for AMI but also enhance retrievals of the corresponding AHI surface products. We evaluate the resulting AMI SR using ground (AERONET) observations and corresponding MODIS products. Further, we discuss potential challenges in utilizing the geostationary satellite data for land surface monitoring.

geostationary satellite↗
NASA NTRS2020

Inversion of multiangular polarimetric measurements from the ACEPOL campaign: an application of improving aerosol property and hyperspectral ocean color retrievals

NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the time frame of late 2022 to early 2023, will carry the Ocean Color Instrument (OCI), a hyperspectral scanning radiometer, and two multiangle polarimeters (MAPs), the UMBC Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). One purpose of the PACE MAPs is to better characterize aerosol properties, which can then be used to improve atmospheric correction for the retrieval of ocean color in coastal waters. Though this is theoretically promising, the use of MAP data in the atmospheric correction of colocated hyperspectral ocean color measurements have not yet been well demonstrated. In this work, we performed aerosol retrievals using the MAP measurements from the Research Scanning Polarimeter (RSP) and demonstrate its application to the atmospheric correction of hyperspectral radiometric measurements from SPEX airborne. Both measurements were collected on the same aircraft from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) field campaign in 2017. Two cases over ocean with small aerosol loading (aerosol optical depth ∼0.04) are identified including colocated RSP and SPEX airborne measurements and Aerosol Robotic Network (AERONET) ground-based observations. The aerosol retrievals are performed and compared with two options: one uses reflectance measurement only and the other uses both reflectance and polarization. It is demonstrated that polarization information helps reduce the uncertainties of aerosol microphysical and optical properties. The retrieved aerosol properties are then used to compute the contribution of atmosphere and ocean surface for atmospheric correction over the discrete bands from RSP measurements and the hyperspectral SPEX airborne measurements. The water-leaving signals determined this way are compared with both AERONET and Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color products for performance analysis. The results and lessons learned from this work will provide a basis to fully exploit the information from the unique combination of sensors on PACE for aerosol characterization and ocean ecosystem research.

Meng Gao↗
NASA NTRS1993

AVIRIS calibration using the cloud-shadow method

More than 90 percent of the signal at an ocean-viewing, satellite sensor is due to the atmosphere, so a 5 percent sensor-calibration error viewing a target that contributes but 10 percent of the signal received at the sensor may result in a target-reflectance error of more than 50 percent. Since prelaunch calibration accuracies of 5 percent are typical of space-sensor requirements, recalibration of the sensor using ground-base methods is required for low-signal target. Known target reflectance or water-leaving radiance spectra and atmospheric correction parameters are required. In this article we describe an atmospheric-correction method that uses cloud shadowed pixels in combination with pixels in a neighborhood region of similar optical properties to remove atmospheric effects from ocean scenes. These neighboring pixels can then be used as known reflectance targets for validation of the sensor calibration and atmospheric correction. The method uses the difference between water-leaving radiance values for these two regions. This allows nearly identical optical contributions to the two signals (e.g., path radiance and Fresnel-reflected skylight) to be removed, leaving mostly solar photons backscattered from beneath the sea to dominate the residual signal. Normalization by incident solar irradiance reaching the sea surface provides the remote-sensing reflectance of the ocean at the location of the neighbor region.

Carder, K. L.↗
NASA NTRS2024

Advanced Libya-4 radiometric and atmospheric characterization utilizing MODIS and VIIRS full-scan reflective solar band measurements

The NASA Clouds and the Earth's Radiant Energy System (CERES) project provides observed flux and cloud products to the climate science community. The CERES instruments, along with the MODIS and VIIRS imagers, are onboard the Terra, Aqua, NPP, and NOAA20 satellites. In order to produce seamless multi-platform integrated products, long-term sensor stability and inter-calibration are required. Inter-calibration between sensors within the same sun-synchronous orbit relies on Earth invariant targets because simultaneous nadir overpasses are not possible. To facilitate inter-calibration efforts, the CERES Imager and Geostationary Calibration Group (IGCG) has improved the Libya-4 target characterization. Improvements include full scan angle characterization to enable daily observations, clear-sky identification using individual scan angle dynamic spatial homogeneity thresholds, and atmospheric corrections. The water vapor correction was found to be effective across the full scan, whereas the ozone and aerosol corrections were less effective. The atmospheric-corrected normalized radiance temporal fluctuations are similar across scan angles, spectral bands, and between the MODIS and VIIRS imagers, suggesting that the fluctuations are a result of the natural variability of the Libya-4 surface reflectance. The Libya-4 surface variability is more than likely caused by changes in the prevailing winds that alter sand dune orientation and resulting shadows. The full-scan-imager atmosphere and angle corrected reflected solar band radiance trend standard errors are between 0.6% and 1.0%, and for near-nadir observations, are between 0.5% and 0.8%. The advanced characterization suggests that the Libya-4 short-term surface reflectance anomalies may need to be considered for imager stability monitoring and inter-calibration efforts.

Libya-4 Pseudo-invariant Calibration Site↗
NASA NTRS2000

SeaWiFS Postlaunch Calibration and Validation Analyses

The effort to resolve data quality issues and improve on the initial data evaluation methodologies of the SeaWiFS Project was an extensive one. These evaluations have resulted, to date, in three major reprocessings of the entire data set where each reprocessing addressed the data quality issues that could be identified up to the time of the reprocessing. Three volumes of the SeaWiFS Postlaunch Technical Report Series (Volumes 9, 10, and 11) are needed to document the improvements implemented since launch. Volume 10 continues the sequential presentation of postlaunch data analysis and algorithm descriptions begun in Volume 9. Chapter 1 of Volume 10 describes an absorbing aerosol index, similar to that produced by the Total Ozone Mapping Spectrometer (TOMS) Project, which is used to flag pixels contaminated by absorbing aerosols, such as, dust and smoke. Chapter 2 discusses the algorithm being used to remove SeaWiFS out-of-band radiance from the water-leaving radiances. Chapter 3 provides an itemization of all significant changes in the processing algorithms for each of the first three reprocessings. Chapter 4 shows the time series of global clear water and deep-water (depths greater than 1,000m) bio-optical and atmospheric properties (normalized water-leaving radiances, chlorophyll, atmospheric optical depth, etc.) based on the eight-day composites as a check on the sensor calibration stability. Chapter 5 examines the variation in the derived products with scan angle using high resolution data around Hawaii to test for residual scan modulation effects and atmospheric correction biases. Chapter 6 provides a methodology for evaluating the atmospheric correction algorithm and atmospheric derived products using ground-based observations. Similarly, Chapter 7 presents match-up comparisons of coincident satellite and in situ data to determine the accuracy of the water-leaving radiances, chlorophyll a, and K(490) products.

Hooker, Stanford B.↗
NASA NTRS1992

Application of split window technique to TIMS data

Absorptions by the atmosphere in thermal infrared region are mainly due to water vapor, carbon dioxide, and ozone. As the content of water vapor in the atmosphere greatly changes according to weather conditions, it is important to know its amount between the sensor and the ground for atmospheric corrections of thermal Infrared Multispectral Scanner (TIMS) data (i.e. radiosonde). On the other hand, various atmospheric correction techniques were already developed for sea surface temperature estimations from satellites. Among such techniques, Split Window technique, now widely used for AVHRR (Advanced Very High Resolution Radiometer), uses no radiosonde or any kind of supplementary data but a difference between observed brightness temperatures in two channels for estimating atmospheric effects. Applications of Split Window technique to TIMS data are discussed because availability of atmospheric profile data is not clear when ASTER operates. After these theoretical discussions, the technique is experimentally applied to TIMS data at three ground targets and results are compared with atmospherically corrected data using LOWTRAN 7 with radiosonde data.

Matsunaga, Tsuneo↗
NASA NTRS1996

Ocean observations with EOS/MODIS: Algorithm development and post launch studies

An investigation of the influence of stratospheric aerosol on the performance of the atmospheric correction algorithm is nearly complete. The results indicate how the performance of the algorithm is degraded if the stratospheric aerosol is ignored. Use of the MODIS 1380 nm band to effect a correction for stratospheric aerosols was also studied. Simple algorithms such as subtracting the reflectance at 1380 nm from the visible and near infrared bands can significantly reduce the error; however, only if the diffuse transmittance of the aerosol layer is taken into account. The atmospheric correction code has been modified for use with absorbing aerosols. Tests of the code showed that, in contrast to non absorbing aerosols, the retrievals were strongly influenced by the vertical structure of the aerosol, even when the candidate aerosol set was restricted to a set appropriate to the absorbing aerosol. This will further complicate the problem of atmospheric correction in an atmosphere with strongly absorbing aerosols. Our whitecap radiometer system and solar aureole camera were both tested at sea and performed well. Investigation of a technique to remove the effects of residual instrument polarization sensitivity were initiated and applied to an instrument possessing (approx.) 3-4 times the polarization sensitivity expected for MODIS. Preliminary results suggest that for such an instrument, elimination of the polarization effect is possible at the required level of accuracy by estimating the polarization of the top-of-atmosphere radiance to be that expected for a pure Rayleigh scattering atmosphere. This may be of significance for design of a follow-on MODIS instrument. W.M. Balch participated on two month-long cruises to the Arabian sea, measuring coccolithophore abundance, production, and optical properties. A thorough understanding of the relationship between calcite abundance and light scatter, in situ, will provide the basis for a generic suspended calcite algorithm.

Gordon, Howard R.↗
NASA NTRS1972

Correcting airborne scanning infrared radiometer measurements for atmospheric effects

Two techniques were developed for determining atmospheric corrections from observations made by a scanning radiometer. Both techniques depend on knowing the radiometer's limb function. The limb function for an RS-18 scanning radiometer is derived from calculations made with a radiation model and used to demonstrate the techniques. One technique requires observations made over an isothermal water surface within the area being remotely sensed. The other technique does not depend on an isothermal water surface but requires a boat measurement of radiometric sea surface temperature within the area being remotely sensed. The radiation model used to derive the limb function does not account for the effects of atmospheric particulates on the correction. It is hypothesized that the effect of particulates on the limb function derived in this study is negligible, and therefore the technique essentially obtains the total correction. The techniques developed can be used over land provided that a section of isothermal land exists within the experiment area or that a radiometric measurement of surface temperature is made at the surface.

Boudreau, R. D.↗
NASA NTRS2017

Generating Land Surface Reflectance for the New Generation of Geostationary Satellite Sensors with the MAIAC Algorithm

The latest generation of geostationary satellite sensors, including the GOES-16/ABI and the Himawari 8/AHI, provide exciting capability to monitor land surface at very high temporal resolutions (5-15 minute intervals) and with spatial and spectral characteristics that mimic the Earth Observing System flagship MODIS. However, geostationary data feature changing sun angles at constant view geometry, which is almost reciprocal to sun-synchronous observations. Such a challenge needs to be carefully addressed before one can exploit the full potential of the new sources of data. Here we take on this challenge with Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, recently developed for accurate and globally robust applications like the MODIS Collection 6 re-processing. MAIAC first grids the top-of- atmosphere measurements to a fixed grid so that the spectral and physical signatures of each grid cell are stacked (“remembered”) over time and used to dramatically improve cloud/shadow/snow detection, which is by far the dominant error source in the remote sensing. It also exploits the changing sun-view geometry of the geostationary sensor to characterize surface BRDF with augmented angular resolution for accurate aerosol retrievals and atmospheric correction. The high temporal resolutions of the geostationary data indeed make the BRDF retrieval much simpler and more robust as compared with sun-synchronous sensors such as MODIS. As a prototype test for the geostationary-data processing pipeline on NASA Earth Exchange (GEONEX), we apply MAIAC to process 18 months of data from Himawari 8/AHI over Australia. We generate a suite of test results, including the input TOA reflectance and the output cloud mask, aerosol optical depth (AOD), and the atmospherically-corrected surface reflectance for a variety of geographic locations, terrain, and land cover types. Comparison with MODIS data indicates a general agreement between the retrieved surface reflectance products. Furthermore, the geostationary results satisfactorily capture the movement of clouds and variations in atmospheric dust/aerosol concentrations, suggesting that high quality land surface and vegetation datasets from the advanced geostationary sensors can help complement and improve the corresponding EOS products.

geostationary satellite sensors↗