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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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248 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Seasonal Monitoring and Estimation of Regional Aerosol Distribution over Po Valley, Northern Italy, Using a High-Resolution MAIAC Product

In this work, the new 1-km-resolved Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is employed to characterize seasonal AOD-PM10 correlations over northern Italy. The accuracy of the new dataset is assessed versus the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5.1 Aerosol Optical Depth (AOD) data, retrieved at 0.55 microns with spatial resolution of 10 km (MYD04). We focused on evaluating the ability of these two products to characterize both temporal and spatial distributions of aerosols within urban and suburban areas. Ground PM10 measurements were obtained from 73 of the Italian Regional Agency for Environmental Protection (ARPA) monitoring stations, spread across northern Italy, for a three-year period from 2010 to 2012. The Po Valley area (northern Italy) was chosen as the study domain because of severe urban air pollution, resulting from the highest population and industrial manufacturing density in the country, being located in a valley where two surrounding mountain chains favor the stagnation of pollutants. We found that the global correlations between PM10 and AOD are R(sup 2) = 0.83 and R(sup 2) = 0.44 for MYD04_L2 and for MAIAC, respectively, suggesting for a greater sensitiveness of the high-resolution product to small-scale deviations. However, the introduction of Relative Humidity (RH) and Planetary Boundary Layer (PBL) depth corrections gave a significant improvement to the PM AOD correlation, which led to similar performance: R(sup 2) = 0.96 for MODIS and R(sup 2) = 0.95 for MAIAC. Furthermore, the introduction of the PBL information in the corrected AOD values was found to be crucial in order to capture the clear seasonal cycle shown by measured PM10 values. The study allowed us to define four seasonal linear correlations that estimate PM10 concentrations satisfactorily from the remotely sensed MAIAC AOD retrieval. Overall, the results show that the high resolution provided by MAIAC retrieval data is much more relevant than 10km MODIS data to characterize PM10 in this region of Italy which has a pretty limited geographical domain, but a broad variety of land usages and consequent particulate concentrations.

MODIS↗
NASA NTRS2012

Aerosol and Surface Parameter Retrievals for a Multi-Angle, Multiband Spectrometer

This software retrieves the surface and atmosphere parameters of multi-angle, multiband spectra. The synthetic spectra are generated by applying the modified Rahman-Pinty-Verstraete Bidirectional Reflectance Distribution Function (BRDF) model, and a single-scattering dominated atmosphere model to surface reflectance data from Multiangle Imaging SpectroRadiometer (MISR). The aerosol physical model uses a single scattering approximation using Rayleigh scattering molecules, and Henyey-Greenstein aerosols. The surface and atmosphere parameters of the models are retrieved using the Lavenberg-Marquardt algorithm. The software can retrieve the surface and atmosphere parameters with two different scales. The surface parameters are retrieved pixel-by-pixel while the atmosphere parameters are retrieved for a group of pixels where the same atmosphere model parameters are applied. This two-scale approach allows one to select the natural scale of the atmosphere properties relative to surface properties. The software also takes advantage of an intelligent initial condition given by the solution of the neighbor pixels.

Broderick, Daniel↗
NASA NTRS2016

Scaling Estimates of Vegetation Structure in Amazonian Tropical Forests Using Multi-Angle MODIS Observations

Detailed knowledge of vegetation structure is required for accurate modelling of terrestrial ecosystems, but direct measurements of the three dimensional distribution of canopy elements, for instance from LiDAR, are not widely available. We investigate the potential for modelling vegetation roughness, a key parameter for climatological models, from directional scattering of visible and near-infrared (NIR) reflectance acquired from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). We compare our estimates across different tropical forest types to independent measures obtained from: (1) airborne laser scanning (ALS), (2) spaceborne Geoscience Laser Altimeter System (GLAS)/ICESat, and (3) the spaceborne SeaWinds/QSCAT. Our results showed linear correlation between MODIS-derived anisotropy to ALS-derived entropy (r(exp 2)= 0.54, RMSE= 0.11), even in high biomass regions. Significant relationships were also obtained between MODIS-derived anisotropy and GLAS-derived entropy(0.52 less than or equal to r(exp 2) less than or equal to 0.61; p less than 0.05), with similar slopes and offsets found throughout the season, and RMSE between 0.26 and 0.30 (units of entropy). The relationships between the MODIS-derived anisotropy and backscattering measurements (sigma(sup 0)) from SeaWinds/QuikSCAT presented an r(exp 2) of 0.59 and a RMSE of 0.11. We conclude that multi-angular MODIS observations are suitable to extrapolate measures of canopy entropy across different forest types, providing additional estimates of vegetation structure in the Amazon.

MODIS↗
NASA NTRS2012

Multi-Angle Implementation of Atmospheric Correction for MODIS (MAIAC). Part 3: Atmospheric Correction

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.

Lyapustin, A.↗
NASA NTRS2008

Mapping Snow Grain Size over Greenland from MODIS

This paper presents a new automatic algorithm to derive optical snow grain size (SGS) at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. Differently from previous approaches, snow grains are not assumed to be spherical but a fractal approach is used to account for their irregular shape. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived SGS shows a good sensitivity to snow metamorphism, including melting and snow precipitation events. Preprocessing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask (CM) is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust performance of CM algorithm in discrimination of clouds over bright snow and ice. As part of the validation analysis, SGS derived from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM\I radiometer, which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS SGS was compared with predictions of the snow model CROCUS driven by measurements of the automatic whether stations of the Greenland Climate Network. We found that CROCUS grain size is on average a factor of two larger than MODIS-derived SGS. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of SGS for four permanent sites, the paper presents SGS maps over the Greenland ice sheet for the March-September period of 2004.

Lyapustin, Alexei↗
NASA NTRS2023

Calibration of the SNPP and NOAA 20 VIIRS Sensors for Continuity of the MODIS Climate Data Records

Accurate long-term sensor calibration and periodic re-processing to ensure consistency and continuity of atmospheric, land and ocean geophysical retrievals from space within the mission period and across different missions is a major requirement of climate data records. In this work, we applied the Multi-Angle Implementation of Atmospheric Correction (MAIAC)-based vicarious calibration technique over Libya-4 desert site to perform calibration analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. For both VIIRS sensors we characterized residual linear calibration trends and cross-calibrated both sensors to MODerate resolution Imaging Spectroradiometer (MODIS) Aqua regarded as a calibration standard. The relative spectral response (RSR) differences were accounted for using the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) hyperspectral surface reflectance data. Our results agree with independent vicarious calibration results of both the MODIS/VIIRS Characterization Support Team as well as the CERES Imager and Geostationary Calibration Group within estimated uncertainty of 1–2%. Analysis of MAIAC geophysical products with the new calibration shows a high level of agreement of MAIAC aerosol, surface reflectance and NDVI records between MODIS and VIIRS. Excluding high aerosol optical depth (AOD), all three sensors agree in AOD with mean difference (MD) less than 0.01 and residual mean squared difference rmsd ∼ 0.04. Spectral geometrically normalized surface reflectance agrees within rmsd of 0.003–0.005 in the visible and 0.01–0.012 at longer wavelengths. The residual surface reflectance differences are fully explained by differences in spectral filter functions. Finally, difference in NDVI is characterized by rmsd ∼ 0.02 and MD less than 0.003 for NDVI based on VIIRS imagery bands I1/I2 and less than 0.01 for NDVI based on VIIRS radiometric bands M5/M7. In practical sense, these numbers indicate consistency and continuity in MAIAC records ensuring the smooth transition from MODIS to VIIRS.

MAIAC↗
NASA NTRS2020

Washington Health & Air Quality: Quantifying Air Quality Parameters and Validating Air Pollution Sources Impacting the Health of Puget Sound Residents Through the Use of NASA and ESA Remote Sensing Data

In the Puget Sound region of Washington, high levels of air pollutants put residents’ health at risk by increasing their likelihood of developing critical respiratory conditions. This project used remotely-sensed data to investigate aerosol optical depth (AOD) from NASA satellite sensors including the Terra and Aqua MODerate Resolution Imaging Spectroradiometer (MODIS) and European Space Agency Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). The team visualized the most recent data in Google Earth Engine (GEE) API to display air pollution trends in Washington State, which will support the Puget Sound Clean Air Agency’s (PSCAA) decision-making processes. The team performed linear regressions using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to form a relationship between ground-level microscopic particles (PM2.5) and AOD in the Puget Sound region, validating the relationship using concentration readings taken from Environmental Protection Agency (EPA) air quality monitors. The team utilized estimated PM2.5 and other satellite data to produce a web-based tool and to evaluate the effectiveness of using such a tool for near real-time air quality monitoring within a particular region. The team found that the tool provides useful supplementary data that fills in the gaps of the PSCAA’s air monitoring network.

Health & Air Quality↗
NASA NTRS2020

Washington Health & Air Quality: Quantifying Air Quality Parameters and Validating Air Pollution Sources Impacting the Health of Puget Sound Residents Through the Use of NASA and ESA Remote Sensing Data

In the Puget Sound region of Washington, high levels of air pollutants put residents’ health at risk by increasing their likelihood of developing critical respiratory conditions. This project used remotely-sensed data to investigate aerosol optical depth (AOD) from NASA satellite sensors including the Terra and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) and European Space Agency Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). The team visualized the most recent data in Google Earth Engine (GEE) API to display air pollution trends from Northern California to British Columbia, which will support the Puget Sound Clean Air Agency’s (PSCAA) decision-making processes. The team performed linear regressions using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to form a relationship between ground-level microscopic particles (PM2.5) and AOD in the Puget Sound region, validating the relationship using concentration readings taken from Environmental Protection Agency (EPA) air quality monitors. The team utilized estimated PM2.5 and other satellite data to produce a web-based tool and to evaluate the effectiveness of using such a tool for near real-time air quality monitoring within a particular region. The team found that the tool provides useful supplementary data that fills in the gaps of the PSCAA’s air monitoring network.

Health & Air Quality↗
NASA NTRS1992

Moderate resolution imaging spectroradiometer (MODIS) and observations of the land surface

The moderate resolution imaging spectroradiometer (MODIS) is a NASA facility instrument that is being designed for flight on the Earth Observing System (EOS) series of missions. It is designed to measure biophysical states and dynamics of the land, atmosphere, and ocean. Plans are required for use of other instruments that will be accompanying MODIS on the EOS missions, such as the High-Resolution Imaging Spectrometer (HIRIS) and the Multi-angle Imaging Spectro-Radiometer (MISR). The HIRIS instrument, a spectrometer operating in the visible to shortwave infrared parts of the spectrum, would be employed in combination with the MODIS to understand the impact of sampling the spectrum and the effects of land cover mixtures within the MODIS pixel. The MISR will help in understanding the effects of anisotropy in reflected solar radiation. Both instruments will work in combination with MODIS to better quantify the effects of the atmosphere on observations of surface properties.

Salomonson, V. V.↗
NASA NTRS2005

The application of support vector machines to analysis of global satellite data sets from MlSR

The Multi-angle Imaging Spectro Radiometer (MISR) is one of a suite of five instruments onboard NASA's Terra EOS satellite, launched in December 1999. Typical satellite imagers view the earth from a single direction, but MISR's cameras image the earth simultaneously from nine different directions in four spectral bands. In this way, MISR provides unique multiangle information about solar radiation scattered from clouds, aerosols and other terrestrial surfaces. One of the primary goals of the MISR mission is to improve our understanding of how clouds and aerosols affect the earth's global energy balance.

support vector machines↗
NASA NTRS2004

Dust aerosol retrieval results from MISR (multi-angle imaging spectro-radiometer)

Satellite measurements provide important tools for understanding the effect of mineral dust aerosols on past and present climate and climate predictions. Multi-angle instruments such as Multi-angle Imaging Spectro- Radiometer (MISR) provide independent constraints on aerosol properties based on their sensitivity to the shape of aerosol scattering phase functions. The current MISR operational retrieval algorithm (version 16 and higher) was modified by incorporating new non-spherical dust models that account for naturally occurring dust shapes and compositions. We present selected examples of MISR version 16 retrievals over AERONET sunphotometer land and ocean sites during the passage of dust fronts. Our analysis shows that during such events MISR retrieves Angstrom exponents characteristic of large particles, having little spectral variation in extinction over the MISR wavelength range (442, 550, 672 and 866 nm channels), as expected. The retrieved fraction of non-spherical particles is also very high. This quantity is not retrieved by satellite instruments having only nadir-viewing cameras. Our comparison of current (version 16) MISR-retrieved aerosol optical thickness (AOT) with AERONET instantaneous AOT shows better coverage and stronger correlations than when making identical comparisons with previous AOT retrievals (version 15). The MISR algorithm successful mixtures include a non-spherical dust component with high frequency in retrievals over dark water and slightly lower frequency over land. Selection frequencies of non-spherical dust models also decrease in dusty regions affected by pollution.

Multiangle Imaging Spectroradiometer (MISR)↗
NASA NTRS2010

MISR CMVs and Multiangular Views of Tropical Cyclone Inner-Core Dynamics

Multi-camera stereo imaging of cloud features from the MISR (Multiangle Imaging SpectroRadiometer) instrument on NASA's Terra satellite provides accurate and precise measurements of cloud top heights (CTH) and cloud motion vector (CMV) winds. MISR observes each cloudy scene from nine viewing angles (Nadir, +/-26(sup o), +/-46(sup o), +/-60(sup o), +/-70(sup o)) with approximatel 275-m pixel resolution. This paper provides an update on MISR CMV and CTH algorithm improvements, and explores a high-resolution retrieval of tangential winds inside the eyewall of tropical cyclones (TC). The MISR CMV and CTH retrievals from the updated algorithm are significantly improved in terms of spatial coverage and systematic errors. A new product, the 1.1-km cross-track wind, provides high accuracy and precision in measuring convective outflows. Preliminary results obtained from the 1.1-km tangential wind retrieval inside the TC eyewall show that the inner-core rotation is often faster near the eyewall, and this faster rotation appears to be related linearly to cyclone intensity.

hurricanes↗
NASA NTRS2012

Analysis of Fine-Mode Aerosol Retrieval Capabilities by Different Passive Remote Sensing Instrument Designs

Remote sensing of aerosol optical properties is difficult, but multi-angle, multi-spectral, polarimetric instruments have the potential to retrieve sufficient information about aerosols that they can be used to improve global climate models. However, the complexity of these instruments means that it is difficult to intuitively understand the relationship between instrument design and retrieval success. We apply a Bayesian statistical technique that relates instrument characteristics to the information contained in an observation. Using realistic simulations of fine size mode dominated spherical aerosols, we investigate three instrument designs. Two of these represent instruments currently in orbit: the Multiangle Imaging SpectroRadiometer (MISR) and the POLarization and Directionality of the Earths Reflectances (POLDER). The third is the Aerosol Polarimetry Sensor (APS), which failed to reach orbit during recent launch, but represents a viable design for future instruments. The results show fundamental differences between the three, and offer suggestions for future instrument design and the optimal retrieval strategy for current instruments. Generally, our results agree with previous validation efforts of POLDER and airborne prototypes of APS, but show that the MISR aerosol optical thickness uncertainty characterization is possibly underestimated.

Knobelspiesse, Kirk↗
NASA NTRS2019

Retrieval of Liquid Water Cloud Properties from POLDER-3 Measurements Using a Neural Network Ensemble Approach

This paper describes a neural network algorithm for the estimation of liquid water cloud optical properties from the Polarization and Directionality of Earth's Reflectances-3 (POLDER-3) instrument aboard the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic multi-angle, multi-wavelength measurements of reflectance and polarization and has been applied to the processing of 1 year of POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate Resolution Imaging Spectroradiometer (MODIS) products show that the neural network algorithm has a low bias of around 2 in cloud optical thickness (COT) and between 1 and 2µm in the cloud effective radius. Comparisons with existing POLDER-3 datasets suggest that the proposed scheme may have enhanced capabilities for cloud effective radius retrieval, at least over land. An additional feature of the presented algorithm is that it provides COT and effective radius retrievals at the native POLDER-3 Level 1B pixel level.

Di Noia, Antonio↗