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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 271 records · Page 15
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NASA NTRS2015

Ocean Color Measurements with the Operational Land Imager on Landsat-8: Implementation and Evaluation in SeaDAS

The Operational Land Imager (OLI) is a multispectral radiometer hosted on the recently launched Landsat8 satellite. OLI includes a suite of relatively narrow spectral bands at 30 m spatial resolution in the visible to shortwave infrared, which makes it a potential tool for ocean color radiometry: measurement of the reflected spectral radiance upwelling from beneath the ocean surface that carries information on the biogeochemical constituents of the upper ocean euphotic zone. To evaluate the potential of OLI to measure ocean color, processing support was implemented in Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), which is an open-source software package distributed by NASA for processing, analysis, and display of ocean remote sensing measurements from a variety of spaceborne multispectral radiometers. Here we describe the implementation of OLI processing capabilities within SeaDAS, including support for various methods of atmospheric correction to remove the effects of atmospheric scattering and absorption and retrieve the spectral remote sensing reflectance (Rrs; sr−1). The quality of the retrieved Rrs imagery will be assessed, as will the derived water column constituents, such as the concentration of the phytoplankton pigment chlorophyll a.

Measurements↗
NASA NTRS2014

Ocean Color Measurements from Landsat-8 OLI using SeaDAS

The Operational Land Imager (OLI) is a multi-spectral radiometer hosted on the recently launched Landsat-8 satellite. OLI includes a suite of relatively narrow spectral bands at 30-meter spatial resolution in the visible to shortwave infrared that make it a potential tool for ocean color radiometry: measurement of the reflected spectral radiance upwelling from beneath the ocean surface that carries information on the biogeochemical constituents of the upper ocean euphotic zone. To evaluate the potential of OLI to measure ocean color, processing support was implemented in SeaDAS, which is an open-source software package distributed by NASA for processing, analysis, and display of ocean remote sensing measurements from a variety of satellite-based multi-spectral radiometers. Here we describe the implementation of OLI processing capabilities within SeaDAS, including support for various methods of atmospheric correction to remove the effects of atmospheric scattering and absorption and retrieve the spectral remote-sensing reflectance (Rrs; sr exp 1). The quality of the retrieved Rrs imagery will be assessed, as will the derived water column constituents such as the concentration of the phytoplankton pigment chlorophyll a.

ocean color↗
NASA NTRS2023

Comparative Analysis of Empirical and Machine Learning Models for Chla Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges

Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial optical interferences of various water constituents and uncertainties in the atmospheric correction (AC) process. Although various algorithms have been developed to estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), the production of both accurate distribution maps and time series of Chla has proven challenging, limiting the use of remote analyses for lake monitoring. Here, we develop a support vector regression (SVR) model, which uses satellite-derived remote-sensing reflectance spectra () from Sentinel-2 and Landsat-8 images as input for Chla retrieval in a representative eutrophic prairie lake, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validated against in situ Chla from seven ice-free seasons (N ∼ 200; 2014–2020), the SVR model outperformed both locally tuned, -fed empirical models (Normalized Difference Chlorophyll Index, 2- and 3-band, and OC3) and Mixture Density Networks (MDNs) by 15–65%, while exhibiting comparable performance to a locally trained MDN, with an error of ∼35%. Comparison of Chla retrieval models, AC processors (iCOR, ACOLITE), and radiometric products (Rayleigh-corrected, surface, and top-of-atmosphere reflectance) showed that the best Chla maps and optimal time series (up to 100 mg m−3) were produced using a coupled SVR-iCOR system.

algal blooms↗
NASA NTRS1993

Retrieval of Surface Directional Reflectance and Hemispherical Albedo Using Multi-angle Measurements, I. Ground Level Observations

Knowledge of the directional reflectance properties of natural surfaces such as soils and vegetation canopies is essential for classification studies and canopy model inversions. Atmospheric correction schemes, using various levels of approximation, are described to retrieve surface bidirectional reflectance factors (BRFs) and directional-hemispherical reflectances (albedos) from multi-angle radiance measurements taken at ground level.

factors (BRFs) radiometric calibration sun-view an↗
NASA NTRS2010

Evaluating the Impact of Vegetation Cover and Atmospheric Characteristics on the Estimation of Snow Water Equivalent from Spaceborne Microwave Radiometry

A radiative transfer model for estimating snow water equivalent (SWE, mm) from satellite-observed brightness temperature (K) at 19 and 37 GHz (respectively, T(sub B(sub, sat,19)) and T(sub B(sub, sat,37)) over partially forested area is presented, as an extension of a previously published model, by considering scattering of radiation within the canopy. For the specific case of dense vegetation covering fractional area f, the model can be written as, SWE = alpha{ A. delta (T(sub B(sub, sat)) + B - C. f}/(l f), where delta T(sub B(sub, sat)), is the difference of T(sub B(sub, sat,19)) and T(sub B(sub, sat,37)), alpha(mm/K) is the slope of SWE vs. brightness temperature difference at 19 and 37 GHz that would be obtained by ignoring the presence of atmosphere, delta(T(sub B)sub g)), for a homogeneous snow cover (which varies with grain size). The parameters A, B, and C, are determined primarily by atmospheric characteristics, and for a likely range of atmospheric conditions appear to be in the range of, respectively, 1.15-1.63, 0.69-2.84 K and 0.59-2.39 K. Ignoring atmospheric correction would introduce bias towards underestimation of SWE (and also, snow cover area and snow depth). Increasing cloud liquid water path (L) has the effect of increasing A, and ignoring this variation of A with L would have the impact of biasing the estimate of SWE (and snow extent). Such biasing is further exacerbated with increasing f, because of the appearance of term (l-f) in the denominator. The impact of ignoring the intercept parameters (B and C) would be noticeable at low values of SWE (appearing as a bias towards underestimation of SWE), which has been determined to be about 6 mm for average environmental conditions. The uncertainty in estimating SWE due to variations in the atmospheric characteristics is likely to be less than 15%, but could be up to 25% for non-vegetated snow-covered areas. Better estimates of SWE (and snow extent) would be obtained by adjusting the parameters of the above model to regional differences in the atmospheric characteristics. The biases in determining SWE arising due to variations in atmospheric conditions and due to changes in fractional forest cover are not independent, since they interact as {A/(l-f)}. The present calculations also show that improvement in determining snow cover area from the microwave data is likely to occur when these data are corrected for atmospheric effects, as demonstrated by a specific case study.

Choudhury, Bhaskar J.↗
NASA NTRS2014

Early Evaluation of the VIIRS Calibration, Cloud Mask and Surface Reflectance Earth Data Records

Surface reflectance is one of the key products fromVIIRS and as withMODIS, is used in developing several higherorder land products. The VIIRS Surface Reflectance (SR) Intermediate Product (IP) is based on the heritageMODIS Collection 5 product (Vermote, El Saleous, & Justice, 2002). The quality and character of surface reflectance depend on the accuracy of the VIIRS Cloud Mask (VCM), the aerosol algorithms and the adequate calibration of the sensor. The focus of this paper is the early evaluation of the VIIRS SR product in the context of the maturity of the operational processing system, the Interface Data Processing System (IDPS). After a brief introduction, the paper presents the calibration performance and the role of the surface reflectance in calibration monitoring. The analysis of the performance of the cloud mask with a focus on vegetation monitoring (no snow conditions) shows typical problems over bright surfaces and high elevation sites. Also discussed is the performance of the aerosol input used in the atmospheric correction and in particular the artifacts generated by the use of the Navy Aerosol Analysis and Prediction System. Early quantitative results of the performance of the SR product over the AERONET sites showthatwith the fewadjustments recommended, the accuracy iswithin the threshold specifications. The analysis of the adequacy of the SR product (Land PEATE adjusted version) in applications of societal benefits is then presented. We conclude with a set of recommendations to ensure consistency and continuity of the JPSS mission with the MODIS Land Climate Data Record.

Surface reflectance↗
NASA NTRS2014

Detecting Trend and Seasonal Changes in Bathymetry Derived from HICO Imagery: A Case Study of Shark Bay, Western Australia

The Hyperspectral Imager for the Coastal Ocean (HICO) aboard the International Space Station has offered for the first time a dedicated space-borne hyperspectral sensor specifically designed for remote sensing of the coastal environment. However, several processing steps are required to convert calibrated top-of-atmosphere radiances to the desired geophysical parameter(s). These steps add various amounts of uncertainty that can cumulatively render the geophysical parameter imprecise and potentially unusable if the objective is to analyze trends and/or seasonal variability. This research presented here has focused on: (1) atmospheric correction of HICO imagery; (2) retrieval of bathymetry using an improved implementation of a shallowwater inversion algorithm; (3) propagation of uncertainty due to environmental noise through the bathymetry retrieval process; (4) issues relating to consistent geo-location of HICO imagery necessary for time series analysis, and; (5) tide height corrections of the retrieved bathymetric dataset. The underlying question of whether a temporal change in depth is detectable above uncertainty is also addressed. To this end, nine HICO images spanning November 2011 to August 2012, over the Shark Bay World Heritage Area, Western Australia, were examined. The results presented indicate that precision of the bathymetric retrievals is dependent on the shallow water inversion algorithm used.Within this study, an average of 70% of pixels for the entire HICO-derived bathymetry dataset achieved a relative uncertainty of less than +/-20%. A per-pixel t-test analysis between derived bathymetry images at successive timestamps revealed observable changes in depth to as lowas 0.4 m. However, the present geolocation accuracy of HICO is relatively poor and needs further improvements before extensive time series analysis can be performed.

Geolocations accuracy↗
NASA NTRS2024

Spectral Correlation in MODIS Water-Leaving Reflectance Retrieval Uncertainty

Spectral remote sensing reflectance, Rrs(λ) (sr−1), is the fundamental quantity used to derive a host of bio-optical and biogeochemical properties of the water column from satellite ocean color measurements. Estimation of uncertainty in those derived geophysical products is therefore dependent on knowledge of the uncertainty in satellite-retrieved R rs . Furthermore, since the associated algorithms require R rs at multiple spectral bands, the spectral (i.e., band-to-band)error covariance in R rs is needed to accurately estimate the uncertainty in those derived properties. This study establishes a derivative-based approach for propagating instrument random noise, instrument systematic uncertainty, and forward model uncertainty into R rs as retrieved using NASA’s multiple-scattering epsilon (MSEPS) atmospheric correction algorithm, to generate pixel-level error covariance in R rs . The approach is applied to measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite and verified using Monte Carlo (MC) analysis. We also make use of this full spectral error covariance in R rs to calculate uncertainty in phytoplankton pigment chlorophyll-a concentration (chl a , mg/m 3 ) and diffuse attenuation coefficient of downwelling irradiance at 490 nm (K d (490), m -1 ). Accounting for the error covariance in R rs generally reduces the estimated relative uncertainty in chl a by ∼1-2% (absolute value) in waters with chl a < 0.25 mg/m 3 where the color index (CI) algorithm is used. The reduction is ∼5-10% in waters with chl a > 0.35 mg/m 3 where the blue-green ratio (OCX) algorithm is used. Such reduction can be higher than 30% in some regions. For K d (490), the reduction by error covariance is generally ∼2%, but can be higher than 20% in some regions. The error covariance in R rs is further verified through forward-calculating chl a from MODIS-retrieved and in situ R rs and comparing estimated uncertainty with observed differences. An 8-day global composite of propagated uncertainty shows that the goal of 35% uncertainty in chl a can be achieved over deep ocean waters (chl a ≤ 0.1 mg/m3). While the derivative-based approach generates reasonable error covariance in R rs some assumptions should be updated as our knowledge improves. These include the inter-band error correlation in top-of-atmosphere reflectance, and uncertainties in the calibration of MODIS 869 nm band, in ancillary data, and in the in situ data used for system vicarious calibration.

Ocean color↗
NASA NTRS2022

Estimating pixel-level uncertainty in ocean color retrievals from MODIS

The spectral distribution of marine remote sensing reflectance, R(rs), is the fundamental measurement of ocean color science, from which a host of bio-optical and biogeochemical properties of the water column can be derived. Estimation of uncertainty in these derived properties is thus dependent on knowledge of the uncertainty in satellite-retrieved R(rs) (u(c)(R(rs))) at each pixel. Uncertainty in R(rs), in turn, is dependent on the propagation of various uncertainty sources through the R(rs) retrieval process, namely the atmospheric correction (AC). A derivative-based method for uncertainty propagation is established here to calculate the pixel-level uncertainty in R(rs), as retrieved using NASA’s multiple-scattering epsilon (MSEPS) AC algorithm and verified using Monte Carlo (MC) analysis. The approach is then applied to measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, with uncertainty sources including instrument random noise, instrument systematic uncertainty, and forward model uncertainty. The uc(Rrs) is verified by comparison with statistical analysis of coincident retrievals from MODIS and in situ Rrs measurements, and our approach performs well in most cases. Based on analysis of an example 8-day global products, we also show that relative uncertainty in R(rs) at blue bands has a similar spatial pattern to the derived concentration of the phytoplankton pigment chlorophyll-a (chl-a), and around 7.3%, 17.0%, and 35.2% of all clear water pixels (chl-a ≤ 0.1 mg/cu.m) with valid u(c)(R(rs)) have a relative uncertainty ≤ 5% at bands 412 nm, 443 nm, and 488 nm respectively, which is a common goal of ocean color retrievals for clear waters. While the analysis shows that u(c)(R(rs)) calculated from our derivative-based method is reasonable, some issues need further investigation, including improved knowledge of forward model uncertainty and systematic uncertainty in instrument calibration.

Pixel-level uncertainty↗
NASA NTRS1982

Atmospheric radiation model for water surfaces

An atmospheric correction model was extended to account for various atmospheric radiation components in remotely sensed data. Components such as the atmospheric path radiance which results from singly scattered sky radiation specularly reflected by the water surface are considered. A component which is referred to as the virtual Sun path radiance, i.e. the singly scattered path radiance which results from the solar radiation which is specularly reflected by the water surface is also considered. These atmospheric radiation components are coded into a computer program for the analysis of multispectral remote sensor data over the Great Lakes of the United States. The user must know certain parameters, such as the visibility or spectral optical thickness of the atmosphere and the geometry of the sensor with respect to the Sun and the target elements under investigation.

Turner, R. E.↗
NASA NTRS1992

Optimization of the information content of multitemporal Landsat TM data sets for monitoring forest cover disturbance

Procedures that were developed to optimize the information content of multitemporal thematic mapper (TM) data sets for forest cover disturbance monitoring in Minnesota are described. TM imagery from three different years was calibrated to exoatmospheric reflectance. An atmospheric correction routine was applied combining two major components, atmospheric normalization over time and transformation to ground reflectance. Atmospheric conditions were modeled over time using regression functions derived from five ground features known to be unchanged over the time interval of interest and spanning the entire image reflectance range. The correlation between digital data and the forest cover was subsequently maximized and irrelevant information content was reduced by converting the band-specific reflectances into seven vegetation indices that were assumed to carry unique information. The application of two change detection algorithms to these seven indices ultimately resulted in 14 change features for each time interval of interest. Results show that the preprocessing sequence is vital to forest cover monitoring methodology.

Coppin, Pol R.↗
NASA NTRS2004

New Measurements of Aerosol Vertical Structure from Space using the NASA Geoscience Laser Altimeter System (GLAS): Applications for Aerosol Transport Models

In the past, satellite measurements of aerosols have only been possible using passive sensors. Analysis of passive satellite data has lead to an improved understanding of aerosol properties, spatial distribution, and their effect on the earth,s climate. However, direct measurement of aerosol vertical distribution has not been possible using only the passive data. Knowledge of aerosol vertical distribution is important to correctly assess the impact of aerosol absorption, for certain atmospheric correction procedures, and to help constrain height profiles in aerosol transport models. On January 12,2003 NASA launched the first satellite-based lidar, the Geoscience Laser Altimeter System (GLAS), onboard the ICESat spacecraft. GLAS is both an altimeter and an atmospheric lidar, and obtains direct measurements of aerosol and cloud heights. Here we show an overview of GLAS, provide an update of its current status, and discuss how GLAS data will be useful for modeling efforts. In particular, a strategy of using GLAS to characterize the height profile of dust plumes over source regions will be presented, along with initial results. Such information can be used to validate and improve output from aerosol transport models. Aerosol height profile comparisons between GLAS and transport models will be shown for regions downwind of aerosol sources. We will also discuss the feasibility of assimilating GLAS profiles into the models in order to improve their output.

Welton, E. J.↗
NASA NTRS2003

New Measurements of Aerosol Vertical Structure from Space Using the NASA Geoscience Laser Altimeter System (GLAS): Applications for Aerosol Transport Models

In the past, satellite measurements of aerosols have only been possible using passive sensors. Analysis of passive satellite data has lead to an improved understanding of aerosol properties, spatial distribution, and their effect on the earth s climate. However, direct measurement of aerosol vertical distribution has not been possible using only the passive data. Knowledge of aerosol vertical distribution is important to correctly assess the impact of aerosol absorption, for certain atmospheric correction procedures, and to help constrain height profiles in aerosol transport models. On January 12,2003 NASA launched the first satellite-based lidar, the Geoscience Laser Altimeter System (GLAS), onboard the ICESat spacecraft. GLAS is both an altimeter and an atmospheric lidar, and obtains direct measurements of aerosol and cloud heights. Here we show an overview of GLAS, provide an update of its current status, and discuss how GUS data will be useful for modeling efforts. In particular, a strategy of using GLAS to characterize the height profile of dust plumes over source regions will be presented, along with initial results. Such information can be used to validate and improve output from aerosol transport models. Aerosol height profile comparisons between GLAS and transport models will be shown for regions downwind of aerosol sources. We will also discuss the feasibility of assimilating GLAS profiles into the models in order to improve their output,

Welton, Ellsworth J.↗
NASA NTRSDate not supplied

GeoNEX-ML: A Machine Learning System for Geostationary Satellite Imagery

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), Himawari-8/9 (JAXA), and GK-2A (Korea), we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary satellites↗
NASA NTRSDate not supplied

GeoNEX-ML: A Machine Learning System for Earth Observations

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary↗
NASA NTRSDate not supplied

Atmospheric winds with deep optical flow

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Atmospheric winds↗