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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Learning spatial response functions from large multi-sensor AIRS and MODIS datasets

We use large datasets from the Atmospheric Infrared Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive AIRS spatial response functions and study their potential variations over the mission. The new reconstructed spatial response functions can be used to reduce errors in the radiances in non-uniform scenes and improve products generated using both AIRS and MODIS data. AIRS spatial response functions are distinct for each of its 2378 channels and each of its 90 scan angles. We develop the mathematical model and the optimization framework for deriving spatial response functions for two AIRS channels with low water vapor absorption and various scan angles. We quantify uncertainties in the derived reconstructions and study how they differ from pre-flight spatial response functions. We show that our approach generates reconstructions that agree with the data more accurately compared to pre-flight spatial responses. We derive spatial response functions using data collected during successive dates in order to ascertain the repeatability of the reconstructed spatial response functions. We also compare the derived spatial response functions based on data collected in the beginning, the middle, and at the current state of the mission in order to study changes in reconstructions over time.

Vese, Luminita↗
NASA NTRS2023

The northernmost hyperspectral FLoX sensor dataset for monitoring of high-Arctic tundra vegetation phenology and Sun-Induced Fluorescence (SIF)

A hyperspectral field sensor (FloX) was installed in Adventdalen (Svalbard, Norway) in 2019 as part of the Svalbard Integrated Arctic Earth Observing System (SIOS) for monitoring vegetation phenology and Sun-Induced Chlorophyll Fluorescence (SIF) of high-Arctic tundra. This northernmost hyperspectral sensor is located within the footprint of a tower for long-term eddy covariance flux measurements and is an integral part of an automatic environmental monitoring system on Svalbard (AsMovEn), which is also a part of SIOS. One of the measurements that this hyperspectral instrument can capture is SIF, which serves as a proxy of gross primary production (GPP) and carbon flux rates. This paper presents an overview of the data collection and processing, and the 4-year (2019–2021) datasets in processed format are available at: https://thredds.met.no/thredds/catalog/arcticdata/infranor/NINA-FLOX/raw/catalog.html associated with https://doi.org/10.21343/ZDM7-JD72 under a CC-BY-4.0 license. Results obtained from the first three years in operation showed interannual variation in SIF and other spectral vegetation indices including MERIS Terrestrial Chlorophyll Index (MTCI), EVI and NDVI. Synergistic uses of the measurements from this northernmost hyperspectral FLoX sensor, in conjunction with other monitoring systems, will advance our understanding of how tundra vegetation responds to changing climate and the resulting implications on carbon and energy balance.

hyperspectral field sensor↗
NASA NTRS2023

An Accelerated Life Testing Dataset for Lithium-Ion Batteries With Constant and Variable Loading Conditions

The dataset repository is organized into three main folders, each containing one group of life cycled battery packs. Within each folder individual battery packs own their dedicated csv file for continuous data logging, which are named with their respective battery pack number. The folders are named: - regular_alt_batteries: Containing one csv file for each battery pack cycled at the same load level or load range throughout lifetime - recommissioned_batteries: Containing one csv file for each battery pack cycled at different load levels at varying life stages - second_life_batteries: Containing one csv file for each second life battery pack cycled at constant current througout the second life

Li-ion Battery↗
NASA NTRS2023

A Global Land Cover Training Dataset From 1984 to 2020

State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.

Radost Stanimirova↗
NASA NTRSDate not supplied

Neural Network Atmospheric Correction of Remote Sensing Imagery Over Water Using a Synthetic Dataset

Remote sensing atmospheric correction methods have primarily focused on imagery over land. However, accurate correction over water is important for monitoring and research of aquatic environments. More research in this area is ongoing, though one of the biggest challenges is enough quality data to develop and validate correction methods. This is especially true for neural network (NN) -based models which have shown promise in this area given enough quality data. To address this deficiency of data, we are leveraging a synthetic dataset produced by a model called SWIPE that uses radiative transfer modeling to simulate the atmospheric effects on water-leaving (WL) reflectance to estimate top-of-atmosphere (TOA) reflectance. This allows us to produce almost unlimited pairs of WL reflectance and corresponding TOA reflectance for model training across a variety of atmospheric conditions. We use two approaches for our atmospheric correction model. One uses a conditional variational autoencoder (VAE) to estimate a single WL reflectance value from a single TOA reflectance value. The second is based on a UNET architecture and estimates an array of WL reflectance values from an array of TOA reflectance values. The goal of the second method is to capture atmospheric effects that occur spatially between values within the array as compared to the first method.

deep learning↗
NASA NTRSDate not supplied

Homogenized Ground-Based and Profile Ozone Datasets From the TOAR-II/HEGIFTOM Project: Methods and Station Trends

Within the framework of the second phase of the Tropospheric Ozone Assessment Report (TOAR-II), it was recognized that an essential first step for deriving accurate trends from the ground-based networks that monitor ozone in the free troposphere is putting the measurements on the same basis with respect to absolute references and processing methods. The relevant procedures are referred to as “harmonization” or “homogenization”. The TOAR II working group, “HEGIFTOM” (Harmonization and Evaluation of Ground-based Instruments for Free-Tropospheric Ozone Measurements), has carried out harmonization for five types of network (Figure below) instruments (mid-1990s to 2020): ozonesondes, commercial aircraft IAGOS landing/takeoff profiles, Fourier-Transform Infrared spectrometer (FTIR), tropospheric Lidar, and Brewer/Dobson Umkehr. First, we summarize the homogenization effort for each network that provides new quality-assessed ozone profile or segment (partial column) data sets, including uncertainty estimates and quality flags. Second, with the HEGIFTOM datasets forming the basis for a global assessment of tropospheric ozone column trends, results derived with various trend detection algorithms will be presented. The ultimate goal is evaluation of the consistency of the calculated trends among different techniques at selected stations and/or regions.

ozone↗
NASA NTRSDate not supplied

Geophysical Retrievals and Cloud Analyses from Merged Airborne Radiometer Datasets Covering 10–684 GHz

Airborne microwave radiometers provide insight about numerous aspects of Earth’s atmosphere and yield critical validation datasets for spaceborne radiometers. Three radiometers that are important to NASA’s airborne remote-sensing arsenal include: the Advanced Microwave Precipitation Radiometer (AMPR), covering 10–85 GHz; the Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR), covering 50–183 GHz; and the Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR), covering 170–684 GHz. The NASA field campaigns of interest to this study include: the Integrated Precipitation and Hydrology Experiment (IPHEx) in 2014, the Olympic Mountains Experiment and Radar Definition Experiment (OLYMPEX/RADEX) in 2015–2016, the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) in 2020–2023, and the Airborne Lightning Observatory for FEGS and TGFs (ALOFT) in 2023. To provide a more comprehensive perspective on clouds and precipitation observed during these airborne field campaigns, AMPR data were merged spatiotemporally with CoSMIR data for IPHEx, OLYMPEX/RADEX, and IMPACTS (2020 and 2022), while considering differences in instrument characteristics and operations, providing brightness temperature (Tb) values from 10–183 GHz in a common background grid throughout each flight. AMPR and CoSSIR data were similarly merged for IMPACTS (2023) and ALOFT, providing a common background grid with Tb values covering 10–684 GHz throughout each flight. These merged Tb data were employed in geophysical retrievals using the Community Radiative Transfer Model (CRTM), an Eddington radiative transfer model, and a one-dimensional variational (1DVAR) inversion method. Retrievals of cloud liquid water path were of primary interest. This presentation will include an overview of the methods for the radiometer data mergers, the radiative transfer methods, the geophysical retrievals, and detailed results from examining trends in Tb and cloud liquid water path in clouds, precipitation, and cloud-to-precipitation transition zones.

Corey G Amiot↗
NASA NTRSDate not supplied

Computer Vision Dataset for Aircraft Taxi Operations

The development and democratization of computer vision algorithms are contingent on the availability of high-quality datasets. In this paper, we introduce a database of forward-facing videos from taxiing aircraft as well as the time-correlated flight data at approximately 1 to10 Hz containing aircraft state data and environmental conditions. The video data is sourced from the National Aeronautics and Space Administration Airborne Science Program archive and includes over 33 hours of 4k, 1080p, and 720p video from twenty-two airports around the world. This paper describes the method of the database construction and a brief analysis of its contents.

Ryan Horn↗