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At least 109 records · Page 6
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NASA NTRS2021

Scaling photosynthetic function and CO2 dynamics from leaf to canopy level for maize – dataset combining diurnal and seasonal measurements of vegetation fluorescence, reflectance and vegetation indices with canopy gross ecosystem productivity

Recent advances in leaf fluorescence measurements and canopy proximal remote sensing currently enable the non-destructive collection of rich diurnal and seasonal time series, which are required for monitoring vegetation function at the temporal and spatial scales relevant to the natural dynamics of photosynthesis. Remote sensing assessments of vegetation function have traditionally used actively excited foliar chlorophyll fluorescence measurements, canopy optical reflectance data and vegetation indices (VIs), and only recently passive solar induced chlorophyll fluorescence (SIF) measurements. In general, reflectance data are more sensitive to the seasonal variations in canopy chlorophyll content and foliar biomass, while fluorescence observations more closely relate to the dynamic changes in plant photosynthetic function. With this dataset we link leaf level actively excited chlorophyll fluorescence, canopy proximal reflectance and SIF, with eddy covariance measurements of gross ecosystem productivity (GEP). The dataset was collected during the 2017 growing season on maize, using three automated systems (i.e., Monitoring Pulse-Amplitude-Modulation fluorimeter, Moni-PAM; Fluorescence Box, FloX; and from eddy covariance tower). The data were quality checked, filtered and collated to a common 30 minutes timestep. We derived vegetation indices related to canopy functioning (e.g., Photochemical Reflectance Index, PRI; Normalized Difference Vegetation Index, NDVI; Chlorophyll Red-edge, Clre) to investigate how SIF and VIs can be coupled for monitoring vegetation photosynthesis. The raw datasets and the filtered and collated data are provided to enable new processing and analyses.

Petya Campbell↗
NASA NTRS2021

AI4MARS: A Dataset for Terrain-Aware Autonomy on Mars

Deep learning has quickly become a necessity for selfdriving vehicles on Earth. In contrast, the self-driving vehicles on Mars, including NASA’s latest rover, Perseverance, which is planned to land on Mars in February 2021, are still driven by classical machine vision systems. Deep learning capabilities, such as semantic segmentation and object recognition, would substantially benefit the safety and productivity of ongoing and future missions to the red planet. To this end, we created the first large-scale dataset, AI4Mars, for training and validating terrain classification models for Mars, consisting of ~326K semantic segmentation full image labels on 35K images from Curiosity, Opportunity, and Spirit rovers, collected through crowdsourcing. Each image was labeled by ~10 people to ensure greater quality and agreement of the crowdsourced labels. It also includes ~1.5K validation labels annotated by the rover planners and scientists from NASA’s MSL (Mars Science Laboratory) mission, which operates the Curiosity rover, and MER (Mars Exploration Rovers) mission, which operated the Spirit and Opportunity rovers. We trained a DeepLabv3 model on the AI4Mars training dataset and achieved over 96% overall classification accuracy on the test set. The dataset is made publicly available.1

Ono, Hiro↗
NASA NTRS2022

Microstructure Segmentation With Deep Learning Encoders Pre-Trained on a Large Microscopy Dataset

This study examined the improvement of microscopy segmentation intersection over union accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures were trained on over 100,000 labeled microscopy images from 54 material classes. These pre-trained encoders were then embedded into multiple segmentation architectures including UNet and DeepLabV3+ to evaluate segmentation performance on created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2% reduction in relative intersection over union error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.

machine learning↗
NASA NTRSDate not supplied

Making Dataset Quality Information FAIR: Supporting Open-Source Science and Enhancing (Re)Use and Trustworthiness of Scientific Data

- Quality information should be documented and readily shared within and across domains. - Sharing of dataset quality information supports open science and trustworthiness of scientific data. - Dataset quality is more than data quality. - Quality tends to be domain-specific and context-dependent. - Community guidelines provide practical steps towards FAIR dataset quality information.

Ge Peng↗
NASA NTRS2022

Recommendations on Funding Mission Operations and Historical Datasets

The Heliophysics Low Cost Access to Space (H-LCAS) and Flight Opportunities in Research and Technology (H-FORT) grant budgets primarily fund the development and construction of instrumentation. A side effect of this approach is that mission operations, including data collection and data processing, tend to be severely under budget (or unfunded). In order to satisfy the requirements of modern missions, we recommend a new funding source for mission operations. This funding source is increasingly vital going forward as new missions collect exponentially more data compared to past missions. Similarly, to take full advantage of underutilized historical datasets, we recommend adding another funding source to analyze these valuable datasets. Without these funding sources, mission datasets will, in the best case, be significantly underdeveloped and underutilized, and more likely, will fall dramatically short of their required scope.

Mykhaylo Shumko↗
NASA NTRSDate not supplied

The Multiplatform Precipitation Feature (MPF) Database: Synthesizing Satellite and Ground-Based Precipitation and Lightning Datasets for Convective Studies

NASA’s Lightning Imaging Sensor (LIS) and the Global Precipitation Measurement (GPM) mission have contributed a wealth of data toward global lightning and precipitation studies, respectively. Combining lightning and precipitation datasets leverages their unique insights into deep convective processes that inform about characteristics of convection and its intensity. Recent efforts to synthesize the LIS and GPM datasets prepare the opportunity for unprecedented large-scale, value-added multiplatform analyses of convection. This data synthesis proof-of-concept study elaborates on the creation of a database of reflectivity-based multiplatform precipitation features (MPFs) that capture a combination of information extracted from spatiotemporally coincident lightning and precipitation data within individual storm features. The space-based GPM Dual-frequency Precipitation Radar (DPR) provides a record of precipitation data, while the GPM Validation Network (VN) additionally incorporates ground-based polarimetric Doppler radar data to provide microphysical and kinematic context to DPR data. The LIS instrument onboard the International Space Station has contributed lightning observations since 2017. MPFs encapsulating information from these datasets are created from isolated regions of filtered, smoothed DPR reflectivity data to which ellipses are fit. Each MPF includes feature location, size, and eccentricity information as well as summary reflectivity characteristics. They also include summaries of precipitation microphysics and derived three-dimensional wind available from ground-based radar data. LIS data provides standard lightning characteristics such as flash count and density to each MPF as well as other informative metrics such as flash area and radiance. Each MPF file includes information about the original data from which the MPF and its characteristics were determined, allowing end-user reconstruction of the ellipse and deeper “level I” analysis of captured data. This database of VN-LIS MPFs enables broad statistical analysis of the relationships between the microphysical, kinematic, and electrical properties of convection. Preliminary results from a demonstration of the database will be described as well as ongoing efforts and avenues for future work.

Lightning↗
NASA NTRSDate not supplied

Low Latency Flux and Concentration Datasets in Support of Greenhouse Gas Monitoring Based on NASA's GEOS Modeling and Data Assimilation System

We present efforts to develop space-based greenhouse gas monitoring systems that can provide low latency information and traceability to independent observations. Through support from its Carbon Monitoring System program, NASA has developed the capability to assimilate XCO2 retrievals from the Orbiting Carbon Observatory, 2 (OCO-2) into the Goddard Earth Observing System (GEOS) Constituent Data Assimilation System (CoDAS) to create gap-filled, three-dimensional (3D) estimates of CO2 mixing ratio. When OCO-2 data are not available, concentration fields are further informed by a bottom-up flux package based on remotely sensed fire radiative power, nighttime lights, and vegetation reflectance combined with estimates of atmospheric growth rate based on surface in situ data. The 3D nature of this dataset supports evaluation with independent aircraft data, helping to ensure transparency of remotely sensed data products. These quasi-operational data are currently produced 2-3 months behind real time and are distributed via NASA and international dashboard services to a variety of end users. In this presentation, we provide an overview of the system as well as remaining data gaps and modeling challenges. We also highlight the application of this dataset for detecting emissions anomalies associated with COVID-19 and comparing against independent emissions estimates. Finally, we highlight a new NASA initiative called the Earth Information System (EIS), which aims to support open science and applications by leveraging emerging cloud computing capabilities to increase access to NASA’s greenhouse gas datasets, opportunities for co-development, and transparency in methods for analysis and flux attribution.

Lesley Ott↗
NASA NTRSDate not supplied

Curating AI-Ready Datasets for Equity and Environmental Justice: A Data-Centric AI Case Study

An equitable and environmentally just community is essentialin order to avoid disproportionate burden borne by vulnerablecommunities. This need becomes pressing in the aftermathof an extreme event such as disaster or hazard when it is diffi-cult for the governing bodies to implement resource allocationas per the need. Artificial Intelligence (AI) algorithms canhelp surface Equity and Environmental Justice (EEJ) issueswhen trained on EEJ datasets. However, curating AI-readyEEJ training datasets is challenging due to differences in fac-tors such as heterogeneity, resolution, modality, and level ofexpertise in labeling. Additionally, EEJ issues involve sensi-tive information where uncertainties and errors could degradethe performance of AI algorithms. For eg. Error in seasonalcrop yield information can highly affect the prediction of an-nual crop yield. To address these challenges, Data-centricAI (DCAI) methods are employed, which enhance AI algo-rithm performance even with limited training samples. DCAIprioritizes data quality, thereby reducing the adverse effectsof uncertainties and errors during the model training process.This research proposes a novel dataset and benchmark for an-alyzing the effect of the Maui Wildfire of 2023 for Equityand Environmental Justice (EEJ) issues. The proposed datasetaligns with the concepts of DCAI such as annotation quality,data preprocessing, privacy, feature engineering, governanceand provenance. We firmly believe that the proposed datasetwould lay a foundation to implement robust and reliable mod-ern AI algorithms for addressing EEJ issues.

Paridhi Parajuli↗
NASA NTRS2015

Comparison of GFED3, QFED2 and FEER1 Biomass Burning Emissions Datasets in a Global Model

Biomass burning contributes about 40% of the global loading of carbonaceous aerosols, significantly affecting air quality and the climate system by modulating solar radiation and cloud properties. However, fire emissions are poorly constrained in models on global and regional levels. In this study, we investigate 3 global biomass burning emission datasets in NASA GEOS5, namely: (1) GFEDv3.1 (Global Fire Emissions Database version 3.1); (2) QFEDv2.4 (Quick Fire Emissions Dataset version 2.4); (3) FEERv1 (Fire Energetics and Emissions Research version 1.0). The simulated aerosol optical depth (AOD), absorption AOD (AAOD), angstrom exponent and surface concentrations of aerosol plumes dominated by fire emissions are evaluated and compared to MODIS, OMI, AERONET, and IMPROVE data over different regions. In general, the spatial patterns of biomass burning emissions from these inventories are similar, although the strength of the emissions can be noticeably different. The emissions estimates from QFED are generally larger than those of FEER, which are in turn larger than those of GFED. AOD simulated with all these 3 databases are lower than the corresponding observations in Southern Africa and South America, two of the major biomass burning regions in the world.

biomass burning↗
NASA NTRS2016

Outcomes of a NASA Workshop to Develop a Portfolio of Low Latency Datasets for Time-Sensitive Applications

It is widely accepted that time-sensitive remote sensing data serve the needs of decision makers in the applications communities and yet to date, a comprehensive portfolio of NASA low latency datasets has not been available. This paper will describe the NASA low latency, or Near-Real Time (NRT), portfolio, how it was developed and plans to make it available online through a portal that leverages the existing EOSDIS capabilities such as the Earthdata Search Client (https:search.earthdata.nasa.gov), the Common Metadata Repository (CMR) and the Global Imagery Browse Service (GIBS). This paper will report on the outcomes of a NASA Workshop to Develop a Portfolio of Low Latency Datasets for Time-Sensitive Applications (27-29 September 2016 at NASA Langley Research Center, Hampton VA). The paper will also summarize findings and recommendations from the meeting outlining perceived shortfalls and opportunities for low latency research and application science.

remote sensing↗
NASA NTRS1988

Interface between astrophysical datasets and distributed database management systems (DAVID)

This is a status report on the progress of the DAVID (Distributed Access View Integrated Database Management System) project being carried out at Louisiana State University, Baton Rouge, Louisiana. The objective is to implement an interface between Astrophysical datasets and DAVID. Discussed are design details and implementation specifics between DAVID and astrophysical datasets.

Iyengar, S. S.↗
NASA NTRS1993

Clementine: Anticipated scientific datasets from the Moon and Geographos

The Clementine spacecraft mission is designed to test the performance of new lightweight and low-power detectors developed at the Lawrence Livermore National Laboratory (LLNL) for the Strategic Defense Initiative Office (SDIO). A secondary objective of the mission is to acquire useful scientific data, principally of the Moon and the near-Earth asteroid Geographos. The spacecraft will be in an elliptical polar orbit about the Moon for about 2 months beginning in February of 1994 and it will fly by Geographos on August 31. Clementine will carry seven detectors each weighing less than about 1 kg: two Star Trackers wide-angle uv/vis wide-angle Short Wavelength IR (SWIR) Long-Wavelength IR (LWIR) and LIDAR (Laser Image Detection And Ranging) narrow-angle imaging and ranging. Additional presentations about the mission detectors and related science issues are in this volume. If fully successful Clementine will return about 3 million lunar images, a dataset with nearly as many bits of data (uncompressed) as the first cycle of Magellan and more than 5000 images of Geographos. The complete and efficient analysis of such large data sets requires systematic processing efforts. Described below are concepts for two such efforts for the Clementine mission: global multispectral imaging of the Moon and videos of the Geographos flyby. Other anticipated datasets for which systematic processing might be desirable include multispectral observations of Earth; LIDAR altimetry of the Moon with high-resolution imaging along each ground track; high-resolution LIDAR color along each lunar ground track which could be used to identify potential titanium-rich deposits at scales of a few meters; and thermal IR imaging along each lunar ground track (including nighttime observations near the poles).

Mcewen, A. S.↗
NASA NTRS1994

The resolution capability of an irregularly sampled dataset: With application to Geosat altimeter data

A formalism is presented for determining the wavenumber-frequency transfer function associated with an irregularly sampled multidimensional dataset. This transfer function reveals the filtering characteristics and aliasing patterns inherent in the sample design. In combination with information about the spectral characteristics of the signal, the transfer function can be used to quantify the spatial and temporal resolution capability of the dataset. Application of the method to idealized Geosat altimeter data (i.e., neglecting measurement errors and data dropouts) concludes that the Geosat orbit configuration is capable of resolving scales of about 3 deg in latitude and longitude by about 30 days.

Chelton, Dudley B.↗
NASA NTRS1999

New NOAA-15 Advanced Microwave Sounding Unit (AMSU) Datasets for Stratospheric Research

The NOAA-15 spacecraft launched in May 1998 carried the first Advanced Microwave Sounding Unit (AMSU). The AMSU has eleven oxygen absorption channels with weighting functions peaking from near the surface to 2 mb. Twice-daily, limb-corrected I degree gridded datasets of layer temperatures have been constructed since the AMSU went operational in early August 1998. Examples of AMSU imagery will be shown, as will preliminary analyses of daily fluctuations in tropical stratospheric temperatures and their relationship to daily variations in tropical-average rainfall measured by the Special Sensor Microwave Imager (SSM/I). The AMSU datasets are now available for other researchers to utilize.

Spencer, Roy W.↗
NASA NTRS1999

The Lunar Crustal Thickness from Analysis of the Lunar Prospector Gravity and Clementine Topography Datasets

The Lunar Prospector spacecraft has mapped the gravity field of the Moon to a level of resolution never achieved before, and a spherical harmonic representation to degree and order 100 is available. When combined with the topography dataset produced by the Clementine mission, the resulting Bouguer anomaly map is interpreted to model the thickness of the lunar crust. Such models are crucial to understanding the lunar thermal history and the formation of geological features such as mascon basins, several more of which have been newly discovered from this dataset. A two-layer planetary model was used to compute the variations of the depth to the lunar Moho. The thickness values ranged from near 0 to 120 km. There is significant agreement with previous work using the Clementine gravitational field data with differences in specific locations such as South Pole-Aitken Basin, for example.

Asmar, S.↗
NASA NTRS2000

Troposphere-Stratosphere Connections in Recent Northern Winters in NASA GEOS Assimilated Datasets

The northern winter stratosphere displays a wide range of interannual variability, much of which is believed to result from the response to the damping of upward-propagating waves. However, there is considerable (growing) evidence that the stratospheric state can also impact the tropospheric circulation. This issue will be examined using datasets generated in the Data Assimilation Office (DAO) at NASA's Goddard Space Flight Center. Just as the tropospheric circulation in each of these years was dominated by differing synoptic-scale structures, the stratospheric polar vortex also displayed different evolutions. The two extremes are the winter 1998/1999, when the stratosphere underwent a series of warming events (including two major warmings), and the winter 1999/2000, which was dominated by a persistent, cold polar vortex, often distorted by a dominant blocking pattern in the troposphere. This study will examine several operational and research-level versions of the DAO's systems. The 70-level-TRMM-system with a resolution of 2-by-2.5 degrees and the 48-level, 1-by-l-degree resolution ''Terra'' system were operational in 1998/1999 and 1999/2000, respectively. Research versions of the system used a 48-level, 2-by-2.5-degree configuration, which facilitates studies of the impact of vertical resolution. The study includes checks against independent datasets and error analyses, as well as the main issue of troposphere-stratosphere interactions.

Pawson, Steven↗
NASA NTRS2001

A 7.5-Year Dataset of SSM/I-Derived Surface Turbulent Fluxes Over Global Oceans

The surface turbulent fluxes of momentum, latent heat, and sensible heat over global oceans are essential to weather, climate and ocean problems. Wind stress is the major forcing for driving the oceanic circulation, while Evaporation is a key component of hydrological cycle and surface heat budget. We have produced a 7.5-year (July 1987-December 1994) dataset of daily, individual monthly-mean and climatological (1988-94) monthly-mean surface turbulent fluxes over the global oceans from measurements of the Special Sensor Microwave/Imager (SSM/I) on board the US Defense Meteorological Satellite Program F8, F10, and F11 satellites. It has a spatial resolution of 2.0x2.5 latitude-longitude. Daily turbulent fluxes are derived from daily data of SSM/I surface winds and specific humidity, National Centers for Environmental Prediction (NCEP) sea surface temperatures, and European Centre for Medium-Range Weather Forecasts (ECMWF) air-sea temperature differences, using a stability-dependent bulk scheme. The retrieved instantaneous surface air humidity (with a 25-km resolution) IS found to be generally accurate as compared to the collocated radiosonde observations over global oceans. The surface wind speed and specific humidity (latent heat flux) derived from the F10 SSM/I are found to be -encrally smaller (larger) than those retrieved from the F11 SSM/I. The F11 SSM/I appears to have slightly better retrieval accuracy for surface wind speed and humidity as compared to the F10 SSM/I. This difference may be due to the orbital drift of the F10 satellite. The daily wind stresses and latent heat fluxes retrieved from F10 and F11 SSM/Is show useful accuracy as verified against the research quality in si -neasurerrients (IMET buoy, RV Moana Wave, and RV Wecoma) in the western Pacific warm pool during the TOGA COARE Intensive observing period (November 1992-February 1993). The 1988-94 seasonal-mean turbulent fluxes and input variables derived from FS and F11 SSM/Is show reasonable patterns related to seasonal variations of atmospheric general circulation. This dataset of SSM/I-derived turbulent fluxes is useful for climate studies, forcing of ocean models, and validation of coupled ocean-atmosphere global models and can be accessed through the NASA/GSFC Distributed Active Archive Center.

Chou, Shu-Hsien↗
NASA NTRS2001

A 7.5-Year Dataset of SSM/I-Derived Surface Turbulent Fluxes Over Global Oceans

The global air-sea turbulent fluxes are needed for driving ocean models and validating coupled ocean-atmosphere global models. A method was developed to retrieve surface air humidity from the radiances measured by the Special Sensor Microwave/Imager (SSM/I) Using both SSM/I-retrieved surface wind and air humidity, they computed daily turbulent fluxes over global oceans with a stability-dependent bulk scheme. Based on this method, we have produced Version 1 of Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF) dataset from the SSM/I data and other data. It provides daily- and monthly-mean surface turbulent fluxes and some relevant parameters over global oceans for individual F8, F10, and F11 satellites covering the period July 1987-December 1994. It also provides 1988-94 annual- and monthly-mean climatologies of the same variables, using only F8 and F1 1 satellite data. It has a spatial resolution of 2.0 degrees x 2.5 degrees lat-long and is archived at the NASA/GSFC DAAC. The purpose of this paper is to present an updated assessment of the GSSTF 1.0 dataset.

Chou, Shu-Hsien↗