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104 results for “earth observation”
Observations of Earth Quasi-Satellite (469219) Kamo`oalewa
<p>Companion dataset to Sharkey et al. (2021).</p>
Empirically estimated electron lifetimes in the Earth's radiation belts: 1. Observations
Open the record for dataset details and reuse information.
GHRSST Level 3U Global Subskin Sea Surface Temperature from the Advanced Scanning Microwave Radiometer - Earth Observing System (AMSR-E) on the NASA Aqua Satellite
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on 4 May 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields. Remote Sensing Systems (RSS, or REMSS) is the provider of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR-E instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by "_rt_" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. "Final" data (currently identified by "v7" within the file name) are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.
GHRSST Level 2P Global Subskin Sea Surface Temperature from the Advanced Scanning Microwave Radiometer - Earth Observing System (AMSR-E) on the NASA Aqua Satellite
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on 4 May 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields. Remote Sensing Systems (RSS, or REMSS) is the provider of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR-E instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by "_rt_" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. "Final" data (currently identified by "v7" within the file name) are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.
Generated datasets for Yue et al. (2020, Earth and Space Science): "Combining In-situ and Satellite Observations to Understand the Vertical Structure of Tropical Anvil Cloud Microphysical Properties During the TC4 Experiment"
<p>This archive contains the data sets generated from the research conducted by Yue et al. (2020) titled "Combining In-situ and Satellite Observations to Understand the Vertical Structure of Tropical Anvil Cloud Microphysical Properties During the TC4 Experiment" published in Earth and Space Science. The method to generated the following data sets is described in Yue et al. (2020) and stored as Matlab .mat files.</p> <p>CombiningTC4_Satellite_eof_cov_mat.mat contains the correlation matrix shown in Figure 1a.</p> <p>TC4_processed.mat contains the correlation matrix shown in Figure 1b.</p> <p>RO_processed.mat contains the correlation matrix shown in Figure 2a.</p> <p>RVOD_processed.mat contains the correlation matrix shown in Figure 2b.</p> <p>ICE_processed.mat contains the correlation matrix shown in Figure 2c.</p> <p> </p> <p> </p>
Observational data for "A New Lake Classification System based on Thermal Profiles to Better Understand the Most Dominant Lake Type on Earth"
<p>Previously unpublished continuous water temperature data used in the study "A New Lake Classification System based on Thermal Profiles to Better Understand the Most Dominant Lake Type on Earth". Table S1 links to previously published data in other manuscripts.</p>
Committee on Earth Observation Satellites (CEOS) International Directory Portal
The CEOS IDN is an international effort developed to assist researchers in locating information on available datasets and services. The directory is sponsored as a service to the Earth science community.
NASA Earth Observations (NEO)
Our mission is to help you picture climate change and environmental changes happening on our home planet. Here you can search for and retrieve satellite images of Earth. Download them; export them to GoogleEarth; perform basic analysis. Tracking regional and global changes around the world just got easier.
EO-1 (Earth Observing-1) Advanced Land Imager (ALI) Instrument Level 1R, Level 1Gs, Level 1Gst Data
Advanced Land Imager (ALI) provides image data from ten spectral bands (band designations). The instrument operates in a pushbroom fashion, with a spatial resolution of 30 meters for the multispectral bands and 10 meters for the panchromatic band. The standard scene width is 37 kilometers. Standard scene length is 42 kilometers, with an optional increased scene length of 185 kilometers (additional information). For Advanced Land Imager (ALI) data, the following levels of correction are available: Level 1R radiometrically corrected with no geometric correction applied. The image data are provided in 16-bit radiance values. The data are available in Hierarchical Data Format (HDF) and are distributed on CD-ROM, DVD, and File Transfer Protocol (FTP). Level 1Gs is geometrically corrected and will be provided as a single "stitched" file. The image data are provided in 16-bit radiance values. The data are available in Hierarchical Data Format (HDF) or Geographic Tagged Image-File Format (GeoTIFF) and are distributed on DVD and File Transfer Protocol (FTP). Level 1Gst is terrain corrected and will be provided as a single "stitched" file. The image data are provided in 16-bit radiance values. The data are available in Hierarchical Data Format (HDF) or Geographic Tagged Image-File Format (GeoTIFF) and are distributed on DVD and File Transfer Protocol (FTP). [Source: USGS/EDC Homepage]
Yale Center for Earth Observation (YCEO) Surface Urban Heat Islands, Version 4, 2003-2018
The Yale Center for Earth Observation (YCEO) Surface Urban Heat Islands, Version 4, 2003-2018 includes annual, summertime, and wintertime Surface Urban Heat Island (SUHI) intensities for daytime and nighttime for over 10,000 global urban extents. This global SUHI data set was created using the Simplified Urban-Extent (SUE) algorithm and is available at the pixel and urban cluster-levels (i.e. at the level of larger urban agglomerations). Monthly composites are also available as urban cluster means. A summary of older versions, including changes from the data set created and analyzed in the originally published manuscript (Chakraborty and Lee, 2019) can be found on the YCEO Global Surface UHI Explorer website (https://yceo.yale.edu/research/global-surface-uhi-explorer).
Earth Radiation Budget through Earth Radiation Budget Satellite Wide-field-of-view Nonscanner Observations Edition
Understanding the mean and variability of the Earth’s radiation budget (ERB) at the Top-of-Atmosphere (TOA) and surface is a fundamental quantity governing climate variability and, for that reason, NASA has been making concerted efforts to observe the ERB since1984 through two projects: ERBE and CERES, that span nearly 30 years to date.The proposed project utilizes knowledge gained in the last 10 years through CERES data analyses and apply the knowledge to existing data to develop long-term (nearly 30 years) consistent and calibrated data product (TOA irradiances at the same radiometric scale) from multiple missions (ERBS and CERES). This project proposes to produce level 3 surface irradiance products that are consistent with observed TOA irradiances in a framework of 1D radiative transfer theory. Based on these TOA and surface irradiance products, a data product will be developed which contains the contribution of atmospheric and cloud property variability to TOA and surface irradiance variability. All algorithms used in the process are based on existing CERES algorithms. All data sets produced by this project will be available from the Atmospheric Science Data Center.
Earth Radiation Budget through Earth Radiation Budget Satellite Wide-field-of-view Nonscanner Observations Edition 4.1
ERBE_S10N_WFOV_SF_ERBS_Regional is the Earth Radiation Budget Experiment (ERBE) through Earth Radiation Budget Satellite (ERBS) Wide-field-of-view Nonscanner Observations Edition 4.1 data product. Understanding the mean and variability of the Earth's radiation budget (ERB) at the Top-of-Atmosphere (TOA) and surface is a fundamental quantity governing climate variability and, for that reason, NASA has been making concerted efforts to observe the ERB since1984 through two projects: ERBE and Clouds and the Earth's Radiant Energy System (CERES), that span nearly 30 years to date.The ERBE MEaSUREs project uses knowledge gained in the last 10 years through CERES data analyses and applies the knowledge to existing data to develop long-term (nearly 30 years) consistent and calibrated data product (TOA irradiances at the same radiometric scale) from multiple missions (ERBS and CERES). This project proposes to produce level 3 surface irradiance products that are consistent with observed TOA irradiances in a framework of 1D radiative transfer theory. Based on these TOA and surface irradiance products, a data product will be developed which contains the contribution of atmospheric and cloud property variability to TOA and surface irradiance variability. All algorithms used in the process are based on existing CERES algorithms. All data sets produced by this project will be available from the Atmospheric Science Data Center.
Retrospective analysis of anthropogenic change in Midwest reservoirs: Integrating earth observing data with statewide reservoir monitoring programs
The dataset comprises in-situ hyperspectral data acquired using the on-water approach (aka skylight-blocked approach), using a combination of a downwelling irradiance sensor and an upwelling radiance sensor. These sensors are specifically TriOS RAMSES hyperspectral radiometers, each associated with two calibration files. The data collection was conducted across different reservoirs in the state of Missouri USA. This NASA-funded project directly addresses how Earth-observing satellite data can better inform critical links between the biogeochemical and optical properties of inland waters. It achieves this by using satellite imagery and in-situ measurements from two long-running water quality monitoring programs in the state of Missouri that annually record more than one thousand measurements of nitrogen, phosphorus, chlorophyll-a, Secchi depth, particulate organic and inorganic matter, and cyanotoxins across 100 reservoirs.
NASA Earth Observations (NEO)
Our mission is to help you picture climate change and environmental changes happening on our home planet. Here you can search for and retrieve satellite images of Earth. Download them; export them to GoogleEarth; perform basic analysis. Tracking regional and global changes around the world just got easier.
GEOSatDB: global civil earth observation satellite semantic database
<p>The new version at <a href="https://doi.org/10.57760/sciencedb.11805">https://doi.org/10.57760/sciencedb.11805</a></p> <p>GEOSatDB is a semantic representation of Earth observation satellites and sensors that can be used to easily discover available Earth observation resources for specific research objectives.</p> <p><strong>Relevant Papers</strong></p> <p>Ming Lin, Meng Jin, Juanzi Li & Yuqi Bai (2024) GEOSatDB: global civil earth observation satellite semantic database, Big Earth Data, DOI: <a href="https://doi.org/10.1080/20964471.2024.2331992">10.1080/20964471.2024.2331992</a></p> <p><strong>Background</strong></p> <p>The widespread availability of coordinated and publicly accessible Earth observation (EO) data empowers decision-makers worldwide to comprehend global challenges and develop more effective policies. Space-based satellite remote sensing, which serves as the primary tool for EO, provides essential information about the Earth and its environment by measuring various geophysical variables. This contributes significantly to our understanding of the fundamental Earth system and the impact of human activities.</p> <p>Over the past few decades, many countries and organizations have markedly improved their regional and global EO capabilities by deploying a variety of advanced remote sensing satellites. The rapid growth of EO satellites and advances in on-board sensors have significantly enhanced remote sensing data quality by expanding spectral bands and increasing spatio-temporal resolutions. However, users face challenges in accessing available EO resources, which are often maintained independently by various nations, organizations, or companies. As a result, a substantial portion of archived EO satellite resources remains underutilized. Enhancing the discoverability of EO satellites and sensors can effectively utilize the vast amount of EO resources that continue to accumulate at a rapid pace, thereby better supporting data for global change research.</p> <p><strong>Methodology</strong></p> <p>This study introduces GEOSatDB, a comprehensive semantic database specifically tailored for civil Earth observation satellites. The foundation of the database is an ontology model conforming to standards set by the International Organization for Standardization (ISO) and the World Wide Web Consortium (W3C). This conformity enables data integration and promotes the reuse of accumulated knowledge. Our approach advocates a novel method for integrating Earth observation satellite information from diverse sources. It notably incorporates a structured prompt strategy utilizing a large language model to derive detailed sensor information from vast volumes of unstructured text.</p> <p><strong>Dataset Information</strong></p> <p>The downloadable files in RDF Turtle format are located in the data directory and contain a total of 130,134 statements:</p> <p>- GEOSatDB_ontology.ttl: Ontology modeling of concepts, relations, and properties.</p> <p>- satellite.ttl: 2,365 Earth observation satellites and their associated entities.</p> <p>- sensor.ttl: 1,021 Earth observation sensors and their associated entities.</p> <p>- sensor2satellite.ttl: relations between Earth observation satellites and sensors.</p> <p>In addition, a user-friendly portal is under development to facilitate easy access to GEOSatDB. The portal currently offers preliminary SPARQL query functionality, enabling the execution of SPARQL query examples.</p> <p>GEOSatDB undergoes quarterly updates, involving the addition of new satellites and sensors, revisions based on expert feedback, and the implementation of additional enhancements.</p>
Sentinel-3A OLCI Level-3B Global Binned Earth-observation Reduced Resolution (ERR) Remote-Sensing Reflectance (RRS) - Near Real-time (NRT) Data, version R2022.0
The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.
Sentinel-3A OLCI Level-3M Global Mapped Earth-observation Reduced Resolution (ERR) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) - Near Real-time (NRT) Data, version R2022.0
The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.
Sentinel-3B OLCI Level-2 Regional Earth-observation Full Resolution (EFR) Ocean Color (OC) - Near Realtime Data, version R2022.0
The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.
Sentinel-3B OLCI Earth-observation Reduced Resolution (ERR) Data, version 1
The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2° and a 0.6° overlap with its neighbors. The whole field of view is shifted across track by 12.6 degrees away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, referred to as Full Resolution(FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km.
Earth Resources Observation and Science (EROS) Center's Earth as Art Image Gallery
The Earth Resources Observation and Science (EROS) Center manages this collection of Landsat 7 scenes created for aesthetic purposes rather than scientific interpretation.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.