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104 results for “Earth Observation”
China Earth Observation Data Cube: The 30m Seamless Annual Leaf-On Landsat Composites from 1985 to 2024
<p>The <strong>30m seamless annual leaf-on Landsat composites from 1985 to 2024</strong> were generated using a comprehensive framework designed to ensure high-quality, consistent data across decades. Starting with preprocessed Level-2 surface reflectance images from multiple Landsat sensors, the dataset is restricted to the Leaf-On season, with rigorous cloud and shadow masking applied based on quality assessment bands. To maintain consistency across sensors, spectral harmonization is conducted, followed by annual composite generation using the medoid method to capture peak vegetation conditions. The resulting composites are structured into a spatially consistent data cube, facilitating efficient analysis and monitoring of vegetation dynamics over time.</p> <p>The band naming convention follows Landsat TM standards, with bands designated as <strong>Blue (B1), Green (B2), Red (B3), NIR (B4), SWIR1 (B5), and SWIR2 (B7)</strong>. Both qualitative and quantitative evaluations were conducted to validate the data quality. Here, we provide 2023 image data covering southwestern forest regions of China as a sample for testing. For access to the full dataset, please visit <strong>Google Earth Engine</strong> at <a href="https://code.earthengine.google.com/6d1ea26ff4463277840eaf6a2662763c">this link</a>, and <strong>Earth Engine App (<a target="_blank">Landsat Yearly Composite Viewer</a>)</strong> at <a href="https://ee-caiyt33-catcd.projects.earthengine.app/view/landsat-yearly-composite-viewer">this link</a>.</p> <p><strong><a target="_blank">The dataset has now been updated to include data up to 2024.</a></strong></p> <p><a target="_blank"><strong>Data citation:</strong> Cai, Y., Li, X., Zhu, P., Nie, S., Wang, C., Liu, X., & Chen, Y. (2025). China Earth Observation Data Cube: The 30m Seamless Annual Leaf-On Landsat Composites from 1985 to 2023. <em>Journal of Remote Sensing</em>. </a><a href="https://doi.org/10.34133/remotesensing.0698">DOI: 10.34133/remotesensing.0698</a></p> <p>For data-related inquiries, please contact Dr. Yaotong Cai at <a href="mailto:caiyt33@mail2.sysu.edu.cn">caiyt33@mail2.sysu.edu.cn</a>.</p>
Global and Sequential Imaging Observation of the Earth's Plasmasphere by PHOENIX Onboard EQUULEUS
<p>A dataset used in "<span>Global and Sequential Imaging Observation of the Earth’s Plasmasphere by PHOENIX Onboard EQUULEUS</span>"</p>
Data for NHESS manuscript by Biass et al. (2022): Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data
<p>This repository contains the data produced in the context of the following paper:</p> <blockquote> <p>Biass S, Jenkins SF, Aberhard WH, Delmelle P, Wilson T (2022): Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data, Accepted in NHESS</p> </blockquote> <p>Naming convention is: `run_date`_`landcover`_`impact_metrics`_`VI`_`anomaly`_test.pkl, where:</p> <ul> <li>Landcover is either crops, shrubs, herbaceous vegetation (grass), forests (trees) or all together</li> <li>Impact metrics is either minV (impact magnitude) or minT (impact duration)</li> <li>VI is the vegetation index (here, EVI)</li> <li>Anomaly is the impact indicator (here, cumulative difference index)</li> </ul> <p>Refer to the associated paper for more information on the methodology.</p> <p>Files are saved as .pkl and were generated by the <a href="https://explainerdashboard.readthedocs.io/en/latest/">explainerdashboard</a> library. They are the result of <a href="https://xgboost.readthedocs.io/en/stable/">XGBoost</a> runs that were optimised with <a href="https://optuna.org">Optuna</a> and analysed with the <a href="https://shap.readthedocs.io/en/latest/">SHAP</a> library. They contain:</p> <ol> <li>The explanatory variables and observed and computed target variables for all features</li> <li>The SHAP values</li> </ol> <p>To load the files, use <a href="https://explainerdashboard.readthedocs.io/en/latest/cli.html?highlight=load#explainerdashboard.explainers.BaseExplainer.from_file">this method</a>.</p> <p> </p>
How to assess similarities and differences between mantle circulation models and Earth using disparate independent observations: Data and Analysis
<p>Dataset includes simulation output produced by a TERRA simulation for `How to assess similarities and differences between mantle circulation models and Earth using disparate independent observations'. </p> <p> </p> <h3><strong>Description of data file contents</strong></h3> <ul> <li><strong>NC*comp.tar.gz</strong> - compressed archives containing NetCDF files (file-per-process) with TERRA grid data including temperature, velocity, interpolated bulk composition, denisty, and voscosity fields. Can be read using <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>. *dump number</li> <li><strong>NC_seis_037.tar.gz</strong> - compressed archive containing NetCDF files (file-per-process) with predicted seismic properties at the resolution of the TERRA grid generated from the present day state of the simulated mantle, including elastic and anelastic Vs and Vp, bulk sound velocity, and predicted density from mineral phyiscs tables. Can be read using <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>.</li> <li><strong>NC_hpes_037.tar.gz</strong> - compressed archive containing NetCDF files (file-per-process) with interpolated abundances at the resolution of the TERRA grid for isotopes including the heat-producing elements ^40^K, ^232^Th, ^235^U and ^238^U. Can be read using <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>.</li> <li><strong>P_files_037.tar.gz</strong> - compressed archive of TERRA P-files (particle files).</li> <li><strong>C_files_037.tar.gz</strong> - compressed archive of TERRA C-files (grid state files) - together with the P-files describe the full present day state of the simulation. </li> <li><strong>seis_filtered_037.tar.gz</strong> - compressed archive (file-per-layer) with reparameterised and seismically filtered (against S40RTS) present day Vs field.</li> <li><strong>seis_tables.tar.gz</strong> - compressed archive contianing lookup tables of seismic properties for the 3 principal lithologies assumed in the TERRA simulation (harzburgite, lherzolite and basaltic crust).</li> <li><strong>density_037.sph</strong> - Spherical harmonic coefficients for the density field in format to be read by the <a title="propagator" href="https://zenodo.org/records/12696774" target="_blank" rel="noopener">propagator matrix code.</a></li> <li><strong>plumes.pkl, ridges.pkl</strong> - Files containing tracer particle information for particles associated with plumes and ridges. </li> <li><strong>plumes_ridges.py</strong> - Python script containing example code for reading and plotting plumes.pkl and ridges.pkl files. </li> <li><strong>ptcls_rdgs_plms.py, interrogate_particles.py</strong> - Python script and module containing required functions for carrying out post processing routine generating the plumes.pkl and ridges.pkl files. Requires <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>.</li> <li><strong>hst.dat </strong>- Time series of key simulation properties including mantle temperature profile used for calcualting CMB heat flux. </li> <li><strong>terra, interra</strong> - TERRA executable and input parameter file.</li> <li><strong>pyflowng.zip</strong> - Compressed directory containing version of the `pyflowng` code used in this work.</li> <li><strong>mode_splitting_methods.zip</strong> - Compressed directory contianing synthetic splitting function predictions and maps.</li> </ul> <p> </p> <h3><strong>Dump Numbers</strong></h3> <p>Below is a table of dump numbers (final three digits of file names) and the corresponding model times.</p> <table> <tbody> <tr> <td><strong>Dump number </strong></td> <td><strong>Model time (Ma)</strong></td> </tr> <tr> <td>037</td> <td>0 (present day)</td> </tr> <tr> <td>027</td> <td>10</td> </tr> <tr> <td>026</td> <td>20</td> </tr> <tr> <td>025</td> <td>30</td> </tr> <tr> <td>024</td> <td>40</td> </tr> <tr> <td>023</td> <td>50</td> </tr> <tr> <td>022</td> <td>60</td> </tr> <tr> <td>021</td> <td>70 </td> </tr> <tr> <td>020</td> <td>80</td> </tr> <tr> <td>019</td> <td>90</td> </tr> <tr> <td>018</td> <td>100</td> </tr> </tbody> </table>
Data for "Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)"
<p>This tar file contains the data produced for the manuscript "Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)". The contents include model data from the simulation presented in the paper, a four-year benchmark simulation using version 10.23.0 of GEOS with GOCART2G, spanning the period of 2016 through 2019. Aerosol mass budget terms including emissions, production, and deposition are included as well as the model data sampled according to the observational data sets for MODIS, AERONET, OMPS-LP, CALIOP, IMPROVE, and EMEP.</p>
EOMORES earth observation and in situ data of water quality in lakes and coastal areas - year 2 and 3
<p>EOMORES is a European innovation project aiming to develop commercial services for monitoring the quality of inland and coastal water bodies, using data from Earth Observation (EO) satellites and in situ sensors to measure, model and forecast water quality parameters.</p> <p>The current data set is a sample of the data generated within the second and third project years (2018, 2019), and consists of Earth Observation (EO) data and in situ data from lakes and coastal areas. For full data sets, please contact the respective contact point listed for each area. Data sets of the first (2017) year of EOMORES have also be submitted.</p> <p>The following is included:<br> - Estonia lakes and coast: in situ data 2019<br> - Finland: links to repositories of EO data<br> - Italy CNR: sample of EO data 2018 and 2019<br> - Italy Trasimeno: sample of WISPstation in situ spectral data and water quality parameters data<br> - Lithuania Curonian Lagoon: sample of WISPstation in situ spectral data and satellite based water quality maps<br> - Netherlands Lake Markermeer: sample of EO data 2018 and 2019 (Chl, Secchi, SPM)<br> - Netherlands Lake Paterswoldsemeer: EO data 2015, 2016, 2017<br> - UK Scotland: links to relevant repositories of in situ data</p> <p>http://eomores-h2020.eu</p>
Equatorial pitch angle distributions of 1 – 50 keV electrons in Earth's inner magnetosphere: an empirical model based on the Van Allen Probes observations
<p>This dataset contains the parameters of the pitch angle distribution model of 1 – 50 keV electrons, constructed using data from the HOPE instrument on the Van Allen Probes A using method described in the manuscript, along with the example IDL programs to read in model coefficients and plot out the model PADs.</p>
Experimental observation of Earth's rotation with quantum entanglement
<p>Raw data of the article titled "Experimental Observation of Earth’s Rotation with Quantum Entanglement".</p>
IECDT AI for Earth Observation
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Seismological observation of Earth's oscillating inner core
<p>This is the dataset for the paper 'Seismological observation of Earth’s oscillating inner core'.</p>
Source Data for Direct observations of energy transfer from resonant electrons to whistler-mode waves in magnetosheath of Earth
<p>Source Data files for figures and Supplementary figures in the manuscript "Direct observations of energy transfer from resonant electrons to whistler-mode waves in magnetosheath of Earth".</p> <p>Source_Data.zip includes data files (.txt or .xlsx) for each panel in the figures.</p> <p>tplot_save_files_w_load_procedures_for_time_series_plots.zip includes, tplot save files (.tplot) of time series plot figures and procedures to load and plot the tplot save files with SPEDAS (bleeding edge >r31062).</p>
Raw Data for Publication "Earth observations reveal impacts of climate variability on maize cropping systems in Sub-Saharan Africa"
<p>Phenological metrics extracted for all agricultural fields used in the study. Data also includes the coordinates of the fields.</p>
Model Spectra for Morley et al. 2017 (Observing the Atmospheres of Known Temperate Earth-sized Planets with JWST)
<p>README</p> <p>This file contains the model spectra presented in Morley et al. 2017, Observing the Atmospheres of Known Temperate Earth-sized Planets with JWST. </p> <p>The models are organized as follows: </p> <p>##########################<br> ## Transmission spectra ##<br> ##########################</p> <p>transmission_spectra contains 4 folders:<br> alb0.0_massradiusrel includes models with Bond albedo=0.0, and planet masses assuming the Weiss & Marcy 2014 mass-radius relationship<br> alb0.3_massradiusrel includes models with Bond albedo=0.3, and planet masses assuming the Weiss & Marcy 2014 mass-radius relationship<br> alb0.0_measured_masses includes models with Bond albedo=0.0, and observed planet masses, as described in Morley et al. 2017<br> alb0.3_measured_masses includes models with Bond albedo=0.3, and observed planet masses, as described in Morley et al. 2017</p> <p>each file contains the wavelength and model transit depth from 0.3 to 250 microns, calculated at 1 cm-1 wavenumber resolution. </p> <p>The file name indicates the planet name, model surface pressure (in bar), Bond albedo, and the assumed composition (Earth-, Venus-, or<br> Titan-based compositions, calculated in chemical equilibrium at each layer of the model, as described in Morley et al. 2017). </p> <p>(e.g. trans_spect_massradiusrel_gj1132b_psurf0.1_alb0.0_chem_earth.p.txt is a model of GJ 1132b, assuming the Weiss/Marcy mass-radius relationship, with a surface pressure of 0.1 bar, Bond albedo of 0.0, and an Earth-based composition). </p> <p>##########################<br> ## Emission spectra ##<br> ##########################</p> <p>emission_spectra contains 4 folders:<br> alb0.0 includes models with Bond albedo=0.0<br> alb0.3 includes models with Bond albedo=0.3<br> alb0.7 includes models with Bond albedo=0.7<br> emission_spectra_dividestar includes models that have already been divided by a model stellar spectrum, for convenience. </p> <p><br> each file contains the wavelength and model thermal emission flux in W/m2/m from 0.3 to 250 microns, calculated at 1 cm-1 wavenumber resolution. </p> <p>The file name indicates the planet name, model surface pressure (in bar), Bond albedo, and the assumed composition (Earth-, Venus-, or<br> Titan-based compositions, calculated in chemical equilibrium at each layer of the model, as described in Morley et al. 2017). </p> <p>(e.g. gj1132b_psurf0.01_alb0.0_chem_earth.spec is a model of GJ 1132b with a surface pressure of 0.01 bar, Bond albedo of 0.0, and an Earth-based composition). </p> <p>The models in emission_spectra_dividestar instead contain the flux of the planet divided by the flux of the star. </p> <p> </p> <p>##########################<br> ## Eclipse Depths ##<br> ##########################</p> <p>eclipse_depths contains a file for each planet with calculated eclipse depths for each of the wide-band JWST MIRI filters (http://ircamera.as.arizona.edu/MIRI/pces.htm). </p> <p>(new version from Jan 29 2018 has fixed a bug in MIRI eclipse depths that affected the longest wavelength filter for GJ 1132b and LHS 1140b only). </p>
Assessing the longwave components of the Earth's radiation budget from modeling and observational perspectives - coarse resolution datasets
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Processed data of "Observations of Ring Current Proton Fast Local Loss Associated with Deepening Local Minimum in Phase Space Density in Earth's Inner Magnetosphere"
<p>The dataset includs the processed observation data of "Observations of Ring Current Proton Fast Local Loss Associated with Deepening Local Minimum in Phase Space Density in Earth’s Inner Magnetosphere". *.sav files are the phase space densities of protons and the corresponding adiabatic invariants (μ, K, and L*) which are calculated under the T89D magnetic field model (using the observation data measured by the RBSPICE on the Van Allen Probes from 24th to 26th Jun 2017), which are used to plot Figure 1. The data in Processed data for Figures 2-5.zip are the processed data for Figures 2-5. </p>
GPS data set used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations"
<p>Data set of continuous GPS observations at CVAN site in Gran Canaria (Canary Islands, Spain). The period of observation spans from July 3, 2013 to November 30, 2015. Data was acquired during the execution of the research project GCL2011-25494 of the Spanish Research Agency. </p> <p>This data set belongs to the Research Group ‘Geodesia’ of the University Complutense of Madrid, Spain, and has been used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations", by Jose Arnoso, Machiel S. Bos, Maite Benavent, Nigel T. Penna, Sergio Sainz-Maza, submitted to Geophysical Journal International, 2022.</p>
Dataset accompanying the submission titled "Adaptive covariance hybridization for the assimilation of SST observations within a coupled Earth system reanalysis"
<p>The dataset contains the data accompanying our submission entitled "Adaptive covariance hybridization for the assimilation of SST observations within a coupled Earth system reanalysis". It contains:</p> <ol> <li>The yearly outputs of the free run</li> <li>The observations</li> <li>The yearly outputs of the runs of the standard hybrid</li> <li>The yearly outputs of the runs of the adaptive hybrid</li> <li>The grid of the model</li> <li>The maps of the hybridization coefficients</li> <li>The python and matlab scripts used to plot the figures of the article</li> </ol>
Maximizing Societal Benefit Across Multiple Hyperspectral Earth Observation Missions: A User Needs Approach - DATA and CODES
<p>In this repository you will find data elicited from Italian and the NASA Surface Biology and Geology (SBG) Designated Observable users. the first page "read me first" provides a description of the first sheet "User requirements merged" where you will find the reqirements codified for both community of users.</p> <p>Other files include the codes used throug the R software to develop several useful figures.</p>
Data from: Biodiversity monitoring, earth observations, and the ecology of scale
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Data from: Habitat mapping of coastal wetlands using expert knowledge and Earth observation data
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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.