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403 results for “Satellite data”
Satellite-derived water quality data for Lake Mulargia (Sardinia, Italy) 2015-2019
This dataset contains satellite-derived water quality (WQ) data of Lake Mulargia (Sardinia, Italy) for the years 2015-2019. Available parameters are: Total Absorption (ABS), Chlorophyll-a (CHL), Harmful Algae Bloom Indicator (HAB), True-color image (RGB), Secchi Disc Depth (SDD), Sea Surface Temperature (SST), Total Suspended Matter (TSM) and Turbidity (TUR). WQ parameters have been calculated using EOMAPs physics-based MIP from Sentinel-2 and Landsat 8. The data are available as GeoTiff files in web-mercator projection (EPSG: 3857). Further information can be found in the readme files. Contains Copernicus data. Credits: ESA (2022). Landsat data courtesy of the United States Geological Survey (2022).
A merged satellite data from SABER, MLS, and HALOE
<p>Dimensions: </p> <p>Time: from 1993.01 to 2020.12 </p> <p>Lev: 55 layers from the surface to 1e-5 hPa</p> <p>Lat: 22 grids from 52.5°N to 52.5°S</p> <p>Lon: 1 grid, zonal mean </p> <p> </p> <p>below 14 hPa: merge MLS and HALOE</p> <p>above 14 hPa: first merging MLS and SABER, then merge with HALOE </p>
Satellite telemetry data of Double-crested cormorant locations
<p>Avian migrants are challenged by seasonal adverse climatic conditions and energetic costs of long-distance flying. Migratory birds may track or switch seasonal climatic niche between the breeding and non-breeding grounds. Satellite tracking enables avian ecologists to investigate seasonal climatic niche and circannual movement patterns of migratory birds. The Double-crested Cormorant (<em>Nannopterum auritum</em>, hereafter cormorant) wintering in the Gulf of Mexico (GOM) migrate to the Northern Great Plains and Great Lakes and is of economic importance because of its impacts on aquaculture. We tested the climatic niche switching hypothesis that cormorants would switch climatic niche between summer and winter because of substantial differences in climate between the non-breeding grounds in the subtropical region and breeding grounds in northern temperate region. The ordination analysis of climatic niche overlap indicated that cormorants had separate seasonal climatic niche consisting of seasonal mean monthly minimum and maximum temperature, seasonal mean monthly precipitation, and seasonal mean wind speed. Despite non-overlapping summer and winter climatic niches, cormorants appeared to be subjected to similar wind speed between winter and summer habitats and were consistent with similar hourly flying speed between winter and summer. Therefore, substantial differences in temperature and precipitation may lead to the climatic niche switching of fish-eating cormorants, a dietary specialist, between the breeding and non-breeding grounds. </p>
Data and scripts for: Satellite-tracking reveals sex-specific migration distance in green turtles (Chelonia mydas)
<p>Data derivates and analysis scripts (in R) used for the paper "Satellite-tracking reveals sex-specific migration distance in green turtles (<em>Chelonia mydas</em>)", published in Biology Letters, on analyzing male and female green turtle movements in West Africa.</p>
Training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series
<p>This dataset contains training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series. The data have been derived from Sentinel-5P total column carbon monoxide observations, using the offline processing stream.</p> <p><strong>Preprocessing</strong></p> <p>The following operations have been applied on the original S5P imagery:</p> <ol> <li>Images have been resampled to 0.1 by 0.1 degree spatial resolution</li> <li>Pixels with quality assessment value less than or equal to 0.5 have been set to NA</li> <li>Images have been aggregated by day of observation</li> <li>Images have been cropped to -60 to 60 degrees latitude</li> <li>Images have been devided into spatiotemporal blocks of size 128 x 128 pixels and 16 days</li> </ol> <p>Imagery has been recorded between 2021-01-01 and 2021-11-25. Notice that both the training and the validation blocks have been randomly sampled from all available blocks.</p> <p><br> <strong>Data Format and Naming Conventions</strong></p> <p>Input and output data blocks are stored as GeoTIFF files, where bands represent time. Notice the following file naming conventions:</p> <ul> <li>Files starting with <em>X</em> represent input measurements for training, where artificial gaps have been added.</li> <li>Files starting with <em>Y</em> represent true measurements without artificially added gaps (but still containing gaps in many cases).</li> <li>Binary masks of input data where all pixels with valid measurements are 1 and others 0 are stored in files whose name starts with <em>MASK</em></li> <li>Files starting with <em>VALMASK</em> contain a binary mask where only pixels that are available in Y but not in X are 1. The latter is used for validation on artificially removed pixels only.</li> </ul> <p>Numbers in filenames encode spatial and temporal block indexes.</p> <p>In addition, the dataset contains prediction of the validation blocks from different models in the `predictions` directory. The subfolders contain output from different models:</p> <ul> <li>mean refers to simple block-wise mean predictions.</li> <li>timeseries refers to simple linear time series interpolation.</li> <li>gapfill refers to the method proposed in [1].</li> <li>stmra refers to the method proposed in [2].</li> <li>STpconv refers to predictions passed on an artificial neural netowork with three-dimensional partial convolutions.</li> </ul> <p><strong>References</strong></p> <p>[1] Gerber, F., de Jong, R., Schaepman, M. E., Schaepman-Strub, G., & Furrer, R. (2018). Predicting missing values in spatio-temporal remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 56(5), 2841-2853.</p> <p>[2] Appel, M., & Pebesma, E. (2020). Spatiotemporal multi-resolution approximations for analyzing global environmental data. Spatial Statistics, 38, 100465.</p>
VIIRS gap-free satellite data for Chla in Chesapeake Bay from 2011 to 2018
<p>The uploaded data are daily and 7-day blended VIIRS satellite Chla data for the Chesapeake Bay. </p> <p>Data are used by the publication "Chlorophyll-a in Chesapeake Bay based on VIIRS satellite data: spatiotemporal variability and prediction with machine learning" submitted to Ocean Modelling</p> <p> </p>
A synchronized estimation of hourly ground-level concentrations of six criteria air pollutants in China using data from the first geostationary air-quality monitoring satellite
<p>This dataset provides the ground-level concentrations of six criteria air pollutants estimated from the first geostationary air quality monitoring satellite GEMS with a multi-output random forest model.</p>
Crop performance, aerial, and satellite data from multistate maize yield trials
<p>Accurate genotype-specific early yield estimates at fields and plots offer potential benefits to farmers in optimizing their agronomic practices, breeders in screening hundreds and thousands of varieties, and policymakers in decisions contributing to the overall improvement of agriculture and food production systems. Effective, generalizable approaches to track plant growth and predict yield at the individual plot level require large matched datasets of remote sensing and ground truth data collected across multiple environments. Low-altitude drone flights are increasingly being used to collect data from field evaluations of new crop varieties, while satellite imagery is being explored to track yield and management practices at the regional and field scales. Despite their lower spatial resolution, satellite platforms exhibit multiple logistical and technical advantages in scalability and accessibility, and could facilitate plot-level predictions, especially with steadily improving spatial resolution. However, genotype-specific, plot-level, high-resolution satellite images from multiple environments integrated with the ground truth measurements are not yet publicly available. Here we generated, described, and evaluated a set of more than 20,000 plot-level images of over 80 hybrid maize (Zea mays) varieties grown in six locations across the US corn belt under various management practices collected from (near simultaneous) satellite and drone flights integrated with ground truth measurements of crop yield. Of the six baseline models examined, models employing data collected from satellite images often matched or exceeded the performance of models employing data collected from drones for both within-environment and cross-environment yield prediction. Large, multimodal, multi-environment, genetically diverse training datasets such as those generated in this study, along with more complex models could help unlock the power of satellite imagery as an important new addition to the tool of farmers, plant geneticists, crop breeders, and policymakers.</p>
Data for "Anthropogenic aerosols have significantly weakened the regional summertime circulation in the Northern Hemisphere during the satellite era"
<p>The dataset supporting the conclusion of the submitted paper is uploaded here.</p> <p>The data are labeled after each figure. The npz files include data required to reproduce our results in python arrays.</p> <p> </p>
Dataset (Global C-responses for CSES and CSES+Swarm+Obs database) presented in the recently submitted AGU manuscript "Electrical conductivity of mantle transition zone and water content revealed by the magnetic data of China Seismo-electromagnetic Satellite".
<p>Dataset (Global C-responses for CSES and CSES+Swarm+Obs database) presented in the recently submitted AGU manuscript "Electrical conductivity of mantle transition zone and water content revealed by the magnetic data of China Seismo-electromagnetic Satellite".</p>
Soil moisture maps of Ukraine based on SMAP satellite data, vegetation season 2018
<p>A set of soil moisture maps of Ukraine based on SMAP satellite data</p> <p>Product: SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture V001</p> <p>Vegetation season 2018</p> <p> </p>
The prediction data analyzed in "Seasonal Arctic sea ice prediction using a newly developed fully coupled regional model with the assimilation of satellite sea ice observations"
<p>The outputs of seasonal predictions with the new modeling system analyzed in the article including:</p> <p>Sea ice concentration (SIC)</p> <p>Sea ice thickness (SIT)</p> <p>Sea surface temperature (SST)</p> <p>Near surface air temperature (T2) </p>
Contrail altitude estimation using GOES-16 ABI data and deep learning: Dataset of contrails collocated with CALIOP satellite measurements
Open the record for dataset details and reuse information.
Source data for "Date of introduction and epidemiologic patterns of SARS-CoV-2 in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020"
<p>Source data for the model fitting code at https://doi.org/10.5281/zenodo.5525349, accompanying the article "<em>Date of introduction and epidemiologic patterns of SARS-CoV-2 in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020</em>"</p>
Generation of combined daily satellite-based precipitation products over Bolivia - Generated Precipitation Data
<p><strong>A journal paper published in Remote Sensing details the method to generate the data.</strong></p> <p>Saavedra, O.; Ureña, J. Generation of Combined Daily Satellite-Based Precipitation Products over Bolivia. <em>Remote Sens.</em> <strong>2022</strong>, <em>14</em>, 4195. https://doi.org/10.3390/rs14174195</p>
DGFI-TUM DSO1 orbits of altimetry satellites TOPEX/Poseidon, Jason-1, Jason-2 and Jason-3 derived from SLR data in the SLRF2014 reference frame
<p>The data set provides DSO1 orbits of altimetry satellites TOPEX/Poseidon (27 September 1992 to 9 October 2005), Jason-1 (13 January 2002 to 30 June 2013), Jason-2 (20 July 2008 to 2 October 2019) and Jason-3 (17 February 2016 to 24 October 2021) computed at the Deutsches Geodätisches Forschungsinstitut of the Technical University of Munich (DGFI-TUM). The orbits are derived using the DGFI-TUM Orbit and Geodetic parameter estimation Software (DOGS). The orbits were computed from SLR data in the SLRF2014 (an extended version of ITRF2014) reference frame using common for all satellites, most precise models and standards available and described in the following paper that serves as a citation of these orbits:</p> <p>Sergei Rudenko, Denise Dettmering, Julian Zeitlhöfler, Riva Alkahal, Dhruv Upadhyay and Mathis Bloßfeld (2023) Radial orbit errors of contemporary altimetry satellite orbits. Surveys in Geophysics, https://doi.org/10.1007/s10712-022-09758-5.</p> <p>For each satellite, a tar file is given comprising compressed files. File names are given as satgpswd.sp3.gz, where “sat” is the abbreviation of the satellite name (JA1, JA2, JA3, TPX), “gpsw” is the 4-digit GPS week, and “d” indicates the day of the GPS week containing the first time instant of the file (0 = Sunday, 6 = Saturday). The orbit files are available in the Extended Standard Product 3 Orbit Format, Version c (SP3-c).</p> <p>The orbits were derived within the project “Mitigation of the current errors in precise orbit determination of altimetry satellites (MEPODAS)” funded by Deutsche Forschungsgemeinschaft (DFG).</p>
Forcing, cloud feedbacks, cloud masking, and internal variability in the cloud radiative effect satellite record (Data)
<p>README file for ERA5-PRP datasets used in:</p> <p>Raghuraman et al., 2023, Journal of Climate,<br> "Forcing, cloud feedbacks, cloud masking, and internal variability in the cloud radiative effect satellite record"</p> <p>Point of Contact: Shiv Priyam Raghuraman, shivr@alumni.princeton.edu</p> <p>30 ERA5-PRP files:</p> <p>2 files: '2000_2020-allsky_small.nc' and '2000_2020-clearsky_small.nc'</p> <ul> <li>all input quantities varying</li> </ul> <p>2 files: 'clim-allsky-clim-var-all.nc' and 'clim-clearsky-clim-var-all.nc'</p> <ul> <li>all input quantities at climatology</li> </ul> <p>13 files: 'clim-var'</p> <ul> <li>Input quantity X at climatology, rest varying</li> <li>X = clouds, q, fal, skt, t, ghg, or o3. Fluxes computed in clear-sky and all-sky, apart from clouds which will only have all-sky.</li> </ul> <p>13 files: 'clim-except-var'</p> <ul> <li>Input quantity X varying, rest at climatology</li> <li>X = clouds, q, fal, skt, t, ghg, or o3. Fluxes computed in clear-sky and all-sky, apart from clouds which will only have all-sky.</li> </ul> <p>Other datasets:</p> <p>GFDL AM4 AMIP and Control<br> https://doi.org/10.5281/zenodo.4784726</p> <p>CMIP6 Control, Historical, RFMIP<br> Downloaded from ESGF</p>
Data for: Ensemble-based data assimilation of significant wave height from Sofar Spotters and satellite altimeters with a global operational wave model
<p>An ensemble-based method for wave data assimilation is implemented using significant wave height observations from the globally distributed network of Sofar Spotter buoys and satellite altimeters. The Local Ensemble Transform Kalman Filter (LETKF) method generates skillful analysis fields resulting in reduced forecast errors out to 2.5 days when used as initial conditions in a cycled wave data assimilation system. The LETKF method provides more physically realistic model state updates that better reflect the underlying sea state dynamics and uncertainty compared to methods such as optimal interpolation. Skill assessment far from any included observations and inspection of specific storm events highlights the advantages of LETKF over an optimal interpolation method for data assimilation. This advancement has immediate value in improving predictions of the sea state and, more broadly, enabling future coupled data assimilation and utilization of global surface observations across domains (atmosphere-wave-ocean).</p>
Data for: Multi-LEO satellite stereo winds
The stereo-winds method follows trackable atmospheric cloud features from multiple viewing perspectives over multiple times, generally involving multiple satellite platforms. Multi-temporal observations provide information about the wind velocity and the observed parallax between viewing perspectives provides information about the height. The stereo-winds method requires no prior assumptions about the thermal profile of the atmosphere to assign a wind height, since the height of the tracked feature is directly determined from the viewing geometry. The method is well developed for pairs of Geostationary (GEO) satellites and a GEO paired with a Low Earth Orbiting (LEO) satellite. However, neither GEO-GEO nor GEO-LEO configurations provide coverage of the poles. In this paper, we develop the stereo-winds method for multi-LEO configurations, to extend coverage from pole to pole. The most promising multi-LEO constellation studied consists of Terra/MODIS and Sentinel-3/SLSTR. Stereo-wind products are validated using clear-sky terrain measurements, spaceborne LiDAR, and reanalysis winds for winter and summer over both poles. Applications of multi-LEO polar stereo winds range from polar atmospheric circulation to nighttime cloud identification. Low cloud detection during polar nighttime is extremely challenging for satellite remote sensing. The stereo-winds method can improve polar cloud observations in otherwise challenging conditions.
Supporting data for Assessing clouds using satellite observations through three generations of global atmosphere models
<p>Monthly data from CAM4, CAM5, and CAM6 that are needed to reproduce the analysis and figures in the manuscript entitled: Assessing clouds using satellite observations through three generations of global atmosphere models by Brian Medeiros, Jonah Shaw, Jennifer Kay, and Isaac Davis.</p>
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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.