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403 results for “satellite data”
Data from: Computational research on mobile pastoralism using agent-based modeling and satellite imagery
Dryland pastoralism has long attracted considerable attention from researchers in diverse fields. However, rigorous formal study is made difficult by the high level of mobility of pastoralists as well as by the sizable spatio-temporal variability of their environment. This article presents a new computational approach for studying mobile pastoralism that overcomes these issues. Combining multi-temporal satellite images and agent-based modeling allows a comprehensive examination of pastoral resource access over a realistic dryland landscape with unpredictable ecological dynamics. The article demonstrates the analytical potential of this approach through its application to mobile pastoralism in northeast Nigeria. Employing more than 100 satellite images of the area, extensive simulations are conducted under a wide array of circumstances, including different land-use constraints. The simulation results reveal complex dependencies of pastoral resource access on these circumstances along with persistent patterns of seasonal land use observed at the macro level.
Data from: A daily global mesoscale ocean eddy dataset from satellite altimetry
Mesoscale ocean eddies are ubiquitous coherent rotating structures of water with radial scales on the order of 100 kilometers. Eddies play a key role in the transport and mixing of momentum and tracers across the World Ocean. We present a global daily mesoscale ocean eddy dataset that contains ~45 million mesoscale features and 3.3 million eddy trajectories that persist at least two days as identified in the AVISO dataset over a period of 1993–2014. This dataset, along with the open-source eddy identification software, extract eddies with any parameters (minimum size, lifetime, etc.), to study global eddy properties and dynamics, and to empirically estimate the impact eddies have on mass or heat transport. Furthermore, our open-source software may be used to identify mesoscale features in model simulations and compare them to observed features. Finally, this dataset can be used to study the interaction between mesoscale ocean eddies and other components of the Earth System.
Satellite-retrieved cloud top radiative cooling data in 2014 over global ocean
<p>Satellite-retrieved cloud-top radiative cooling data in 2014 over global ocean, used in a manuscript submitted to GRL (Zheng et al., 2021, GRL).</p>
Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning with Satellite AOD
<p>Input data for random forest model. </p> <p> </p> <p>1) UM_RDAPS.egg file: It provides analysis and forecast products four times a day (00, 06, 12, 18 UTC) in 12 km x 12 km spatial resolution. In this study, analysis products were only considered as the input variables (i.e., 2m temperature and dew-point temperature, relative humidity (RH), maximum wind speed, visibility at height above the ground, planetary boundary layer height (PBLH), and surface pressure). The accumulated maximum wind speed during 1, 3, 5, 7 days were also used in this study.</p> <p>2) data_1.zip file: GOCI Aerosol product, MODIS Land cover, MODIS NDVI, Population density, Road density, SRTM_DEM. </p> <p> </p> <p>The detailed information of input variables is written in the supporting information of the paper.</p> <p> </p> <p> </p> <p> </p>
Data for "Satellite-observed strong subtropical ocean warming as an early signature of global warming"
<p>Data for “Satellite-observed strong subtropical ocean warming as an early signature of global warming”</p>
The NO2 monitoring data for "Satellite-based long-term spatiotemporal trends in ambient NO2 concentrations and attributable health burdens in China from 2005 to 2020"
<p>The current dataset is about NO2 monitoring data used in Geohealth manuscript entitled "Satellite-based long-term spatiotemporal trends in ambient NO2 concentrations and attributable health burdens in China from 2005 to 2020". It was collected from the air quality monitoring stations administered by the China National Environmental Monitoring Center. </p>
CO2 retrievals at global power plants using PRISMA satellite data
<p>PRISMA (prisma.asi.it) data for a set of global power plants tasked between 2021-2022. Each scene includes a NETCDF file containing raw radiance data (1e-4 * W/(str µm m<sup>2</sup>)), retrieved XCO2 (using an IMAP-DOAS algorithm), and retrieval precision. Also included is a spreadsheet "PRISMA tracking-2023-06-23.xlsx" that details the result of an analyst's QC of each scene regarding retrieval quality and CO2 plume detection. Another tracking sheet "PRISMA emissions-2023-06-23.xlsx" lists derived emission rates (via Integrated Mass Enhancement approach) with uncertainties and ERA5 wind speeds.</p> <p>Also included for each scene is an RGB and XCO2 PNG file that allows the user to quickly scan retrieval results. </p>
Geolocating and Measuring Offshore Infrastructure with Multimodal Satellite Data
<p><em>Please cite this when using the dataset and code.</em></p> <p>The offshore infrastructure is rapidly spreading in the Scottish waters to satisfy the increasing energy demand. The up-to-date knowledge of their distribution and size is critical for the development and management of marine ecosystems. With the development of remote sensing techniques, satellite data have been widely used in offshore infrastructure detection on the vast ocean. However, the automatic and accurate identification on remote sensing data is still challenging that every kind of data have limitations. Here we combine the Sentinel-1 SAR data and Sentinel-2 Multi-Spectral Instrument (MSI) imagery to propose an automatic method for the location detection and size evaluation of offshore infrastructure in Scottish waters. Specifically, three strategies (transformed median composite, 2D-SSA filtering and threshold segmentation) were designed to first extract the contour range on Sentinel-1 data. Then morphological operations were applied on Sentinel-2 true color image to obtain the precise location and size of each offshore infrastructure.</p> <p>All the Sentinel-1 and Sentinel-2 data are downloaded from https://www.sentinel-hub.com/explore/eobrowser/</p> <p>The file "loc_S1" is used for the contour range detection (guided area) in Sentinel-1;</p> <p>The file "loc_S2" is for the specific location detection and size evaluation of oil/gas platforms and wind turbines;</p>
PEATCLSM(Tb): A land surface data assimilation product for peatlands using PEATCLSM and brightness temperature (Tb) satellite observations (Northern Hemisphere output, Jan 2010 through Sep 2021)
<p>The dataset archived here includes an extended version of the analysis output shown in the paper, “Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework”, published in Remote Sensing of Environment Journal (Bechtold et al., 2020). The output was produced by combining peatland-specific land surface modeling (Bechtold et al., 2019b) embedded in the NASA Catchment Land Surface Model (CLSM) with L-band brightness temperature (Tb) observations (SMOS), applying the data assimilation framework of the SMAP Level‐4 Soil Moisture product (Reichle et al., 2019). We provide a single NetCDF file of the analysis output (9-km resolution EASEv2 grid, period Jan 2010 through Sep 2021, and between 45°N and 70°N, NE Asia excluded):<br> • daily_images.nc: Daily land states and fluxes (Table 1), provided as netCDF image-chunked image stack</p> <p>The file content is described in the file PEATCLSM_Tb_Documentation_20230830.pdf</p> <p>Please contact Michel Bechtold (michel.bechtold@kuleuven.be) for any questions.</p> <p>Data usage statement:<br> This work is licensed under a Creative Commons Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0/<br> If you decide to work with this data, we kindly ask to be informed at the outset of the nature of this work. If the data are essential to the work, or if an important result or conclusion depends on the PEATCLSM(Tb) data product, we would appreciate that you discuss these findings with us to ensure correct use and interpretation of the PEATCLSM(Tb) product. Furthermore, we are continuously improving the data assimilation product, a discussion of your work at an early stage may (i) help us to improve our product, and (ii) allow us to provide you with a newer version. Thanks!</p> <p>References:</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., & Koster, R. D. (2019a). PEAT-CLSM simulation output (Northern Peatlands) version 1. https://doi.org/10.17605/OSF.IO/E58YM</p> <p>Bechtold, M. et al. (2019b). PEAT‐CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(7), 2130–2162. https://doi.org/10.1029/2018MS001574</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., Roose, D., Balliston, N., Burdun, I., Devito, K., Kurbatova, J., Strack, M., & Zarov, E. A. (2020). Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework. <em>Remote Sensing of Environment</em>. https://doi.org/10.1016/j.rse.2020.111805</p> <p>Reichle, R. H., Liu, Q., Koster, R. D., Crow, W. T., De Lannoy, G. J. M., Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M., Kolassa, J., Mahanama, S. P., Prueger, J., Starks, P., & Walker, J. P. (2019). Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(10), 3106–3130. https://doi.org/10.1029/2019MS001729</p>
Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations (Data)
<p>README file for LBL-ERA5, LBL-GCM, and band datasets used in:<br> Raghuraman et al., 2023, Geophysical Research Letters,<br> "Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations "</p> <p>Point of Contact: Shiv Priyam Raghuraman, shivr@alumni.princeton.edu</p> <p> </p> <p>LBL-ERA5</p> <p>6 files (3 experiments with olr and olr_clr output separately):</p> <p>2003-2021.GFDL.all-2010-o3_*.nc - varying WMGHG,Ts,T,q,clouds, fixed o3</p> <p>2003-2021.GFDL.fo3_*.nc - varying WMGHG and fixed Ts,T,q,clouds,surface albedo,o3</p> <p>2003-2021.GFDL.ff_*.nc - fixed Ts,T,q,clouds,surface albedo, varying o3</p> <p> </p> <p>LBL-GCM (clear-sky only)</p> <p>4 AM4 files:</p> <p>AM4piclim-control.nc (for ERF)</p> <p>AM4piclim-4xCO2_1xCO2IRF.nc (for ERF)</p> <p>AM4piclim-control_STRAT.nc (for SARF)</p> <p>AM4piclim-4xCO2_1xCO2IRF_STRAT.nc (for SARF)</p> <p>4 CM3/AM3 files:</p> <p>CTLAM3CM3_P1_APR_allctm_E1_RFM_1CM_A2_WN3_1xCO2N.nc (for \lambda)</p> <p>EXPAM3CM3_P1_APR_allctm_E1_RFM_1CM_A2_WN3_1xCO2N.nc (for \lambda)</p> <p>CTLAM3CM3_P1_2003C.nc (for IRF)</p> <p>CTLAM3CM3_P1_2021C.nc (for IRF)</p> <p> </p> <p>Band-AM4/MERRA-Total: GFDL AM4 with prescribed SSTs and sea-ice (AMIP) and nudged with MERRA winds</p> <p>2 files:</p> <p>atmos.200001-202112.olr.nc - all-sky OLR</p> <p>atmos.200001-202112.olr_clr.nc - clear-sky OLR</p> <p> </p> <p>Observational and reanalysis files used in paper (AIRS, CERES EBAF and SSF, GISTEMP, ERA5 input data) can be downloaded from their respective websites (see paper's "Open Research" section).</p> <p> </p>
De novo reconstruction of satellite repeat units from sequence data
<p>Results generated for preprint 'De novo reconstruction of satellite repeat units from sequence data".</p>
Investigation of the summer 2018 European ozone air pollution episodes using novel satellite data and modelling - Dataset
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Data from: Global, satellite-driven estimates of heterotrophic respiration
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Data from: Effects of sea ice cover on satellite-detected primary production in the Arctic Ocean
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Data from: Immediate and carry-over effects of insect outbreaks on vegetation growth in West Greenland assessed from cells to satellite
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Data from: Assessing the spatial ecology and resource use of a mobile and endangered species in an urbanized landscape using satellite telemetry and DNA faecal metabarcoding
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Data from: Using satellite AIS to improve our understanding of shipping and fill gaps in ocean observation data to support marine spatial planning
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Data from: First satellite tracks of South Atlantic sea turtle ‘lost years’: seasonal variation in trans-equatorial movement
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Data from: Predicting bird phenology from space: satellite-derived vegetation green-up signal uncovers spatial variation in phenological synchrony between birds and their environment
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Data from: Alternative reproductive tactics arising from a continuous behavioral trait: callers vs. satellites in field crickets
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ScienceDex guides
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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.