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748 results for “surface temperature”
Fig. 4 in Retrieving climate change dependent Sea Surface Temperature (SST) in Southern Turkey by using Landsat thermal imagery
Fig. 4 — Average annual temperature in Gökova Bay
Interacting effects of surface water and temperature on wild and domestic large herbivore aggregations and contact rates
<p>Earth's climate is rapidly changing, bringing forth questions of how domestic and wild animals will alter their behavior in response to increasing temperatures and dryland expansion. Dwindling water availability will likely impact animal behavior and water foraging, potentially increasing animal aggregations and interspecific contacts. These interspecific contacts are especially important for competition, predation, and disease transmission among wildlife and domestic animals.</p> <p>In this study, we analyzed interspecific wildlife and cattle contacts using two years of camera trap data at an experimental water manipulation site at a conservancy in central Kenya.</p> <p>We found that on average, the hourly probability of any interspecific contact was approximately 3.4 times higher at water sources versus drained water sources, and 18 times higher than surrounding matrix areas, and that this relationship was amplified by dry and hot conditions.</p> <p>Species-specific analyses revealed variation in the magnitude of responses across wildlife and domestic cattle, although all animals had approximately 2-3 times higher interspecific contact probability with other species at water in hot conditions versus other conditions. Notably, we observed the largest behavioral changes for relatively water-independent species, such as giraffe, which had 3.6 times higher interspecific contact probability at water sources in hot versus other conditions.</p> <p><em>Synthesis and applications:</em> These findings show how elevated temperatures that will become increasingly common with future climate changes can increase interspecific contacts around critical water resources. In mixed wildlife-livestock systems, maintaining wildlife-only water sources may be a practical management tool to mitigate human-wildlife conflict and disease transmission at this interface, especially during dry and hot conditions.</p>
Figure 1 in Climate Changes of the Temperature of the Surface and Level of the Black Sea by the Data of Remote Sensing at the Coast of the Krasnodar Krai and the Republic of Abkhazia
Figure 1. The Black Sea coast of the Krasnodar Krai and the Republic of Abkhazia.
Greenland Ice Sheet precipitation and surface temperature from CloudSat and ECMWF
<p>This dataset contains code, data, and instructions for recreating the figures and analysis in Thompson-Munson et al. (submitted), "An Observational Constraint for Future Greenland Rainfall in a Warmer Atmosphere". Please see the readme for instructions and descriptions of the data and code.</p>
Evaluation of the influence of rain on air surface temperature measurements
<h2>Description</h2> <p>The dataset is constituted by three .csv files, which contain the measurements performed in an experiment aiming to evaluate the influence of rain on temperature readings. Two devices under tests (DUTs), one naturally ventilated and one artificially ventilated, are compared with a reference system. A .csv file is produced for DUT1, DUT2 and the reference system. Here below the content of each file is briefly described:</p> <ul> <li>Dataset_reference: accurate air temperature measurements obtained using the reference system, which is not affected by rain. The system is constituted by four aspirated thermometers (called Meteo1, Meteo2, Meteo 3, Meteo 4) manufactured at the Danish Technology Institute. The column "PT500" contains instead the rain temperature measurements. The readings are produced using a Fluke Super-DAQ (1586A). </li> <li>Dataset_DUT1: measurements of the naturally ventilated thermometer under an artificially generated rainfall. The readings are produced using the manufacturer datalogger.</li> <li>Dataset_DUT2: measurements of the artificially ventilated thermometer under an artificially generated rainfall. The readings are produced using the manufacturer datalogger.</li> </ul>
BDO Export of NASA GISS Surface Temperature
<p><a href="https://zenodo.org/api/files/15d77e79-1ae0-4828-b9d3-35a67695817d/BDO%20Export%20of%20NASA%20GISS%20Surface%20Temperature%20%28GISTEMP%29.zip">BDO Export of NASA GISS Surface Temperature </a></p>
Movies and temperature and pressure measurements associated with the study "Experimental evidence for lava-like mud flows under Martian surface conditions"
<p>Movies and temperature and pressure data associated with the study "<strong>Experimental evidence for lava-like mud flows under Martian surface conditions</strong>".</p>
Figure 1 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 1. Drifter device [Lagrangian drifter laboratory, 2024]
Satellite-ground synchronous in-situ dataset of water optical parameters and surface temperature for typical lakes in China
<p>Remote sensing technology has the potential to significantly enhance the lakes large-scale and long-term dynamic monitoring capabilities. High-quality in-situ datasets are essential for improving the accuracy and reliability of remote sensing retrieval of water optical parameters. This dataset provides satellite-ground synchronized in-situ data on water optical parameters for typical lakes in China spanning the period between 2020 and 2023. The dataset includes quality-checked remote sensing reflectance ( ) data and water optical parameter data for chlorophyll-a (Chl-a), total suspended matter (TSM), Secchi disk depth (SDD), andwater surface temperature (WST). It encompasses 586 sampling points across 18 lakes. The dataset exhibits two significant highlights: Firstly, synchronous observations from multiple satellites are coordinated during the data collection process, effectively supporting the retrieval and validation of water remote sensing products. Secondly, it encompasses diverse data types, collecting synchronous measurements of and various water optical parameters. This dataset will be continuously updated, thereby making a substantial contribution to enhancing regional and global lake monitoring capabilities through satellite remote sensing data.</p>
Ocean surface temperature
Open the record for dataset details and reuse information.
CRITER 1.0: Sea Surface Temperature Evaluation Datasets
<p>Training and evaluation datasets used in CRITER 1.0: A coarse reconstruction with iterative refinement network for sparse spatio-temporal satellite data .</p>
Planktonic foraminifera faunal data and derived sea-surface temperatures for the Marine Isotope Stage (MIS) 18 to MIS 28 interval of IODP Site U1387, Gulf of Cadiz
<p>Planktonic foraminifera faunal data and sea-surface temperature reconstructions for the early-middle Pleistocene interval between 750 and 1006 kilo years from IODP Site U1387 in the Gulf of Cadiz (southern Portuguese margin). This data was used to reconstruct the paleoecological and paleoclimatical changes at the southern Portuguese margin in Mega et al. (2025), The Early–Middle Pleistocene Transition in the Gulf of Cadiz (NE Atlantic) – an interplay between subtropical gyre and extremely cold surface waters. Clim. Past 21, 919-939, doi: 10.5194/cp-21-919-2025.</p> <p>The data is also available from the world data center Pangaea as a bundled data set:</p> <p>Voelker, Antje H L; Mega, Aline; Rodrigues, Teresa (2025): Planktonic foraminifera faunal data and sea-surface temperatures for the Marine Isotope Stage (MIS) 18 to MIS 28 interval of IODP Site 339-U1387, Gulf of Cadiz [dataset bundled publication]. PANGAEA, <a href="https://doi.org/10.1594/PANGAEA.974451" target="_blank" rel="nofollow noopener">https://doi.org/10.1594/PANGAEA.974451</a></p>
Varying partitioning of surface turbulent fluxes regulates temperature-humidity dissimilarity in the convective atmospheric boundary layer
<p>This dataset contains the data used in the submitted manuscript of Liu, Liu, Huang, and Xiao 2021. Please refer to the manuscript for the detailed description of the dataset.</p>
PhanSST - A global database of Phanerozoic sea surface temperature proxy data
<p>Beta pre-publication release of the PhanSST database of globally distributed Phanerozoic paleo-sea surface temperature proxy data. The database is currently accepted at Scientific Data.</p>
Data for "Clouds increasingly influence Arctic sea surface temperatures as CO2 rises" part 1
<p>Data for "Clouds increasingly influence Arctic sea surface temperatures as CO2 rises" submitted to Geophysical Research Letters.</p> <p>Data are included from three fully-coupled CESM2 simulations with variable CO2 concentrations: pre-industrial climate ('control'), 424 ppm CO2 ('yr40'), and 1139 ppm CO2 ('yr140'). Data in all files are restricted to 40-90<sup>o</sup>N. Variables include surface temperature (TS), sea ice concentration (ICEFRAC), CALIPSO total cloud fraction (CLDTOT_CAL), total grid cell cloud liquid water path (TGCLDLWP), sea surface temperature (SST), surface downwelling shortwave radiation (FSDS), surface downwelling longwave radiation (FLDS), surface net shortwave radiation (FSNS), clear-sky surface net shortwave radiation (FSNSC), and clear-sky surface downwelling longwave radiation (FLDSC).</p>
Daily surface temperature and current from a 10 members ensemble simulation of June-September 2018 over the South China Sea
<p>This file contains the outputs from an ensemble of 10 members of simulation performed over the South China Sea for summer 2018.</p> <p>Members are numbered from 09 to 18.</p> <p>member_11_daily_surface_tem_u_v_JJAS.nc contains the surface daily temperature and current simulated by member 11</p> <p>grid.nc contains all information about the Arakawa C grid (longitude, latitude, mask, mesh size etc).</p> <p>wstress_surf_2018_JJAS_daily.nc contains the daily wind stress for summer 2018</p>
The Influence of Land Surface Temperature on Mental Health in Lausanne - Data
<p>Dataset contains LST data for Lausanne used for the report.</p> <p>GeoPackage is in ESPG:21781 and provides coordinated in ESPG:4326 as well.</p> <p>Data contains mean and median LST for Summer (June - August) 1998, 2008 and 2018 and the delta. The delta is calculated by subtractig the data from 2018 (for example deltalstmedian0818 = lst_2018 - lst_2008).</p> <p>Find the code here: https://github.com/aamir-s18/InfluenceLSTGAF</p>
Data for: Connecting hemispheric asymmetries of planetary albedo and surface temperature
<p>Satellite measurements show that the Northern and Southern hemispheres reflect equal amounts of short-wave radiation ("albedo symmetry"), but no theory exists on if, how, and why the symmetry is established and maintained. Ambiguously, climate models are strongly biased in albedo symmetry but agree in the sign of the response to CO<sub>2</sub>. We find that mean-state biases in albedo symmetry and surface temperature asymmetry correlate negatively. Similarly, the response of albedo asymmetry to CO<sub>2</sub> forcing correlates negatively with the magnitude of the asymmetry in surface warming. This is true across many and within single climate model simulations: a too warm or stronger warming hemisphere is darker or darkens more than its counterpart. In the 21 years of observations we find the same tendency and hypothesize a) albedo symmetry is a function of the current climate state and b) we will observe an evolution towards albedo asymmetry in coming decades.</p>
A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data
<p> </p> <p>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data.</p> <p>Introduction</p> <p>This dataset is the Planet Labs PBC (VanderSat B.V.) contribution to the ESA 4DMED hydrology project (<a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org/</a>). It includes Soil Moisture, Land Surface Temperature and Vegetation Optical Depth for the 4DMED spatial domain and time period (2015-2021) at 1km pixel size. If you use the data please include the following reference:</p> <blockquote> <p>Jaap Schellekens, Tessa Kramer, Michel van Klink, Robin van der Schalie, Yoann Malbeteau, Arjan Geers, Richard de Jeu. (2022) <em>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data</em>. DOI: 10.5281/zenodo.7684993. Planet Labs PBC/VanderSat B.V., ESA Contract No. 4000136272/21/I-EF</p> </blockquote> <p> </p> <p><em>Figure 1: Average L-Band Soil moisture for 2020 over the 4dmed spatial domain</em></p> <p>Variables and files</p> <p>The dataset consists of the following files and products for the 4DMED domain. Detailed information about the products can also be found at <a href="https://docs.vandersat.com/data_products/soil_water_content/specification.html">docs.vandersat.com</a>:</p> <ul> <li><strong><code>planet-teff-4dmed-V4.0.zip</code></strong> - LST (TEFF) ascending (daytime) and descending (nighttime) <ul> <li><code>TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime (13:30 solar time) at 1 km</li> </ul> </li> <li><code>TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature nighttime (01:30 solar time) at 1 km</li> </ul> </li> </ul> </li> <li><strong><code>planet-teff-qf-4dmed-V4.0.zip</code></strong> - LST (TEFF) quality flags <ul> <li><code>QF-TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> <li><code>QF-TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> </ul> </li> <li><strong><code>planet-vod-4dmed-V4.1.zip</code></strong> - vegetation optical depth C and X band (interpolated from C3S passive soil moisture) <ul> <li><code>VOD_AMSR2_C1_DESC_V41_1000</code> <ul> <li>C1 band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> <li><code>VOD_AMSR2_X_DESC_V41_1000</code> <ul> <li>X band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-4dmed-V4.0.zip</code></strong> - All soil moisture products (C1, X and L-band) <ul> <li><code>SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-qf-4dmed-V4.0.zip</code></strong> - Soil moisture quality maps see <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python</a> <ul> <li><code>QF-SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture quality flags (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-cor-4dmed-V4.0.zip</code></strong> - Yearly correlation maps of soil moisture derived from the difference microwave bands. To be used as an extra quality indicator (for example undetected RFI) or for uncertainty estimation <ul> <li><code>SM-CORR-C1-X-DESC_V4.0_1000</code> - yearly C1 vs X band pearson's correlation maps</li> <li><code>SM-CORR-L-C1-DESC_V4.0_1000</code> - yearly L vs C1 band pearson's correlation maps</li> <li><code>SM-CORR-L-X-DESC_V4.0_1000</code> - yearly L vs X band pearson's correlation maps</li> </ul> </li> <li><strong><code>planet-aux-flags-4dmed-V4.0</code></strong> - Extra flags for frozen soil and bare soil. Determined at 0.25 degree and interpolated to the 4dmed grid <ul> <li><code>QF-SNOWFROZEN-AMSR2-ASC_1000::RD</code> - Frozen soil determined from dayttime data</li> <li><code>QF-SNOWFROZEN-AMSR2-DESC_1000::RD</code> - Frozen soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-DESC_1000::RD</code> - Bare soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-ASC_1000::RD</code> - Bare soil determined from daytime data</li> </ul> </li> </ul> <p>All files are archived into one zip file per product group. Each individual netcdf file in the zip file consists of one observation for the whole domain. If you need you can combine the files into one file using the cdo software <a href="https://code.mpimet.mpg.de/projects/cdo">https://code.mpimet.mpg.de/projects/cdo</a> (e.g. <code>cdo -f nc4c mergetime *.nc outfile.nc</code>).</p> <p>License</p> <p>The data for 4DMED is released under the Creative Commons license: CC BY-NC-SA 4.0 (<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>)</p> <ul> <li>Contains modified Copernicus Sentinel data 2015-2021</li> <li>Contains modified JAXA GCOM-W1/AMSR2 data 2015-2021</li> <li>Contains modified SMAP L1B Radiometer data: Piepmeier, J. R., P. Mohammed, J. Peng, E. J. Kim, G. De Amici, J. Chaubell, and C. Ruf. 2020. SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures, Version 4,5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: <a href="https://doi.org/10.5067/ZHHBN1KQLI20">https://doi.org/10.5067/ZHHBN1KQLI20</a></li> </ul> <p>Contact</p> <p>Jaap Schellekens: <a href="mailto:jaap@planet.com">jaap@planet.com</a></p> <p>Versions</p> <ul> <li>1.0 Initial creation</li> <li>1.1 Adjusted 4DMED Mask. Data itself unchanged but more LST (TEFF) measurements added</li> <li>1.2 Removed VOD and replaced by 25km C3S VOD interpolated to 1km (V4.1)</li> </ul> <p>Further information</p> <p>More information on the data and the flags can be found at <a href="https://docs.vandersat.com/">https://docs.vandersat.com</a> and <a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org</a></p> <p>Background publications</p> <p>R.A.M. De Jeu, A.H.A. De Nijs, M.H.W. Van Klink (2016) <em>Method and system for improving the resolution of sensor data</em>, US10643098B2,EP3469516B1, WO2017216186A1</p> <p>De Jeu, R. A., Holmes, T. R., Parinussa, R. M., & Owe, M. (2014). <em>A spatially coherent global soil moisture product with improved temporal resolution</em>. Journal of hydrology, 516, 284-296.</p> <p>Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I. and Forkel, M., 2020. <em>The global long-term microwave vegetation optical depth climate archive (VODCA)</em>. Earth System Science Data, 12(1), pp.177-196.</p> <p>Schmidt, L., Forkel, M., Zotta, R.-M., Scherrer, S., Dorigo, W. A., Kuhn-Régnier, A., van der Schalie, R., and Yebra, M.: <em>Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties, Biogeosciences Discuss.</em> [preprint], <a href="https://doi.org/10.5194/bg-2022-85">https://doi.org/10.5194/bg-2022-85</a>, in review, 2022</p> <p>Van der Schalie, R., de Jeu, R.A.M., Kerr, Y.H., Wigneron, J.P., Rodríguez-Fernández, N.J., Al- Yaari, A., Parinussa, R.M., Mecklenburg, S. and Drusch, M. (2017), <em>The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E</em>, Remote Sensing of Environment, 189, pp.180-193.</p> <p>van der Vliet, M., van der Schalie, R., Rodriguez-Fernandez, N., Colliander, A., de Jeu, R., Preimesberger, W., Scanlon, T., Dorigo, W., 2020. Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records. Remote Sensing 12, 3439. <a href="https://doi.org/10.3390/rs12203439">https://doi.org/10.3390/rs12203439</a></p>
Sensitivity of Arctic Surface Temperature to Including a Comprehensive Ocean Interior Reflectance to the Ocean Surface Albedo within the Fully Coupled CESM2
<p>CESM2 simulations were performed to study the light attenuation effects at the ocean surface layer on Arctic surface temperature. This dataset provides some simulated variables analyzed in our study.</p>
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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.