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748 results for “surface temperature”
Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2020.5-2020.8)
<p>This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W).</p> <p>This is the LST&E dataset in 2020.05-2020.08</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E)</li> <li>Temporal Coverage: 2020.05-2020.08</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Projection: GCS_WGS_1984</li> <li>Data Format: NetCDF</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124</p> </li> <li> <p>Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2021.1-2021.4)
<p>This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W).</p> <p>This is the LST&E dataset in 2021.01-2021.04</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E)</li> <li>Temporal Coverage: 2021.01-2021.04</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Projection: GCS_WGS_1984</li> <li>Data Format: NetCDF</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124</p> </li> <li> <p>Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2021.5-2021.8)
<p>This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W).</p> <p>This is the LST&E dataset in 2021.05-2021.08</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E)</li> <li>Temporal Coverage: 2021.05-2021.08</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Projection: GCS_WGS_1984</li> <li>Data Format: NetCDF</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124</p> </li> <li> <p>Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2021.9-2021.12)
<p>This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W).</p> <p>This is the LST&E dataset in 2021.09-2021.12</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E)</li> <li>Temporal Coverage: 2021.09-2021.12</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Projection: GCS_WGS_1984</li> <li>Data Format: NetCDF</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124</p> </li> <li> <p>Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
Time series of electrical conductivity, temperature, relative stream stage and total pressure recorded in surface water and streambed sediments of River Erpe and River Ammer, Germany, and the Sturt River, South Australia
<p>Time series of electrical conductivity (mS cm<sup>-1</sup>), temperature (degC), relative stream stage (cm) and total pressure (hPa) recorded in the surface water and in streambed sediments (depth in cm) of River Erpe and River Ammer, Germany, and the Sturt River, South Australia.</p>
ELITE land surface temperature: seamless 1km LST over China (2014)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the ELITE seamless 1km LST over China landmass (2002-2020). Firstly, a look-up-table-based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled using the geographically weighted regression to obtain 1km LST. Finally, the multi-scale kalman filter is used to fuse MODIS LST and AMSR-E/AMSR2 LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST.</p> <p>This is the seamless LST dataset in 2014. Please <a href="https://zenodo.org/record/8274917"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2013 and <a href="https://zenodo.org/record/8274959"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2015.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2014</li> <li>Spatial Resolution: 1 KM</li> <li>Temporal Resolution: 2 times per day</li> <li>Data Format: hdf</li> <li>Scale: 0.02</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Xu, S., & Cheng, J. (2021). A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254, 112256</li> <li>Zhang, Q., Wang, N., Cheng, J., & Xu, S. (2020). A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5669-5681 </li> <li>Zhang, Q., & Cheng, J. (2020). An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables. Earth and Space Science, 7, e2019EA001006. https://doi.org/10.1029/2019EA001006 </li> </ol> <p> </p> <p>If you have any questions, please contact Prof. Jie Cheng (<a href="mailto:eliteqrs@126.com">eliteqrs@126.com</a>).</p>
ELITE land surface temperature: Landsat LST over China (2012.1)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the ELITE Landsat LST dataset over China generated by the radiative transfer method (Cheng et al., 2021). Firstly, a new scheme was used to determine the real-time Landsat 5/7/8 narrowband emissivity . Then, the MERRA2 reanalysis product was used for thermal infrared data atmospheric correction (Meng and Cheng, 2018). Finally, an LST product with 30m spatial resolution was generated using the radiative transfer equation method.</p> <p>This is the ELITE Landsat LST dataset from Landsat 7 in January 2012. Please <a href="https://zenodo.org/record/8275591"><em>click here</em></a> to download the ELITE Landsat LST from Landsat 7 in July 2012.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2012.1</li> <li>Spatial Resolution: 30m</li> <li>Temporal Resolution: 16 days</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Cheng, J., Meng, X., Dong, S., & Liang, S. (2021). Generating the 30-m land surface temperature product over continental China and USA from landsat 5/7/8 data. Science of Remote Sensing, 4, 100032</p> </li> <li> <p>Meng, X., & Cheng, J. (2018). Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data. Remote Sensing, 10, 474</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).</p>
ELITE land surface temperature: Global Landsat LST (2020.1.1-2020.1.5)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn/">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the Global ELITE Landsat LST dataset generated by the radiative transfer method (Cheng et al., 2021). Firstly, a new scheme was used to determine the real-time Landsat 5/7/8 narrowband emissivity . Then, the MERRA2 reanalysis product was used for thermal infrared data atmospheric correction (Meng and Cheng, 2018). Finally, an LST product with 30m spatial resolution was generated using the radiative transfer equation method.</p> <p>This is the ELITE Landsat LST dataset for Landsat 8 from January 1, 2020 to January 5, 2020. Please <a href="https://zenodo.org/record/8312637"><em>click here</em></a> to download the ELITE Landsat LST for Landsat 8 from January 6, 2020 to January 10, 2020.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage:Global landmass</li> <li>Temporal Coverage: 2020.1.1-2020.1.5</li> <li>Spatial Resolution: 30m</li> <li>Temporal Resolution: 16 days</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Cheng, J., Meng, X., Dong, S., & Liang, S. (2021). Generating the 30-m land surface temperature product over continental China and USA from landsat 5/7/8 data. Science of Remote Sensing, 4, 100032</p> </li> <li> <p>Meng, X., & Cheng, J. (2018). Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data. Remote Sensing, 10, 474</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).</p>
ELITE land surface temperature: Global Landsat LST (2020.1.16-2020.1.20)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn/">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the Global ELITE Landsat LST dataset generated by the radiative transfer method (Cheng et al., 2021). Firstly, a new scheme was used to determine the real-time Landsat 5/7/8 narrowband emissivity . Then, the MERRA2 reanalysis product was used for thermal infrared data atmospheric correction (Meng and Cheng, 2018). Finally, an LST product with 30m spatial resolution was generated using the radiative transfer equation method.</p> <p>This is the ELITE Landsat LST dataset for Landsat 8 from January 16, 2020 to January 20, 2020. Please <a href="https://zenodo.org/record/8312641"><em>click here</em></a> to download the ELITE Landsat LST for Landsat 8 from January 11, 2020 to January 15, 2020 and <a href="https://zenodo.org/record/8313496"><em>click here</em></a> to download the ELITE Landsat LST for Landsat 8 from January 21, 2020 to January 25, 2020.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global landmsss</li> <li>Temporal Coverage: 2020.1.16-2020.1.20</li> <li>Spatial Resolution: 30m</li> <li>Temporal Resolution: 16 days</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Cheng, J., Meng, X., Dong, S., & Liang, S. (2021). Generating the 30-m land surface temperature product over continental China and USA from landsat 5/7/8 data. Science of Remote Sensing, 4, 100032</p> </li> <li> <p>Meng, X., & Cheng, J. (2018). Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data. Remote Sensing, 10, 474</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).</p>
ELITE land surface temperature: Global Landsat LST (2020.1.21-2020.1.25)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn/">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the Global ELITE Landsat LST dataset generated by the radiative transfer method (Cheng et al., 2021). Firstly, a new scheme was used to determine the real-time Landsat 5/7/8 narrowband emissivity . Then, the MERRA2 reanalysis product was used for thermal infrared data atmospheric correction (Meng and Cheng, 2018). Finally, an LST product with 30m spatial resolution was generated using the radiative transfer equation method.</p> <p>This is the ELITE Landsat LST dataset for Landsat 8 from January 21, 2020 to January 25, 2020. Please <a href="https://zenodo.org/record/8312646"><em>click here</em></a> to download the ELITE Landsat LST for Landsat 8 from January 16, 2020 to January 20, 2020 and <a href="https://zenodo.org/record/8313500"><em>click here</em></a> to download the ELITE Landsat LST for Landsat 8 from January 26, 2020 to January 30, 2020.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global landmsss</li> <li>Temporal Coverage: 2020.1.21-2020.1.25</li> <li>Spatial Resolution: 30m</li> <li>Temporal Resolution: 16 days</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Cheng, J., Meng, X., Dong, S., & Liang, S. (2021). Generating the 30-m land surface temperature product over continental China and USA from landsat 5/7/8 data. Science of Remote Sensing, 4, 100032</p> </li> <li> <p>Meng, X., & Cheng, J. (2018). Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data. Remote Sensing, 10, 474</p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).</p>
Effect of Topical Application of Iodex® Balm on Local Surface Temperature
ClinicalTrials.gov study NCT02424565. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Sea surface temperature and habitat effects on juvenile reef fish communities along a tropicalising coastline
Open the record for dataset details and reuse information.
Data from: Hot wings: thermal impacts of wing colouration on surface temperature during bird flight.
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Distinct impacts of major El Niño events on Arctic temperatures due to differences in eastern tropical Pacific sea surface temperatures
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Data from: Sea surface temperature dictates movement and habitat connectivity of Atlantic cod in a coastal fjord system
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MODIS/Terra Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Night V006
The MOD21A1N Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD21A1N Version 6.1](https://doi.org/10.5067/MODIS/MOD21A1N.061) data product.A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6. The MOD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the [MOD11](https://doi.org/10.5067/modis/mod11_l2.006) LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MOD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily [MOD21](http://doi.org/10.5067/MODIS/MOD21.006) swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MOD21A1N product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. MOD21A1N products are available two months after acquisition due to latency of data inputs. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).Known Issues * Forward processing of Terra MODIS LST&E Version 6 data products was discontinued on December 31, 2005. Users are encouraged to use the [MOD21A1N Version 6.1](https://doi.org/10.5067/MODIS/MOD21A1N.061) data product.* Users of MODIS LST products may notice an increase in occurrences of [extreme high temperature outliers](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=117) in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows.* High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination [example documentation](https://landweb.modaps.eosdis.nasa.gov/data/userguide/MOD21_dust_QC_examples.pdf).* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).Improvements/Changes from Previous Versions* New product for MODIS Version 6.
GHRSST L3C NOAA/ACSPO GOES-18/ABI West America Region Sea Surface Temperature v2.90 dataset
The G18-ABI-L3C-ACSPO-v2.90 dataset produced by the NOAA ACSPO system is used to derive Subskin and Depth Sea Surface Temperature (SST) from the ABI sensor onboard the G18 satellite. NOAA’s G18 (aka GOES-T before launch) was launched on March 1, 2022, replacing G17 as GOES West in Jan'2023. It is the third satellite in the US GOES–R Series, the Western Hemisphere’s most sophisticated weather-observing and environmental-monitoring system. The ABI is the primary instrument on the GOES-R Series for imaging Earth’s weather, oceans, and environment. <br><br>The G18-ABI-L3C-ACSPO-v2.90 dataset is a gridded version of the G18-ABI-L2P-ACSPO-v2.90 dataset (https://podaac.jpl.nasa.gov/dataset/G18-ABI-L2P-ACSPO-v2.90). The L3C (Level 3 Collated) outputs 24 hourly granules per day, with a daily volume of 0.7 GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST. <br><br>The ACSPO G18/ABI L3C product is validated against iQuam in situ data (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). The NRT files are replaced with Delayed Mode (DM) files, with a latency of ~2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing).
GHRSST Level 4 RAMSSA_9km Australian Regional Foundation Sea Surface Temperature Analysis v1.0 dataset (GDS2)
A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a regional 1/12 degree grid over the Australian region (20N - 70S, 60E - 170W). This Regional Australian Multi-Sensor SST Analysis (RAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers, with in situ data from ships, Argo floats, XBTs, CTDs, drifting buoys and moorings from the Global Telecommunications System (GTS). SST observations that have experienced recent surface wind speeds less than 6 m/s during the day or less than 2 m/s during night are rejected from the analysis. The processing results in daily foundation SST estimates that are largely free of nocturnal cooling and diurnal warming effects. Sea ice concentrations are supplied by the NOAA/NCEP 12.7 km sea ice analysis. In the absence of observations, the analysis relaxes to the BoM Global Weekly 1 degree OI SST analysis, which relaxes to the Reynolds and Smith (1994) Monthly 1 degree SST climatology for 1961 - 1990.
MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V006
The MOD11A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the [MOD11A2 Version 6.1](https://doi.org/10.5067/MODIS/MOD11A2.061) data product.The MOD11A2 Version 6 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding [MOD11A1](https://doi.org/10.5067/MODIS/MOD11A1.006) LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.Known Issues* Production of V6 Terra MODIS Land Surface Temperature and Emissivity (LST&E) data products was discontinued on November 16, 2022, due to [significant loss of data retrieval](https://landweb.modaps.eosdis.nasa.gov/displayissue?id=98) following the Constellation Exit Maneuvers.* For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=6).Improvements/Changes from Previous Versions* Removed cloud-contaminated LSTs from Level 2 and Level 3 LST products.* Updated the coefficient look-up table (LUT) for the split-window algorithm with comprehensive regression analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) simulation data in bands 31 and 32 over wide ranges of surface and atmospheric conditions, especially extending the upper boundary for (LST – Ts-air) in arid and semi-arid regions. Increased the overlap between various sub-ranges to reduce the sensitivity of the algorithm to uncertainties in the input data (i.e., column water vapor and air surface temperature from MOD07).* Made minor adjustments in the classification-based surface emissivity values, especially for bare soil and rocks land cover types.* Tuned the day/night algorithm by adjusting weights to improve performance in desert regions where the incorporated split-window algorithm may not work well.* Generated new gridded LST&E products with a 6 km spatial resolution for 8-day ([MOD11B2](https://doi.org/10.5067/MODIS/MOD11B2.006)) and monthly ([MOD11B3](https://doi.org/10.5067/MODIS/MOD11B3.006)) intervals in response to user community requests.
ASTER L2 Surface Temperature V003
The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Surface Kinetic Temperature (AST_08) is generated using the five Thermal Infrared (TIR) bands (acquired either during the day or night time) between 8 and 12 µm spectral range. It contains surface temperatures at 90 m spatial resolution for the land areas only. Surface kinetic temperature provides a vital input to studies of volcanism, thermal inertia, surface energy, and high-resolution mapping of fires. This product is derived using the same algorithm as the ASTER Surface Emissivity [AST_05](http://doi.org/10.5067/ASTER/AST_05.003) Product. Surface kinetic temperature is determined by applying Planck's Law using the emissivity values from the Temperature/Emissivity Separation (TES) algorithm, which uses atmospherically corrected ASTER surface radiance (TIR) data. The TES algorithm first estimates emissivity in the TIR channels using the Normalized Emissivity Method (NEM). These estimates are used along with Kirchoff's Law to account for the land-leaving TIR radiance that is due to sky irradiance. That figure is subtracted from TIR radiance iteratively to estimate the emitted radiance from which temperature is calculated using the NEM module.The ASTER L2 Surface Kinetic Temperature data product is only available through [NASA's Earthdata Search](https://search.earthdata.nasa.gov/search?q=C1299783630-LPDAAC_ECS). The ASTER Order Instructions provide step-by-step directions for ordering this product.Known Issues* Data acquisition gaps: On November 28, 2024, one of Terra's power-transmitting shunt units failed. As a result, there was insufficient power to maintain functionality of the ASTER instrument. ASTER resumed acquisitions for the VNIR bands on January 18, 2025, and for the TIR bands on April 15, 2025. Users should note the data gap in ASTER acquisitions from November 28, 2024, through January 16, 2025, for VNIR observations, and a gap from November 28, 2024, through April 15, 2025, for TIR acquisitions.Improvements/Changes from Previous Version* The Science Scalable Scripts-based Science Processor for Missions (S4PM) Version 3.4 algorithm, which is used to generate L2 Product Generation Executables (PGEs), is relying on a new ancillary input for atmospheric parameters. Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) is global atmospheric reanalysis that combines remote sensing observations and interactions with the climate system. It will be one of the primary ozone and water vapor, pressure, and temperature inputs for L2 PGEs. MERRA-2 will provide a finer geographic resolution grid since it is a 3-dimensional, 3-hourly data collection with 50-km (latitudinal direction) spatial resolution.* The fallback options for L2 PGEs are as follows: * Ozone: [TOVS Ozone (OZ_DLY ) > AURA Ozone Monitoring Instrument (AURAOMI) > Total Ozone Analysis from Stratospheric and Tropospheric (TOAST) > Earth Probe-Total Ozone Mapping Spectrometer (EPTOMS)] or [MERRA-2] > National Centers for Environmental Prediction (NCEP)/Global Data Assimilation System (GDAS) > Climatology * Water Vapor, Pressure, and Temperature: [MOD07_L2] or [MERRA-2] > NCEP/GDAS > Climatology* Caveat: The temporal range for MERRA-2 covers 1980 to present; however, there is latency of ~3 weeks after the end of a month. Hence, NCEP/GDAS > Climatology fallback sequence will be applied for on-demand requests that fall outside of MERRA-2's temporal range or if the data is not science grade.* Starting June 23, 2021, radiometric calibration coefficient Version 5 (RCC V5) will be applied to newly observed ASTER data and archived ASTER data products. Details regarding RCC V5 are described in the following journal article. * Tsuchida, S., Yamamoto, H., Kouyama, T., Obata, K., Sakuma, F., Tachikawa, T., Kamei, A., Arai, K., Czapla-Myers, J.S., Biggar, S.F., and Thome, K.J., 2020, Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations: Remote Sensing, v. 12, no. 3, at https://doi.org/10.3390/rs12030427.* As of December 15, 2021, the LP DAAC has implemented changes to ASTER PGE Version 3.4, which will affect all ASTER Level 2 on-demand products. Changes include: * Aura Ozone Monitoring Instrument (OMI) has been added as one of the ancillary ozone inputs for any observations made after May 27, 2020. The sequence of fallbacks for ozone will remain the same. * Toolkit has been updated from Version 5.2.17 to 5.2.20. Users may notice minor differences between the two versions. Differences may include minuscule changes in digital numbers around the peripheral of the granule and boundaries of a cloud for Surface Reflectance and Surface Radiance (AST07 and AST09) QA Data Plane depending on the Operating System and libraries being used by the user to process the data.* Additionally, Climatology, which is one of the inputs for Ozone and Moisture, Temperature and Pressures (MTP) will be remo
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.