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Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer
<p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> GLACIER METEOROLOGICAL DATA<br> SWISS ALPS -2022<br>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br>Data gathered and structured by Thomas Shaw (WSL, Switzerland (until Oct 2022)).</p><p>On and off-glacier meteorological data were gathered and analysed as part of a Marie-Curie project 'TEMPEST' (tempestglacier.com).<br>The dataset consists of hourly low-cost AWS (Davis Vantage Pro2) and simple temperature ('T-')logger (Onset TidBitv2) sensor records on three glaciers in the Swiss Alps (Canton Valais).</p><p>The glaciers are:<br>Haut Glacier d'Arolla (45.967°N, 7.526°E)<br>Glacier d'Otemma (45.956°N, 7.454°E)<br>Glacier du Corbassière (45.975°N, 7.303°E)</p><p>Data are provided in individual Excel files per glacier that contain all hourly data for the sub-period of comparison (11 August-18 September, 2022).<br>Data are quality controlled and checked for obvious errors. Any uncertain values are set to NaN.<br>Air temperature data at 'T-Logger' stations were corrected for heating errors using the comparison of measurements in artificially (AWS) and naturally ventilated (T-Logger) radiation shields on Arolla and Corbassiere glaciers.<br>A multiple linear regression model was applied to estimate these differences at all T-Loggers on all glaciers as a function of incoming shortwave radiation (MeteoSwiss station-derived) and wind speed (measured at AWS).</p><p>Each Excel file contains a 'META' tab for simple metadata related to station locations (latitude 'LAT' (°), longitude 'LON' (°), elevation 'ELE' (m a.s.l.) and flowpath length 'FPL' (m)) and a 'DATA' tab for the hourly data. <br>Suffixes to the station names in each column provide the variable measured at that site:<br>'TA' - 2m air temperature (°C)<br>'TA_Hi' - Maximum air temperature for timestep (°C)<br>'TA_Lo' - Minimum air temperature for timestep (°C)<br>'RH' - 2m relative humiditiy (%)<br>'FF' - Wind speed (m s^-1)<br>'FF_Hi' - Maximum wind speed for timestep (m s^-1)<br>'FF_Lo' - Minimum wind speed for timestep (m s^-1)<br>'DIR' - Wind direction (°)<br>'DEW' - Dewpoint temperature (°C)<br>'PRESS' - Air pressure (mbar)<br>'CHILL' - Calculated wind chill temperature (°C)<br>'Heat_idx' - Calculated heat index (°C)<br>'THSW' - A calculated index that uses humidity and temperature like for the Heat Index, but also includes the heating effects of sunshine and the cooling effects of wind (like Wind Chill) to calculate an apparent temperature of what it "feels" like out in the shade</p><p>Wind speeds and direction measured at off-glacier sites 'OG' are for the lower off-glacier station ('OG_Low'). </p><p>-------------------------</p><p> </p><p>This work was funded by the EU Horizon 2020 Marie Skłodowska-Curie Actions Grant 101026058.<br> </p>
Late Cenozoic Sea Surface Temperature evolution of the South Atlantic Ocean (Data sets)
<p>Supplementary datasets affiliated with the Hoem et al., 2023 publication in Climate of the past: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-291/ </p> <p>Supplementary Table 3: GDGT results Site 696</p> <p>Supplementary Table 4: GDGT results SiteU1536</p>
ELITE land surface temperature: seamless 1km LST over China (2012)
<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 2012. Please <a href="https://zenodo.org/record/8274911"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2011 and <a href="https://zenodo.org/record/8274917"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2013.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2012</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: seamless 1km LST over China (2015)
<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 2015. Please <a href="https://zenodo.org/record/8274965"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2014 and <a href="https://zenodo.org/record/8274961"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2016.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2015</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: seamless 1km LST over China (2013)
<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 2013. Please <a href="https://zenodo.org/record/8274913"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2012 and <a href="https://zenodo.org/record/8274965"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2014.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2013</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: seamless 1km LST over China (2017)
<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 2017. Please <a href="https://zenodo.org/record/8274961"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2016 and <a href="https://zenodo.org/record/8274971"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2018.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2017</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: seamless 1km LST over China (2011)
<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 2011. Please <a href="https://zenodo.org/record/8273154"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2010 and <a href="https://zenodo.org/record/8274913"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2012.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2011</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: seamless 1km LST over China (2009)
<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 2009. Please <a href="https://zenodo.org/record/8273134"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2008 and <a href="https://zenodo.org/record/8273154"><em><strong>click here</strong></em> </a>to download the ELITE LST product in 2010.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2009</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: seamless 1km LST over China (2007)
<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 2007. Please <a href="https://zenodo.org/record/8271732"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2006 and <a href="https://zenodo.org/record/8273134"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2008.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2007</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: seamless 1km LST over China (2008)
<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 2008. Please <a href="https://zenodo.org/record/8271953"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2007 and <a href="https://zenodo.org/record/8273152"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2009.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2008</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: seamless 1km LST over China (2010)
<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 2010. Please <a href="https://zenodo.org/record/8273152"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2009 and <a href="https://zenodo.org/record/8274911"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2011.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2010</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: seamless 1km LST over China (2005)
<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 2005. Please <a href="https://zenodo.org/record/8271726"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2004 and <a href="https://zenodo.org/record/8271732"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2006.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2005</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: seamless 1km LST over China (2006)
<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 2006. Please <a href="https://zenodo.org/record/8271728"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2005 and <a href="https://zenodo.org/record/8271953"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2007.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2006</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: seamless 1km LST over China (2018)
<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 2018. Please <a href="https://zenodo.org/record/8274969"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2017 and <a href="https://zenodo.org/record/8274980"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2019.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2018</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: seamless 1km LST over China (2019)
<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 2019. Please <a href="https://zenodo.org/record/8274971"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2018 and <a href="https://zenodo.org/record/8274982"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2020.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2019</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: seamless 1km LST over China (2004)
<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 2004. Please <a href="https://zenodo.org/record/8271722"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2003 and <a href="https://zenodo.org/record/8271728"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2005.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2004</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>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: seamless 1km LST over China (2016)
<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 2016. Please <a href="https://zenodo.org/record/8274959"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2015 and <a href="https://zenodo.org/record/8274969"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2017.</p> <p> </p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2016</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: seamless 1km LST over China (2020)
<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 2020. Please <a href="https://zenodo.org/record/8274980"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2019.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2020</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:Jie_Cheng@bnu.edu.cn">Jie_Cheng@bnu.edu.cn</a>).</p>
ELITE land surface temperature: seamless 1km LST over China (2003)
<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 2003. Please <a href="https://zenodo.org/record/8271665"><em><strong>click here</strong></em> </a>to download the ELITE LST product in 2002 and <a href="https://zenodo.org/record/8271726"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2004.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2003</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> <p>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</p> </li> <li> <p>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 </p> </li> <li> <p>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 </p> <p> </p> </li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
ELITE land surface temperature: Landsat8 LST over CONUS (2014.7)
<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 the CONUS 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 8 in July 2014. Please <a href="https://zenodo.org/record/8275604"><em>click here</em></a> to download the ELITE Landsat LST from Landsat 8 in January 2014.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Continental United States</li> <li>Temporal Coverage: 2014.7</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>
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.