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78 results for “LST”
Long-term MODIS LST day-time and night-time temperatures, sd and differences at 1 km based on the 2000–2020 time series
<p>Layers include: Land Surface Temperature daytime monthly median value 2000–2017, Land Surface Temperature daytime monthly sd value 2000–2017, Land Surface Temperature daytime monthly day-night difference 2000–2017. Derived using the <a href="https://gitlab.com/openlandmap/global-layers/-/tree/master/input_layers/MOD11A2">data.table package and quantile function in R</a>. We derived four standard statistics: (1) lower 2.5% probability (l.025), median (m), upper 97.5% probability (u.975) and standard deviation (sd). Updated long-term values for 2000–2022+ are pending.</p> <p>Includes also long-term trends (trend.logit.ols) which was produced by fitting regression models to de-seasonalized time-series as explained in this <strong><a href="https://gitlab.com/openlandmap/global-layers/-/blob/master/input_layers/MOD13Q1/03-data-access.ipynb">python tutorial</a></strong>. Basically models are fitted for <strong>each pixel</strong> and the model parameters are saved as images.</p> <p>For more info about the MODIS LST product see: <a href="https://lpdaac.usgs.gov/products/mod11a2v006/"><strong>https://lpdaac.usgs.gov/products/mod11a2v006/</strong></a>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">https://gitlab.com/openlandmap/global-layers/-/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>clm = theme: climate,</li> <li>lst = variable: land surface temperature,</li> <li>mod11a2.oct.day = determination method: MOD11A2 product, day time values for October,</li> <li>d = median value / sd = standard deviation / u.975 = aggregation/statistics method: 97.5% probability upper quantile,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: from 2000 to 2017,</li> <li>v1.0 = version number: 1.0,</li> </ul>
MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2000 at 1-km
<p>Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2000. Derived using the <a href="https://gitlab.com/openlandmap/global-layers/-/tree/master/input_layers/MOD11A2">data.table package and quantile function in R</a>. For more info about the MODIS LST product see: <a href="https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006">https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006</a>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>To download and compute with maps using Cloud-Optimized-GeoTIFF see <a href="https://gitlab.com/openlandmap/global-layers/-/blob/master/tutorial/OpenLandMap_COG_tutorial.md"><strong>this tutorial</strong></a>.</p> <p>If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">https://gitlab.com/openlandmap/global-layers/-/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>clm = theme: climate,</li> <li>lst = variable: land surface temperature,</li> <li>mod11a2.daytime = determination method: MOD11A2 product, day time values,</li> <li>d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2020 = time reference: from 2000 to 2020,</li> <li>v1.1 = version number: 1.1,</li> </ul>
2000-2018 SUHII and Rural LST Monthly Means used in Sismanidis et al. 2022
<p>This dataset provides the 2000-2018 SUHII and rural LST monhtly means used in Sismanidis et al. (2022). The source of the LST data is the the <a href="https://climate.esa.int/en/odp/#/project/land-surface-temperature">v1.0 Terra MODIS data product</a> created by the <a href="https://climate.esa.int/en/projects/land-surface-temperature/">ESA-CCI project on Land Surface Temperature (LST_cci)</a>.</p>
Great Lakes WRF-FVCOM model ensemble outputs: Summer 2018 daily LST and T2m
<p>Postprocessed model data for the paper: "Coupled Lake-Atmosphere-Land Physics Uncertainties in a Great Lakes Regional Climate Model"</p> <p>Perturbed Physics Ensemble outputs from a coupled lake-atmosphere-land Great Lakes regional model: <br>- Time period: May, June, July of 2018 <br>- Computational domain: Great Lakes region as contained within <a href="../api/records/10806629/draft/files/wrf_grid.nc/content" target="_blank" rel="noopener noreferrer">wrf_grid.nc</a> (atmosphere-land) and <a href="../api/records/10806629/draft/files/fvcom_grid.nc/content" target="_blank" rel="noopener noreferrer">fvcom_grid.nc</a> (lake).<br>- Quantities of interest: lake surface temperature and 2-m near-surface air temperature<br>- Training set: "<a href="../api/records/10806629/draft/files/wfv_global_daily_temperature_training_set.pkl/content" target="_blank" rel="noopener noreferrer">wfv_global_daily_temperature_training_set.pkl</a>" [18 members]. Associated with "<a href="../api/records/10806629/draft/files/perturbation_matrix_9variables_korobov18.nc/content" target="_blank" rel="noopener noreferrer">perturbation_matrix_9variables_korobov18.nc</a>" input model configuration matrix.<br>- Test set: "<a href="https://zenodo.org/api/records/13863491/draft/files/wfv_global_daily_temperature_test_set.pkl/content" target="_blank" rel="noopener noreferrer">wfv_global_daily_temperature_test_set.pkl</a>" [9 members]. Associated with "<a href="https://zenodo.org/api/records/13863491/draft/files/perturbation_matrix_9variables_latin_hypercube9.nc/content" target="_blank" rel="noopener noreferrer">perturbation_matrix_9variables_latin_hypercube9.nc</a>" input model configuration matrix.</p>
Global Hourly, 5-km, All-sky Land Surface Temperature (GHA-LST) from 2011 - now
<p>GHA-LST is a global, hourly, 5-km, all-sky, gap-free, and all-weather land surface temperature (LST) dataset. The manuscript describing this dataset has been accepted by <em>Earth System Science Data (ESSD)</em> (<a href="https://doi.org/10.5194/essd-15-869-2023" target="_new" rel="noopener">https://doi.org/10.5194/essd-15-869-2023</a>). Due to storage limitations on Zenodo, the full dataset is available at <a href="http://glass.umd.edu/allsky_LST/GHA-LST" target="_new" rel="noopener">glass.umd.edu/allsky_LST/GHA-LST</a>. The dataset is updated annually. For further details, please contact Dr. Aolin Jia at <a rel="noopener">aolin@terpmail.umd.edu</a>.</p>
Validation of MODIS11A2 LST and glacier surface heatwave during 2001-2020 over Tibetan Plateau
<p>1,Validation of MODIS11A2 LST in 2019 using AWS temperature on the glacier</p> <p>2,Validation of MODIS11A2 LST during 2001-2020 using CMA station temperature over the Tibetan Plateau</p> <p>3,Glacier surface heatwave during 2001-2020 over the Tibetan Plateau glacier </p>
DATASET - Improving Remote Sensing of Extreme Events with Machine Learning: Application to IASI LST Retrievals
<p>Data for experiments presented in the paper "Improving Remote Sensing of Extreme Events with Machine Learning: Application to IASI LST Retrievals" </p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2021)
<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 hourly seamless 0.02 ° LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the seamless LST dataset in 2021. Please<a href="../records/10668883" target="_blank" rel="noopener"> <strong><em>click here</em></strong></a> to download the ELITE LST product in Januray-June, 2022.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2021</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</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>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (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 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2020. Please <a href="https://zenodo.org/record/8260245"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2019 and <a href="https://zenodo.org/record/8260240"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2021.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2020</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</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>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (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 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2018. Please <a href="https://zenodo.org/record/8266456"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2017 and <a href="https://zenodo.org/record/8260245"><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: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2018</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</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>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (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 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2019. Please <a href="https://zenodo.org/record/8256087"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2018 and <a href="https://zenodo.org/record/8264798"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2020.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2019</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</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>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
GLASS Land Surface Temperature product (1981-2000): instantaneous LST
<ul> <li>GLASS Land Surface Temperature product (1981-2000): instantaneous LST have been produced from historical NOAA AVHRR data. The LST data were generated by integrating several Split-Window Algorithms with the Random Forest method (RF-SWA). The individual SWAs and the RF-SWA were trained and tested on simulation datasets obtained from globally representative atmospheric profiles. The validation against <em>in-situ</em> LST shows that the GLASS LST product has a precision of 1.18 K.</li> <li>The dataset is organized by year and a sample data is provided in simple.zip</li> <li>Further details can be found in the readme.pdf.</li> </ul>
Crab Nebula DL3 example dataset from the LST-1 performance study
<h1>DL3 example dataset from Crab Nebula observations with LST-1</h1> <p>This repository contains a subsample of DL3 files from Crab Nebula observations used in the performance study of the Large-Sized Telescope prototype (LST-1, <a href="https://www.lst1.iac.es/">https://www.lst1.iac.es/</a>) for the Cherenkov Telescope Array Observatory (CTAO, <a href="https://www.ctao.org/">https://www.ctao.org/</a>). The results of this performance study [1] were obtained from a larger sample of the Crab Nebula observations than the one compiled here.</p> <p>This reduced dataset aims to serve as an example for analyzing observed data by one of the telescopes that will be part of the future CTAO. These data files are intended to be used in the hands-on sessions for the 1D high-level DL3 analysis in the CTAO Shcool (<a href="https://www.school.cta-observatory.org/" target="_blank" rel="noopener">https://www.school.cta-observatory.org/</a>).</p> <h2>Information about the data and the reduction process</h2> <p>The DL3 files included in this repository are a subsample of 1.9 hours of Crab Nebula observations data taken on March 4th and 5th, 2022. They were produced with <a href="https://github.com/cta-observatory/cta-lstchain" target="_blank" rel="noopener">cta-lstchain</a> in FITS format following the Gamma-ray Astronomy Data Format (GADF; [3]). They can be directly read and analyzed with <a href="https://gammapy.org/" target="_blank" rel="noopener">Gammapy</a> [4]. Data were processed following the source-independent analysis approach described in [1]. The gamma-hadron separation and directional cuts (<em>gammaness</em> and <em>theta</em> parameters) for the gamma-ray-like event selection were chosen to keep 70% of gamma-ray-like simulated events in each bin of reconstructed energy. The point-like instrument response functions (IRF) were produced (using <a href="https://github.com/cta-observatory/pyirf" target="_blank" rel="noopener">pyirf</a> [5]) using the same energy-dependent efficiency cuts from simulated gamma rays in an all-sky grid of pointing positions. Final IRFs for each observation run were produced by linear interpolation among the closest simulated pointing nodes to the actual telescope pointing while observing the Crab Nebula.</p> <h2>List of files</h2> <ul> <li>dl3_LST-1.Run07253.fits</li> <li>dl3_LST-1.Run07254.fits</li> <li>dl3_LST-1.Run07255.fits</li> <li>dl3_LST-1.Run07256.fits</li> <li>dl3_LST-1.Run07274.fits</li> <li>dl3_LST-1.Run07275.fits</li> <li>dl3_LST-1.Run07276.fits</li> <li>dl3_LST-1.Run07277.fits</li> <li>hdu-index.fits.gz</li> <li>obs-index.fits.gz</li> </ul> <p> </p> <h2>Acknowledgements</h2> <p>The production of these files has been possible thanks to the LST Collaboration work at different levels, namely, hardware and software development, data-taking, production of simulations, and data analysis.</p> <h2>References</h2> <p>[1] H. Abe <em>et al</em> 2023 <em>ApJ</em> <strong>956</strong> 80 (<strong>DOI</strong> <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ace89d" target="_blank" rel="noopener">10.3847/1538-4357/ace89d</a>)</p> <p>[2] <em>cta-lstchain</em>: <a href="https://doi.org/10.5281/zenodo.10849683" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10849683</a></p> <p>[3] Data formats for gamma-ray astronomy. <a href="https://github.com/open-gamma-ray-astro/gamma-astro-data-formats" target="_blank" rel="noopener">https://github.com/open-gamma-ray-astro/gamma-astro-data-formats</a></p> <p>[4] A&A, 678, A157 (2023) DOI <a href="https://doi.org/10.1051/0004-6361/202346488" target="_blank" rel="noopener">https://doi.org/10.1051/0004-6361/202346488 </a></p> <p>[5] <em>pyirf</em>: <a href="https://doi.org/10.5281/zenodo.8348922" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8348922</a></p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (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 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the ELITE seamless LST product in 2017. Please <a href="https://zenodo.org/record/7306248"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2016 and <a href="https://zenodo.org/record/8256087"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2018.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2017</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</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>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> <p> </p>
ELITE land surface temperature: seamless 1km LST over China (2002)
<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 2002. Please <a href="https://zenodo.org/record/8271722"><em><strong>click here</strong></em></a> to download the ELITE LST product in 2003.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: China</li> <li>Temporal Coverage: 2002</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>
GLASS Land Surface Temperature product (1981-2000): Orbital Drift Corrected LST
<ul> <li>GLASS Land Surface Temperature product (1981-2000): the ODC LST product is an orbital drift corrected (ODC) version of the instantaneous GLASS LST product. To compensate the effect of orbital drift on LST, an improved ODC method was used to normalize the instantaneous GLASS LSTs to 14:30 solar time.</li> <li>The dataset is organized by year and a sample data is provided in simple.zip</li> <li>Further details can be found in the readme.pdf.</li> </ul>
GLASS Land Surface Temperature product (1981-2000): monthly averaged LST
<ul> <li>The LST product contains monthly averages of GLASS ODC LST.</li> <li>The dataset is organized by year and a sample data is provided in simple.zip</li> <li>Further details can be found in the readme.pdf.</li> </ul>
ELITE land surface temperature: FY-4A/AGRI hourly 4km seamless LST (2023.1-2023.5)
<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 ELITE hourly seamless 4 km LST dataset covering the FY-4A/AGRI nominal fixed disc (80.6°N-80.6°S, 24.1°E-174.7°W). First, an improved temperature and emissivity separation algorithm was used to obtain the clear-sky LST. Then, under the framework of the SEB theory, a unique way was proposed to solve the temperature difference between the cloudy-sky LST and hypothetical clear-sky LST caused by cloud radiative effects. The in situ validation results show that the bias (RMSE) of the AGRI hourly seamless LST is 0.02 K (2.84 K). The temporal resolution and spatial resolution of this dataset are 1 hour and 4 km, respectively.</p> <p>This is the ELITE FY-4A/AGRI seamless LST product in 2023. Please <a href="../records/10595576"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2022.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: AGRI nominal fixed disc (80.6°N-80.6°S, 24.1°E-174.7°W)</li> <li>Temporal Coverage: 2023.1-2023.5</li> <li>Spatial Resolution: 4 km (subsatellite point)</li> <li>Temporal Resolution: one hour</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Liu, W., Cheng, J. & Wang, Q. (2023). Estimating Hourly All-Weather Land Surface Temperature From FY-4A/AGRI Imagery Using the Surface Energy Balance Theory. <em>IEEE Transactions on Geoscience and Remote Sensing, 61</em>,<em> 5001518</em></li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).</p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2022.7-2022.12)
<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 hourly seamless 0.02 ° LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the seamless LST dataset in 2022.7-2022.12. Please <a href="../records/8260240"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2021.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2022.7-2022.12</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</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>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
Monthly MODIS LST data related to the article: A new fully gap-free time series of land surface temperature from MODIS LST data
<p>Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. With a novel method [1] we fully reconstructed the daily global MODIS LST products MOD11C1 and MYD11C1 (spatial resolution: 3 arc-min, i.e. approximately 5.6 km at the equator). For this, we combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. Here we provide a time series of these reconstructed LST data aggregated as monthly average, minimum and maximum LST maps.</p> <p>[1] Metz M., Andreo V., Neteler M. (2017): A new fully gap-free time series of Land Surface Temperature from MODIS LST data. Remote Sensing, 9(12):1333. DOI: http://dx.doi.org/10.3390/rs9121333</p> <p>LICENSE: Open Data Commons Open Database License (ODbL) http://opendatacommons.org/licenses/odbl/</p> <p>Acknowledgments: We are grateful to the NASA Land Processes Distributed Active Archive Center (LP DAAC) for making the MODIS LST data available. The dataset is based on MODIS Collection V006.</p> <p><strong>The data available here for download are the reconstructed global MODIS LST products MOD11C1/MYD11C1 at a spatial resolution of 3 arc-min</strong> (approximately 5.6 km at the equator; see https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table), <strong>aggregated to monthly data</strong>. The data are provided in GeoTIFF format. The Coordinate Reference System (CRS) is identical to the MOD11C1/MYD11C1 product as provided by NASA. In WKT as reported by GDAL:<br> <br> GEOGCS["Unknown datum based upon the Clarke 1866 ellipsoid",<br> DATUM["Not specified (based on Clarke 1866 spheroid)",<br> SPHEROID["Clarke 1866",6378206.4,294.9786982138982,<br> AUTHORITY["EPSG","7008"]]],<br> PRIMEM["Greenwich",0],<br> UNIT["degree",0.0174532925199433]]<br> </p> <p><strong>File name</strong> abbreviations:</p> <ul> <li>avg = average of daily averages</li> <li>min = minimum of daily minima</li> <li>max = maximum of daily maxima</li> </ul> <p>Meaning of <strong>pixel values</strong>:</p> <ul> <li>The <strong>pixel values</strong> are coded in <strong>degree Celsius * 100</strong> (hence, to obtain °C divide the pixel values by 100.0).</li> </ul> <p>Version <strong>changelog</strong>:</p> <ul> <li>V1.1.0: GeoTIFF metadata updated.</li> <li>V1.0.0: original upload</li> </ul>
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