ELITE land surface temperature: seamless 1km LST over China (2023)
Fuente:
Zenodo
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Recurso digital |
| Veröffentlicht: |
Zenodo
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866902017876688896 |
|---|---|
| author | Jie, Cheng Shengyue, Dong Jiancheng, Shi Helin, Wang |
| author_facet | Jie, Cheng Shengyue, Dong Jiancheng, Shi Helin, Wang |
| contents | <p class="MsoNormal">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 class="MsoNormal">This dataset is the ELITE seamless 1km LST over China landmass (2002-2024). 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 class="MsoNormal">This is the seamless LST dataset in 2023. Please <strong><em>click here</em></strong> to download the ELITE LST product in 2022 and <a href="https://zenodo.org/records/19677256"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2024.</p> <p class="MsoNormal"> </p> <p class="MsoNormal"><strong>Dataset Characteristics:</strong></p> <ul> <li class="MsoNormal">Spatial Coverage: China</li> <li class="MsoNormal">Temporal Coverage: 2023</li> <li class="MsoNormal">Spatial Resolution: 1 km</li> <li class="MsoNormal">Temporal Resolution: 2 times per day</li> <li class="MsoNormal">Data Format: Tiff</li> <li class="MsoNormal">Scale: 0.02</li> </ul> <p class="MsoNormal"><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 class="MsoNormal"> </p> <p class="MsoNormal">If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19677103 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | ELITE land surface temperature: seamless 1km LST over China (2023) Jie, Cheng Shengyue, Dong Jiancheng, Shi Helin, Wang land surface temperature multiresolution kalman filtering CDF thermal infrared remote sensing microwave remote sensing data fusion <p class="MsoNormal">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 class="MsoNormal">This dataset is the ELITE seamless 1km LST over China landmass (2002-2024). 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 class="MsoNormal">This is the seamless LST dataset in 2023. Please <strong><em>click here</em></strong> to download the ELITE LST product in 2022 and <a href="https://zenodo.org/records/19677256"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2024.</p> <p class="MsoNormal"> </p> <p class="MsoNormal"><strong>Dataset Characteristics:</strong></p> <ul> <li class="MsoNormal">Spatial Coverage: China</li> <li class="MsoNormal">Temporal Coverage: 2023</li> <li class="MsoNormal">Spatial Resolution: 1 km</li> <li class="MsoNormal">Temporal Resolution: 2 times per day</li> <li class="MsoNormal">Data Format: Tiff</li> <li class="MsoNormal">Scale: 0.02</li> </ul> <p class="MsoNormal"><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 class="MsoNormal"> </p> <p class="MsoNormal">If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).</p> |
| title | ELITE land surface temperature: seamless 1km LST over China (2023) |
| topic | land surface temperature multiresolution kalman filtering CDF thermal infrared remote sensing microwave remote sensing data fusion |
| url | https://doi.org/10.5281/zenodo.19677103 |