ELITE land surface temperature: seamless 1km LST over China (2009)

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 (https://elite.bnu.edu.cn). 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. This is the seamless LST dataset in 2009. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2008 and <em><strong>click here</strong></em> to download the ELITE LST product in 2010. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2009 Spatial Resolution: 1 KM Temporal Resolution: 2 times per day Data Format: hdf Scale: 0.02 <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Xu, S., &amp; 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 Zhang, Q., Wang, N., Cheng, J., &amp; 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 Zhang, Q., &amp; 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 If you have any questions, please contact Prof. Jie Cheng ([e-mail]).**热红外遥感核心（Essential Thermal Infrared Remote Sensing，ELITE）**产品套件目前包含四类产品：地表温度（Land Surface Temperature, LST：晴空与全天空条件下）、发射率（NBE：窄带发射率；BBE：宽带发射率；光谱发射率）、地表辐射与能量收支分量（SLUR：地表长波上行辐射；SLDR：地表长波下行辐射；SLNR：地表长波净辐射）以及地球辐射收支分量（OLR：向外长波辐射；RSR：反射太阳辐射）。该ELITE产品的时空分辨率主要取决于所采用的卫星数据源。有关ELITE产品的更多信息，请访问官网：https://elite.bnu.edu.cn。 本数据集为覆盖中国陆域的ELITE无缝1km分辨率地表温度数据集（时间范围2002-2020年）。具体生成流程如下：首先，开发了基于查找表的经验反演算法，用于从AMSR-E/AMSR2观测数据中反演微波LST；随后，采用地理加权回归对AMSR-E/AMSR2反演得到的LST进行降尺度处理，获取1km分辨率LST；最后，利用多尺度卡尔曼滤波融合MODIS LST与AMSR-E/AMSR2 LST，生成1km分辨率无缝LST数据集。 地面验证结果表明，该1km无缝LST的均方根误差（Root Mean Square Error, RMSE）约为3K。此外，该1km无缝LST的空间分布与MODIS LST及CLDAS LST保持一致。本次提供的为2009年的无缝LST数据集。 如需下载2008年ELITE LST产品，请点击此处；如需下载2010年ELITE LST产品，请点击此处。 **数据集特征：** 空间覆盖范围：中国陆域 时间覆盖范围：2009年 空间分辨率：1KM 时间分辨率：每日2次 数据格式：HDF 尺度因子：0.02 **引用说明（使用本数据集时请引用以下文献）：** 1. 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. 2. 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. 3. 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. 如有任何疑问，请联系程杰教授（邮箱：[e-mail]）。