全球无间隙月均地表温度数据集（2003-2018）

地表温度（LST）在能量平衡、水文学、气象学、地理学和生态学等领域的研究中起着至关重要的作用，是一项广泛关注的重要输入指标。本数据集是通过综合两个遥感LST数据集和两个再分析LST数据集（这四种LST产品分别是：①占文凤生产的2003年-2019年全球逐日1km分辨率地表温度日均温产品数据集（http://www.nesdc.org.cn/sdo/detail?id=60f4e35e7e28173cf0c8a771），②刘向阳生产的2000年-2020年全球月平均地表温度数据集（https://zenodo. org/record/6618442），③GLDAS提供的月均地表温度（https://disc.gsfc.nasa.gov）以及④ERA5-land提供的月均地表温度（https://cds.climate.copernicus.eu）），利用FLUXNET、ChinaFlux、SURFARD以及TPDC网络的170个地面实测站点评估四种LST产品在不同条件下（月份、气候、土地覆盖类型以及海拔）的表现，分析了它们的偏差（bias）、均方根误差（RMSE）、平均绝对误差（MAE）、确定系数（R^2）和Nash-Sutcliffe效率系数（NSE）。根据评估结果为每种产品赋予了合成时的权重，最终合成得到了一个综合月均地表温度产品（该产品时间覆盖范围是2003-2018年，空间分辨率是1km）。验证结果显示，合成产品的RMSE约为2.3K，优于其他四个产品，并且精度提高了0.4-0.7K。此外，合成产品在不同土地覆盖类型下（草地、农田、林地、湿地、贫瘠等）的RMSE始终优于其他四个产品。合成产品与其他四个产品之间的空间差异分析显示，GLDAS在整体和季节性比较中与合成产品最接近，但是在一些地区（如格陵兰岛，落基山脉，安第斯山脉以及昆仑山脉等）存在显著差异。

Land Surface Temperature (LST) plays a critical role in research domains including energy balance, hydrology, meteorology, geography and ecology, serving as a widely recognized important input indicator. This dataset was synthesized by integrating two remote sensing LST datasets and two reanalysis LST datasets. The four LST products are as follows: ① The global daily 1km-resolution daily average LST product dataset from 2003 to 2019 produced by Zhan Wenfeng (http://www.nesdc.org.cn/sdo/detail?id=60f4e35e7e28173cf0c8a771), ② The global monthly average LST dataset from 2000 to 2020 produced by Liu Xiangyang (https://zenodo.org/record/6618442), ③ Monthly average LST provided by GLDAS (https://disc.gsfc.nasa.gov), and ④ Monthly average LST provided by ERA5-land (https://cds.climate.copernicus.eu). We evaluated the performance of the four LST products across diverse conditions (month, climate zone, land cover type and elevation) using 170 ground-based observation sites from the FLUXNET, ChinaFlux, SURFARD and TPDC networks, and analyzed their bias, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R²) and Nash-Sutcliffe Efficiency Coefficient (NSE). We assigned synthesis weights to each product based on the evaluation outcomes, and ultimately generated a comprehensive monthly average LST product, which spans the temporal range of 2003–2018 with a spatial resolution of 1 km. Verification results indicate that the RMSE of the synthesized product is approximately 2.3 K, which is superior to the other four products, with an accuracy improvement of 0.4–0.7 K. Furthermore, the RMSE of the synthesized product across different land cover types (grassland, cropland, woodland, wetland, barren land, etc.) consistently outperforms that of the four competing products. Spatial difference analysis between the synthesized product and the four competing products reveals that GLDAS is the most similar to the synthesized product in overall and seasonal comparisons, yet exhibits significant discrepancies in certain regions including Greenland, the Rocky Mountains, the Andes and the Kunlun Mountains.

搜集汇总

背景与挑战

背景概述

该数据集是一个全球无间隙月均地表温度数据集，覆盖2003年至2018年，空间分辨率为1公里，通过综合四个遥感和再分析地表温度产品并基于地面实测站点评估加权合成而成。验证表明合成产品均方根误差约为2.3K，精度优于其他产品，且在不同土地覆盖类型下表现稳定，适用于能量平衡、水文学和生态学等领域的研究。