Comprehensive In Situ Validation of Five Satellite Land Surface Temperature Data Sets over Multiple Stations and Years
Comprehensive In Situ Validation of Five Satellite Land Surface Temperature Data Sets over Multiple Stations and Years
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DOI：
10.3390/rs11050479
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
M. Martin;D. Ghent;A. Pires;F. Göttsche;J. Cermak;J. Remedios
中科院分区：
文献类型：
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作者：
M. Martin;D. Ghent;A. Pires;F. Göttsche;J. Cermak;J. Remedios
从基于卫星的红外辐射测量得到的全球陆地表面温度(LST)数据在气候研究的各种应用中具有很高的价值。虽然卫星LST数据集的现场验证是一项具有挑战性的任务，但需要获得关于其准确性的定量信息。在本文首次提出的多传感器验证的标准化方法中，从几个传感器(AATSR、GOES、MODIS和SEVIRI)使用最先进的检索算法获得的LST数据集与来自全球分布的代表各种土地覆盖类型的站点多年的现场数据以一致的方式在空间和时间上进行匹配。处理的共同性对这一办法至关重要：所有卫星数据集都被投射到同一空间网格，并转换成一种共同的协调格式，从而能够以相同的方法和数据处理与现场数据进行比较。欧洲航天局的全球温度项目提供的标准化卫星LST的大型数据库使以前难以进行的LST研究和应用变得更加可行和更容易实施。根据数据的可用性，卫星数据集将在三年或十年内得到验证。整个时间跨度的平均精度在夜间一般在±2.0K以内，白天在±4.0K以内。对各个站点的时间序列分析揭示了季节性周期。根据台站的不同，它们源于地表各向异性、地形或不均匀的土地覆盖。这些结果证明了LST产品的成熟度，但也强调了在将它们用于科学目的时需要仔细考虑它们的时间和空间特性。
Global land surface temperature (LST) data derived from satellite-based infrared radiance measurements are highly valuable for various applications in climate research. While in situ validation of satellite LST data sets is a challenging task, it is needed to obtain quantitative information on their accuracy. In the standardised approach to multi-sensor validation presented here for the first time, LST data sets obtained with state-of-the-art retrieval algorithms from several sensors (AATSR, GOES, MODIS, and SEVIRI) are matched spatially and temporally with multiple years of in situ data from globally distributed stations representing various land cover types in a consistent manner. Commonality of treatment is essential for the approach: all satellite data sets are projected to the same spatial grid, and transformed into a common harmonized format, thereby allowing comparison with in situ data to be undertaken with the same methodology and data processing. The large data base of standardised satellite LST provided by the European Space Agency’s GlobTemperature project makes previously difficult to perform LST studies and applications more feasible and easier to implement. The satellite data sets are validated over either three or ten years, depending on data availability. Average accuracies over the whole time span are generally within ±2.0 K during night, and within ± 4.0 K during day. Time series analyses over individual stations reveal seasonal cycles. They stem, depending on the station, from surface anisotropy, topography, or heterogeneous land cover. The results demonstrate the maturity of the LST products, but also highlight the need to carefully consider their temporal and spatial properties when using them for scientific purposes.