Spatial and Temporal Variations of Land Surface Temperature Over the Tibetan Plateau Based on Harmonic Analysis
Spatial and Temporal Variations of Land Surface Temperature Over the Tibetan Plateau Based on Harmonic Analysis
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DOI：
10.1659/mrd-journal-d-12-00090.1
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发表时间：
2013-02
影响因子：
13.4
通讯作者：
Yongming Xu;Yan Shen;Ziyue Wu
中科院分区：
文献类型：
--
作者：
Yongming Xu;Yan Shen;Ziyue Wu
摘要地表温度（LST）是地表过程物理学中的一个重要参数。考虑云污染对遥感LST产品数据的影响，采用时间序列谐波分析（HANTS）算法消除云量的影响，对LST周期性信号进行描述。利用79个气象站的气温观测数据，对HANTS算法的拟合性能进行了评价。结果表明，HANTS能有效拟合地表温度时间序列，并能消除云层的影响。基于汉斯平均项和年谐波，讨论了年平均地表温度、季节波动和地表温度年周期的峰值时间。研究区年平均地表温度的空间分布总体上与海拔高度一致，季节振荡的空间分布与降水密切相关。然而，地表温度峰值的发生时间并没有明显的规律性。研究了不同土地覆被类型的地表温度特征。裸地平均地表温度最高，且季节性波动显著。雪、冰或两者均表现出最低的平均温度，森林表现出最弱的季节性。不同土地覆被类型对地表温度年周期峰值的反映也不同，其中草地的年相位值最低。HANTS算法对于理解遥感地表温度的时空变化是有效的，特别是对于那些稠密云层导致地表温度数据存在较大差距的地区。
Abstract Land surface temperature (LST) is an essential parameter in the physics of land surface processes. The spatiotemporal variations of LST on the Tibetan Plateau were studied using AQUA Moderate Resolution Imaging Spectroradiometer LST data. Considering the data gaps in remotely sensed LST products caused by cloud contamination, the harmonic analysis of time series (HANTS) algorithm was used to eliminate the influence of cloud cover and to describe the periodical signals of LST. Observed air temperature data from 79 weather stations were employed to evaluate the fitting performance of the HANTS algorithm. Results indicate that HANTS can effectively fit the LST time series and remove the influence of cloud cover. Based on the HANTS-derived mean term and annual harmonics, annual mean LST, seasonal fluctuation, and peak time of the LST annual cycle are discussed. The spatial distribution of annual mean LST generally exhibits consistency with altitude in the study area, and the spatial distribution of seasonal oscillation is closely related to precipitation. However, the timing of the peak LST does not exhibit an obvious regular pattern. The LST characteristics of different land cover types were also studied. Bare land has the highest mean LST and exhibits remarkable seasonal fluctuation. Snow, ice, or both show the lowest mean temperature, and forest shows the weakest seasonality. Different land cover types also reflect different peak occurrences of the LST annual cycle, with grassland showing the lowest annual phase value. This paper provides detailed information on the LST variations on the Tibetan Plateau, with the cloud contamination removed. The HANTS algorithm is demonstrated to be effective for understanding spatiotemporal variations of remotely sensed LST, especially for regions over which dense clouds cause large gaps in the LST data.