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Xu, YM; Shen, Y; Wu, ZY (2013). Spatial and Temporal Variations of Land Surface Temperature Over the Tibetan Plateau Based on Harmonic Analysis. MOUNTAIN RESEARCH AND DEVELOPMENT, 33(1), 85-94.

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.



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