Spatial and Temporal Variation of Land Surface Temperature in Fujian Province From 2001 to 2015

Abstract

ABSTRACT:Land surface temperature (LST) is an essential parameter in the physics of land surface processes. The spatiotemporal variations of LST on the Fujian province were studied using AQUA Moderate Resolution Imaging Spectroradiometer LST data. Considering the data gaps in remotely sensed LST products caused by cloud contamination, the Savitzky-Golay (S-G) filter method was used to eliminate the influence of cloud cover and to describe the periodical signals of LST. Observed air temperature data from 27 weather stations were employed to evaluate the fitting performance of the S-G filter method. Results indicate that S-G can effectively fit the LST time series and remove the influence of cloud cover. Based on the S-G-derived result, Spatial and temporal Variations of LST in Fujian province from 2001 to 2015 are analysed through slope analysis. The results show that: 1) the spatial distribution of annual mean LST generally exhibits consistency with altitude in the study area and the average of LST was much higher in the east than in the west. 2) The annual mean temperature of LST declines slightly among 15 years in Fujian. 3) Slope analysis reflects the spatial distribution characteristics of LST changing trend in Fujian.Improvement areas of LST are mainly concentrated in the urban areas of Fujian, especially in the eastern urban areas. Apparent descent areas are mainly distributed in the area of Zhangzhou and eastern mountain area.

Reconstruction of All-Weather Daytime and Nighttime MODIS Aqua-Terra Land Surface Temperature Products Using an XGBoost Approach

“…The multi-temporal information-based methods which utilize the information of time sequence images have also been well developed. These approaches include the linear temporal approaches [30,31], the harmonic analysis of time-series (HANTS) method [32], the Savitzky-Golay (SG) filtering method [33][34][35], the diurnal temperature cycle (DTC) method [36,37], and the annual temperature cycle (ATC) method [38,39], etc. Such approaches are appropriate for LST images with adequate pixels and sufficient multi-temporal information, but generally ignore information from geographically neighboring information and thus are only suitable for the images with regular variation [25].…”

Changing temperature trends at subtropical mountains in southeastern China

“…Because the southeastern coast has low topography, alluvial plains downstream of rivers, medium and large cities are mainly located there, and there is much human activity, so it is warming up more. In contrast, the density of human activity in the northwestern mountainous areas is lower, and the warming rate is correspondingly lower (Li et al 2018). Second, human activities can alter the vegetation cover, resulting in an increase in latent heat ux from transpiration, which in turn reduces the temperature (Peng et al 2012); therefore, the change in vegetation cover in Fujian Province has a signi cant impact on the distribution of temperatures (Li et al 2020;Li et al 2018).…”

“…In contrast, the density of human activity in the northwestern mountainous areas is lower, and the warming rate is correspondingly lower (Li et al 2018). Second, human activities can alter the vegetation cover, resulting in an increase in latent heat ux from transpiration, which in turn reduces the temperature (Peng et al 2012); therefore, the change in vegetation cover in Fujian Province has a signi cant impact on the distribution of temperatures (Li et al 2020;Li et al 2018). Moreover, human activities are often concentrated in areas with signi cant urbanization, increasing urban construction intensity (Yow, 2007), and decreasing surface albedo (Stewart and Oke, 2012).…”