Abstract
Abstract. Spatiotemporal mapping and modeling of Land Surface Temperature (LST) variations and characterization of parameters affecting these variations are of great importance in various environmental studies. The aim of this study is a spatiotemporal modeling the impact of surface characteristics variations on LST variations for the studied area in Samalghan Valley. For this purpose, a set of satellite imagery and meteorological data measured at the synoptic station during 1988–2018, were used. First, single-channel algorithm, Tasseled Cap Transformation (TCT) and Biophysical Composition Index (BCI) were employed to estimate LST and surface biophysical parameters including brightness, greenness and wetness and BCI. Also, spatial modeling was used to modeling of terrain parameters including slope, aspect and local incident angle based on DEM. Finally, the principal component analysis (PCA) and the Partial Least Squares Regression (PLSR) were used to modeling and investigate the impact of surface characteristics variations on LST variations. The results indicated that surface characteristics vary significantly for case study in spatial and temporal dimensions. The correlation coefficient between the PC1 of LST and PC1s of brightness, greenness, wetness, BCI, DEM, and solar local incident angle were 0.65, −0.67, −0.56, 0.72, −0.43 and 0.53, respectively. Furthermore, the coefficient coefficient and RMSE between the observed LST variation and modelled LST variation based on PC1s of brightness, greenness, wetness, BCI, DEM, and local incident angle were 0.83 and 0.14, respectively. The results of study indicated the LST variation is a function of s terrain and surface biophysical parameters variations.
Highlights
Land surface temperature (LST) is considered as controller parameter of surface energy exchanges top of land surface (Anderson et al 2008; Prata et al 1995; Weng et al 2019)
The principal component analysis (PCA) was applied for the deeper analysis of the LST variations and each of the surface characteristics at the pixel scale from 1988 to 2018 (Figure 4)
The variations of LST and surface characteristics in the temporal dimension can be examined on a pixel scale using PCA
Summary
Land surface temperature (LST) is considered as controller parameter of surface energy exchanges top of land surface (Anderson et al 2008; Prata et al 1995; Weng et al 2019). LST is extremely changeable and affected by various parameters in both spatial and temporal dimensions (Guo et al 2015). These parameters include temporal characteristics, geographic coordinates, topographic factors, thermal surface properties, biophysical parameters, soil texture, meteorological parameters and sub-surface features (geothermal, hydrothermal and volcanic areas) (Weng et al 2019). Study of LST variations and the parameters affecting these changes is important (Karimi Firozjaei et al 2018; Weng et al 2019)
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