Abstract: Based on the Landsat images of June 11, 2004, June 25, 2009, June 27, 2014 and May 22, 2020, the single-window algorithm was used to invert land surface temperature in Shijiazhuang City, China, and to analyze the spatiotemporal dynamic variation characteristics of land surface temperature, and then the spatiotemporally and geographically weighted model was used to explore the mechanism of each driving factor. The results showed that: (1) In the four panoramic images studied, the maximum land surface temperature in Shijiazhuang area showed an upward trend from 2004 to 2020 with a slope of 1.997 ℃/a, and reached the maximum in 2020. (2) With the passage of time, the land surface temperature showed an overall upward trend under different topographic factors. Spatially, land surface temperature first increased and then decreased with the increase of elevation, and on the other hand, it increased with the increase of slope, but there were differences in different slope directions, with the maximum difference of land surface temperature between the negative slope and the sunny slope of 0.566 ℃. (3) Compared with ordinary least square regression and geographically weighted regression, spatio-temporal geographically weighted model is the optimal model with multiple driving factors, in which the driving force of vegetation index and relative soil moisture on land surface temperature is the largest, while the driving force of monthly total precipitation is the weakest. The results of this study can provide reference for urban layout planning and ecological environment improvement.

Key words: land surface temperature, single window algorithm, geographically spatiotemporal weighted regression, spatial distribution characteristics, driving force