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Under the influence of anthropogenic and climate change, the problems caused by urban heat island (UHI) has become increasingly prominent. In order to promote urban sustainable development and improve the quality of human settlements, it is significant for exploring the evolution characteristics of urban thermal environment and analyzing its driving forces. Taking the Landsat series images as the basic data sources, the winter land surface temperature (LST) of the rapid urbanization area of Fuzhou City in China was quantitatively retrieved from 2001 to 2021. Combing comprehensively the standard deviation ellipse model, profile analysis and GeoDetector model, the spatio-temporal evolution characteristics and influencing factors of the winter urban thermal environment were systematically analyzed. The results showed that the winter LST presented an increasing trend in the study area during 2001–2021, and the winter LST of the central urban regions was significantly higher than the suburbs. There was a strong UHI effect from 2001 to 2021 with an expansion trend from the central urban regions to the suburbs and coastal areas in space scale. The LST of green lands and wetlands are significantly lower than croplands, artificial surface and unvegetated lands. Vegetation and water bodies had a significant mitigation effect on UHI, especially in the micro-scale. The winter UHI had been jointly driven by the underlying surface and socio-economic factors in a nonlinear or two-factor interactive enhancement mode, and socio-economic factors had played a leading role. This research could provide data support and decision-making references for rationally planning urban layout and promoting sustainable urban development. [ABSTRACT FROM AUTHOR]

The thermal infrared sensor-2 (TIRS-2) carried on Landsat 9 is the newest thermal infrared (TIR) sensor for the Landsat project and provides two adjacent TIR bands, which greatly benefits the land surface temperature (LST) retrieval at high spatial resolution. In this article, a radiance based split window (RBSW) algorithm for retrieving LST from Landsat 9 TIRS-2 data was proposed. In addition, the split-window covariance-variance ratio (SWCVR) algorithm was improved and applied to Landsat 9 TIRS-2 data for estimating atmospheric water vapor (AWV) that is required for accurate LST retrieval. The performance of the proposed method was assessed using the simulation data and satellite observations. Results reveal that the retrieved LST using the RBSW algorithm has a bias of 0.06 K and root-mean-square error (RMSE) of 0.51 K based on validation with the simulation data. The sensitivity analysis exhibited a LST error of