Good knowledge of the surface air temperature (SAT) is critical for scientific understanding of ecological environment changes and land-atmosphere thermodynamic interactions. However, sparse and uneven spatial distribution of the temperature gauging stations introduces remarkable uncertainties into analysis of the SAT pattern.From a geo-intelligent perspective, here we proposed a new SAT reconstruction method based on the multisource data and machine learning technique which was developed by considering autocorrelation of the in situ observed SAT in both space and time, or

Urbanization is mainly characterized by the expansion of impervious surface (IS) and hence modifies hydrothermal properties of the urbanized areas. This process results in rising land surface temperature (LST) of the urbanized regions, i.e., urban heat island (UHI). Previous studies mainly focused on relations between LST and IS over individual city. However, because of the spatial heterogeneity of UHI from individual cities to urban agglomerations and the influence of relevant differences in climate background across urban agglomerations, the spatial-temporal scale independence of the IS-LST relationship still needs further investigation. In this case, based on Landsat-8 Operational Land Imager and Thermal Infrared Sensor (Landsat 8 OLI/TIRS) remote sensing image and multi-source remote sensing data, we extracted IS using VrNIR-BI (Visible red and NIR-based built-up Index) and calculated IS density across three major urban agglomerations across eastern China, i.e., the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) to investigate the IS-LST relations on different spatial and temporal scales and clarify the driving factors of LST. We find varying warming effects of IS on LST in diurnal and seasonal sense at different time scales. Specifically, the IS has stronger impacts on increase of LST during daytime than during nighttime and stronger impacts on increase of LST during summer than during winter. On different spatial scales, more significant enhancing effects of IS on LST can be observed across individual city than urban agglomerations. The Pearson correlation coefficient (r) between IS and LST at the individual urbanized region can be as high as 0.94, indicating that IS can well reflect LST changes within individual urbanized region. However, relationships between IS and LST indicate nonlinear effects of IS on LST. Because of differences in spatial scales, latitudes, and local climates, we depicted piecewise linear relations between IS and LST across BTH when the IS density was above 10% to 17%. Meanwhile, linear relations still stand between IS density and LST across YRD and PRD. Besides, the differences in the IS-LST relations across urban agglomeration indicate more significant enhancing effects of IS on LST across PRD than YRD and BTH. These findings help to enhance human understanding of the warming effects of urbanization or UHI at different spatial and temporal scales and is of scientific and practical merits for scientific urban planning.

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