Quantifying the spatial and temporal relationship between air and land surface temperatures of different land-cover types in Southeastern China

Near surface air temperature (Tₐ) is an important variable utilized for various fields of research such as agriculture, epidemic forecasting, and climate change. It is usually obtained from a limited number of weather stations that are unevenly distributed within a region. Thus, the spatial information of Tₐ is rarely provided especially in complex topographical and underdeveloped regions. To compensate for this shortcoming, Tₐ can be estimated by using the land surface temperature (Tₛ) from satellite data because remote sensing has an advantage of describing the spatial heterogeneity over a large geographical area. In previous research, authors focused on analysing the effect of limited factors on Tₐ observations by using a small number of weather stations. However, this study explores a thorough sensitivity analysis of several factors on the relationship between Tₐ (obtained from a dense network of automatic weather stations) and Tₛ of different land-cover types using statistical analysis. This article discovered the relationships between Tₐ and Tₛ. First, comparisons between daytime and night-time Moderate Resolution Imaging Spectroradiometer (MODIS) Tₛ data with Tₐ data showed that a better agreement was achieved during the night than the day. Second, when comparing the results of different land-cover types, the correlation coefficient of the vegetated area was higher than that of water and impervious surface. Third, a comparison of statistical results for different seasons indicated that the correlation between Tₐ and Tₛ was weakest in a hot season. Fourth, altitude seemed to have no significant effect on the Tₐ–Tₛ relationship. Finally, the relationship between Tₐ and Tₛ strengthened with increasing window size except for the vegetated area but tended to saturate as the Tₛ window increased to the optimal size. These results will be helpful for building an appropriate model to derive air temperature directly from the remotely sensed data in the future.