An Integrated Framework for Mapping Nationwide Daily Temperature in China.

Air temperature (T_a) is an essential parameter for science research and engineering practice. While the traditional site-based approach is only able to obtain observations in limited and discrete locations, satellite remote sensing is promising to retrieve some environmental variables with spatially continuous coverage. Nowadays, land surface temperature (T_s) measurements can be obtained from some satellite sensors (e.g., MODIS), further enabling us to estimate T_a in view of the relationship between T_a and T_s. In this article, we proposed a two-phase integrated framework to estimate daily mean T_a nationwide. In the first phase, multivariate linear regression models were fitted between site-based observations of daily mean air temperature (T_a-mean) and MODIS land surface temperature products (including Terra day: T_MOD-day, Terra night: T_MOD-night, Aqua day: T_MYD-day, and Aqua night: T_MYD-night) conditional on some covariates of environmental factors. The fitted models were then used to predict T_a-mean from those covariates at unobserved locations. The predicted T_a-mean were looked on as stochastic variables, and their distributions were also obtained. In the second phase, Bayesian maximum entropy (BME) methods were used to produce spatially continuous maps of T_a-mean taking the meteorological station observations as hard data and the predicted T_a-mean in the first phase as soft data. It is shown that the proposed approach is promising to improve the interpolation accuracy significantly, comprehensively considering the prior knowledge and the context of space variability and correlation, which will enable it to compile spatially continuous air temperature products with higher accuracy. [ABSTRACT FROM AUTHOR]