Remotely sensed retrieval of midday air temperature considering atmospheric and surface moisture conditions

Abstract

Near-surface air temperature (T a) is an important variable for various scientific communities, and many previous studies have attempted to estimate T a with high spatial resolution using remotely sensed data. The present study proposes a new, practical method for estimating T a near the land surface based on moisture conditions of the atmosphere and surface. First, match-up data sets of ground observations from 76 meteorological stations and satellite remote-sensing observations were established over the Republic of Korea during 2006. Four cases of atmospheric and surface moisture conditions were then determined using Moderate Resolution Imaging Spectroradiometer brightness temperatures (at 11 and 12 μm) and the normalized difference water index for all match-ups. Using stepwise multiple regression analysis for each case, land surface temperature and the normalized difference vegetation index were statistically selected as significant independent variables for estimating T a using the development data set (75% of the match-ups for each case). The T a values estimated from the multiple regression algorithm with different constants and coefficients for each case showed reasonably good performance (nearly zero mean bias and approximately 3°C root mean square error) compared with the measured T a values used as the validation data set (25% of the match-ups for each case). Thus, the proposed T a estimation approach based on moisture conditions of the atmosphere and surface is not confined to a specific season or land type and can be applied to all areas and seasons in the Republic of Korea.