Estimating midday near-surface air temperature by weighted consideration of surface and atmospheric moisture conditions using COMS and SPOT satellite data

The measurement of near-surface air temperature (T_a) is critically important for understanding the Earth’s energy and water circulation system and for diverse modelling applications. T_a data obtained from meteological ground stations are basically available but not suitable for large-scale areas, because of their spatial limitation. Remote-sensing techniques can provide a spatially well-distributed T_a map. However, the current remote-sensing methodology for T_a mapping has accuracy inferior to common expectations in terms of the region of various terrestrial ecosystems and climatic conditions. Our aim was to develop a midday T_a retrieval algorithm with reasonable accuracy over Northeast Asia during one seasonal year. In consideration of the various environmental conditions in our study area, T_a was calculated using land surface temperature and the normalized difference vegetation index in the nine cases derived from the combination of surface and atmospheric moisture conditions, and a weighting factor was applied to reduce the bias error among T_a results from nine cases. The reasonable pixel window size was established as 13 × 13. The validation process yielded a coefficient of determination (R²), root mean square error, and bias values of 0.9401, 2.8865 K, and 0.4920 K, respectively. Although the study area includes diverse land-cover and climatic conditions, our satellite-derived T_a data provided better results compared with a previous study of only four cases with no weighting function in the Korean peninsula. Our suggested methodology will be useful in estimating T_a using satellite data, particularly over complex land surfaces.