Development of Daily Maximum Air Temperature Estimation Algorithm for the Korean Peninsula Using Modis Data

Air temperature is one of the most important variables in a wide range of application such as weather forecasts, climate, crop yield forecasting, agriculture and environment. In particular, continuous observations of daily maximum air temperature can identify the phenomenon and trend of the heat wave and prevent the heat related disaster. In this study, we have developed the daily maximum air temperature estimation algorithm using multiple linear regression equations. The input data is MODIS and ASOS data from 2013 to 2016 (June to September). The developed MODIS-based daily maximum air temperature estimation algorithm was applied to the MODIS images acquired from June to September in 2017. Then, we verified our result to the Seoul, Daejeon, and Ulsan areas in South Korea.