Development of Estimation Algorithm of Near-Surface Air Temperature for Warm and Cold Seasons in Korea

Title & Authors
Development of Estimation Algorithm of Near-Surface Air Temperature for Warm and Cold Seasons in Korea
Kim, Do Yong;

Abstract
Spatial and temporal information on near-surface air temperature is important for understanding global warming and climate change. In this study, the estimation algorithm of near-surface air temperature in Korea was developed by using spatial homogeneous surface information obtained from satellite remote sensing observations. Based on LST(Land Surface Temperature), NDWI(Normalized Difference Water Index) and NDVI(Normalized Difference Vegetation Index) as independent variables, the multiple regression model was proposed for the estimation of near-surface air temperature. The different regression constants and coefficients for warm and cold seasons were calculated for considering regional climate change in Korea. The near-surface air temperature values estimated from the multiple regression algorithm showed reasonable performance for both warm and cold seasons with respect to observed values (approximately $\small{3^{\circ}C}$ root mean-square error and nearly zero mean bias). Thus;the proposed algorithm using remotely sensed surface observations and the approach based on the classified warm and cold seasons may be useful for assessment of regional climate temperature in Korea.
Keywords
Air Temperature;Satellite Remote Sensing;Regression Algorithm;Warm and Cold seasons;
Language
Korean
Cited by
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