Downscaling Advanced Microwave Scanning Radiometer 2 (AMSR2) Soil Moisture Data Using Regression-kriging (회귀크리깅을 이용한 AMSR2 토양수분자료의 다운스케일링)

김대선; NO WOOK PARK (박노욱); Kim, Na Ri (김나리); 김광진; Soo-Jin Lee (이수진); KIM YEONGHO (김영호); KIM JIWON (김지원); Daeyun Shin (신대윤); Young-Hyun Cho (조영현); Yang-Won LEE (이양원)

Downscaling Advanced Microwave Scanning Radiometer 2 (AMSR2) Soil Moisture Data Using Regression-kriging

Journal of the Korean Cartographic Association
Abbr : JKCA
2017, 17(2), pp.99-110
Publisher : The Korean Cartographic Association
Research Area : Social Science > Geography > Geography in general > Cartography
Published : August 31, 2017

김대선 ¹, NO WOOK PARK ², Kim, Na Ri ¹, 김광진 ³, Soo-Jin Lee ¹, KIM YEONGHO ¹, KIM JIWON ¹, Daeyun Shin ⁴, Young-Hyun Cho ⁵, Yang-Won LEE ¹

¹부경대학교
²인하대학교
³부경대학교 공간정보시스템공학전공
⁴한국수자원공사
⁵한국수자원공사 수문기상협력센터

Accredited

ABSTRACT

Soil moisture is an important meteorological factor to understand hydrological circulation and is closely associated with disasters such as drought, flood and wildfire. However, the spatial resolution of satellite-based soil moisture data is too coarse to be applied directly to local analysis. To solve the problem of the restricted spatial resolution of soil moisture data retrieved from microwave satellite sensors, this study presents a downscaling method that combines spatial statistical models with various land surface variables. Regression-kriging, which is known as the most elaborated downscaling technique, was employed to downscaling of the daily soil moisture data from AMSR2 (Advanced Microwave Scanning Radiometer 2) at the resolution of 10km and 25km for the period between April and October, 2013-2014. The downscaled result at the resolution of 2km and 4km showed very good consistency with the original data, which means the spatial patterns and the data properties were well preserved even after downscaling. Our approach will be applied to other meteorological factors and can be a viable option for overcoming the problem of limited spatial resolution in satellite images and numerical model data.

KEYWORDS

Soil moisture, AMSR2, Downscaling, Spatial statistics, Regression-kriging

Citation status

* References for papers published after 2023 are currently being built.

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