Deep Learning-based Retrieval of Daily 500-m Soil Moisture for South Korea (딥러닝을 이용한 남한 500미터 해상도의 일단위 토양수분 산출)

KIM YEONGHO (김영호); 김광진; Soo-Jin Lee (이수진); KIM JIWON (김지원); Yang-Won LEE (이양원)

Deep Learning-based Retrieval of Daily 500-m Soil Moisture for South Korea

Journal of the Korean Cartographic Association
Abbr : JKCA
2017, 17(3), pp.109-121
Publisher : The Korean Cartographic Association
Research Area : Social Science > Geography > Geography in general > Cartography
Published : December 31, 2017

KIM YEONGHO ¹, 김광진 ², Soo-Jin Lee ¹, KIM JIWON ¹, Yang-Won LEE ¹

¹부경대학교
²부경대학교 지구환경시스템과학부

Accredited

ABSTRACT

As soil moisture is an important hydro-meteorological factor which affects Earth’s energy budget and surface-atmosphere interaction, accurate observation of soil moisture and the understanding of spatio-temporal change is very important for Earth environmental studies. Point-based in-situ observation of soil moisture has favorable accuracy, but it does not have spatial continuity. Meanwhile, satellite observation provides spatially continuous dataset, but its accuracy and spatial resolution are not satisfactory. For quality improvement of soil moisture data in South Korea, we developed a deep learning-based retrieval model to produce daily 500-m soil moisture content by using satellite and meteorological dataset. Through 200 iterations of training-validation experiment, our deep learning model yielded more favorable results than NASA’s target accuracy and showed a good agreement with the in-situ observations. We built maps for soil moistur

KEYWORDS

Soil moisture content, Numerical weather prediction, Remote sensing, Artificial intelligence, Deep learnin

Citation status

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

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