Deep Learning-based Estimation and Mapping of Evapotranspiration in Cropland using Local Weather Prediction Model and Satellite Data (국지예보모델자료와 위성자료를 이용한 딥러닝 기반의농지 증발산량 산출 및 지도화)

Soo-Jin Lee (이수진); Kwang-Jin Kim (김광진); Yeong-Ho Kim (김영호); Ji-Won Kim (김지원); Seong-Wook Park (박성욱); Ye-Seul Yun (윤예슬); Kim, Na Ri (김나리); Yang-Won LEE (이양원)

doi:10.16879/jkca.2018.18.3.105

Deep Learning-based Estimation and Mapping of Evapotranspiration in Cropland using Local Weather Prediction Model and Satellite Data

Journal of the Korean Cartographic Association
Abbr : JKCA
2018, 18(3), pp.105-116
DOI : 10.16879/jkca.2018.18.3.105
Publisher : The Korean Cartographic Association
Research Area : Social Science > Geography > Geography in general > Cartography
Published : December 31, 2018

Soo-Jin Lee ¹, Kwang-Jin Kim ¹, Yeong-Ho Kim ¹, Ji-Won Kim ¹, Seong-Wook Park ¹, Ye-Seul Yun ¹, Kim, Na Ri ², Yang-Won LEE ³

¹부경대학교 지구환경시스템과학부 공간정보공학전공
²부경대학교 지오메틱연구소
³부경대학교

Accredited

ABSTRACT

Evapotranspiration is an important factor for Earth energy balance which transports vapor into atmosphere in the form of latent heat using net radiation energy. Measuring the evapotranspiration is actually limited to a point, so the modeling on a spatially continuous grid has been conducted for a long time using meteorological and satellite data. In addition to the PM (Penman-Monteith) equation-based METRIC (Mapping Evapotranspiration with Internalized Calibration) model and the PT (Priestley-Taylor) equation-based MS-PT (Modified Satellite-based Priestley-Taylor) model, the DNN (deep neural network) as an emerging technique can be a viable option. We conducted a DNN modeling of evapotranspiration through optimization for hidden layer structure, loss function, optimizer, active function, L1/L2 regularization, and dropout ratio. The result showed a quite favorable accuracy with the RMSE of 0.326 mm/day and the correlation coefficient of 0.975. This is because we used optimal input variables associated with the mechanism of evapotranspiration from numerical weather prediction model and satellite data, in addition to the DNN optimization. However, a more delicate modeling by increasing the volume and kind of training dataset will be necessary for future work.

KEYWORDS

Evapotranspiration, Deep learning, Numerical weather prediction model, Satellite data

Citation status

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

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Journal of the Korean Cartographic Association 2023 KCI Impact Factor : 1.34

Deep Learning-based Estimation and Mapping of Evapotranspiration in Cropland using Local Weather Prediction Model and Satellite Data

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

Journal of the Korean Cartographic Association 2023 KCI Impact Factor : 1.34

Deep Learning-based Estimation and Mapping of Evapotranspiration in Cropland using Local Weather Prediction Model and Satellite Data

ABSTRACT

KEYWORDS

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