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Article

Retrieval of Daily Reference Evapotranspiration for Croplands in South Korea Using Machine Learning with Satellite Images and Numerical Weather Prediction Data

1
Geomatics Research Institute, Pukyong National University, Busan 48513, Korea
2
National Meteorological Satellite Center, Korea Meteorological Administration, Chungbuk 27803, Korea
3
Department of Applied Plant Science, Chonnam National University, Gwangju 61186, Korea
4
Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(21), 3642; https://doi.org/10.3390/rs12213642
Received: 5 October 2020 / Revised: 2 November 2020 / Accepted: 4 November 2020 / Published: 6 November 2020
(This article belongs to the Special Issue Remote Sensing for Agrometeorology)
Evapotranspiration (ET) is an important component of the Earth’s energy and water cycle via the interaction between the atmosphere and the land surface. The reference evapotranspiration (ET0) is particularly important in the croplands because it is a convenient and reasonable method for calculating the actual evapotranspiration (AET) that represents the loss of water in the croplands through the soil evaporation and vegetation transpiration. To date, many efforts have been made to retrieve ET0 on a spatially continuous grid. In particular, the Moderate Resolution Imaging Spectroradiometer (MODIS) product is provided with a reasonable spatial resolution of 500 m and a temporal resolution of 8 days. However, the applicability to the local-scale variabilities due to complex and heterogeneous land surfaces in countries like South Korea is not sufficiently validated. Meanwhile, the AI approaches showed a useful functionality for the ET0 retrieval on the local scale but have rarely demonstrated a substantial product for a spatially continuous grid. This paper presented a retrieval of the daily reference evapotranspiration (ET0) over a 500 m grid for croplands in South Korea using machine learning (ML) with satellite images and numerical weather prediction data. In a blind test for 2013–2019, the ML-based ET0 model produced the accuracy statistics with a root mean square error of 1.038 mm/day and a correlation coefficient of 0.870. The results of the blind test were stable irrespective of location, year, and month. This outcome is presumably because the input data of the ML-based ET0 model were suitably arranged spatially and temporally, and the optimization of the model was appropriate. We found that the relative humidity and land surface temperature were the most influential variables for the ML-based ET0 model, but the variables with lower importance were also necessary to consider the nonlinearity between the variables. Using the daily ET0 data produced over the 500 m grid, we conducted a case study to examine agrometeorological characteristics of the croplands in South Korea during the period when heatwave and drought events occurred. Through the experiments, the feasibility of the ML-based ET0 retrieval was validated, especially for local agrometeorological applications in regions with heterogeneous land surfaces, such as South Korea. View Full-Text
Keywords: agrometeorology; evapotranspiration; cropland; machine learning agrometeorology; evapotranspiration; cropland; machine learning
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MDPI and ACS Style

Kim, N.; Kim, K.; Lee, S.; Cho, J.; Lee, Y. Retrieval of Daily Reference Evapotranspiration for Croplands in South Korea Using Machine Learning with Satellite Images and Numerical Weather Prediction Data. Remote Sens. 2020, 12, 3642. https://doi.org/10.3390/rs12213642

AMA Style

Kim N, Kim K, Lee S, Cho J, Lee Y. Retrieval of Daily Reference Evapotranspiration for Croplands in South Korea Using Machine Learning with Satellite Images and Numerical Weather Prediction Data. Remote Sensing. 2020; 12(21):3642. https://doi.org/10.3390/rs12213642

Chicago/Turabian Style

Kim, Nari, Kwangjin Kim, Soobong Lee, Jaeil Cho, and Yangwon Lee. 2020. "Retrieval of Daily Reference Evapotranspiration for Croplands in South Korea Using Machine Learning with Satellite Images and Numerical Weather Prediction Data" Remote Sensing 12, no. 21: 3642. https://doi.org/10.3390/rs12213642

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