Improving the UAV-based yield estimation of paddy rice by using the solar radiation of geostationary satellite Himawari-8

Akira Hama, Kei Tanaka, Atsushi Mochizuki, Yasuo Tsuruoka, Akihiko Kondoh
Received 2019/11/27, Accepted 2019/02/23, Published 2020/03/24

Akira Hama1), Kei Tanaka2), Atsushi Mochizuki3), Yasuo Tsuruoka3), Akihiko Kondoh4)

1) College of Education, Yokohama National University, Japan
2) Japan Map Center, Japan
3) Chiba Prefectural Agriculture and Forestry Research Center, Japan
4) Center for Environmental Remote Sensing, Chiba University, Japan

The objectives of this study were to improve the yield estimation of paddy rice based on the unmanned aerial vehicle remote sensing (UAV-RS) and solar radiation data sets. The study used the UAV-RS-based normalized difference vegetation index (NDVI) at the heading stage, the solar radiation data of geostationary satellite Himawari-8 and the solar radiation data of polar orbiting satellite Aqua/MODIS. A comparison of two satellite-based solar radiation data sets (Himawari-8 and MODIS PAR) showed that the coefficient of determination (R2) of estimated yield based on Himawari-8 solar radiation was 0.7606 while the R2 of estimated yield based on the MODIS PAR was 0.4749. Additionally, the root mean square error (RMSE) of Himawari-8 solar radiation was 26.5 g/m2 while the RMSE of estimated yield based on the MODIS PAR was 39.2 g/m2 (The average observed yield was 489.3 g/m2). The Estimated yield based on Himawari-8 solar radiation, therefore, outperformed the MODIS PAR-based estimated yield. The improvement of the temporal resolution of the satellite-based dataset allowed by using the Himawari-8 data set contributed to the improvement of estimation accuracy. Satellite-based solar radiation data allow yield estimation based on remote sensing in regions where there are no ground observation data of solar radiation.

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Copyright (c) 2020 The Author(s) CC-BY 4.0

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