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Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery: A Case Study in South Korea

1,†, 1,*,† and 2,†
1
Department of Applied Plant Science, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Korea
2
Satellite Information Center, Korea Aerospace Research Institute, 169-84 Gwahak-ro, Yuseong-gu, Daejeon 34133, Korea
*
Author to whom correspondence should be addressed.
Equal contribution goes to these authors.
Remote Sens. 2018, 10(10), 1665; https://doi.org/10.3390/rs10101665
Received: 2 September 2018 / Revised: 16 October 2018 / Accepted: 19 October 2018 / Published: 21 October 2018
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Abstract

The Geostationary Ocean Color Imager (GOCI) of the Communication, Ocean, and Meteorological Satellite (COMS) increases the chance of acquiring images with greater clarity eight times a day and is equipped with spectral bands suitable for monitoring crop yield in the national scale with a spatial resolution of 500 m. The objectives of this study were to classify nationwide paddy fields and to project rice (Oryza sativa) yield and production using the grid-based GRAMI-rice model and GOCI satellite products over South Korea from 2011 to 2014. Solar insolation and temperatures were obtained from COMS and the Korea local analysis and prediction systems for model inputs, respectively. The paddy fields and transplanting dates were estimated by using Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and land cover products. The crop model was calibrated using observed yield data in 11 counties and was applied to 62 counties in South Korea. The overall accuracies of the estimated paddy fields using MODIS data ranged from 89.5% to 90.2%. The simulated rice yields statistically agreed with the observed yields with mean errors of −0.07 to +0.10 ton ha−1, root-mean-square errors of 0.219 to 0.451 ton ha−1, and Nash–Sutcliffe efficiencies of 0.241 to 0.733 in four years, respectively. According to paired t-tests (α = 0.05), the simulated and observed rice yields were not significantly different. These results demonstrate the possible development of a crop information delivery system that can classify land cover, simulate crop yield, and monitor regional crop production on a national scale. View Full-Text
Keywords: GRAMI model; remote sensing; rice yield; satellite imagery; vegetation index GRAMI model; remote sensing; rice yield; satellite imagery; vegetation index
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Jeong, S.; Ko, J.; Yeom, J.-M. Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery: A Case Study in South Korea. Remote Sens. 2018, 10, 1665.

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