Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 2 September 2018 / Approved: 3 September 2018 / Online: 3 September 2018 (09:33:37 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Remote Sens. 2018, 10, 1665
DOI: 10.3390/rs10101665

Abstract

The Geostationary Ocean Color Imager (GOCI) of the Communication, Ocean, and Meteorological Satellite (COMS) increases the chance of acquiring images with greater clarity at eight times a day and is equipped with spectral bands suitable for monitoring crop yield with a moderate spatial resolution. 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 system for model inputs, respectively. The paddy fields and transplanting dates were estimated 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 detected paddy fields ranged from 89.5 to 90.2%. The simulated rice yields statistically agreed with observed yields, with 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.

Subject Areas

GRAMI model; remote sensing; rice yield; satellite imagery; vegetation index

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