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.
Highlights
Satellite-based remote sensing techniques have been applied for estimating crop yield throughout the world [1,2,3]
The previous evaluated the performance of the GRAMI model in farm fields or small-region to mid-region scales, the current study focuses on the application at the scale of an entire nation
Regional rice productions were estimated from detected paddy fields and simulation of rice yield for 73 counties of South Korea from 2011 to 2014
Summary
Satellite-based remote sensing techniques have been applied for estimating crop yield throughout the world [1,2,3]. Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery have been used most widely to estimate crop yield from a regional to a global scale [3,4,5]. It is challenging to obtain invariably high-quality imagery data in monsoon climate areas because of many inadequate or missing pixels caused by frequent cloud coverage during a crop growing season. For this reason, an image processing technique that retrieves the pixels contaminated by clouds is an important issue for obtaining reliable spectral information on crop conditions. All of these approaches have been proven to be practically applicable, it is difficult to ensure reliable imagery data in the case of prolonged cloudy days [13,14]
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