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

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.

Assessment of a Proximal Sensing-integrated Crop Model for Simulation of Soybean Growth and Yield

“…Secondly, optimising the model improves simulation performance. Last, RSCM can be integrated with proximal or remote sensing data from different platforms, for example, an unmanned aerial system [54] and operational optical satellites [14,15,55]. Meanwhile, RSCM has some limitations, such as the strong dependence on proximal or remote sensing data as well as the inapplicability of reproducing the effects of photoperiod and phenology, N fixation, and N dynamics in comparison with other conventional process-based crop models.…”

“…Moreover, a crop simulation model incorporated with RS data can produce solid performances even with no data about the objective crop and field available except for RS information [13,14]. RS data assimilation in crop modelling has become widespread in crop growth and yield monitoring because of its improved simulation performance in crop growth and yield estimation and forecasting [15,16]. RS information can be integrated into a crop model in several ways: using a state variable directly obtained from RS information; updating state variables based on regression coefficients; re-initializing the model by changing initial conditions; and recalibrating the model by adjusting model parameters [17].…”

Mathematical Integration of Remotely-Sensed Information into a Crop Modelling Process for Mapping Crop Productivity

“…The current study was dedicated to the statistical hybrid approach of crop modelling and remote sensing, as well as the method to project spatiotemporal crop growth information using remote sensing data from various platforms and optical sensors with different geospatial resolutions. On the other hand, the previous studies using GRAMI-rice were focused on applying for different aspects of crop monitoring, most likely as case studies using images from a specific platform, e.g., an unmanned aerial system, UAS [55] or an optical satellite with either a high ground resolution [21] or a coarse ground resolution [23,56,57].…”

“…Second, the optimization method allows the model to advance the simulation performance. Third, the GRAMI model can be assimilated with remotely sensed information from various platforms, e.g., a UAS [55] and operational optical satellites on-board different ground resolution sensors [21,56,57]. Finally, once the model is applied to the satellite-based remote sensing, it is applicable for any region of interest on the Earth's surface.…”

“…The CIDS is designed to project geospatial information of crop growth and yield using the GRAMI model integrated with remote sensing images from various platforms of remote sensing from an UAS [18,55] to various operational optical satellites with different ground resolutions [21,56,57]. There have been similar practical efforts to assimilate a crop model with satellite images to improve the predictive performance of crop yields [11][12][13].…”