## 3 Results
### Geospatial Projections of Crop Yield and Growth  
The CIDS well projected geospatial variations in rice yield and growth using satellite images from both the COMS with a medium ground resolution of 500 m (Figure 6) and the RapidEye with a high ground resolution of 5 m (Figure 7). When comparing simulated and observed rice yields (Figure 6b), the simulated mean value (\(\mu\) = 6.77 t ha\({}^{-1}\)) was not significantly different (\(p\) = 0.392) from the observed mean value (\(\mu\) = 6.86 t ha\({}^{-1}\)), according to a two-sample t-test (\(\alpha\) = 0.05). These agreed with an RMSE of 0.44 t ha\({}^{-1}\) and an \(ME\) of 0.24. The CIDS was also designed to project crop productivity information with seasonal patterns, as well as two-dimensional variations on any given day of interest during the crop season. The CIDS produced two-dimensional predictive maps of rice grain yield and growth variables well, with a \(5\times 5\) m pixel resolution (Figure 8). Predicted grain yields ranged between 4150.6 and 8483.2 kg ha\({}^{-1}\), with a mean of 6029.1 kg ha\({}^{-1}\). The predicted average value agreed with the field  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Division** & **Year** & **Cultivar** & **Simulation** & **Observation\({}^{\lx@sectionsign}\)** & **RMSE** & **ME** \\ \hline \multirow{3}{*}{Gwangju} & \multirow{3}{*}{2011} & \multirow{3}{*}{Hopum} & \multicolumn{4}{c}{m\({}^{2}\) m\({}^{-2}\)} \\ \cline{4-7}  & & & 3.93 & 3.84 & 0.71 & 0.80 \\ \cline{3-7}  & & 2012 & Hwasunchal & 2.98 & 2.84 & 0.56 & 0.86 \\ \cline{3-7}  & & 2013 & Unkwang & 2.73 & 2.69 & 0.33 & 0.95 \\ \cline{3-7}  & & 2014 & Sindongjin & 4.52 & 4.57 & 1.05 & 0.75 \\ \cline{3-7}  & & 2014 & Saenuri & 4.70 & 4.62 & 0.99 & 0.84 \\ \cline{3-7}  & & 2014 & Ilmi & 5.04 & 5.10 & 0.88 & 0.86 \\ \hline \hline \end{tabular} \({}^{\lx@sectionsign}\) Observation was based on the GRAMI model projections using four vegetation indices (VIs), i.e., normalized difference vegetation index (NDVI), re-normalized difference vegetation index (RDVI), modified triangular vegetation index (MTVI), and optimized soil adjusted vegetation index (OSAVI).  
\end{table}
Table 3: Comparisons between simulated and observed mean leaf area index (LAI) of paddy for model calibration at Chonnam National University (CNU), Gwangju and for model validation at Buan and Gimje, Chonbuk in South Korea. Comparison statistics applied were root mean square error (RMSE) and model efficiency (\(ME\)).  
Figure 5: Simulated (S) and observed (O) leaf area index (LAI) and simulated above ground dry mass (SAGDM) of Sindongin (**a**), Saenuri (**b**), and Ilmi (**c**), and comparison between simulated (Sim) and observed (Obs) yields (**d**) at Buan and Gimje, Chonbuk, S. Korea in 2014 for model validation.  
measurement of 6381.9 kg ha\({}^{-1}\). Spatiotemporal projections of LAI and AGDM reproduced spatial variations of each growth variable well in the scene during the crop season.  
Figure 6: Simulated regional variation in rice yield in South Korea (**a**) and simulated mean rice yields in comparison with the observed mean rice yields in 62 administrative districts of the country, representing more than 5,000 ha of the paddy areas (**b**) in 2014. The yield data were simulated using Communication Ocean and Meteorological Satellite (COMS) imageries with a 500-m ground resolution.  
Figure 7: Simulated regional variations in above ground dry mass, AGDM (**a**) and leaf area index, LAI (**b**) at 100 days after transplanting and yield (**c**) of rice in Gimpje in 2014. The data were simulated using RapidEye satellite imageries with a 5-m ground resolution.