## 4 Results
### Mapping the Biomass and Yield  
The winter wheat biomass and yield in Yucheng and Guantao were estimated using the generated time-series NDVI and LSWI datasets. Comparisons between the simulated and measured biomass are presented in Figure 9. The estimated biomass from the blended 100-m data shown in Figure 9a,b were generally in good agreement with the observed biomass (\(R^{2}=0.864\), RMSE = 191 g/m\({}^{2}\) and RRMSE = 16.7% at Site 1; \(R^{2}=0.871\), RMSE = 168 g/m\({}^{2}\) and RRMSE = 12.7% at Site 2); most of the scatter points are distributed along the fitting line, and the slopes are between 0.888 and 0.916. Figure 10 shows that the simulated yields were also correlated with the measured yields (\(R^{2}=0.663\), RMSE = 62.8 g/m\({}^{2}\) and RRMSE = 7.27% for Site 1; \(R^{2}=0.631\), RMSE = 41.8 g/m\({}^{2}\) and RRMSE= 4.88% for Site 2), which indicates that the fusion dataset can be used for crop biomass and yield estimations when the original PROBA-V 100-m data are limited. It also can be observed from Figure 9 that the measured biomass was more correlated with the estimations that involved the blended data (they had higher \(R^{2}\), and lower RMSE and RRMSE) than were the 300-m products. This is because the 100-m data, with its higher spatial resolution, can detect spatial details that ignored by the 300-m data. Because the agricultural landscape in the NCP is extremely complex, monitoring crop yields using higher resolution remote sensing data is appropriate on a local scale.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**100-m**} & \multicolumn{2}{c}{**300-m**} \\ \cline{2-5}  & **Producer’s Accuracy** & **User’s Accuracy** & **Producer’s Accuracy** & **User’s Accuracy** \\ \hline Wheat & 86.96\% & 81.63\% & 72.83\% & 65.05\% \\ Others & 82.69\% & 87.76\% & 65.38\% & 73.91\% \\ \hline \multicolumn{5}{c}{Overall Accuracy: 84.69\%; Kappa: 0.7198} & Overall Accuracy: 68.88\%; Kappa: 0.3795} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classification accuracies of the 300-m and 100-m time-series data at Site 1.  
Figure 8: Classification results of the time-series 300-m data over the study areas: (**a**) Site 1; and (**b**) Site 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**100-m**} & \multicolumn{2}{c}{**300-m**} \\ \cline{2-5}  & **Producer’s Accuracy** & **User’s Accuracy** & **Producer’s Accuracy** & **User’s Accuracy** \\ \hline Wheat & 80.95\% & 73.91\% & 69.05\% & 63.04\% \\ Others & 76.62\% & 83.1\% & 66.88\% & 72.54\% \\ \hline \hline \multicolumn{5}{c}{Overall Accuracy: 78.57\%; Kappa: 0.5708} & Overall Accuracy: 67.86\%; Kappa: 0.3562} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Classification accuracies of the 300-m and 100-m time-series data at Site 2.  
The spatial distributions of the wheat biomass and yield (Figures 11 and 12, respectively) can be obtained by combining the field maps derived in Section 4.2 and the estimation results. The biomass was greater than 1000 g/m\({}^{2}\) in mid-May, whereas the yields ranged from 750 to 1000 g/m\({}^{2}\) in most parts of the study areas. Figures 11a and 12a indicate that the biomass and yield were much lower in the northern part of Yucheng, which has infertile farmland, whereas the southern region is a major grain-producing area in the small county due to its fertile soil and convenient agricultural facilities. In contrast, Figures 11b and 12b demonstrate that the biomass and yield were relatively uniform in Guantao because of its excellent irrigation facilities. In addition, wheat production can be easily acquired from the yield distribution maps.  
Figure 10: Comparisons between the measured and estimated winter wheat yields: (**a**) Site 1; and (**b**) Site 2.  
Figure 9: Comparisons between the measured and estimated winter wheat biomass: (**a**) With the 100-m data at Site 1; (**b**) With the 100-m data at Site 2; (**c**) With the 300-m data at Site 1; and (**d**) With the 300-m data at Site 2.