## 3 Results
### Mapping Disease Infestations with PL Satellite Imagery  
The normalized two-stage VIs were used as input variables for building the feature space and producing disease distribution maps based on the proposed PLS-DA mapping frame (Figure 4). The spatial distribution of different diseases produced by the damage map was generally consistent with our field surveys. Thus, the damage caused by rice dwarf occurred in the center of Yulin City, north of Beiliu City, and north of Luchuan County. The damage caused by rice blast occurred in most of Yulin City, north of Beiliu City, and north-center of Luchuan County. The damage caused by glume \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Means of Normalized Two-Stage VIs**} & \multicolumn{4}{c}{**Means on 30 October**} \\ \hline  & **Healthy Rice** & **Rice Dwarf** & **Rice Blast** & **Glume Blight** & **Healthy Rice** & **Rice Dwarf** & **Rice Blast** & **Glume Blight** \\ \hline NDVI & 0.032 \({}^{**}\) & 0.014 \({}^{**}\) & 0.058 & 0.065 & 0.078 \({}^{*}\) & 0.018 \({}^{**}\) & 0.08 \({}^{*}\) & 0.102 \\ SAVI & 0.047 \({}^{*}\) & 0.046 \({}^{*}\) & 0.036 \({}^{*}\) & 0.079 & 0.032 \({}^{*}\) & 0.031 \({}^{*}\) & 0.166 & 0.052 \({}^{*}\) \\ TVI & 0.032 \({}^{*}\) & 0.02 \({}^{*}\) & 0.04 \({}^{*}\) & 0.012 \({}^{*}\) & 0.037 \({}^{*}\) & 0.104 & 0.142 & 0.051 \({}^{*}\) \\ RDVI & 0.01 \({}^{*}\) & 0.009 \({}^{*}\) & 0.045 \({}^{*}\) & 0.066 \({}^{*}\) & 0.048 \({}^{*}\) & 0.074 \({}^{*}\) & 0.125 & 0.091 \\ MSR & 0.057 & 0.095 & 0.027 \({}^{**}\) & 0.091 & 0.073 \({}^{*}\) & 0.125 & 0.077 & 0.054 \({}^{*}\) \\ SIFT & 0.039 \({}^{*}\) & 0.027 \({}^{*}\) & 0.087 & 0.036 \({}^{*}\) & 0.056 \({}^{*}\) & 0.124 & 0.145 & 0.09 \\ \hline \hline \end{tabular} Note: \({}^{*}\) indicates the different significance at 0.95 confidence level. \({}^{**}\) indicates the different significance at 0.99 confidence level.  
\end{table} 
Table 4: The sensitivity of normalized two-stage and single date VIs based on ANOVA analysis. Figure 3: The mean and standard deviations of pixel-based single-date VIs shown on (**left**) and normalized two-stage VIs shown on (**right**). blight was identified in the north of Beiliu City and Luchuan County. The disease infestations that occurred in the northern edge of Yulin City were confirmed through telephone interviews with the local plant protection department. The PLS-DA model was based on single-date VIs (calculated from the October 30 images) and a damage map was produced for comparison (Figure 4). The map aided in examining whether the normalized two-stage VI PLS-DA model had a significant improvement over the single-date image. The results revealed that the diseased area produced by single-date VIs was significantly less than that on the normalized two-stage map, especially for the identification of rice blast and glume blight. A confusion matrix and kappa value were calculated to provide a quantifiable classification assessment, shown in Table 5. The normalized two-stage VI model returned an overall accuracy of 75.62% (kappa = 0.47), 13.95% higher than the single-date VI model. The classification accuracy for individual diseases ranged from 64.29 to 87.1%. Rice dwarf disease featured the highest classification accuracy of the three diseases. By comparison, in Table 5, the single-date VI model produced more  
Figure 4: A map of healthy and diseased rice based on the PLS-DA classifier from normalized two-stage and single-date VIs. commission errors in the detection and classification of different diseases (kappa = 0.27). These results suggest that the infestation was underestimated by single-date VIs and their combinations, which resulted in a significant reduction in disease mapping. Figure 5 illustrates the contributions of each individual spectral feature in the PLS-DA model by the VIP method. It suggests that the normalized two-stage VIs had a better performance than single-date VIs in detection the progressive development of internal and external symptoms during the disease detection.