## 4 Discussion
For example, the VIs that were sensitive to plant growth (such as the normalized two-stage NDVI, SAVI, MSR) made more contributions to the identification of rice dwarf. The normalized two-stage TVI, RDVI, and SIPI selected by VIP scores features a high correlation to pigments and chloroplast variations and indicated their relative importance in discriminating rice blast. The VIP method captured the typical symptoms of specific diseases and was important for monitoring and discriminating between diseased species. The results obtained from PL imagery revealed finer spatial detail at a 3 m spatial resolution and helped to resolve the heterogeneity in different disease infestations. For example, based on field surveys, the greatest concentrations of disease were highlighted and mapped using the optimal normalized two-stage VIs based PLS-DA model (Figure 6). The pixel-based classification of thediseases was highly consistent with our field observations which confirmed the effectiveness of the method (the photos from field surveys are provided in Figure 6). In contrast to manual interpretation, the proposed model aided in eliminating error and increasing the consistency and reliability of disease detection and discrimination for large areas. More importantly, our method provided a fast and effective way to assess the impact of diseases ranging from a single point observation to an entire region. This approach proved to be effective for assessing losses in crop yields. Our study successfully obtained damage information on rice diseases for the disease outbreak in the autumn of 2017 in Yulin City, Beiliu City, and Luchuan County. This information was forwarded to the Plant Protection Department in Guangxi and to insurance companies for agricultural management and damage assessment. Overall, the novel developed normalized two-stage VIs based PLS-DA model performed better in monitoring and classification of diseases on rice based on the PlanetScope satellite image data, with an acceptable accuracy of 75.62%. On one hand, we expect PlanetScope's high spatial resolution imagery to cover gaps in finer scale disease detection. On the other hand, its high revisiting cycle made it possible to mosaic and composite cloud-free imagery from the continues dates. However, because of mountain-dominated topographic conditions, rice fields are small and scattered, and it is still impossible to eliminate completely the influence of mixed pixel problem. Additionally, the error from noises from the co-registration between images will also bring uncertainty in the modeling processes. Hyperspectral data is capable of partly overcoming the limitation of mixed pixels, and providing more detailed spectral information for disease detection [60], but the high-dimensional information provided by the hyperspectral sensors always raises the computational complexity and cost. Therefore, our future research would investigate whether the hyperspectral satellite data could provide more information on diseases detection and eliminating the mixed pixel problem, and further improve the  
Figure 6: Mapping results of rice diseases in a sub-region based on the optimal normalized two-stage VIs based PLS-DA. accuracies in disease species classification. Furthermore, by assimilating the high spatio-temporal resolution, multispectral data and the moderate resolution, hyperspectral data, we expect to develop time- and cost-effective strategies for early detection and monitoring of diseases before the specific symptoms become visible.