## 4 Discussion  
The damage produced by the rice dwarf virus primarily impacts plant growth, which thereby results in a significant reduction in leaf area and biomass [54]. Similarly, the glume blight infestation first affects the foliage and induces wilting and structural change in the canopy [55]. By comparison, the first symptom of rice blast infestation is dehydration and destruction of the pigmentary system, which leads to discoloration in appearance and a series of physiological and biochemical variations in leaves [56]. Changes in external architecture and internal biophysical parameters provide evidences for remote detection of diseases infestations. PlanetScope satellite data products collected high spatial resolution (3-m) imagery in broad blue, green, red, and NIR spectral bands. Among the four original bands, the NIR band showed evident difference between healthy and diseased rice, which was associated with variation in canopy variations. In this study, the selected VIs exhibit great performance on separating healthy rice from the rice infected with different diseases. These VIs enhance the original spectral response from different aspects. It was also noted that all of the VIs contained a NIR band, which thereby had the potential to be sensitive to the changes in canopy parameters at the regional scale, such as LAI and green biomass, and might explain their good performance in detecting diseases infestations. This finding is consistent with [PERSON] et al. [57; 58]'s studies. Compared with the single-date VIs, the novel proposed normalized two-stage VIs performed better on characterizing the biophysical and canopy structure variations caused by disease infestations (Tables 3 and 4). It is noteworthy that, apart from the disease infestations, there were other factors leading to responses of the same spectral features. Thus, within a single-date scene, the spectral features would not only respond to disease infestations, but also fluctuate following the phenological differences, cultivation differences, and plant condition diversities between fields [59]. The bi-temporal spectral variations characterized by the normalized two-stage VIs and the high spatial resolution (3 m) provided by the PL imagery helps to eliminate field anomalies other than the disease infestations and mixed pixel effects. For example, among the identified features, the normalized two-stage NDVI showed stronger sensitivity to rice dwarf for disease mapping, which was associated with variation in canopy morphology driven by change in leaf area. Normalized two-stage TVI performed better on rice blast classification owing to its sensitivity of radiant absorption of chlorophyll. And the normalized two-stage NDVI and MSR provide significant contributions for glume blight classification, which were highly correlated with canopy structure. These spectral responses extracted by the normalized two-stage VIs may explain the sound improvement in detecting different diseases. From the perspective of classification, this study revealed the potential of normalized two-stage VIs for accurately classifying rice diseases. PLS-DA provides an ideal framework for the combined purpose of integrating spectral features and pathological mechanisms. For instance, PLS-DA successfully reduced the redundant information and collinearity effect hidden in the input feature space. Furthermore, PLS-DA provides valuable information on important spectral features, based on the VIP approach. The normalized two-stage VI based PLS-DA model produced a better damage map and lower commission error for specific disease classification. In contrast, the classification produced by single-date VIs (30 October) had a reduced performance. The analysis of important features determined by the VIP procedure has shown that the highest scoring structures for the classification were consistent with the pathological progress of certain diseases.