## 3 Results
### Mapping and Classification Accuracy of Crop Types and Practices Using NDVI Time Series  
Figure 5 shows the classification accuracy of each vegetation index time series evaluated from 10 random samplings of training data. High classification accuracy was obtained with all vegetation indices (Figure 5). All vegetation indices have an average OA exceeding 0.91, and the average Kappa exceeding 0.89. Vegetation indices based on both visible and NIR bands (NDVI, SAVI, MSAVI, and EVI) provided slightly better classification accuracy than those based on visible bands only (VARI, GRVI, and GR). This can be confirmed by the threshold based on the average value of classification by all vegetation indices (red line in Figure 5). No significant difference was found between NDVI and other vegetation indices based on visible and NIR bands. This suggested that the NIR band, which reflects vegetation better than visible wavelengths [80], could be effectively used after band-to-band re-registering. Because NDVI did not differ from other NIR-based vegetation indices, we recommend the traditional and versatile NDVI classification.  
We created a crop type and practice map using the NDVI time series (Figure 6). In this map, the class with the maximum average value of the class probabilities when the classification was conducted 10 times by RF. Class probability indicates the probability that a pixel belongs to each class when classified by RF. Even though classification in this map was on a pixel basis, the segments of crop types and practices were adequately represented in most of the agricultural parcels.  
We compared the percentage of pure pixels that can be classified with the Planet Dove imagery with other representative medium-resolution satellite images. The ratio of pure pixel area to agricultural land area (agricultural field polygon data) was 85.2% in Planet Dove imagery (spatial resolution, 3 m), 55.5% in Sentinel-2 (10 m), and 11.3% in Landsat images (30 m). The presence or absence of target pixels in the polygon data was 99.8% in Planet Dove imagery, 90.3% in Sentinel-2 imagery, and 16.4% in Landsat imagery.  
Table 2 shows a confusion matrix that combines the results of 10 trials of classification using the NDVI time series. The classification accuracy of crop type and practice using NDVI time series was 0.93 for OA and 0.92 for Kappa (Table 2). The accuracy of classification into sugarcane vs. others was 0.98 for OA and 0.97 for Kappa. The producer's accuracy and user's accuracy of sugarcane were 0.98 and 0.99 respectively, and the other's were 0.99 and 0.98 respectively. This result is superior to the published values of OA (0.84-0.96) and Kappa (0.59-0.87) obtained using remotely sensed satellite imagery (combination of Formosat/MS, ALOS/AVNIR2, SPOT/HRV and HRG [37], combination of Landsat sensors [81,82], MODIS [83]). However, it should be noted that the previous studies were not  
Figure 5: Overall accuracy (OA) and Kappa of each vegetation index time series (n = 10). Red line shows the average of all values of each evaluation index.  
necessarily limited to crop type classification as in this study (for example, forests were included in other class [37]).