## 4 Discussion
### Mapping and Classification Accuracy of Crop Type and Practice Using NDVI Time Series  
The classification accuracy of SAVI was slightly higher than that of NDVI (Figure 5). SAVI was developed to reduce soil effects, which affect NDVI [57]. This may be the reason for the slightly higher classification accuracy of SAVI, because the amount of vegetation could be estimated more accurately than with NDVI on agricultural land with low vegetation cover. MSAVI and EVI had slightly lower classification accuracy than NDVI, possibly because the parameters in their equations were not properly estimated owing to the dispersion of reflectance observed by Planet Dove sensors. EVI uses the blue band in addition to the red and near-infrared bands [59], so the increased number of bands may have increased the variability of the index. Moreover, EVI used the parameters developed for the MODIS sensor, which may not be suitable for broadband Planet Dove sensors. The effects of these parameters on EVI should be clarified by more detailed research. Therefore, we used NDVI to create a crop type and practice map.  
To the best of our knowledge, this study was the first to attempt to classify typical sugarcane crop practices in Japan using satellite remote sensing. The NDVI time series generated by Planet Dove satellite imagery classified sugarcane crop practices with good accuracy, exhibiting 0.93 for OA and 0.88 for Kappa, calculated based on Table 2. The slight confusion was mainly between ratoon and spring planting of sugarcane. There is no large difference in the growing season between these crop practices (Figure 4). Variations in the NDVI values due to individual differences in sensors [85] could have obscured the slight differences in the NDVI time series of the ratoon and spring planting, which have similar growth periods, resulting in a slight confusion of the two classes shown in Table 2. The only difference is that for spring planting season, vegetation is completely removed in spring planting fields, whereas the soil of ratoon fields is covered with dry sugarcane leaves remaining after harvest (mulching); therefore, in the NDVI time series, spring planting showed a slightly lower value than that of the ratoon (Figure 4). Hence, the presence or absence of mulching in the early stages of growth is the key to discriminating between ratoon and spring planting. It is necessary to examine the sub-classification algorithm for ratoon and spring planting with a focus on the spring planting season. At the same time, the classification between crop practices that show similar crop growth patterns requires further efforts to suppress noise in the NDVI time series based on Planet Dove imagery caused by individual differences in sensors [85].  
The results for the user's and producer's accuracy of each class were good (Table 2). The producer's accuracy of ratoon was the lowest and ratoon was the most underestimated class (Table 2), indicating that ratoon sugarcane fields had large spatio-temporal variability. The pineapple was the most overestimated class, and pastures and sugarcane (summer planting in 2018 and ratoon) were often misclassified as pineapple (Table 2). Pasture and ratoon misclassification may have been caused by the presence of vegetation throughout the year in unharvested areas of agricultural fields. The confusion may have also been caused by the overlap between summer planting of sugarcane and pineapple growth season; pineapples are harvested in July and August, plowed between August and October, and then replanted. Classification may be improved by extending the time series period from 3 to 5 years.  
Only pure pixels, not mixed pixels with non-agricultural areas, were classified in this study. Hence, mixed pixels can be complemented by the neighboring pure pixels, and the coverage ratio of pure pixels within an agricultural field at the spatial resolution of Planet Dove imagery (99.8%) is very important in the classification of small-scale agricultural areas.