# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 5 Discussion  
Paddy rice classification was conducted by expanding the dimensionality of data on both spectral and temporal domains using two machine learning approaches, RF and SVM. The six schemes that were composed of various combinations of input data by sensor and collection date were carried out, and the effect of data dimensionality was analyzed over two different regions. The spectral and temporal characteristics of paddy rice were identified, and Paddy rice Mapping Index (PMI) was proposed based on the phenological characteristics of paddy rice. SAR data contributed to paddy rice classification more for site A than site B, which implies that it might not be ideal to use SAR data only for paddy rice classification in areas with rugged terrains and heterogeneous land cover. Nonetheless, SAR data were able to improve classification accuracy when using with Landsat data. Although it is considered that the fusion of Landsat and SAR might not be  
Figure 9: Spatial distribution of paddy rice maps at California State based on Google Earth Engine. (a) RGB image from Landsat 8 on 26 May 2016 (planting season); (b) RGB image from Landsat 8 on 30 August 2016; (c) PMI result; and, (d) reference map. Paddy rice fields are shown in yellow. necessary for areas with low cloud cover, such a fusion can be effective to paddy rice classification for Asia because most regions in Asia are very cloudy, especially for the growing season, which limits the availability of optical time series data. The performance of SVM was better than that of RF at both sites, which is consistent with [PERSON] et al. [PERSON] et al. (2016) and [PERSON] & [PERSON] [PERSON] and [PERSON] (2016). In this study, classifications were conducted in a 10-dimensional feature space for scheme 1, and more than 60-dimensional feature space for the other schemes. As SVM is known to work well in a large dimensional feature space, it could be better for SVM to classify paddy rice than RF [PERSON] and [PERSON] (2016) stated that the number of training samples has an impact on the performance of a classifier, and [PERSON] & [PERSON] [PERSON] et al. (2016) stated that more than 400 pixels per class are needed for robust classification. In this study, a relatively small number of training samples (less than 200 pixels per class) were used, which might explain the better performance of SVM than RF because SVM works well with a small sample size as well as mixed pixels [PERSON] et al. (2016); [PERSON] et al. (2016). Land cover was more heterogeneous in site B than site A with a higher number of classes (six classes in site A and eight classes in site B). The population density of site B (249 people/km\({}^{2}\)) was about four times more than that of site A (61 people/km\({}^{2}\)), and classes were more patched and thus more mixed within a pixel in site B than site A. Those factors influenced the classification accuracy at site B. The size of paddy rice fields needs to be considered in the future research as discussed in [PERSON] et al. [PERSON] et al. (2016). Higher resolution data, such as RapidEye, are needed to classify paddy rice where the size of paddy rice field is small, such as in site B. The approach proposed in this study have benefited from the use of phenological characteristics of paddy rice (i.e., time series data). The phenology of paddy rice in terms of various spectral bands and vegetation indices can be used for numerous applications because such phenology is a relative characteristic that varies by region. There are many fields that use the phenological characteristics of crops (e.g., paddy rice), such as classifying different vegetation (crop) types ([PERSON] et al., 2016; [PERSON] et al., 2016), crop yield estimation ([PERSON] et al., 2016; [PERSON] et al., 2016), gross and net primary production (GPP/NPP) estimation ([PERSON] et al., 2016), and quantifying crop water requirements ([PERSON] et al., 2016).