# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 6 Conclusions  
As the population in the world increases, the demand of rice also increases. Thus, providing accurate and up-to-date paddy rice maps is very important. A satellite-based approach produces paddy rice maps with a fast update cycle in comparison with the traditional survey approach. In this study, Landsat, SAR, and SRTM data were used to classify paddy rice at two sites that have different climatic and environmental characteristics. This study evaluated six schemes to identify the improvement of classification performance through the investigation of the characteristics of study areas, classifiers, and fusion of multi-sensor time series data. As expected, the classification accuracy was the highest when using multi-sensor time series data (scheme 6). Site A showed better performance than site B because Site A consisted of more homogeneous land cover and had higher data availability during the growing season than site B. The performance of SVM classifier was slightly better (accuracy of 1-3%) than RF, especially at site B because SVM works well in a small sample size and at a mixed pixel. Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice was proposed in this study. NIR and SAR backscattering coefficients detected the phenological characteristic of paddy rice well. Although the performance of PMI is slightly worse than the RF or SVM classifications based on multi-sensor time series data, PMI is useful to operationally map paddy rice, because PMI is simply calculated only using two scenes and does not require training data, which implies that the PMI-based approach can be applied to different regions. In addition, paddy rice can be easily mapped using the Google Earth Engine platform.  
There are some limitations in the use of PMI. PMI can be applied only where paddy rice areas are flooded during the transplanting season. As the current threshold, greater than 0.21, based on the results for 13 years is a little arbitrary, more robust and adaptive thresholding approaches should be considered. There are some misclassified pixels due to the heterogeneous composition of the land cover within a pixel at site B. In further studies, the size of paddy rice fields need to be considered when selecting satellite images, and higher resolution data, such as RapidEye or Worldview series, need to be evaluated to detect small size paddy rice patches.  
The following are available online at www.mdpi.com/2072-4292/10/3/447/sl.  
**Acknowledgments: This research was supported by the Space Technology Development Program and the Basic Science Research Program through the National Foundation of Korea (NRF) funded by the Ministry of Science, ICT, & Future Planning and the Ministry of Education of Korea, respectively (Grant: NRF-2017M1A3A3A02015981; NRF-2017R1D1A1B03028129).**  
**Author Contributions:** [PERSON] led manuscript writing and contributed to the data analysis and research design. [PERSON] supervised this study, contributed to the research design and manuscript writing, and served as the corresponding author. [PERSON] and [PERSON] contributed to data processing and analysis. [PERSON] and [PERSON] contributed to the discussion of the results and manuscript writing.  
**Conflicts of Interest:** The authors declare no conflict of interest.