# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 1 Introduction
Over the years, research efforts have been made in fusion of different sensor data in the remote sensing field [1; 23; 26; 27]. [PERSON] et al. [26] aimed to monitor paddy rice agriculture fusing optical vegetation indices from Landsat 8 and dual-polarization C and L band backscattering coefficients from Sentinel-1 and PALSAR-2 across the Myanmar using a random forest (RF) approach. The results showed the accuracy with R\({}^{2}\) of 0.78 for the harvested rice areas in comparison with census statistics. They concluded that the fusion of optical and SAR data has a great potential to monitor and analyze paddy rice production. Given the increasing interests in remote sensing-based paddy rice mapping and monitoring, several review papers on this topic have been published [5; 17; 28]. [PERSON] et al. [17] divided paddy rice mapping studies into four categories, using (1) reflectance data and statistical approaches, (2) vegetation index (VI) and enhanced statistical approaches, (3) temporal analysis approaches with VI or RADAR data, and (4) phenology-based approaches. They discussed the current challenges and opportunities in future paddy rice mapping: to improve algorithms with the development of land cover classification approaches, to improve data acquisition capacity with high spatial resolution (\(\sim\)30 m) such as combined Landsat and Sentinel-2 data, and to improve computing capacity such as using Google Earth Engine. In particular, the Landsat series have been provided for free with an opening from the archive datasets to the general public. Added to this benefit are large swaths with a width of 185-km and a 16-day temporal resolution, which produces valuable information for the repeated measurement of space and time [29; 30]. Moreover, accumulated archives with more than 40 years of data have the potential to fuel long-term studies with regional and global land cover classifications [31]. Given this background, we identified some issues that should be improved. This present study investigated the following approaches to improve the identification of paddy rice areas over two distinct regions with different climatic and environmental characteristics:  
1. paddy rice classification using multi-sensor fusion (optical sensor and SAR) and multi-temporal data through machine learning approaches (RF and SVM);
2. development of a paddy rice mapping index (PMI) while considering spectral and phenological characteristics; and,
3. paddy rice classification using Google Earth Engine based on the PMI approach over larger areas. The present study aims at advance paddy rice classification by expanding the dimensionality of data on both spectral and temporal domains. We conducted paddy rice classification through fusion of optical sensor and SAR time series data using two machine learning approaches, RF and SVM. This study examined six schemes to identify the effect of data dimensionality on paddy rice classification. The six schemes composed of various combinations of input data by sensor and collection date considering the phenology of paddy rice over two different regions. This study also proposed Paddy rice Mapping Index (PMI) when considering the spectral and temporal characteristics of paddy rice that can be applied to areas that have different climatic and environmental characteristics.