# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 1 Introduction  
Rice is one of the important food resources for most of the world's population, especially those in Asia [1, 2]. The annual average of rice consumption per capita was approximately 54 kg during 2017-2018 (Agricultural Market Information System (AMIS), [[http://statistics.amis-outlook.org/](http://statistics.amis-outlook.org/)]([http://statistics.amis-outlook.org/](http://statistics.amis-outlook.org/))), and the consumption has increased with increasing world population [3]. [PERSON] et al. [4] noted that the demand for rice over the next 30 years is expected to increase by 90% in Asia. Paddy rice mapping and monitoring are crucial for food security and agricultural mitigation because they allow for identifying and forecasting rice production [5]. In general, the annual or seasonal surveys have been conducted for measuring cultivated areas during the crop growing season [5, 6]. The sampling survey, however, is costly, labor-intensive, and time-consuming. In addition, the survey based paddy rice mapping has a relatively slow update cycle (e.g., every five years). Rice cultivated areas often change due to human activities as well as natural disasters such as drought, floods, and wildfire. Thus, up-to-dateinformation on paddy rice areas is essential for decision makers, scientists, and also farmers to monitor and manage the areas in a timely and sustainable manner. Satellite remote sensing has been employed as an alternative with advantages of high temporal resolution and spatial continuity. There are several studies for paddy rice classification using single optical sensor data, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) [7; 8]. However, mixed pixels have often caused misclassification when using single sensor data (e.g., MODIS) [9]. Multi-temporal and high-resolution data have been used to overcome such a mixed pixel problem [10]. Recent studies have emphasized the capability of multi-temporal data to identify paddy rice areas because paddy rice has its own phenology (e.g., transplanting), which is a distinct characteristic of paddy rice compared to other crop or land cover types [11; 12; 13; 14; 15; 16; 17]. [PERSON] et al. [18] mapped paddy rice in China using Support Vector Machine (SVM) with MODIS time series data. Their approach performed well to map paddy rice with an overall accuracy of 90%. [PERSON] et al. [9] attempted to map paddy rice distribution using multi-temporal Landsat data at regional scale with phenology information. They used a rule-based decision tree approach to determine optimal threshold values to map paddy rice using multi-temporal Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) as input data. The results showed the coefficient of determination (R\({}^{2}\)) of 0.85 between satellite-based and census recorded paddy rice areas. [PERSON] et al. [19] developed a time series Landsat-based paddy rice mapping platform to produce paddy rice maps from 1986 to 2010. Vegetation indices were used to conduct paddy rice mapping with a five-year interval based on a land cover-adapted thresholding approach. Although optical sensor data are useful to map paddy rice, there is a limitation for data acquisition due to cloud contamination. Synthetic Aperture Radar (SAR) data have been used to complement the cloud problems of optical sensor images because SAR data are not influenced by weather conditions [20; 21]. Several studies employed SAR data for land cover/land use as well as paddy rice mapping [1; 21; 22; 23; 24]. Optical sensor and SAR data have complementary information. Multi- or hyperspectral data provide information of reflective and emissive features of the Earth's surface, while the SAR data provide information about the surface roughness, texture and dielectric properties of natural and man-made objects [22; 25]. Thus, the integration of multispectral and SAR data produces more reliable results than those from single sensor data [25].