###### Abstract  
Timely and accurate regional rice paddy monitoring plays a significant role in maintaining the sustainable rice production, food security, and agricultural development. This study proposes an operational automatic approach to mapping rice paddies using time-series SAR data. The proposed method integrates time-series Sentinel-1 data, auxiliary data of global surface water, and rice phenological characteristics with Google Earth Engine cloud computing platform. A total of 402 Sentinel-1 scenes from 2017 were used for mapping rice paddies extent in the Mun River basin. First, the calculated minimum and maximum values of the backscattering coefficient of permanent water (a classification type within global surface water data) in a year was used as the threshold range for extracting the potential extent. Then, three rice phenological characteristics were extracted based on the time-series curve of each pixel, namely the date of the beginning of the season (DBS), date of maximum backscatter during the peak growing season (DMP), and length of the vegetative stage (LVS). After setting a threshold for each phenological parameter, the final rice paddy extent was identified. Rice paddy map produced in this study was highly accurate and agreed well with field plot data and rice map products from the International Rice Research Institute (IRRI). The results had a total accuracy of 89.52% and an F1 score of 0.91, showing that the spatiotemporal pattern of extracted rice cover was consistent with ground truth samples in the Mun River basin. This approach could be expanded to other rice-growing regions at the national scale, or even the entire Indochina Peninsula and Southeast Asia. rice paddy; time-series; Sentinel-1; Google Earth Engine; permanent water; phenological characteristics 2020 IEEE/RSJ International Conference on Intelligent Systems and Applicationswater management and climate change [4; 5]. Therefore, efficient and timely collection of accurate information on the geographic location, distribution, and extent of rice paddies is important for sustainable rice production, water and food security, greenhouse gas emissions, environmental sustainability, effective decision making, and policy management [2; 6; 7; 8; 9; 10; 11; 12; 13]. Despite its significance for water and food security and global change research, the extent of rice paddies worldwide still remains uncertain [14; 15]. Those existing maps covering the continental or global scale have been derived primarily from regional or national statistics, which cannot reflect the extent or distribution of rice paddies across large areas [16; 17]. Remote sensing has gradually become the core alternative for rice paddy mapping over large areas due to its low cost, scalability, unbiased estimates, high temporal resolution, and spatial continuity [18; 19]. Remotely sensed rice paddy mapping has been performed since the launch of the first-generation Landsat satellite [9], and subsequent research has grown more and more. Early traditional unsupervised and supervised image classification methods have been applied using single and multitemporal optical images such as Landsat, SPOT, or other higher spatial resolution satellite images [20; 21; 22; 23; 24; 25] to fulfill the needs of rice paddy mapping. With the development of classification methods, new classification algorithms such as neural networks (NNs), support vector machines (SVMs), random forest (RF), and deep learning have been gradually introduced into rice paddy mapping [3; 26; 27]. However, these methods commonly require local prior knowledge to map rice paddies at relatively fine scales, which is not always directly exportable to other regions at larger scales. In recent years, time-series vegetation indices were gradually increasingly used for extracting the extent of rice paddies at regional or larger scales [9; 13; 14; 20; 22; 24; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37].