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Rice is one of the main staple food of human beings, timely and accurate access to distribution of paddy rice cropped area and its spatial-temporal variations are of great significance for food policy formulation. Focusing on the research topic of "paddy rice remote sensing mapping", firstly, we systematically summarized the physiological growing process and main cropping patterns of paddy rice based on the review of domestic and foreign literature. Rice cultivation is mainly concentrated in Southeast Asia globally. In China, the single cropping rice production areas are mainly located in the northeastern region and the middle and lower reaches of the Yangtze River. The double cropping and three-cropping rice production areas are located in southern provinces, such as Hunan, Jiangxi, and Guangdong. Then, from the view of remote sensing data sources, the optical remote sensing data and radar remote sensing data commonly used at present in mapping paddy rice with were summarized. Based on the emphasized analysis of ‘spectrum-space-time" characteristics of paddy rice, typical vegetation index in paddy rice mapping was discussed and the mainstream methods of paddy rice mapping were concluded from traditional machine learning and deep learning. Afterwards, the application status of paddy rice remote sensing mapping was summarized from three aspects: the typical machine learning model, multi-source remote sensing data fusion and remote sensing computing cloud platform. It is concluded that the following issues exist in the current research on rice mapping: (1) Rice is misclassified due to the lack of remotely-sensed data or the similar plants with comparable growth cycle; (2) It is dependent on machine learning model parameters; (3) Rice mapping is difficult in broken terrain or paddy fields subject to multiple seasons or rotation; (4) Different regions have different planting structures, which make it difficult for existing methods to be compatible and applicable universally. Based on this, the development direction of paddy rice mapping was explored from the aspects of rice phenological feature mining, acquisition methods of time-series observations on paddy rice, and improvement of finer spatial resolution of paddy rice mapping: (1) the characteristics of remote sensing signals in rice phenological period have changed from single to diversified; (2) time series optical imagery acquisition covering the entire growth period of rice; (3) improving the spatial resolution of paddy rice mapping; (4) effective fusion and application of multi-source remote sensing data.