Knowledge of rice phenology is essential for understanding the agricultural practices and studying its impact on ecosystem services. However, so far, available global-scale rice phenology maps do not provide fine spatiotemporal details of rice phenology in a consistent framework because they rely on the compilation of statistical data. Thus, this paper proposes an algorithm that combines the complementary advantages of Sentinel-1 and Sentinel-2 satellite images to produce large-scale maps that depict rice phenology dynamics. The novelty of this algorithm lies in the correlation with rice phenology features, i.e., rice in water condition and rice color change. The time series of backscattering at Vertical-Horizontal (VH) polarization and Enhanced Vegetation Index (EVI) are proposed to recognize rice planting and heading dates, respectively. For the same time, the Normalized Difference Yellow Index (NDYI) is utilized to detect the rice harvest date for the first time. The proposed algorithm is applied to multiple spatial scales (prefecture, 0.5° gridcell, and site scales) and to multiple rice cropping systems (single, double, and triple croppings) in monsoon Asia. Results reveal that the algorithm is able to accurately detect the rice planting and harvest dates across two rice paddy field distribution maps with moderate-to-high spatial resolution, different validation data, and different rice cropping systems. The bias values of detected planting dates are 2, 0, and 4 days, while that of harvest dates are -2, -5, and -13 days at the prefecture, 0.5° gridcell, and site scales, respectively. These results highlight the potential of this algorithm to generate national, continental, or even global maps of rice phenology dynamics in an efficient manner, which can facilitate research on the impact of rice phenology on rice ecosystem services that echoes environmental and climate change.