South and Southeast Asia, a major global hub for paddy rice cultivation, exhibits the highest rice cropping intensity worldwide due to its favourable hydrothermal conditions, and also has experienced considerable spatiotemporal changes due to climate change and anthropogenic activities. However, the absence of long-term spatial distribution and cropping intensity of paddy rice hinders effective agricultural and environmental management. This gap is particularly critical especially in the 21st century, with enhanced impacts from changing climate, water resources, and food trade pattern. Using all the available Landsat and Sentinel-2 archives, we refined a phenology-based algorithm to generate 30-m rice maps and cropping intensity across South and Southeast Asia for the years 1995, 2005, 2015, and 2024. The algorithm overcomes the challenge of detecting rice cropping intensity in long time-series and comprises three core steps: (1) identifying pixel-level rice phenological peaks using an enhanced peak detection method, thereby defining potential transplanting windows and minimizing monsoon-induced cloud and precipitation interference; (2) detecting paddy flooding signals and delineating rice cultivation areas based on phenological rules derived from the relationship between the Land Surface Water Index (LSWI) and Enhanced Vegetation Index (EVI); (3) determining rice cropping intensity according to the number of valid crop peaks and associated flooding signals within a single year. The resulting maps were validated using 23,396 samples collectively derived from a field photo library, visual interpretation of Sentinel-1/2 satellite imagery, and a sample migration algorithm. Across the four periods, the maps achieved overall accuracies ranging from 83 % to 87 %. In addition, the resultant products were compared with existing regional and period-specific rice datasets (e.g., NESEA-RICE10 and Open-SEA-Rice-10) for further evaluation. The comparisons demonstrated that the refined approach achieved higher accuracy and robustness in mapping both rice distribution and cropping intensity, whereas the existing products performed well only in partial environments. When compared with the FAO official statistics for South and mainland Southeast Asian countries, the derived maps yielded R² values exceeding 0.9. This dataset holds great potential for applications such as methane emission estimation, water resource management, and crop yield monitoring, thereby supporting sustainable agricultural practices and policy development in the region.