Under the 12th Malaysia Plan (RMK-12), the Malaysian government has set a goal to achieve a self-sufficiency level (SSL) of 70% for rice production. Accurate and timely spatiotemporal information on harvested rice extent is required to measure progress towards achieving the SSL. Remote sensing technology has been widely used to provide rapid information on the extent of rice. This study aims to create maps of rice extent and cropping patterns in the IADA Barat Laut Selangor (BLS), combining Synthetic Aperture Radar (SAR) imagery data using Sentinel-1 and optical imagery data using Sentinel-2. The monthly composite data of VH polarization and the Normalized Difference Vegetation Index (NDVI) were stacked as bands in an imagery dataset, forming a time series dataset. The unsupervised K-means clustering method and the Google Earth Engine (GEE) cloud-based computing platform were used for the analysis of the data. The results allowed the classification of the rice and non-rice groups. The rice cropping patterns were generated from the temporal composites of VH backscatter values of Sentinel-1 data and NDVI of Sentinel-2 data. The map products were evaluated using accuracy measures using visual interpretation techniques applied to very high-resolution imagery acquired from Google Earth (GE). The rice extent map generated at 10-meter resolution exhibited excellent accuracy with an overall accuracy of 98% and a kappa coefficient of 0.95. The estimated rice parcel area in IADA BLS for 2021 was 17,864 ha, which is close to the recorded data (18,785 ha). The comparison of results in the irrigation block also indicated that the rice field area agreed well with the statistical data, with an R2 of 0.95, RMSE of 357 ha, and a relative discrepancy of 4.9%. The cropping pattern also showed satisfactory results as compared to the existing data. These findings demonstrate that the proposed methodology can provide high-accuracy rice extent map products and has the potential to be applied to rice fields across Malaysia and other tropical regions to address food security issues. Subsequent investigations should prioritize addressing the challenge of detecting conversions into other land uses, which were not addressed in this study, by exploring alternative data sources, advanced machine learning algorithms, and incorporating ground-based surveys.