The Malaysian government, under the 12th Malaysia Plan (RMK-12), has set a goal to achieve a self-sufficient 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 for rice. Remote sensing technology has been widely used to provide information on rice extent in large areas rapidly. The aim of this study is to create maps of rice extent and cropping patterns in the IADA Barat Laut Selangor (BLS) using a combination of Synthetic Aperture Radar (SAR) imagery data from Sentinel-1, optical imagery data at level-2A or Surface Reflectance (SR) from Sentinel-2, the unsupervised K-means clustering method, and the Google Earth Engine (GEE) cloud-based computing platform. The clustering results were then classified into rice and non-rice groups, and used to generate rice cropping patterns based on their representative profiles of temporal composite values of VH backscatter of Sentinel-1 data and NDVI of Sentinel-2 data. The accuracy of the map products was evaluated using a confusion matrix based on visual interpretation using very high-resolution imagery in Google Earth (GE) and compared with the existing data from IADA BLS. 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 existing data of 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 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.