With a population of 267 million, Indonesia faces the significant challenge of inaccurate rice production data leading to a flawed national rice import policy and supply problems. Its 2018 rice production and harvest area data were generated through the Area Sampling Frame (ASF) method which incurred high labour and financial costs as well as failing to optimize accuracy; hence, an alternative method needs exploration. This study compares ASF and remote sensing Synthetic Aperture Radar (SAR) methods to calculate rice growth stages (RGS) using Indramayu Regency, the highest rice producer in West Java Province, as the study area. The SAR-based method used time-series of Vertical Horizontal (VH) polarization of Sentinel-1A data that employed a combination of k-means clustering, hierarchical cluster analysis (HCA), a visual interpretation and support vector machine (SVM) classifier. Both SAR and ASF methods can generate results on a monthly basis, although remote sensing satellite time revisits can be shortened (every 12 days). Whilst the ASF, a basic technique for collecting agricultural statistics, was easy to implement in large-scale areas its accuracy depended on the quantity and representativeness of the samples. This study applied the ASF by simulating a sample size of 1.7%, 3.3% and 5% of a rice field area with unmanned aerial vehicles (UAVs) data as a reference. Whilst remote sensing SAR methods involve complex data processing the image classification process can be conducted automatically and cost-effectively (data and its software are free of charge). Moreover, it yields not only statistical data on RGS but also determines the spatial planting patterns and the RGS distribution at 10 m pixel resolution. This method showed more accurate results with overall accuracy of image classification of 81.89% and a kappa coefficient (κ) of 0.73. The comparative result was relatively small, i.e., 4,094.89 ha more than the ASF results (3.5% difference), since this study covered a limited research area. Nonetheless, with evidence of more accurate results remote sensing holds the potential for replication across the country’s 416 regencies, so enabling government to develop more appropriate policies that minimize the risks of either a surplus or shortage in the nation’s most important food supply.