During the kharif season, cloud cover, shadows, and haze many a time do not give a perfect assessment of the cropped area. Microwave datasets like Sentinel-1 synthetic aperture radar (SAR) open possibilities of penetrating clouds and hence are of great utility. Extracting crop-related information from these datasets is not easy, as it will be affected by the phenology of the crop, the presence of speckles, polarization characteristics, and the classifier chosen for information extraction. In this research, the focus is on mapping paddy acreage for the kharif season 2023 in Kurukshetra District, Haryana, using Sentinel-1 VH polarization data. The data were filtered for speckles using the intensity refined Lee filter, which appeared to be quite effective with respect to classification. We have compared a rule-based model with a random forest classifier. While the rule-based model came up with an estimated 128,824 hectares of paddy-covered area, the RF model, another ensemble learning methodology in vogue, arrived at only 111,817 hectares. This huge difference of 17,007 hectares reveals how such choices are indeed influential for results. On the one hand, the rule-based model requires predefined conditions specified in view of paddy cultivation complexities, and on the other hand, the RF model uses nonlinear relationships by multiple decision trees. It performed well in discriminating paddy, non-paddy areas, and fallow land. Results further emphasize the appropriate strategies that have to be considered for classification in agricultural mapping, wherein rule-based methods can give better estimates for specific crops like rice, since they put more focus on phenological features and temporal VH data. This study, therefore, brings out the potentials of remote sensing technologies and particularly Sentinel-1 SAR in delivery of accurate and timely crop area estimates for effective agricultural planning and resource allocation.