The groundnut, also known as peanut, is a legume crop that is classified as both a grain legume and an oil crop (due to its high oil content). About 85% of the total groundnut in India is sown in the kharif season under rainfed conditions. This makes it extremely difficult to apply the optical remote sensing methodologies for groundnut discrimination and acreage estimation. We develop a remote sensing strategy based on timeseries synthetic aperture radar (SAR) imagery, which is immune to the rainy conditions of the observation period. Further, our work is distinct from the traditional multitemporal SAR backscatter analysis as it incorporates polarimetric parameters such as the alpha angle (α) and the polarimetric radar vegetation index to aid discrimination. These parameters were shortlisted on the basis of maximal sensitivity to groundnut growth stages. Random forest classifier, an ensemble learning-based classifier, is used to carryout the crop classification using these parameters. This approach is contrasted with SAR backscatter-based analysis (using the same RF classifier) and with a Euclidean distance (ED)-based model matching method (with the same polarimetric parameters). A detailed comparative analysis of classification accuracy of groundnut discrimination is carried out using extensive ground truth data over the agricultural fields of Junagadh district in the Gujarat state of Western India. The backscatter-based analysis with RF achieves 86.2% overall accuracy in discriminating groundnut (with the competing cotton crop), whereas polarimetric parameters with ED model matching achieves 76%. Using the proposed method, with dual-pol parameters derived out of Sentinel-1 SAR data, we report 92.7% overall accuracy with a kappa coefficient of 0.81. The major insight is that combining timeseries polarimetric information together with a good machine learning classifier improves the classification accuracy of short crops compared to nonpolarimetric approach.