Rice is one of the world’s major agricultural crops and is the staple food for more than half of the world population. Consequently, there is a need to develop spatio-temporal monitoring system that can accurately assess rice area planted. In the past years, many remote sensing projects on rice crop monitoring have been carried out, predominantly the use of space-borne Synthetic Aperture Radar (SAR). In the Mekong Delta, Vietnam, the changes in cultural practices have been gradually adopted in the last ten years. These changes have impacts on remote sensing methods developed for rice monitoring. The aim of this study was to compare the accuracy obtained by different ENVISAT ASAR modes (APP and Wide Swath) and the classifiers used. Using ASAR Alternating Polarisation Precision (APP) data, the study showed that the radar backscattering behaviour is much different from that of the traditional rice, due to changes brought by modern cultural practices. The polarisation ratio image of rice fields at a single date during a long period of the rice season could be used to derive the rice/non-rice mapping algorithm. The results of this thresholding algorithm achieved higher and consistent accuracies between seasons and districts (i.e. 99% and 98%, respectively) when compared to other classifiers, such as the minimum distance, maximum likelihood, SAM, ISODATA and K-Means. Regarding the ASAR modes of data acquisition, the classification accuracy of Envisat ASAR APP data is higher (99%) than that of the Envisat ASAR Wide Swath (WS) image (97%) of the same seasons and regions.