Paddy transplantation rate mapping can provide important information regarding water demand for its transplantation. It is a time-tagged activity that cannot be accurately mapped using a single date image. Further, many times, single sensor data may not have enough temporal resolution required to map it. Furthermore, due to cloud cover, a single optical sensor is not enough to provide required temporal scenes for mapping the transplanted paddy fields. Therefore, to have a finer temporal resolution, a multisensor approach was applied in our research work by integrating an optical sensor data with a synthetic aperture radar data to map transplanted paddy fields at a frequency of 5- to 10-day intervals. In our research, the transplanted paddy fields were mapped as a specific class of interest using suitable fuzzy-based classifiers. The specific fuzzy-based classifiers explored here were modified possibilistic c-mean (MPCM) and noise clustering (NC). For evaluating the results, mean membership difference (MMD) and entropy values were computed within transplanted paddy fields and for noninterest classes. Moreover, an error matrix criterion was also used to validate the results. The computed MMD and entropy values were found to be lower within transplanted paddy fields in comparison to the other classes of noninterest, indicating accurate classification. NC algorithm outperformed the MPCM algorithm while observing MMD and entropy values for assessment. However, using the fuzzy error matrix criterion, the performance of both algorithms was identical with an average overall accuracy and kappa coefficient of 90.88% and 0.82 for MPCM and 90.66% and 0.81 for NC, respectively.