Cotton is one of the major cash crops in the world, which is the main fiber crop that delivers fibers to textile industries across the globe. The efficient and timely information of the cotton distribution is essential for cotton production, agricultural management, as well as the sustainability of agricultural systems. However, there still keep challenges in accurately extracting cotton cultivated area over large areas. To address this issue, we explored the feasibility of combining time series enhanced vegetation index (EVI) calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data with the fused representation-based classification (FRC) algorithm to identify the cotton pixels and map cotton cultivated area in the major cotton production regions of China. The Fourier transform was used to obtain the harmonic features of the annual time series EVI at a pixel scale over the study area. Then the FRC algorithm was applied for the identification of cotton cultivated area with harmonic features as input. By calculating the fused residual for each pixel, and setting the threshold value for each province based on the statistical data, the cotton cultivated area was extracted in the study area during 2015–2017. Finally, the performance of the FRC algorithm for cotton mapping was evaluated and compared with those of the collaborative representation-based classification (CRC) and sparse representation-based classification (SRC) algorithms. The results showed that there was a good correlation between the MODIS-derived cotton cultivated area and statistical data at the municipal level, with the coefficient of determination (R²) of 0.83 for 3 years. The mapping results of the cotton cultivated area derived by the FRC algorithm had a higher precision compared with those derived by the CRC and SRC algorithms. It demonstrated that the combination of time series MODIS EVI data, statistical data and the FRC algorithm is effective in identifying the cotton cultivated area. Furthermore, the applicability of this approach to other crop mapping and higher spatial resolution images is worth investigating.