Abstract: Abstract: In order to improve the automatic extraction of cultivated land in irrigation area in remote sensing images, according to the planting structure characteristics in Shenwu irrigation area, Hetao Irrigation District, the NDVI (normalized difference vegetation index) time series of main crops in the study area were constructed based on field survey results, Google earth and GF1-WFV remote sensing images. OIF index was used to select the best band combination. Furthermore, the harmonic analysis of time series (HANTS: An improved algorithm based on Fourier transform, which can flexibly deal with the problem of unequal intervals of data that constitute the time series) method was employed to smooth the NDVI time series. Visual interpretation based on remote sensing and Google earth, supervised classification (support vector machine), and the combination method of supervised classification and decision tree classification based on NDVI time series (before and after smoothed by HANTS filtering method) were used to extract the cultivated land area of the irrigation area. The extraction errors of different methods were verified by visual interpretation and 100 000 000 random verification points whose attributes were given by the means of Google earth and visual interpretation. Moreover, 3 indices, i.e. accuracy (equivalent to the user precision in the confusion matrix), integrity rate (equivalent to the producer accuracy in the confusion matrix) and overall accuracy (ratio of extracted land area to actual area) were used to evaluate the results. The results demonstrated that the accuracy, integrity rate and overall accuracy of supervised classification (support vector machine) were only 84.82%, 64.4% and 75.68%, respectively; for the combination method of supervised classification with decision tree classification based on NDVI time series (unsmoothed), the 3 indices were 94.28%, 84.21% and 89.1%, respectively; the combination method of supervised classification with decision tree classification based on NDVI time series (smoothed) was further improved, and the 3 indices reached 94.47%, 87.32% and 92.24%, respectively. The GF1-WFV data can be used for extraction of cultivated land area, which has better spatial and temporal resolution, and has stronger ground identification ability in the irrigation area with more complex underlying surface. The NDVI time series based on the GF1-WFV data can describe the crop growth law in the study area completely, and can be used to extract the crop spatial information accurately and efficiently through the difference in the amplitude and the phase of the NDVI curve between different crops. It avoids the phenomenon of pixel-based traditional classification, for example, different objects have the same spectrum and the same objects have different spectrum, and overcomes the limitations of single image data. Compared to the results of supervised classification, the accuracy is greatly improved. After smoothing by HANTS method, the NDVI time series keep the basic shape of the original curve, and effectively eliminate the influence of outliers and noise, which more tally with the actual growth law of crops. Through the combination of supervised classification with decision tree classification based on NDVI time series (smoothed), the extraction precision of cultivated land is improved effectively. The method combining crop growth law and remote sensing information can improve the extraction accuracy of cultivated land area effectively.