Abstract: Accurate identification of winter wheat irrigation events and precise estimation of irrigation frequency are crucial for the rational use of water resources and the accurate estimation of irrigation water consumption. Site observation records can provide accurate irrigation information, but such data are difficult to obtain, and the number of observation sites providing irrigation information is limited. As a result, accurately identifying regional-scale irrigation events and frequency remained a challenge. This study proposed a method for identifying winter wheat irrigation events and estimating irrigation frequency. First, the spatial distribution of winter wheat was obtained. The study used winter wheat distribution data at three different spatial scales (field scale and two pixel scales), where the pixel-scale data had spatial resolutions of 500 m and 30 m, using publicly available data. The field-scale winter wheat distribution was extracted in this study, with the process as follows: acquiring the best cloud-free Sentinel-2 images from 2018 to 2020 for the study area, extracting spectral features, constructing sample sets for each year's image, and using the nearest neighbor classifier based on GEOBIA(geographic object-based image analysis) to extract winter wheat field distribution. Next, grids of 500 m × 500 m or 2 km × 2 km were generated in the study area, and the average values of the Sentinel-1 SAR VV (Vertical-Vertical) single-polarization backscatter coefficient for winter wheat (each field or pixel) and the grid were calculated. By analyzing and comparing the standard deviation and correlation coefficient of winter wheat (each field or pixel) and the grid within a certain time range, combined with daily precipitation data, irrigation events were identified. Finally, the irrigation event identification results at the field and pixel scales from 2018 to 2020 were validated using observed irrigation time and frequency data, and irrigation frequency was calculated based on the identified irrigation events. This provided the spatial distribution of winter wheat irrigation frequency at the field and two pixel scales in the study area. The study applied the proposed method to identify winter wheat irrigation events in Yucheng, Shandong Province, at three different spatial scales (field scale, 500 m, and 30 m pixel scales), and used observed irrigation data to validate the results. Irrigation frequency was then estimated at the spatial scale with the highest irrigation event identification accuracy, and the spatial distribution of winter wheat irrigation frequency in Yucheng from 2018 to 2020 was obtained. The results showed that the recall rates for identifying irrigation events at the field scale, 500 m, and 30 m pixel scales were 85.71%, 78.57%, and 57.14%, respectively, with F-scores of 70.59%, 66.67%, and 49.71%. The accuracy of field scale applications is better than that of applications at 500m and 30m pixel scales. The "Strict" overall accuracy for identifying the winter wheat irrigation frequency distribution at the field scale was 69.75%, while the "Loose" overall accuracy was 90.24%. The irrigation frequency for winter wheat in Yucheng from 2018 to 2020 concentrated between 1 and 3 times. This study provides a reliable method for identifying irrigation events and estimating irrigation frequency at regional scales.