【摘要】 及时准确地提取冬小麦种植信息，对开展冬小麦农情遥感监测具有重要的意义.以杭州市余杭区冬小麦越冬期(2021-12-04)、扬花期(2022-04-08)和乳熟期(2022-05-03)Sentinel-2遥感影像为数据源，分别采用最大似然法、支持向量机、归一化差值植被指数(normalized difference vegetation index, NDVI)相加和相减合成运算提取冬小麦种植面积.结合冬小麦地面调查数据与实测种植面积，对不同方法的提取结果进行精度评价.结果显示，利用越冬期影像NDVI阈值将常绿植被区(茶园、林地)掩膜处理，对非常绿植被区(建筑、水体、冬小麦)扬花期与乳熟期影像NDVI值进行和值运算，是提取余杭区冬小麦种植面积的最佳方法，面积精度为91.96%,说明基于多时相遥感影像结合植被物候特征与典型地物类型，能够实现冬小麦种植面积的高精度提取.更多还原

【Abstract】 Timely and accurate extraction of winter wheat planting information is of great research significance for remote sensing monitoring of winter wheat growth. In this study, the Sentinel-2 satellite remote sensing images of winter wheat during overwintering stage(2021-12-04), flowering stage(2022-04-08) and milk ripening stage(2022-05-03) in Yuhang District were used as data sources. The winter wheat planting area was extracted by the maximum likelihood classification, support vector machine, normalized difference vegetation index(NDVI) addition and subtraction synthetic operation methods, respectively. Combining the field survey data with the measured planting area of winter wheat, the accuracy of the results extracted by different classification methods were evaluated. The results showed that using threshold value of NDVI during overwintering stage to mask evergreen vegetation areas(tea garden, woodland) and performing addition operations on the NDVI values of non-evergreen vegetation areas(buildings, water bodies, winter wheat) during flowering and milk ripening stages was the optimum method for extracting the planting area of winter wheat in Yuhang District, with an area accuracy of 91.96%. The results indicated that multi-temporal remote sensing images combined with the phenological characteristics of vegetation and typical land types could obtain high-precision planting area extraction of winter wheat.更多还原