Crop type mapping is relevant to a wide range of food security applications. Supervised classification methods commonly generate these data from satellite image time-series. Yet, their successful implementation is hindered by the lack of training samples. Solutions like transfer learning, development of temporal-spectral signatures of the target classes, re-utilization of existing inventories, or crowdsourcing initiatives are commonly applied to generate samples for thematically coarser classifications. These methods are rarely used for generating crop types samples. In this study, we leverage the phenology information of existing data inventories using Time-Weighted Dynamic Time Warping (TWDTW) to address the problem of automatic crop sample generation in two target areas. Resulting labeled samples are refined using proximity measures obtained from Random Forests (RF). Sentinel-2 time-series are used to obtain phenology information from two study areas. The proposed methodology achieved promising results for classes with a reduced inter-classes similarity such as sugar beets (user’s accuracy, UA, of 98% and producer’s accuracy, PA, of 100%) or grains (UA of 98% and PA of 90%). The crops with a high inter-classes similarity yielded less satisfactory results. Potatoes, for example, obtained a high PA of 95%, but a UA of only 36% because of the spectral-temporal similarity with maize. The methodology works well for areas with balanced crop samples. Yet, it favors prevalent classes in areas with imbalanced crops at the expense of a low accuracy for the minority crops. Despite these shortcomings, the proposed methodology offers a viable option to generate crop samples in regions with few ground labels.

作物类型图谱与广泛的粮食安全应用有关。监督分类方法通常从卫星图像时间序列生成这些数据。然而，由于缺少训练样本，阻碍了它们的成功实施。诸如转移学习，开发目标类别的时光谱签名，重新利用现有清单或众包计划之类的解决方案通常用于生成用于主题上较粗分类的样本。这些方法很少用于生成农作物类型样本。在这项研究中，我们使用时间加权动态时间规整（TWDTW）来利用现有数据清单的物候信息，以解决两个目标区域中自动生成农作物样品的问题。使用从Random Forests（RF）获得的邻近度度量来精炼得到的标记样本。Sentinel-2时间序列用于从两个研究领域获得物候信息。对于类别间相似度降低的类，例如甜菜（用户的准确度，UA，98％，生产者的准确度，PA，100％）或谷物（UA，98％，PA，90％），该方法论在类间相似度降低的情况下取得了可喜的结果）。类间相似性高的农作物收效差。例如，由于马铃薯与玉米的光谱时间相似性，其马铃薯的PA高达95％，UA仅为36％。该方法适用于作物样品平衡的地区。然而，它偏爱作物不平衡地区的普遍阶级，但牺牲了少数作物的准确性。尽管有这些缺点，