Detailed parcel-level crop type mapping for the whole European Union (EU) is necessary for the evaluation of agricultural policies. The Copernicus program, and Sentinel-1 (S1) in particular, offers the opportunity to monitor agricultural land at a continental scale and in a timely manner. However, so far the potential of S1 has not been explored at such a scale. Capitalizing on the unique LUCAS 2018 Copernicus in-situ survey, we present the first continental crop type map at 10-m spatial resolution for the EU based on S1A and S1B Synthetic Aperture Radar observations for the year 2018. Random Forest classification algorithms are tuned to detect 19 different crop types. We assess the accuracy of this EU crop map with three approaches. First, the accuracy is assessed with independent LUCAS core in-situ observations over the continent. Second, an accuracy assessment is done specifically for main crop types from farmers declarations from 6 EU member countries or regions totaling >3 M parcels and 8.21 Mha. Finally, the crop areas derived by classification are compared to the subnational (NUTS 2) area statistics reported by Eurostat. The overall accuracy for the map is reported as 80.3% when grouping main crop classes and 76% when considering all 19 crop type classes separately. Highest accuracies are obtained for rape and turnip rape with user and produced accuracies higher than 96%. The correlation between the remotely sensed estimated and Eurostat reported crop area ranges from 0.93 (potatoes) to 0.99 (rape and turnip rape). Finally, we discuss how the framework presented here can underpin the operational delivery of in-season high-resolution based crop mapping.

整个欧盟 (EU) 的详细地块级别作物类型映射对于评估农业政策是必要的。哥白尼计划，特别是 Sentinel-1 (S1)，提供了在大陆范围内及时监测农业用地的机会。然而，到目前为止，S1 的潜力还没有得到如此大规模的探索。利用独特的 LUCAS 2018 哥白尼原位调查，我们基于 2018 年的 S1A 和 S1B 合成孔径雷达观测，为欧盟展示了第一张 10 米空间分辨率的大陆作物类型图。随机森林分类算法调整为检测 19 种不同的作物类型。我们使用三种方法评估该欧盟作物图的准确性。首先，使用独立的 LUCAS 内核评估准确性对大陆的原位观测。其次，针对来自 6 个欧盟成员国或地区的农民申报的主要作物类型进行了准确评估，总计 >300 万个 包裹和 8.21 嗯。最后，将通过分类得出的作物面积与欧盟统计局报告的地方 (NUTS 2) 面积统计数据进行比较。地图的整体精度在对主要作物类别进行分组时报告为 80.3%，在分别考虑所有 19 个作物类型类别时为 76%。用户对油菜和萝卜油菜的准确率最高，生产的准确率高于 96%。遥感估计值与欧盟统计局报告的作物面积之间的相关性范围从 0.93（马铃薯）到 0.99（油菜和萝卜油菜）。最后，我们讨论了此处介绍的框架如何支持基于季节性高分辨率作物测绘的业务交付。