The process of crop type mapping generates land use maps, which serve as critical tools for efficient evaluation of production factors and impacts of agricultural practice. Yet, despite the necessity for comprehensive solutions in space and time, the state of research still exhibits significant limitations in these two dimensions: (1) From a temporal perspective, the primary focus of past research in crop type mapping has been on the economically most meaningful, main-season crops, thereby largely neglecting the explicit study of off-season vegetation despite its pivotal roles in year-round management cycles. (2) Viewed spatially, study areas in crop type mapping show distinct limitations from a multi- and transnational standpoint, despite intense cross-regional and international interrelations of agricultural production and an increasing number of countries publishing crop reference data. With a focus on Europe, this research aims to tackle the two described shortcomings (a) by investigating to what extent a selection of major off-season, winter vegetation types in continental Europe can be classified and (b) by analyzing the transnational applicability of the Hierarchical Crop and Agriculture Taxonomy (HCAT) for remote sensing-based crop type mapping across the European Union (EU). This study uses ESA’s Sentinel-2 satellite data, EU’s administrative farming declarations, and HCAT labels to analyze off-season farming measures, based on a study period from late summer to spring, in Austria, France, Germany, and Slovenia. We demonstrate that deep learning models effectively identify major productive and agroecogically significant winter vegetation in continental Europe. HCAT proves thereby valuable for transnational crop classification, excelling in mixed-country experiments and showing potential for transfer learning. This study’s findings provide a solid foundation for advancing transnational as well as winter and all-year crop type mapping, thereby serving as contribution towards temporally and spatially holistic research on agricultural practices’ sociocultural, economic, and environmental impacts.

中文翻译：

---

通过冬季植被分类和分层作物和农业分类法绘制欧洲整体作物类型图


作物类型绘图过程生成土地利用地图，这是有效评估生产要素和农业实践影响的关键工具。然而，尽管有必要在空间和时间上进行全面的解决方案，但研究现状在这两个维度上仍然表现出很大的局限性：（1）从时间角度来看，过去作物类型制图研究的主要重点是经济上最重要的作物类型制图。有意义的主季作物，从而在很大程度上忽视了对淡季植被的明确研究，尽管它在全年管理周期中发挥着关键作用。 (2) 从空间上看，尽管农业生产的跨区域和国际相互关系密切，而且越来越多的国家发布作物参考数据，但从多国和跨国角度来看，作物类型制图的研究领域显示出明显的局限性。本研究以欧洲为重点，旨在解决上述两个缺点（a）通过调查欧洲大陆主要淡季、冬季植被类型的选择可以在多大程度上进行分类，以及（b）通过分析跨国适用性作物和农业分层分类法 (HCAT)，用于整个欧盟 (EU) 基于遥感的作物类型制图。本研究基于奥地利、法国、德国和斯洛文尼亚从夏末到春季的研究时期，使用欧空局的 Sentinel-2 卫星数据、欧盟的行政农业声明和 HCAT 标签来分析淡季农业措施。我们证明深度学习模型可以有效识别欧洲大陆主要生产性和具有农业生态意义的冬季植被。 由此证明，HCAT 对于跨国作物分类很有价值，在混合国家实验中表现出色，并显示出迁移学习的潜力。这项研究的结果为推进跨国以及冬季和全年作物类型绘图奠定了坚实的基础，从而为农业实践的社会文化、经济和环境影响的时空整体研究做出了贡献。