Organic farming is a key element in achieving more sustainable agriculture. For a better understanding of the development and impact of organic farming, comprehensive, spatially explicit information is needed. This study presents an approach for the discrimination of organic and conventional farming systems using intra-annual Sentinel-2 time series. In addition, it examines two factors influencing this discrimination: the joint learning of crop type information in a concurrent task and the role of spatial context. A Vision Transformer model based on the Temporo-Spatial Vision Transformer (TSViT) architecture was used to construct a classification model for the two farming systems. The model was extended for simultaneous learning of the crop type, creating a multitask learning setting. By varying the patch size presented to the model, we tested the influence of spatial context on the classification accuracy of both tasks. We show that discrimination between organic and conventional farming systems using multispectral remote sensing data is feasible. However, classification performance varies substantially across crop types. For several crops, such as winter rye, winter wheat, and winter oat, F1 scores of 0.8 or higher can be achieved. In contrast, other agricultural land use classes, such as permanent grassland, orchards, grapevines, and hops, cannot be reliably distinguished, with F1 scores for the organic management class of 0.4 or lower. Joint learning of farming system and crop type provides only limited additional benefits over single-task learning. In contrast, incorporating wider spatial context improves the performance of both farming system and crop type classification. Overall, we demonstrate that a classification of agricultural farming systems is possible in a diverse agricultural region using multispectral remote sensing data.

翻译：有机农业是实现更可持续农业的关键要素。为更好地理解有机农业的发展与影响，需要全面且具有空间明确性的信息。本研究提出了一种利用年内哨兵-2时间序列区分有机与常规农业系统的方法，并探讨了影响这种区分的两个因素：在并发任务中联合学习作物类型信息以及空间上下文的作用。采用基于时空视觉Transformer（TSViT）架构的Vision Transformer模型构建用于两种农业系统的分类模型。该模型扩展为同时学习作物类型，形成了多任务学习设置。通过改变输入模型的图像块大小，我们测试了空间上下文对两项任务分类精度的影响。研究表明，使用多光谱遥感数据区分有机与常规农业系统是可行的。然而，不同作物类型的分类性能差异显著。对于冬黑麦、冬小麦和冬燕麦等多种作物，F1分数可达0.8或更高。相反，其他农业用地类别（如永久草地、果园、葡萄园和啤酒花）无法可靠区分，有机管理类别的F1分数为0.4或更低。联合学习农业系统和作物类型相比单一任务学习仅带来有限的额外收益。相比之下，整合更广泛的空间上下文能提升农业系统和作物类型分类的性能。总体而言，我们证实了在多样化农业区域中利用多光谱遥感数据实现农业系统分类是可行的。