New data platforms have made satellite data freely available and farmers can now produce variable rate application (VRA) maps for nitrogen fertilisers and other inputs based on satellite images. The data platforms are currently attracting attention for site-specific weed management because they are free and user-friendly. Satellite imagery is mainly relevant for detecting large, dense weed patches that have unique spectral characteristics in low-resolution images. The objective of this study was to examine whether Sentinel-2 images are useful in the detection of Cirsium arvense and other green vegetation in pre-harvest cereals. It was hypothesised that there is a lower limit for detection and that weeds in large patches are easier to detect than weeds in small patches. Fifteen fields infested with C. arvense were used to evaluate the possibilities of utilising the normalised difference vegetation index (NDVI) from Sentinel-2 images as a weed classifier. High-resolution RGB images from unmanned aerial vehicles (UAV) were used as ground-truthing after classifying green pixels as weeds with C. arvense as the main contributor. The study showed that C. arvense-dominated weed populations were much less aggregated than previously reported. On average, 90 % of the weeds occurred on 50 % of the field area, in a range from 21 % to 72 %. The potential herbicide savings using Sentinel-2 images were in the range of 6 % to 46 %, averaging 24 %. The low weed aggregation combined with low image resolution limited the prospects of using Sentinel-2 images for weed detection. The study showed that UAV imagery offers much greater potential for herbicide savings due to higher image resolution, allowing the detection of individual C. arvense shoots.

新的数据平台使卫星数据免费可用，农民现在可以根据卫星图像制作氮肥和其他投入物的可变速率施用 (VRA) 地图。数据平台目前正在吸引针对特定地点杂草管理的关注，因为它们是免费且用户友好的。卫星图像主要用于检测在低分辨率图像中具有独特光谱特征的大而密集的杂草斑块。本研究的目的是检查 Sentinel-2 图像是否可用于检测收获前谷物中的Cirsium arvense和其他绿色植被。假设存在检测下限，并且大斑块中的杂草比小斑块中的杂草更容易检测。十五块田地C. arvense用于评估利用来自 Sentinel-2 图像的归一化差异植被指数 (NDVI) 作为杂草分类器的可能性。在将绿色像素分类为杂草后，来自无人机 (UAV) 的高分辨率 RGB 图像被用作地面实况，其中C. arvense作为主要贡献者。研究表明，C. arvense- 占主导地位的杂草种群比以前报告的要少得多。平均而言，90% 的杂草出现在 50% 的田间面积上，范围从 21% 到 72%。使用 Sentinel-2 图像的潜在除草剂节省范围在 6% 到 46% 之间，平均为 24%。低杂草聚集和低图像分辨率限制了使用 Sentinel-2 图像进行杂草检测的前景。研究表明，由于图像分辨率更高，无人机图像在节省除草剂方面提供了更大的潜力，可以检测单个C. arvense芽。