Mapping the within-field variability of crop status is of great importance in precision agriculture, which seeks to balance agronomic inputs with spatial crop demands. Satellite imagery and the delineation of management zones based on remote sensing plays a key role. However, satellite imagery is dependent on a cloud-free view, which is especially challenging in temperate regions such as Northern Europe. This disadvantage can be overcome with unmanned aerial vehicles (UAV), which provide an alternative to satellites. An investigation was conducted to establish whether UAV imagery can generate similar crop heterogeneity maps to satellites (Sentinel 2) and the extent to which crop heterogeneity and management zones can be reproduced by repeated data collection within short time intervals. Three winter wheat fields were monitored during the growing season. Two vegetation indices (NDVI and MSAVI2) based on red and near-infrared (NIR) reflectance were calculated to delineate fields into five management zones based on NDVI raster maps using quintiles. The Pearson correlation coefficient, the Nash–Sutcliffe agreement coefficient and the smallest real difference coefficient (SRD), also called the reproducibility coefficient were used to evaluate the reproducibility. NDVI and MSAVI2 gave similar results, but NDVI was a slightly better descriptor of crop heterogeneity after canopy closure and NDVI was used for the remainder of the study. The results showed that substitution of satellite data with UAV data resulted in an average reclassification of 10 m by 10 m management zones corresponding to 58% of the total field area. Reclassification means that management pixels were classified differently according to origin of images. Repeated satellite and UAV imagery resulted in 39% and 47% reclassification, respectively. The results showed that the reproduction of remote sensing data with different sensor systems added more measurement error to measurements than was the case with repeated measurements using the same sensor systems. In this study, SRD averaged 2.5 management zones, which means that differences up to 2.5 management zones were within the measurement error. This paper discusses the practical aspects of these findings and clarifies that the reclassification of management zones is depending on the heterogeneity of the studied fields. Therefore, the achieved results may not be generalized but the presented methodology can be used in future studies.

中文翻译：

---

在管理区和精准农业的背景下，从卫星和无人机 (UAV) 复制遥感数据的挑战

绘制作物状态的田间变异在精准农业中非常重要，精准农业旨在平衡农艺投入与作物空间需求。卫星图像和基于遥感的管理区划定起着关键作用。然而，卫星图像依赖于无云视图，这在北欧等温带地区尤其具有挑战性。无人驾驶飞行器 (UAV) 可以克服这一缺点，它是卫星的替代品。进行了一项调查，以确定无人机图像是否可以生成与卫星相似的作物异质性图（哨兵 2），以及在短时间内通过重复数据收集可以再现作物异质性和管理区域的程度。在生长季节监测了三个冬小麦田。计算基于红色和近红外 (NIR) 反射率的两个植被指数（NDVI 和 MSAVI2），以使用五分位数基于 NDVI 栅格图将田地划分为五个管理区。使用 Pearson 相关系数、Nash-Sutcliffe 一致性系数和最小实差系数 (SRD)，也称为再现性系数来评估再现性。NDVI 和 MSAVI2 给出了相似的结果，但 NDVI 是冠层关闭后作物异质性的一个稍好的描述，并且 NDVI 用于研究的其余部分。结果表明，用无人机数据替换卫星数据导致平均重新分类 10 m 乘以 10 m 管理区，相当于总场地面积的 58%。重分类是指根据图像的来源对管理像素进行不同的分类。重复的卫星和无人机图像分别导致 39% 和 47% 的重新分类。结果表明，与使用相同传感器系统重复测量的情况相比，使用不同传感器系统再现遥感数据给测量增加了更多的测量误差。在这项研究中，SRD 平均有 2.5 个管理区，这意味着多达 2.5 个管理区的差异在测量误差范围内。本文讨论了这些发现的实际方面，并阐明了管理区的重新分类取决于研究领域的异质性。因此，所取得的结果可能无法推广，但所提出的方法可以用于未来的研究。