Vol.:(0123456789) Precision Agriculture https://doi.org/10.1007/s11119-020-09759-7 1 3 The challenge of reproducing remote sensing data from satellites and unmanned aerial vehicles (UAVs) in the context of management zones and precision agriculture Jesper Rasmussen, et al. [full author details at the end of the article] Accepted: 24 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Mapping the within-feld variability of crop status is of great importance in precision agriculture, which seeks to balance agronomic inputs with spatial crop demands. Satel- lite 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 over- come 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 inter- vals. Three winter wheat felds were monitored during the growing season. Two vegeta- tion indices (NDVI and MSAVI2) based on red and near-infrared (NIR) refectance were calculated to delineate felds into fve management zones based on NDVI raster maps using quintiles. The Pearson correlation coefcient, the Nash–Sutclife agreement coefcient and the smallest real diference coefcient (SRD), also called the reproducibility coef- cient 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 reclassifcation of 10 m by 10 m man- agement zones corresponding to 58% of the total feld area. Reclassifcation means that management pixels were classifed diferently according to origin of images. Repeated sat- ellite and UAV imagery resulted in 39% and 47% reclassifcation, respectively. The results showed that the reproduction of remote sensing data with diferent 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 diferences up to 2.5 management zones were within the measurement error. This paper discusses the practical aspects of these fndings and clarifes that the reclas- sifcation of management zones is depending on the heterogeneity of the studied felds. Therefore, the achieved results may not be generalized but the presented methodology can be used in future studies. Keywords Variable rate application (VRA) · CropSAT · Sentinel 2 · Reproducibility · Crop heterogeneity · Management zones