TY - JOUR

T1 - The challenge of reproducing remote sensing data from satellites and unmanned aerial vehicles (UAVs) in the context of management zones and precision agriculture

AU - Rasmussen, Jesper

AU - Azim, Saiful

AU - Boldsen, Søren Kjærgaard

AU - Nitschke, Thomas

AU - Jensen, Signe M.

AU - Nielsen, Jon

AU - Christensen, Svend

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Variable rate application (VRA)

KW - CropSAT

KW - Sentinel 2

KW - Reproducibility

KW - Crop heterogeneity

KW - Management zones

KW - SITE-SPECIFIC MANAGEMENT

KW - TECHNOLOGY

KW - ALGORITHM

KW - CAMERAS

KW - SENSORS

KW - SYSTEMS

KW - IMAGERY

KW - GROWTH

KW - WHEAT

U2 - 10.1007/s11119-020-09759-7

DO - 10.1007/s11119-020-09759-7

M3 - Journal article

VL - 22

SP - 834

EP - 851

JO - Precision Agriculture

JF - Precision Agriculture

SN - 1385-2256

ER -