Papaver rhoeas L. mapping with cokriging using UAV imagery

Abstract

Accurately mapping the spatial distribution of weeds within a field is a first step towards effective Site-specific Weed Management. The main objective of this study was to investigate if the multivariate geostatistical method of cokriging (COK) can be used to improve the accuracy of Papaver rhoeas L. infestations maps in winter wheat fields using high-resolution UAV imagery as ancillary information. The primary variable was obtained by intensive grid weed density field samplings and the secondary variables were derived from the UAV imagery taken the same day as the weed field samplings (e.g. wavebands and derivative products, such as band ratios and vegetation indexes). Univariate Ordinary Kriging (OK) and multivariate cokriging (COK) interpolation methods were used and compared for Papaver density mapping. The performances of the different methods were assessed by cross-validation. The results indicated that COK outperformed OK in the spatial interpolation of Papaver density. COK reduced the prediction errors and enhanced the accuracy of Papaver estimates maps. The best performances were obtained when COK was performed with the UAV-secondary variables that yielded the highest correlation with Papaver density and produced the strongest spatial cross-semivariograms. On average, the COK with UAV-derived ancillary variables improved the accuracy of mapping Papaver density by 11 to 21% compared with OK. The results suggest the great potential of high-resolution UAV imagery as a source of ancillary information to improve the accuracy of spatial mapping of sparsely sampled target variables using COK.