Upscaling proximal sensor N-uptake predictions in winter wheat (Triticum aestivum L.) with Sentinel-2 satellite data for use in a decision support system

S Wolters,M Söderström,K Piikki,M Stenberg,H Reese

doi:10.1007/s11119-020-09783-7

Abstract

Total nitrogen (N) content in aboveground biomass (N-uptake) in winter wheat (Triticum aestivum L.) as measured in a national monitoring programme was scaled up to full spatial coverage using Sentinel-2 satellite data and implemented in a decision support system (DSS) for precision agriculture. Weekly field measurements of N-uptake had been carried out using a proximal canopy reflectance sensor (handheld Yara N-Sensor) during 2017 and 2018. Sentinel-2 satellite data from two processing levels (top-of-atmosphere reflectance, L1C, and bottom-of-atmosphere reflectance, L2A) were extracted and related to the proximal sensor data (n = 251). The utility of five vegetation indices for estimation of N-uptake was compared. A linear model based on the red-edge chlorophyll index (CI) provided the best N-uptake prediction (L1C data: r2 = 0.74, mean absolute error; MAE = 14 kg ha−1) when models were applied on independent sites and dates. Use of L2A data, rather than L1C, did not improve the prediction models. The CI-based prediction model was applied on all fields in an area with intensive winter wheat production. Statistics on N-uptake at the end of the stem elongation growth stage were calculated for 4169 winter wheat fields > 5 ha. Within-field variation in predicted N-uptake was > 30 kg N ha−1 in 62% of these fields. Predicted N-uptake was compared against N-uptake maps derived from tractor-borne Yara N-Sensor measurements in 13 fields (1.7–30 ha in size). The model based on satellite data generated similar information as the tractor-borne sensing data (r2 = 0.81; MAE = 7 kg ha−1), and can therefore be valuable in a DSS for variable-rate N application.

Highlights

Winter wheat (Triticum aestivum L.) is an important crop globally, and is often the main crop in northern European cropping schemes
As can be seen from the diagram, the chlorophyll index (CI) values were better correlated to the proximal sensor measurements (L1C: r2 = 0.78; L2A: r2 = 0.76)
A linear model based on the CI showed the best N-uptake prediction performance for new sites and dates (L1C data: r2 = 0.74 and mean absolute error (MAE) = 14 kg ha−1)

Summary

Introduction

Winter wheat (Triticum aestivum L.) is an important crop globally, and is often the main crop in northern European cropping schemes. The estimation of the N concentration in aboveground plant tissues multiplied by above-ground dry matter mass (here denominated as N-uptake) during the period of supplementary fertilisation is an important component in the formulation of a fertilisation strategy (Schils et al 2018). There are both economic and environmental benefits in optimising N fertilisation, since it optimises the quantity and quality of the crop in relation to the production costs and at the same time helps prevent losses of excess N through leaching or denitrification Variable application of N fertiliser can be carried out for the purpose of reaching target levels of grain protein content, which is an important quality aspect (Basnet et al 2003; Börjesson et al 2019; Söderström et al 2010)

Methods

Results

Discussion

Conclusion