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Research article - Peer-reviewed, 2021

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

Wolters, S.; Soderstrom, M.; Piikki, K.; Reese, H.; Stenberg, M.


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: r(2) = 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 (r(2) = 0.81; MAE = 7 kg ha(-1)), and can therefore be valuable in a DSS for variable-rate N application.


Decision support system; L2A; Nitrogen fertilisation; Precision agriculture; Sentinel-2; Variable rate application

Published in

Precision Agriculture
2021, volume: 22, number: 4, pages: 1263-1283
Publisher: SPRINGER

Authors' information

Swedish University of Agricultural Sciences, Department of Soil and Environment
Swedish University of Agricultural Sciences, Department of Soil and Environment
Piikki, Kristin (Persson, Kristin)
Swedish University of Agricultural Sciences, Department of Soil and Environment
Reese, H.
University of Gothenburg
Stenberg, M.
Swedish Board of Agriculture

UKÄ Subject classification

Soil Science
Agricultural Science

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