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.

冬小麦（Triticum aestivum L ）地上生物量（N吸收）中的总氮（N）含量。国家监测计划中测量的）已使用Sentinel-2卫星数据扩大到完整的空间覆盖范围，并在精准农业的决策支持系统（DSS）中实施。在2017年和2018年期间，使用近侧冠层反射传感器（手持式Yara N传感器）对每周的N吸收进行了实地测量。Sentinel-2卫星数据来自两个处理级别（大气层顶部反射率，L1C和底部）提取大气反射率L2A，并将其与近端传感器数据相关（n = 251）。比较了五种植被指数在估算氮素吸收方面的效用。基于红边叶绿素指数（CI）的线性模型提供了最佳的N吸收预测（L1C数据：r ²  = 0.74，平均绝对误差； MAE = 14 kg ha ^-1）何时将模型应用于独立的网站和日期。使用L2A数据而不是L1C并不能改善预测模型。基于CI的预测模型已应用于冬小麦集约化生产的所有领域。计算了4169公顷> 5公顷冬小麦田茎伸长期结束时的氮吸收统计数据。在这些田地中，有62％的田间预测氮素吸收的田间变化> 30 kg N ha ^-1。将预测的氮素吸收量与在13个田地（大小为1.7-30公顷）中的拖拉机载Yara N传感器测量值得出的氮素吸收图进行了比较。基于卫星数据的模型所产生的信息与拖拉机感应数据相似（r ²  = 0.81； MAE = 7 kg ha ^-1），因此在可变速率N应用的DSS中可能很有价值。