Improving agricultural nitrogen management is one of the key objectives of the recent Green Deal in Europe. Current technological developments in agriculture offer new opportunities to improve nitrogen fertilization practices. The aim of this study was to adapt to Sentinel-2 data a proven delineation method initially developed for yield maps, in order to facilitate precise nitrogen management by farmers. The study was conducted in two steps. Firstly, an analysis at annual level was conducted to assess the relationship between vegetation indices and yield at the subfield scale, for different sensing period. The second step consisted in performing a pluri- annual analysis through the delineation of management zones and compare the results achieved from yield maps and from NDVI maps. Among different vegetation indices, NDVI proved to be an interesting candidate for subfield detection of yield variation, specifically when the index was sensed was sensed around the second half of May. In this area, this period usually corresponds to phenological development between the flag leaf stage and heading stage, just prior the initiation of winter wheat flowering. Using NDVI maps within Blackmore’s delineation approach instead of yield maps. Allowed to reach an accuracy of 69% on zone classification. However, as yields and NDVI distribution do not respond to similar statistical distributions, we considered that the delineation threshold used to differentiate high from low yielding zones had to be adapted. The adaptation of the “performance threshold” in favor of the median NDVI, made it possible to achieve a higher accuracy (71%) of the delineation. But above all, the improvement lies also in a more robust satellite-based delineation.

改善农业氮肥管理是欧洲近期绿色协议的主要目标之一。当前农业技术的发展为改善氮肥施用提供了新的机遇。本研究的目的是适应 Sentinel-2 数据，这是一种最初为产量图开发的行之有效的描绘方法，以促进农民精确的氮管理。该研究分两步进行。首先，进行了年度水平的分析，以评估不同传感周期的子田尺度植被指数与产量之间的关系。第二步是通过划定管理区进行多年分析，并比较产量图和 NDVI 地图获得的结果。在不同的植被指数中，NDVI 被证明是产量变化子场检测的一个有趣的候选者，特别是在 5 月下旬左右检测到该指数时。在该地区，这一时期通常对应于旗叶期和抽穗期之间的物候发育，就在冬小麦开花开始之前。在 Blackmore 的描绘方法中使用 NDVI 地图而不是产量地图。区域分类准确率达到69%。然而，由于产量和 NDVI 分布并不响应类似的统计分布，我们认为必须调整用于区分高产区和低产区的划分阈值。采用有利于 NDVI 中值的“性能阈值”，可以实现更高的描绘精度（71%）。但最重要的是，改进还在于更强大的基于卫星的描绘。