Estimating crop nitrogen status to optimize production and minimize environmental pollution is a major challenge for modern agriculture. The study objective was to develop a multivariate spatiotemporal dynamic clustering approach to generate Nitrogen (N) Management Zones (MZs) in a citrus orchard during the growing season. The research was conducted in four citrus plots in the coastal area of Israel. Five variables were selected to characterize each plot’s spatiotemporal variability of canopy N content. These were split into constant (i.e., elevation, northness, and slope) and non-constant (i.e., canopy N content and tree height) variables. The non-constant data were obtained via bi-monthly imaging campaigns with a multispectral camera mounted on an unmanned aerial vehicle (UAV) throughout the growing season of 2019. The selected variables were then standardized to define the clusters by applying the Getis-Ord Gi* z-score. These were used to develop a spatiotemporal dynamic clustering model using Fuzzy C-means (FCM). Four input variables were investigated in this final stage, including the constant variables only and different combinations of constant and non-constant variables. The support vector machine regression model results for estimating canopy N-content from multispectral images were R² = 0.771 and RMSE = 0.227. This model was used to predict monthly canopy-level N content and classify the N content levels based on the October N-to-yield content envelope curve. Delineating MZs was followed by the comparison of spatial association among cluster maps. This process may support site-specific and time-specific nitrogen management.

估算作物氮素状况以优化生产并最大程度地减少环境污染是现代农业面临的主要挑战。研究目标是开发一种多变量时空动态聚类方法，以在生长季节在柑橘园中生成氮 (N) 管理区 (MZ)。该研究在以色列沿海地区的四个柑橘园进行。选择了五个变量来描述每个地块冠层氮含量的时空变异性。这些被分成常量（即海拔、北度和坡度）和非常量（即冠层氮含量和树高）变量。在整个 2019 年的生长季节，使用安装在无人机 (UAV) 上的多光谱相机通过双月成像活动获得了非常数数据。然后通过应用 Getis-Ord Gi* z 分数对所选变量进行标准化以定义集群。这些用于使用模糊 C 均值 (FCM) 开发时空动态聚类模型。在这个最后阶段研究了四个输入变量，包括仅常数变量以及常数和非常变量的不同组合。从多光谱图像估计冠层 N 含量的支持向量机回归模型结果为 R²  = 0.771 和 RMSE = 0.227。该模型用于预测每月冠层水平的 N 含量，并根据 10 月的 N-to-yield 含量包络曲线对 N 含量水平进行分类。描绘 MZs 之后是集群地图之间空间关联的比较。该过程可以支持特定地点和特定时间的氮管理。