Accurate, spatially extensive, and frequent assessments of plant nitrogen (N) enabled by remote sensing allow growers to optimize fertilizer applications and reduce environmental impacts. Standard remote sensing methods for N assessment typically involve the use of chlorophyll-sensitive vegetation indices calculated from multispectral or hyperspectral reflectance data. However, the chlorophyll a + b derived from spectral indices is indirectly related to leaf N and saturates at high leaf N levels, dramatically reducing the sensitivity with leaf N under these conditions. Furthermore, these relationships are heavily influenced by canopy structure, variability in leaf area density, proportion of sunlit-shaded tree-crown components, soil background, and understory. Recent studies in uniform crops have demonstrated that estimation of plant N can be improved by considering leaf biochemical constituents derived from radiative transfer model (RTM) and solar-induced fluorescence (SIF). However, it is unclear whether these methods are transferable to tree crops due to their intrinsic physiological differences, structural complexity, and within-tree crown heterogeneity. We investigated how various hyperspectrally derived proxies for leaf N, including RTM-based traits and SIF, could be combined to assess N status on a 1200-ha almond orchard across two growing seasons. RTM-based chlorophyll a + b content (C_ab) and SIF were found to be the most important and consistent predictors for leaf N compared to other leaf biochemical and biophysical traits. C_ab alone was a modest predictor of leaf N variability (r² = 0.49, RMSE = 0.16%, p-value <0.001), but when the non-collinear SIF and C_ab traits were coupled together, predictions improved dramatically (r² = 0.95, RMSE = 0.05%, p-value <0.001). Leaf area index (LAI) was poorly associated with leaf N, suggesting that leaf physiological traits may be more important than structural traits in quantifying leaf N in well-managed orchards characterized by high N levels. Consistent results across the 2 years suggests the importance of airborne SIF coupled with C_ab for precision agriculture and leaf N status assessment in almond orchards.

通过遥感对植物氮 (N) 进行准确、空间广泛和频繁的评估，使种植者能够优化肥料施用并减少对环境的影响。用于 N 评估的标准遥感方法通常涉及使用从多光谱或高光谱反射率数据计算的叶绿素敏感植被指数。然而，叶绿素a  +  b来自光谱指数的结果与叶片 N 间接相关，并在高叶片 N 水平下饱和，在这些条件下显着降低叶片 N 的敏感性。此外，这些关系在很大程度上受到冠层结构、叶面积密度的变化、阳光照射的树冠成分的比例、土壤背景和林下的影响。最近对均匀作物的研究表明，通过考虑源自辐射传递模型 (RTM) 和太阳诱导荧光 (SIF) 的叶片生化成分，可以改进对植物 N 的估计。然而，由于它们内在的生理差异、结构复杂性和树冠内的异质性，尚不清楚这些方法是否可以转移到林木作物上。我们研究了叶子 N 的各种高光谱衍生代理，包括基于 RTM 的性状和 SIF，可以结合起来评估 1200 公顷杏仁园在两个生长季节的 N 状态。基于 RTM 的叶绿素与其他叶片生化和生物物理性状相比， a  +  b含量 (C _ab ) 和 SIF 是叶片 N 最重要和最一致的预测因子。C _ab单独是叶片 N 变异性的适度预测因子（r ²  = 0.49，RMSE = 0.16%，p值 <0.001），但当非共线 SIF 和 C _ab性状耦合在一起时，预测显着改善（r ²  = 0.95，RMSE = 0.05%，p-值 <0.001)。叶面积指数 (LAI) 与叶片 N 的相关性较差，这表明在以高 N 水平为特征的管理良好的果园中，在量化叶片 N 时，叶片生理性状可能比结构性状更重要。两年内一致的结果表明，空气传播 SIF 与 C _ab相结合对于杏仁果园精准农业和叶片 N 状态评估的重要性。