Remote sensing-based mapping of crop nitrogen (N) status is beneficial for precision N management over large geographic regions. Both leaf/canopy level nitrogen content and accumulation are valuable for crop nutrient diagnosis. However, previous studies mainly focused on leaf nitrogen content (LNC) estimation. The effects of growth stages on the modeling accuracy have not been widely discussed. This study aimed to estimate different paddy rice N traits—LNC, plant nitrogen content (PNC), leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA)—from unmanned aerial vehicle (UAV)-based hyperspectral images. Additionally, the effects of the growth stage were evaluated. Univariate regression models on vegetation indices (VIs), the traditional multivariate calibration method, partial least squares regression (PLSR) and modern machine learning (ML) methods, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM), were evaluated both over the whole growing season and in each single growth stage (including the tillering, jointing, booting and heading growth stages). The results indicate that the correlation between the four nitrogen traits and the other three biochemical traits—leaf chlorophyll content, canopy chlorophyll content and aboveground biomass—are affected by the growth stage. Within a single growth stage, the performance of selected VIs is relatively constant. For the full-growth-stage models, the performance of the VI-based models is more diverse. For the full-growth-stage models, the transformed chlorophyll absorption in the reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI) performs best for LNC, PNC and PNA estimation, while the three band vegetation index (TBVI${}_{Tian}$) performs best for LNA estimation. There are no obvious patterns regarding which method performs the best of the PLSR, ANN, RF and SVM in either the growth-stage-specific or full-growth-stage models. For the growth-stage-specific models, a lower mean relative error (MRE) and higher R${}^2$ can be acquired at the tillering and jointing growth stages. The PLSR and ML methods yield obviously better estimation accuracy for the full-growth-stage models than the VI-based models. For the growth-stage-specific models, the performance of VI-based models seems optimal and cannot be obviously surpassed. These results suggest that building linear regression models on VIs for paddy rice nitrogen traits estimation is still a reasonable choice when only a single growth stage is involved. However, when multiple growth stages are involved or missing the phenology information, using PLSR or ML methods is a better option.

中文翻译：

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从无人机高光谱图像估计水稻叶片和植物水平的氮含量和积累

基于遥感的作物氮 (N) 状态测绘有利于大地理区域的精确氮管理。叶/冠层氮含量和积累对于作物营养诊断都很有价值。然而，以前的研究主要集中在叶片氮含量 (LNC) 估计上。生长阶段对建模精度的影响尚未得到广泛讨论。本研究旨在通过基于无人机 (UAV) 的高光谱图像估计不同的水稻 N 性状——LNC、植物氮含量 (PNC)、叶片氮积累 (LNA) 和植物氮积累 (PNA)。此外，还评估了生长阶段的影响。植被指数 (VI) 的单变量回归模型，传统的多变量校准方法，偏最小二乘回归 (PLSR) 和现代机器学习 (ML) 方法，包括人工神经网络 (ANN)、随机森林 (RF) 和支持向量机 (SVM)，在整个生长季节和每个单生长阶段（包括分蘖、拔节、孕穗和抽穗生长阶段）。结果表明，4个氮性状与其他3个生化性状——叶片叶绿素含量、冠层叶绿素含量和地上生物量——之间的相关性受生长阶段的影响。在单个增长阶段内，所选 VI 的性能相对稳定。对于完全成长阶段的模型，基于 VI 的模型的表现更加多样化。对于全生长阶段模型，${}_{吨\mathrm{一世}\mathrm{一种}n}$) 最适合 LNA 估计。在特定于生长阶段或完全生长阶段的模型中，关于哪种方法在 PLSR、ANN、RF 和 SVM 中表现最好，没有明显的模式。对于特定生长阶段的模型，较低的平均相对误差 (MRE) 和较高的 R${}^2$可以在分蘖和拔节生长阶段获得。PLSR 和 ML 方法对全生长阶段模型的估计精度明显优于基于 VI 的模型。对于特定成长阶段的模型，基于 VI 的模型的性能似乎是最优的，不能明显超越。这些结果表明，当仅涉及单个生长阶段时，建立用于水稻氮性状估计的 VI 线性回归模型仍然是一个合理的选择。但是，当涉及多个生长阶段或缺少物候信息时，使用 PLSR 或 ML 方法是更好的选择。