Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production. In the past, empirical algorithms have been widely employed to retrieve information on this biochemical plant component from canopy reflectance. However, these approaches do not seek for a cause-effect relationship based on physical laws. Moreover, most studies solely relied on the correlation of chlorophyll content with nitrogen, and thus neglected the fact that most N is bound in proteins. Our study presents a hybrid retrieval method using a physically-based approach combined with machine learning regression to estimate crop N content. Within the workflow, the leaf optical properties model PROSPECT-PRO including the newly calibrated specific absorption coefficients (SAC) of proteins, was coupled with the canopy reflectance model 4SAIL to PROSAIL-PRO. The latter was then employed to generate a training database to be used for advanced probabilistic machine learning methods: a standard homoscedastic Gaussian process (GP) and a heteroscedastic GP regression that accounts for signal-to-noise relations. Both GP models have the property of providing confidence intervals for the estimates, which sets them apart from other machine learners. Moreover, a GP-based sequential backward band removal algorithm was employed to analyze the band-specific information content of PROSAIL-PRO simulated spectra for the estimation of aboveground N. Data from multiple hyperspectral field campaigns, carried out in the framework of the future satellite mission Environmental Mapping and Analysis Program (EnMAP), were exploited for validation. In these campaigns, corn and winter wheat spectra were acquired to simulate spectral EnMAP data. Moreover, destructive N measurements from leaves, stalks and fruits were collected separately to enable plant-organ-specific validation. The results showed that both GP models can provide accurate aboveground N simulations, with slightly better results of the heteroscedastic GP in terms of model testing and against in situ N measurements from leaves plus stalks, with root mean square error (RMSE) of 2.1 g/m². However, the inclusion of fruit N content for validation deteriorated the results, which can be explained by the inability of the radiation to penetrate the thick tissues of stalks, corn cobs and wheat ears. GP-based band analysis identified optimal spectral settings with ten bands mainly situated in the shortwave infrared (SWIR) spectral region. Use of well-known protein absorption bands from the literature showed comparative results. Finally, the heteroscedastic GP model was successfully applied on airborne hyperspectral data for N mapping. We conclude that GP algorithms, and in particular the heteroscedastic GP, should be implemented for global agricultural monitoring of aboveground N from future imaging spectroscopy data.

高光谱采集已被证明是用于估算氮 (N) 含量的信息最丰富的地球观测数据源，氮 (N) 含量是植物生长和农业生产的主要限制养分。过去，经验算法已被广泛采用从冠层反射率中检索有关这种生化植物成分的信息。然而，这些方法并不寻求基于物理定律的因果关系。此外，大多数研究仅仅依赖于叶绿素含量与氮的相关性，从而忽略了大多数氮结合在蛋白质中的事实。我们的研究提出了一种混合检索方法，使用基于物理的方法与机器学习回归相结合来估计作物氮含量。在工作流程中，叶子光学特性模型 PROSPECT-PRO（包括新校准的蛋白质比吸收系数 (SAC)）与冠层反射率模型 4SAIL 耦合到 PROSAIL-PRO。然后，后者被用来生成用于高级概率机器学习方法的训练数据库：标准同方差高斯过程（GP）和解释信噪关系的异方差 GP 回归。这两种 GP 模型都具有为估计提供置信区间的特性，这使它们与其他机器学习器不同。此外，采用基于 GP 的顺序后向波段去除算法来分析 PROSAIL-PRO 模拟光谱的波段特定信息内容，以估计地上氮。来自多个高光谱现场活动的数据，在未来卫星的框架内进行任务环境测绘和分析程序（EnMAP）被用于验证。 在这些活动中，获取了玉米和冬小麦的光谱来模拟光谱 EnMAP 数据。此外，还分别收集了叶子、茎和果实的破坏性氮测量值，以便进行植物器官特异性验证。结果表明，两种 GP 模型都能提供准确的地上氮模拟，异方差 GP 在模型测试和叶加茎原位氮测量方面的结果略好，均方根误差 (RMSE) 为 2.1 g/平方米。然而，纳入水果氮含量进行验证使结果恶化，这可以用辐射无法穿透茎、玉米芯和麦穗的厚组织来解释。基于 GP 的波段分析确定了最佳光谱设置，其中 10 个波段主要位于短波红外 (SWIR) 光谱区域。使用文献中众所周知的蛋白质吸收带显示了比较结果。最后，异方差GP模型成功应用于机载高光谱数据的N映射。我们得出的结论是，GP 算法，特别是异方差 GP，应该用于根据未来成像光谱数据对地上氮进行全球农业监测。