Nitrogen is one of the most important macronutrients for plant growth and timely estimation of canopy nitrogen content (CNC) is crucial for agricultural applications. Remote sensing has emerged as an important tool to quantify CNC using either empirically or physically based methods. Most empirical methods use chlorophyll related spectral indices and are dependent on the relationship between nitrogen and chlorophyll, which varies with vegetation types and growth stages. In contrast, physically based methods use the full-range reflectance data and retrieve CNC from coupled leaf and canopy radiative transfer models (such as PROSPECT-PRO + 4SAIL, PROSAIL-PRO). However, the subtle absorption features of nitrogen and protein in fresh leaves hinder the accurate estimation of CNC. Therefore, this study proposed an efficient and mechanistic framework to estimate CNC (PROSAIL-NAM) by coupling PROSAIL-PRO with a nitrogen allocation model, which divided the total nitrogen into non-photosynthetic nitrogen (NPN) and photosynthetic nitrogen (PN). At the canopy level, PN and NPN are assumed to be proportional to canopy chlorophyll content (CCC) and canopy dry matter content (CDM), respectively. The PROSAIL-PRO model was first used to estimate CCC and CDM, and then the resulting CCC and CDM were fed to the nitrogen allocation model to estimate CNC. The estimation accuracy of CNC was assessed with six diverse datasets: four from field crop experiments across geographic sites, one from multiple ecosystems, and one from a satellite-ground joint experiment. Our results showed that satisfactory estimations of CNC were obtained when CCC and CDM were estimated using a model inversion method (RMSE = 0.54–1.56 g/m2) and a hybrid retrieval method (RMSE = 0.49–2.25 g/m2). The model inversion method was comparable with the hybrid retrieval method for ground platforms, but performed better for airborne and satellite platforms. In addition, the traditional protein-nitrogen conversion model obtained CNC from the canopy protein content and led to clear overestimations of CNC with RMSE > 1.95 g/m2. This study represents a first attempt to develop a robust approach by coupling PROSAIL-PRO with a nitrogen allocation model for accurate estimation of CNC across geographic sites, ecosystems, and platforms. These finding will advance the monitoring of CNC from regional to global scales.

中文翻译：

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通过将 PROSAIL-PRO 与氮分配模型耦合来估算冠层氮含量


氮是植物生长中最重要的常量营养素之一，及时估计冠层氮含量 （CNC） 对于农业应用至关重要。遥感已成为使用基于经验或物理的方法量化 CNC 的重要工具。大多数实证方法使用与叶绿素相关的光谱指数，并依赖于氮和叶绿素之间的关系，这种关系随植被类型和生长阶段而变化。相比之下，基于物理的方法使用全范围反射数据，并从耦合的叶子和冠层辐射传输模型（例如 PROSPECT-PRO + 4SAIL、PROSAIL-PRO）中检索 CNC。然而，鲜叶中氮和蛋白质的细微吸收特征阻碍了 CNC 的准确估计。因此，本研究提出了一个高效的机械框架，通过将 PROSAIL-PRO 与氮分配模型耦合来估计 CNC （PROSAIL-NAM），该模型将总氮分为非光合氮 （NPN） 和光合氮 （PN）。在冠层水平上，假设 PN 和 NPN 分别与冠层叶绿素含量 （CCC） 和冠层干物质含量 （CDM） 成正比。首先使用 PROSAIL-PRO 模型估算 CCC 和 CDM，然后将得到的 CCC 和 CDM 馈送到氮分配模型估算 CNC。使用六个不同的数据集评估了 CNC 的估计精度： 四个来自跨地理位置的田间作物实验，一个来自多个生态系统，一个来自卫星-地面联合实验。我们的结果表明，当使用模型反转法 （RMSE = 0.54–1.56 g/m2） 和混合检索法 （RMSE = 0.49–2.25 g/m2） 估计 CCC 和 CDM 时，获得了令人满意的 CNC 估计。 模型反演方法与地面平台的混合反演方法相当，但机载和卫星平台的性能更好。此外，传统的蛋白-氮转化模型从冠层蛋白质含量中获得 CNC，导致 RMSE > 1.95 g/m2 的 CNC 明显高估。本研究代表了通过将 PROSAIL-PRO 与氮分配模型耦合来开发一种稳健方法的首次尝试，用于跨地理位置、生态系统和平台准确估计 CNC。这些发现将推进 CNC 的监测从区域到全球范围。