Models of radiative transfer (RT) are important tools for remote sensing of vegetation, as they facilitate forward simulations of remotely sensed data as well as inverse estimation of biophysical and biochemical properties from vegetation optical properties. The remote sensing estimation of foliar protein content is a key to monitoring the nitrogen cycle in terrestrial ecosystems in particular to better understand photosynthetic capacity of plants and improve nitrogen management in agriculture. However, no physically based leaf RT model currently allows for proper decomposition of leaf dry matter into nitrogen-based proteins and carbon-based constituents (CBC), estimated from optical properties of fresh or dry foliage. We developed a new version of the PROSPECT model, named PROSPECT-PRO, which separates nitrogen-based constituents (proteins) from CBC (including cellulose, lignin, hemicellulose and starch). PROSPECT-PRO was calibrated and validated on subsets of the LOPEX dataset, accounting for both fresh and dry broadleaf and grass samples. We applied an iterative model inversion optimization algorithm to identify optimal spectral subdomains for retrieval of leaf protein and CBC contents, with 2125-2174 nm optimal for proteins and 2025-2349 nm optimal for CBCs. PROSPECT-PRO inversions revealed a better performance in estimating proteins from optical properties of fresh than dry leaves. We further tested the ability of PROSPECT-PRO to estimate leaf mass per area (LMA) as the sum of proteins and CBC using independent datasets acquired for numerous plant species. Results showed that PROSPECT-PRO is fully compatible and comparable with its predecessor PROSPECT-D in indirect estimation of LMA. We can conclude from findings of this study that PROSPECT-PRO has a high potential in establishing the carbon-to-nitrogen ratio based on the retrieved CBC-to-proteins ratio.

中文翻译：

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PROSPECT-PRO 用于估算含氮叶蛋白和其他碳基成分的含量

辐射传输 (RT) 模型是植被遥感的重要工具，因为它们有助于对遥感数据进行正向模拟，以及根据植被光学特性对生​​物物理和生化特性进行逆估计。叶面蛋白质含量的遥感估计是监测陆地生态系统氮循环的关键，特别是可以更好地了解植物的光合作用能力并改善农业中的氮管理。然而，目前没有基于物理的叶子 RT 模型允许将叶子干物质适当分解为氮基蛋白质和碳基成分 (CBC)，根据新鲜或干燥叶子的光学特性估计。我们开发了一个新版本的 PROSPECT 模型，名为 PROSPECT-PRO，它将氮基成分（蛋白质）与 CBC（包括纤维素、木质素、半纤维素和淀粉）分离。PROSPECT-PRO 在 LOPEX 数据集的子集上进行了校准和验证，包括新鲜和干燥的阔叶和草样本。我们应用迭代模型反演优化算法来识别用于检索叶蛋白和 CBC 内容的最佳光谱子域，2125-2174 nm 最适合蛋白质，2025-2349 nm 最适合 CBC。PROSPECT-PRO 反演显示，在从新鲜叶的光学特性估计蛋白质方面比干叶具有更好的性能。我们进一步测试了 PROSPECT-PRO 使用为许多植物物种获得的独立数据集估计每面积叶质量 (LMA) 作为蛋白质和 CBC 总和的能力。结果表明，在间接估计 LMA 方面，PROSPECT-PRO 与其前身 PROSPECT-D 完全兼容并具有可比性。我们可以从这项研究的结果中得出结论，PROSPECT-PRO 在根据检索到的 CBC 与蛋白质的比率建立碳氮比方面具有很高的潜力。