Abstract Nitrogen (N) is considered as one of the most important plant macronutrients and proper management of N therefore is a pre-requisite for modern agriculture. Continuous satellite-based monitoring of this key plant trait would help to understand individual crop N use efficiency and thus would enable site-specific N management. Since hyperspectral imaging sensors could provide detailed measurements of spectral signatures corresponding to the optical activity of chemical constituents, they have a theoretical advantage over multi-spectral sensing for the detection of crop N. The current study aims to provide a state-of-the-art overview of crop N retrieval methods from hyperspectral data in the agricultural sector and in the context of future satellite imaging spectroscopy missions. Over 400 studies were reviewed for this purpose, identifying those estimating mass-based N (N concentration, N%) and area-based N (N content, Narea) using hyperspectral remote sensing data. Retrieval methods of the 125 studies selected in this review can be grouped into: (1) parametric regression methods, (2) linear nonparametric regression methods or chemometrics, (3) nonlinear nonparametric regression methods or machine learning regression algorithms, (4) physically-based or radiative transfer models (RTM), (5) use of alternative data sources (sun-induced fluorescence, SIF) and (6) hybrid or combined techniques. Whereas in the last decades methods for estimation of Narea and N% from hyperspectral data have been mainly based on simple parametric regression algorithms, such as narrowband vegetation indices, there is an increasing trend of using machine learning, RTM and hybrid techniques. Within plants, N is invested in proteins and chlorophylls stored in the leaf cells, with the proteins being the major nitrogen-containing biochemical constituent. However, in most studies, the relationship between N and chlorophyll content was used to estimate crop N, focusing on the visible-near infrared (VNIR) spectral domains, and thus neglecting protein-related N and reallocation of nitrogen to non-photosynthetic compartments. Therefore, we recommend exploiting the estimation of nitrogen via the proxy of proteins using hyperspectral data and in particular the short-wave infrared (SWIR) spectral domain. We further strongly encourage a standardization of nitrogen terminology, distinguishing between N% and Narea. Moreover, the exploitation of physically-based approaches is highly recommended combined with machine learning regression algorithms, which represents an interesting perspective for future research in view of new spaceborne imaging spectroscopy sensors.

中文翻译：

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作物氮监测：成像光谱任务背景下的最新进展和主要发展

摘要 氮（N）被认为是最重要的植物常量营养素之一，因此对氮的适当管理是现代农业的先决条件。对这一关键植物性状的持续卫星监测将有助于了解单个作物的氮利用效率，从而实现针对特定地点的氮管理。由于高光谱成像传感器可以提供与化学成分的光学活性相对应的光谱特征的详细测量，因此它们在检测作物 N 方面比多光谱传感具有理论优势。目前的研究旨在提供一种最新的-从农业部门的高光谱数据和未来卫星成像光谱任务的背景下，作物 N 检索方法的艺术概述。为此目的审查了 400 多项研究，使用高光谱遥感数据确定基于质量的 N（N 浓度，N%）和基于区域的 N（N 含量，Narea）估计值。本综述中选择的 125 项研究的检索方法可分为：（1）参数回归方法，（2）线性非参数回归方法或化学计量学，（3）非线性非参数回归方法或机器学习回归算法，（4）物理-基于或辐射传输模型（RTM），（5）使用替代数据源（太阳诱导荧光，SIF）和（6）混合或组合技术。在过去的几十年中，从高光谱数据估计 Narea 和 N% 的方法主要基于简单的参数回归算法，例如窄带植被指数，而使用机器学习、RTM 和混合技术的趋势越来越大。在植物中，N 被投资于储存在叶细胞中的蛋白质和叶绿素，其中蛋白质是主要的含氮生化成分。然而，在大多数研究中，N 和叶绿素含量之间的关系被用来估计作物 N，侧重于可见 - 近红外 (VNIR) 光谱域，因此忽略了与蛋白质相关的 N 和氮向非光合作用区室的重新分配。因此，我们建议使用高光谱数据，特别是短波红外 (SWIR) 光谱域，通过蛋白质代理来估计氮。我们进一步强烈鼓励氮术语的标准化，区分 N% 和 Narea。此外，强烈建议将基于物理的方法与机器学习回归算法结合使用，