气候变化领域集成服务门户(CCPortal): Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions

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DOI	10.1016/j.rse.2020.111758
	Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions
	Berger K.; Verrelst J.; Féret J.-B.; Wang Z.; Wocher M.; Strathmann M.; Danner M.; Mauser W.; Hank T.
发表日期	2020
ISSN	00344257
卷号	242
英文摘要	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. © 2020 Elsevier Inc.
英文关键词	Biochemical traits; Hybrid techniques, machine learning; Hyperspectral; Radiative transfer modelling
语种	英语
scopus关键词	Chemical detection; Chlorophyll; Crops; Information retrieval; Infrared devices; Infrared radiation; Learning algorithms; Learning systems; Nitrogen; Parameter estimation; Proteins; Radiative transfer; Regression analysis; Remote sensing; Spectroscopy; Transfer learning; Biochemical traits; Hybrid techniques; HyperSpectral; Hyperspectral imaging sensors; Hyperspectral remote sensing data; Non-parametric regression; Parametric regression methods; Radiative transfer modelling; Hyperspectral imaging; chlorophyll; machine learning; nitrogen; radiative transfer; regression analysis; remote sensing; satellite data; spectral analysis; standardization
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179350
作者单位	Department of Geography, Ludwig-Maximilians-Universitaet München, Luisenstr 37, Munich, 80333, Germany; Image Processing Laboratory (IPL), Parc Científic, Universitat de València, Paterna, València 46980, Spain; TETIS, INRAE, AgroParisTech, CIRAD, CNRS, Université Montpellier, Montpellier, France; Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706, United States
推荐引用方式 GB/T 7714	Berger K.,Verrelst J.,Féret J.-B.,et al. Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions[J],2020,242.
APA	Berger K..,Verrelst J..,Féret J.-B..,Wang Z..,Wocher M..,...&Hank T..(2020).Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions.Remote Sensing of Environment,242.
MLA	Berger K.,et al."Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions".Remote Sensing of Environment 242(2020).