Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions

REMOTE SENSING OF ENVIRONMENT

卷:242

Review

Berger, Katja¹  Verrelst, Jochem²  Feret, Jean-Baptiste³  Wang, Zhihui⁴  Wocher, Matthias¹  Strathmann, Markus¹  Danner, Martin¹  Mauser, Wolfram¹  Hank, Tobias¹ 

[1] Ludwig Maximilians Univ Munchen, Dept Geog, Luisenstr 37, D-80333 Munich, Germany

[2] Univ Valencia, IPL, Parc Cientif, Valencia 46980, Spain

[3] Univ Montpellier, CNRS, CIRAD, AgroParisTech,TETIS,INRAE, Montpellier, France

[4] Univ Wisconsin, Dept Forest & Wildlife Ecol, 1630 Linden Dr, Madison, WI 53706 USA

关键词: Hyperspectral;    Biochemical traits;    Radiative transfer modelling;    Hybrid techniques;    machine learning;   

DOI  :  10.1016/j.rse.2020.111758

来源: Elsevier

【 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, N-area) 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.

【 授权许可】

Free