Non-uniform vertical nitrogen distribution within plant canopy and its estimation by remote sensing: A review

Abstract Crop growth and production are dependent not only on the amount of total nitrogen (N) absorbed by plants, but also on the vertical leaf N distribution within canopies. The non-uniform leaf N distribution has been reported for various plant canopies. Remote sensing has been widely used for determination of crop N status, but such analysis seldom takes N distribution into consideration, ultimately leading to limited accuracy and decreased practical value of the related results. This paper has reviewed the results of previous studies that investigated the ecophysiological aspects of non-uniform N distribution, and the remote sensing methods that have been proposed to monitor this phenomenon. Additionally, this study used field data to analyze the differences in leaf N distribution in wheat canopies with different plant types (i.e. spread type, semi-spread type, and erect type), and provided insights into the estimation of vertical leaf N distribution by means of remote sensing. The process of reviewing research related to the ecophysiological issues of leaf N distribution led to identification of several important inadequacies in the current body of research. We propose that future work should aim to strengthen an understanding of the dynamic response of vertical N distribution within canopies to the various related environment factors and field management strategies. When a more thorough understanding of vertical N distribution is achieved, researchers will be able to improve related quantitative modeling, and may be able to comprehensively reveal the effect of vertical N distribution on canopy photosynthesis performances. In addition, through a comparison of the leaf N profiles of spread, semi-spread, and erect wheat canopies, it was found here that the semi-spread wheat canopy had a more non-uniform N distribution than did the other two types, despite all types having large and full vegetation coverage at the booting stage. Regarding detection of leaf N distribution using remote sensing, the few existing studies can be grouped into three classes according to the hyperspectral data used. One class employed the spectral data obtained from top-view observations; another class used multi-angle canopy reflectance data, while the third mainly focused on the relationships between spectral reflectance and fluorescence characteristics and the leaf N or chlorophyll content for different vertical layers. Despite important progress having been made, the results of the studies and the methods therein face key limitations in practical application. The present paper suggests two possibilities for the estimation of vertical leaf N distribution with remote sensing. One possibility is based upon hyperspectral imaging, and the other possibility combines a vertical N distribution model and canopy reflectance data. The former method requires investigation of the ability of hyperspectral imaging to obtain pure spectral information of different vertical leaf layers. The latter method requires that the key determinants of vertical N distribution be identified to improve quantitative modeling, and that the parameters of the N distribution model be determined from remotely sensed data. Because of the current lack of adequate data, a concrete case study and a very thorough test analysis could not be presented here. But it is still hoped that this work can provide helpful information for future studies.

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