Universität zu Köln

Ye, Huichun, Huang, Wenjiang, Huang, Shanyu, Wu, Bin, Dong, Yingying and Cui, Bei (2018). Remote Estimation of Nitrogen Vertical Distribution by Consideration of Maize Geometry Characteristics. Remote Sens., 10 (12). BASEL: MDPI. ISSN 2072-4292

Full text not available from this repository.

Abstract

The vertical leaf nitrogen (N) distribution in the crop canopy is considered to be an important adaptive response of crop growth and production. Remote sensing has been widely applied for the determination of a crop's N status. Some studies have also focused on estimating the vertical leaf N distribution in the crop canopy, but these analyses have rarely considered the plant geometry and its influences on the remote estimation of the N vertical distribution in the crop canopy. In this study, field experiments with three types of maize (Zea mays L.) plant geometry (i.e., horizontal type, intermediate type, and upright type) were conducted to demonstrate how the maize plant geometry influences the remote estimation of N distribution in the vertical canopy (i.e., upper layer, middle layer, and bottom layer) at different growth stages. The results revealed that there were significant differences among the three maize plant geometry types in terms of canopy architecture, vertical distribution of leaf N density (LND, g m(-2)), and the LND estimates in the leaves of different layers based on canopy hyperspectral reflectance measurements. The upright leaf variety had the highest correlation between the lower-layer LND (R-2 = 0.52) and the best simple ratio (SR) index (736, 812), and this index performed well for estimating the upper (R-2 = 0.50) and middle (R-2 = 0.60) layer LND. However, for the intermediate leaf variety, only 25% of the variation in the lower-layer LND was explained by the best SR index (721, 935). The horizontal leaf variety showed little spectral sensitivity to the lower-layer LND. In addition, the growth stages also affected the remote detection of the lower leaf N status of the canopy, because the canopy reflectance was dominated by the biomass before the 12th leaf stage and by the plant N after this stage. Therefore, we can conclude that a more accurate estimation of the N vertical distribution in the canopy is obtained by canopy hyperspectral reflectance when the maize plants have more upright leaves.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Ye, Huichun UNSPECIFIED UNSPECIFIED UNSPECIFIED Huang, Wenjiang UNSPECIFIED UNSPECIFIED UNSPECIFIED Huang, Shanyu UNSPECIFIED UNSPECIFIED UNSPECIFIED Wu, Bin UNSPECIFIED UNSPECIFIED UNSPECIFIED Dong, Yingying UNSPECIFIED UNSPECIFIED UNSPECIFIED Cui, Bei UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-162877
DOI:	10.3390/rs10121995
Journal or Publication Title:	Remote Sens.
Volume:	10
Number:	12
Date:	2018
Publisher:	MDPI
Place of Publication:	BASEL
ISSN:	2072-4292
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language LEAF-AREA INDEX; CHLOROPHYLL CONTENT; HYPERSPECTRAL REFLECTANCE; CANOPY PHOTOSYNTHESIS; VEGETATION INDEXES; DILUTION CURVE; WHEAT CANOPY; LIGHT; RICE; DYNAMICS Multiple languages Remote Sensing Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/16287