期刊
BIOSYSTEMS ENGINEERING
卷 158, 期 -, 页码 38-50出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.biosystemseng.2017.03.006
关键词
Crop monitoring; Remote sensing; Hyperspectrograms; Multivariate data analysis; VIS-NIR canopy reflectance
资金
- Department of Agricultural and Environmental Sciences of University degli Studi di Milano (UNIMI) through Development Plan UNIMI
This work had the goal to assess the capability of hyperspectral line scan imaging (400-1000 nm) to estimate crop variables in the greenhouse under combined water and nitrogen stress using multivariate data analysis and two data compression methods: canopy average spectra and hyperspectrogram extraction. Hyperspectral images contain far more information than do multispectral ones, which permits discrimination among minute pattern differences in canopy spectral reflectance. A pot greenhouse experiment of eight treatments, from the combination of four nitrogen supply levels and two water supply levels, was designed to test widely varied spinach canopies. Using partial least square regression models, the fresh and dry matter of aboveground biomasses and water and nitrogen contents were estimated from a 76-sample dataset. Both the canopy reflectance-based and hyperspectrogram-based models performed well in estimating variables strictly related to canopy leaf area index (LAI) and geometry, i.e., water content and fresh and dry matters, such that R-2 in independent validation reached values of 0.87, 0.65, 0.65, and 0.86, 0.74, 0.72, respectively. Estimation of nitrogen concentration from single leaf spectra hyperspectral images produced a high cross-validation R-2 (0.83), as opposed to the poor predictive results produced from canopy scans. This latter result arose from orientation effects due to canopy architecture. Finally, for estimation purposes, image hyperspectrogram compression without spatial information loss produced more encouraging results while considering canopy structure in crop variables than did average canopy spectra. (C) 2017 IAgrE. Published by Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据