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	地理加权回归模型结合高光谱反演盐生植物叶片盐离子含量
其他题名	Leaf salt ion content estimation of halophyte plants based on geographically weighted regression model combined with hyperspectral data
	袁婕 1; 张飞2 ; 葛翔宇 1; 郭婉臻 1; 邓来飞 1
来源期刊	农业工程学报
ISSN	1002-6819
出版年	2019
卷号	35 期号:10 页码:115-124
中文摘要	快速、无损地估算盐生植物叶片盐离子含量在植物生长监测、耐盐植物筛选和土壤盐渍化监测等方面有实用价值。该研究以新疆艾比湖保护区内盐生植物为研究对象,通过分析植物叶片盐离子(K~+、Na~+、Ca~(2+)、Mg~(2+))含量与冠层高光谱数据的光谱变换和二维植被指数(比值型植被指数(ratio vegetation index,RVI)、差值型植被指数(difference vegetation index,DVI)、归一化型植被指数(normalized difference vegetation index,NDVI))的相关性选取特征波段,构建基于地理加权回归模型(geographically weighted regression,GWR)的叶片盐离子含量估算模型,并与BP神经网络模型(back propagation neural network)进行对比,研究基于GWR模型估算干旱区盐生植物叶片盐离子的可行性。结果表明,选取特征波段集中表现在红及短波红外波段:K~+含量在反射率倒数的对数选取的红光区域内波段使用GWR估算效果最佳;Na~+的特征波段在光谱变换下集中于短波红外区域,二维植被指数集中在近红外、短波近红外及黄、橙、红区域,各种波段选取下GWR对Na~+的含量估算均有较好效果,但反射率对数的一阶估算效果最好;Ca~(2+)含量在反射率平方根的一阶微分下选取的短波红外波段通过GWR模型估算效果最好;Mg~(2+)含量在DVI选取的位于红光区域特征波段估算效果最佳,但使用GWR模型对Mg~(2+)的估算精度不及BP模型。分析基于GWR盐离子模型估算模型发现,含量较高的离子估算效果更好,K~+、Na~+的模型精度优于Ca~(2+)、Mg~(2+)。在使用GWR模型估算植物叶片盐离子含量时,特征波段均指向红及短波红外波段,符合植被光谱机理的响应。
英文摘要	Rapid and non-destructive estimation of leaf salt ion concentrations in halophytes can provide valuable information for plant growth monitoring, selection of salt-tolerant plants and soil salinity monitoring. In this study, the canopy reflectance (350-2 500 nm) and the leaf salt ion (K~+, Na~+, Ca~(2+), Mg~(2+)) concentration in the halophytes were measured in the Ebinur Lake Protection Zones, Xinjiang, China. Data collected includes hyperspectral data and leaf salt ion data, and the relationships between the leaf ion concentrations and the selected spectral indices were analyzed. K~+ sensitive wave bands on the photosynthetic effective radiation area of the 400- 700 nm (photosynthetically available radiation, PAR), and focused on the red and yellow areas without differential transform; The sensitive bands of Na~+ are concentrated in the near infrared region of 949- 1 355 nm. Ca~(2+) sensitive bands were concentrated in the visible red and near-infrared regions of 665-672 and 919-1 283 nm. Mg~(2+) sensitive bands were mainly concentrated in 384, 651- 669 nm, mainly in the visible red light region. There was a certain correlation with the ultraviolet region band, but the correlation was generally small. The correlation between the original spectrum and K~+ and Na~+ was relatively high, reaching a significant level. Spectral transformation increased the correlation between the contents of Ca~(2+) and Mg~(2+) and the spectrum, so that modeling bands could be selected according to the standard. Spectral transformation could improve the correlation between the content of salt ions and the spectrum. There were 64 samples in total, and the proportion of samples used for modeling and verification was 3:1. R2 and root mean squared error (RMSE) were used as accuracy evaluation criteria. A Geographically Weighted Regression (GWR) model and a back propagation (BP) model were constructed for estimating leaf salt ion concentrations with the spectral transform and the spectral indices as ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference vegetation index, and achieved a promising accuracy. The GWR estimation was the best in the bands in the red light region selected by the reciprocal logarithm of reciprocal of reflectance. The characteristic bands of Na~+ were concentrated in the short-wave infrared region under the spectral transformation, and the two-dimensional vegetation index was concentrated in the near-infrared region, short-wave near-infrared region, yellow, orange and red region.
中文关键词	干旱 ; 叶片 ; 高光谱 ; GWR模型 ; 盐生植物 ; 盐离子
英文关键词	drought leaf hyperspectra GWR model halophyte saline ions
语种	中文
收录类别	CSCD
WOS类目	ENGINEERING AEROSPACE
WOS研究方向	Engineering
CSCD记录号	CSCD:6530828
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315822
作者单位	1.新疆大学资源与环境科学学院;;新疆大学, ;;绿洲生态教育部重点实验室, 乌鲁木齐;;乌鲁木齐, ;; 830046;;830046; 2.新疆大学资源与环境科学学院;;新疆大学;;新疆大学资源与环境科学学院, ;;绿洲生态教育部重点实验室;;智慧城市与环境建模自治区普通高校重点实验室, 乌鲁木齐;;乌鲁木齐;;乌鲁木齐, ;;;; 830046;;830046;;830046
推荐引用方式 GB/T 7714	袁婕,张飞,葛翔宇,等. 地理加权回归模型结合高光谱反演盐生植物叶片盐离子含量[J],2019,35(10):115-124.
APA	袁婕,张飞,葛翔宇,郭婉臻,&邓来飞.(2019).地理加权回归模型结合高光谱反演盐生植物叶片盐离子含量.农业工程学报,35(10),115-124.
MLA	袁婕,et al."地理加权回归模型结合高光谱反演盐生植物叶片盐离子含量".农业工程学报 35.10(2019):115-124.