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Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance

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Ecosystem Health and Sustainability 2020年第1期6卷 240-250,239页

作者： Weiwei Liu Mengjie Li Manyin Zhang DaanWang Ziliang Guo Songyuan Long Si Yang Henian Wang Wei Li Yukun Hu Yuanyun Wei Hongye Xiao Institute of Wetland Research Chinese Academy of ForestryBeijingChina Beijing Key Laboratory of Wetland Services and Restoration BeijingChina cHengshuihu National Wetland Ecosystem Research Station HengshuiHebeiChina Hanshiqiao National Wetland Ecosystem Research Station BeijingChina

Introduction:Highmercury(Hg)concentrations affect the chlorophyll in leaves,therebymodifying leaf *** is a promising technique for the rapid,nondestructive evaluation of leaf Hg *** this study,we investigated Hg contents and reflective hyperspectra of reed leaves(Phragmites communis)in a gold mining(Jilin province,China).Spectral parameters sensitive to Hg content were identified through basic spectral transformations,continuous wavelet transformation(CWT),and spectral indices *** Hg inversion models were developed using stepwise multiple linear regression,partial least squares regression,and random forest ***:The results indicated that:1)leaf Hg content decreased with increasing distance from the mine:Jiapigou(JPG)>Erdaocha(EDC)>Laojingchang(LJC)>Erdaogou(EDG)>Lingqian(LQ)>Weishahe(WSH).2)Hg–sensitive wavelengths were primarily in the visible region;CWT increased the correlation between hyperspectral data and leaf Hg content,and improved the regression and accuracy of inversion;3)the continuumremoval–CWT–stepwise multiple linear regression was better for estimating low leaf Hg content;while the differential spectral index–partial least squares regression was better for estimating high leaf Hg ***:These hyperspectral inversion methods could be used for rapid,nondestructive monitoring of wetland plants.

关键词： Mercury pollution reed leaf hyperspectrum inversion model nondestructive monitoring

Estimating Canopy Nitrogen Contents of an Apple Tree Using Hyperspectral Remote Sensing

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遥感科学（中英文版） 2016年第2期4卷 42-50页

作者： Xicun ZHU Lulu GAO Xianyi FANG Gengxing ZHAO Ling WANG College of Resources and Environment Shandong Agricultural UniversityTaianShandong271018China

Estimating an apple tree canopy nitrogen contents using hyperspectral techniques is important in theoretical and realistic significance for fertilization and *** 80 Fuji apple trees at the early fruiting stage as the research objects,the hyperspectral characteristics of the apple canopy were analyzed *** apple canopy hyperspectral and the canopy nitrogen contents were measured *** canopy hyperspectral characteristics under different nitrogen contents were analyzed and selected the sensitive wave *** apple canopy nitrogen content monitoring models were established by using multiple regression method,robust regression and BP neural network *** results showed that the canopy hyperspectral reflectance had obvious differences under different nitrogen *** sensitive bands concentrate on 724~1136 *** models based on hyperspectral indices are not *** based on robust regression(M regression)and BP neural network are better than multiple statistical model,and the accuracy of the BP neural network monitoring model is the *** results of the study provide a certain reference for estimating apple nutrition using hyperspectral technology.

关键词： Apple Ttrees Canopy Nitrogen Contents hyperspectrum Estimating Model

Monitoring Soil Nitrate Nitrogen Based on Hyperspectral Data in the Apple Orchards

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Agricultural Sciences 2017年第1期8卷 21-32页

作者： Yu Wei Xicun Zhu Cheng Li Lizhen Cheng Ling Wang Gengxing Zhao Yuanmao Jiang College of Resources and Environment Shandong Agricultural University Tai’an China Key Laboratory of Agricultural Ecology and Environment Shandong Agricultural University Tai’an China College of Horticulture Science and Engineering Shandong Agricultural University Tai’an China

This paper is aimed to monitor the soil nitrate nitrogen content in the apple orchards rapidly, accurately and in real time by making full use of the effective information of soil spectra. The 96 air-dried soil samples of the apple orchards in Qixia county, Yantai city, Shandong province were used as the data source. Spectral measurements of soil samples were carried out by ASD Fieldspec 3 in the darkroom, and the content of the soil nitrate nitrogen was determined by chemical method. Then the hyperspectral reflectance of soil samples were preprocessed by Multivariate Scatter Correction (MSC) and First Derivative (FD), the correlation analysis was carried out with the soil nitrate nitrogen content. The sensitive wavelength of soil nitrate nitrogen was screened. Finally, the Support Vector Machine (SVM) model for the soil nitrate nitrogen content was established. The results showed that the selected sensitive wavelength were 617 nm, 760 nm, 1239 nm, 1442 nm, 1535 nm, 1695 nm, 1776 nm, 1907 nm and 2088 nm. Hyperspectral monitoring model was established by SVM, in which the prediction set R2 was 0.959, RMSE was 0.281, RPD was 3.835;the correction set R2 was 0.822, RMSE was 0.392, RPD was 2.037. The SVM model could be used to monitor the soil nitrate content accurately.

关键词： hyperspectrum Nitrate Nitrogen Content Support Vector Machine Sensitive Wavelength

Improve the Prediction Accuracy of Apple Tree Canopy Nitrogen Content through Multiple Scattering Correction Using Spectroscopy

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Agricultural Sciences 2016年第10期7卷 651-659页

作者： Lulu Gao Xicun Zhu Cheng Li Lizhen Cheng Ling Wang Gengxing Zhao Yuanmao Jiang College of Resources and Environment Shandong Agricultural University Taian Shandong National Engineering Laboratory on Efficient Utilization of Soil and Fertilization Taian Shandong College of Horticulture Science and Engineering Shandong Agricultural University Taian Shandong

Method: Use Multiple Scattering Correction to eliminate the interference of scattering on spectrum in the process of field measurement so as to improve the accuracy of prediction model of tree canopy nitrogen content. Apple trees in Qixia of Yantai City were taken as the test material. The spectral reflectivity of apple tree canopy went through the First Derivative (FD) and Multiple Scattering Correction (MSC) plus first derivative, respectively. The correlation coefficients were calculated between spectral reflectivity and nitrogen content. The Support Vector Machine (SVM) method was used to establish the prediction model. The result indicates that the MSC pre-processing can improve the correlation between spectral reflectivity and nitrogen content. The SVM model with MSC + FD pre-processing was a good way to predict the nitrogen content. The calibration R2 of the model was 0.746;the validation R2 was 0.720;and its RMSE was 0.452 g·kgˉ1. MSC can commendably eliminate scattering error to improve the prediction accuracy of prediction model.

关键词： Multiple Scattering Correction hyperspectrum Apple Tree Canopy Nitrogen Content Support Vector Machine

Analysis on Reflectance Spectral Characteristics of Typical Saline Soil in Arid Area

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Agricultural Science & Technology 2014年第1期15卷 148-151,158页

作者：蒲智孟晓燕陈艳红吴艳新疆农业大学计算机与信息工程学院新疆乌鲁木齐830052

The spectral characteristic of geography objects is not only the important content of remote sensing mechanism, but also the important basis for remote sensing application. The reflectance spectral characteristics reflect the physiochemi-cal properties of saline soil. With 3 kinds of typical saline soils in the arid area as the study objects, the reflectance spectrums of soils with different salt contents and soil moistures were measured, and the spectral characteristics of the spectrums were analyzed. The results showed that under dry condition, the reflectance of the three kinds of saline soils presented obvious high-low patterns, while under damp condition, there was no obvious pattern. With continuum removed ,the three kinds of saline soils showed significant difference in reflectance spectral characteristics. There was significant difference in the absorption depth of the two absorption val eys un-der dry and damp conditions, which could be used to identify these 3 saline soils. The result of this research can be used for the parametric inversion and classifica-tion of saline soil retrieval and classification, as wel as for the remote sensing monitoring on saline soil.

关键词： Saline soil Spectral characteristic Continuum removal hyperspectrum