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
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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(ratiovegetationindex,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。
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. R~2 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. The short-wave infrared band selected under first order of square root for Ca~(2+) content had the best estimation effect through GWR model. Mg~(2+) content was best estimated in the characteristic bands in the red light region selected by DVI, but the GWR model was not as accurate as BP model in estimating Mg~(2+) content. Based on the GWR salt ion model, the estimation of ions with higher content was better, and the accuracy of K~+ and Na~+ models was better than that of Ca~(2+) and Mg~(2+). When the GWR model was used to estimate the salt ion content in plant leaves, the characteristic bands all pointed to red and short-wave infrared bands. The model based on logarithms of reciprocal of reflectance and GWR for estimated K~+ produced the superior performance(R~2=0.930, RMSE=0.018 mg/kg). The optimal GWR model with the highest R~2 and lowest RMSE was estimation model on Na~+(R~2=0.984, RMSE=0.041 mg/kg) via processing. For the estimation model on Ca~(2+), the model produced reasonable outcome using first order of square root of reflectance-GWR strategy. Moreover, compared with BP model, the GWR model had insufficient estimation for Mg~(2+) whereas DVI scheme contributed to improve accuracy of the BP estimated model. By comparison, the GWR model yielded better results in higher-content ion models. In conclusion, our study showed GWR model was effective for estimating leaf salt ions through vegetation spectral information. Sensitive bands for salt ions were prominent in the red bands and short-wave infrared bands, which were consistent with the response of vegetation spectral mechanism.
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. R~2 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. The short-wave infrared band selected under first order of square root for Ca~(2+) content had the best estimation effect through GWR model. Mg~(2+) content was best estimated in the characteristic bands in the red light region selected by DVI, but the GWR model was not as accurate as BP model in estimating Mg~(2+) content. Based on the GWR salt ion model, the estimation of ions with higher content was better, and the accuracy of K~+ and Na~+ models was better than that of Ca~(2+) and Mg~(2+). When the GWR model was used to estimate the salt ion content in plant leaves, the characteristic bands all pointed to red and short-wave infrared bands. The model based on logarithms of reciprocal of reflectance and GWR for estimated K~+ produced the superior performance(R~2=0.930, RMSE=0.018 mg/kg). The optimal GWR model with the highest R~2 and lowest RMSE was estimation model on Na~+(R~2=0.984, RMSE=0.041 mg/kg) via processing. For the estimation model on Ca~(2+), the model produced reasonable outcome using first order of square root of reflectance-GWR strategy. Moreover, compared with BP model, the GWR model had insufficient estimation for Mg~(2+) whereas DVI scheme contributed to improve accuracy of the BP estimated model. By comparison, the GWR model yielded better results in higher-content ion models. In conclusion, our study showed GWR model was effective for estimating leaf salt ions through vegetation spectral information. Sensitive bands for salt ions were prominent in the red bands and short-wave infrared bands, which were consistent with the response of vegetation spectral mechanism.
引文
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[3]赵可夫,范海,江行玉,等.盐生植物在盐渍土壤改良中的作用[J].应用与环境生物学报,2002,8(1):31-35.Zhao Kefu,Fan Hai,Jiang Xingyu,et al.Improvement and utilization of saline soil by planting halophytes[J].Chin JAppl Environ Biol,2002,8(1):31-35.(in Chinese with English abstract)
[4]赵丹,余冬梅,胡夏嵩,等.青海柴达木盆地大柴旦盐湖区盐生植物降盐效应[J].盐湖研究,2016,24(4):37-47.Zhao Dan,YüDongmei,Hu Xiasong,et al.The biodesalinization effect by halophytes in Da Qaidam salt lake area[J].Journal of Salt Lake Research.2016,24(4):37-47.(in Chinese with English abstract)
[5]黄建,田长彦,卞卫国,等.陈春秀.4种盐生植物生长对土壤石油污染的响应[J].干旱区研究,2014,31(1):100-104.Huang Jian,Tian Changyan,Bian Weiguo,et al.Response of growth of four halophyte species in oil-contaminated soil[J].Arid Zone Research,2014,31(1):100-104.(in Chinese with English abstract)
[6]郗金标,张福锁,毛达如,等.新疆盐生植物群落物种多样性及其分布[J].林业科学,2006,42(10):6-12.Xi Jinbiao,Zhang Fusuo,Mao Daru,et al.Species diversity and distribution of halophytic vegetation in Xinjiang[J].Scientia Silvae Sinicae,2006,42(10):6-12.(in Chinese with English abstract)
[7]Yue J,Feng H,Jin X,et al.A comparison of crop parameters estimation using images from UAV-mounted snapshot hyperspectral sensor and high-definition digital camera[J].Remote Sensing,2018,10(7):1138.
[8]刘炜,常庆瑞,郭曼,等.夏玉米可见/近红外光小波主成分提取与氮素含量神经网络检测[J].红外与毫米波学报,2011,30(1):48-54.Liu Wei,Chang Qingrui,Guo Man,et al.Detection of leaf nitrogen content of summer corn using visible/near infrared spectra[J].Journal of Infrared and Millimeter Waves,2011,30(1):48-54.(in Chinese with English abstract)
[9]刘克,赵文吉,郭逍宇,等.基于地面实测光谱的湿地植物全氮含量估算研究[J].光谱学与光谱分析,2012,32(2):465-471.Liu Ke,Zhao Wenji,Guo Xiaoyu,et al.Estimating Total nitrogen content in wetland vegetation based on measured reflectance spectra[J].Spectroscopy and Spectral Analysis,2012,32(2):465-471.(in Chinese with English abstract)
[10]Broge N H,Leblanc E.Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density[J].Remote Sensing of Environment,2001,76(2):156-172.
[11]易秋香,黄敬峰,王秀珍.玉米粗纤维含量高光谱估算模型研究[J].红外与毫米波报,2007,26(5):393-395.Yi Qiuxiang,Huang Jingfeng,Wang Xiuzhen.Hyperspectral estimation models for crude fibre concentration of cone[J].Journal of Infrared and Millimeter Waves,2007,26(5):393-395.(in Chinese with English abstract)
[12]刘冰峰,李军,贺佳,等.基于高光谱植被指数的夏玉米地上干物质量估算模型研究[J].农业机械学报,2016,47(3):254-262.Liu Bingfeng,Li Jun,He Jia,et al.Estimation models of above-ground dry matter accumulation of summer maize based on hyperspectral remote sensing vegetation indexes[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(3):254-262.(in Chinese with English abstract)
[13]Wang Q,Jin J.Leaf transpiration of drought tolerant plant can be captured by hyperspectral reflectance using PLSRanalysis[J].iForest-Biogeosciences and Forestry,2015,9:e1-e8.
[14]Sampson P H,Zarcotejada P J,Mohammed G H,et al.Hyperspectral remote sensing of forest condition:estimating chlorophyll content in tolerant hardwoods.[J].Forest Science,2003,49(3):381-391.
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