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遥感技术与应用  2015, Vol. 30 Issue (4): 638-644    DOI: 10.11873/j.issn.1004-0323.2015.4.0638
模型与反演     
GA-PLS方法提取土壤水盐光谱特征的精度分析
柴思跃1,2,马维玲1,2,刘高焕1,黄翀1,刘庆生1
(1.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101;
2.中国科学院大学,北京 100049)
Accuracy Analysis of GA-PLS based Soil Water Salinity Hyperspectral Characteristics Mining Approach
Chai Siyue1,2,Ma Weiling1,2,Liu Gaohuan1,Huang Chong1,Liu Qingsheng1
(1.Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences,Beijing 100101,China;
2.University of Chinese Academy of Sciences,Beijing 100049,China)
 全文: PDF(2075 KB)  
摘要:

光谱定量遥感已成为土壤盐渍化大尺度调查的有效手段之一,但黄河三角洲地区盐渍化土壤的光谱响应特征尚未明确。以黄河三角洲野外测定土壤体积含水率、电导率为例,应用遗传偏最小二乘法(GA-PLS)在小样本集条件下提取盐渍土壤的水分—盐分的光谱响应特征,利用蒙特卡罗方法随机模拟结果表明:在不同土壤水盐含量条件下,GA-PLS方法所提取的光谱特征具有鲁棒性,含水率模型稳定在23个波段变量,即响应特征为365~425,500~515,720~740,755~765与955~965 nm;土壤电导率模型的特征集数目为20个波段变量,特征为370~385,405~425\,500~535,650~660,755~760与1 030~1 050 nm。实验在不同预处理模型下,GA-PLS算法所建立水盐光谱模型较PLSR模型均显示出更高的精度。其中,包络线预处理方法与GA-PLS算法相结合效果最优,其水分光谱模型测试集拟合精度(R2),预测残差平方和(PRSS)与残差预测方差(RPD)分别为0.88,9.36与15.80;土壤光谱模型测试集精度R2,PRSS与RPD分别为0.71,15.68与13.76。

关键词: 遗传-偏最小二乘算法(GA-PLS)土壤电导率高光谱黄河三角洲    
Abstract:

Hyperspectral remote sensing data is one of effective ways which can be used to retrieve salinity quantitatively in soil monitoring.But the quantitative structure-property relationship between soil salinity and soil spectral reflection characters has not been found in yellow river delta region.Genetic Algorithm with Partial Least Square kernel(GA-PLS)method is applied to mine spectral features of volumetric moisture content(V%)and Electrical Conductivity(EC)using the in-stu salinity soil sampling in Yellow River Delta region.MC simulation result shows GA-PLS method mines stable characters numbers and fitness under different of water\|salt level,which prove the robustness of the algorithm.Therefore,the spectral features of V% exist in 365~425,500~515,720~740,755~765 and 955~965 nm bands,compared with the spectral features of EC b appear in 370~385,405~425,500~535,650~660,755~760 and 1 030~1 050 nm bands.According to the experiment result,through 4 different preprocessing approaches,water content model and electric conductivity model of both PLS and GA-PLS are all evaluated by R2t,Predicted Residual Sum of Squares(PRSS)and Residual Predictive Deviation(RPD),GA-PLS models got the better point in prediction accuracy rather than PLS regression.The continuum removal approach leads to the highest prediction accuracy among all other preprocessing methods,with R2,PRSS and RPD equal 0.88,9.36 and 15.80 in soil water content model and 0.71,15.68 and 13.76 in EC model.

Key words: Genetic algorithm-partial least square(GA-PLS)    Soil electrical conductivity    Hyperspectral    Yellow river delta
收稿日期: 2014-06-18 出版日期: 2015-09-22
:  TP 75  
基金资助:

国家自然科学基金项目“现代黄河三角洲地下水—土壤—大气相互作用模式研究”(41271407), 海洋公益性行业科研专项项目(201105020)。

作者简介: 柴思跃(1985-),男,北京人,博士研究生,主要从事时空数据挖掘研究。Email: chaisy@lreis.ac.cn。
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引用本文:

柴思跃,马维玲,刘高焕,黄翀,刘庆生. GA-PLS方法提取土壤水盐光谱特征的精度分析[J]. 遥感技术与应用, 2015, 30(4): 638-644.

Chai Siyue,Ma Weiling,Liu Gaohuan,Huang Chong,Liu Qingsheng. Accuracy Analysis of GA-PLS based Soil Water Salinity Hyperspectral Characteristics Mining Approach. Remote Sensing Technology and Application, 2015, 30(4): 638-644.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2015.4.0638        http://www.rsta.ac.cn/CN/Y2015/V30/I4/638

[1]Metternicht G I,Zinck J A.Remote Sensing of Soil Salinity:Potentials and Constraints[J].Remote Sensing of Environment,2003,85(1):1-20.

[2]Hamzeh S,Naseri A A,AlaviPanah S K,et al.Estimating Salinity Stress in Sugarcane Fields with Spaceborne Hyperspectral Vegetation Indices[J].International Journal of Applied Earth Observation and Geoinformation,2013,21:282-290.

[3]Zhang Xiaoguang,Huang Biao,Ji Junfeng,et al.Quantitative Prediction of Soil Salinity Content with Visible-near Infrared Hyper-spectral in Northeast China[J].Spectroscopy and Spectral Analysis,2012,(8):2075-2079.[张晓光,黄标,季峻峰,等.基于可见近红外高光谱的东北盐渍土盐分定量模型研究[J].光谱学与光谱分析,2012,(8):2075-2079.]

[4]Sidike A,Zhao S,Wen Y.Estimating Soil Salinity in Pingluo County of China Using QuickBird Data and Soil Reflectance Spectra[J].International Journal of Applied Earth Observation and Geoinformation,2014,26:156-175.

[5]Wang Jing,Liu Xiangnan,Huang Fang,et al.Salinity Forecasting of Saline Soil based on ANN and Hyperspectral Remote Sensing[J].Transactions of the Chinese Society of Agricultural Engineering,2009,(12):161-166.[王静,刘湘南,黄方,等.基于ANN技术和高光谱遥感的盐渍土盐分预测[J].农业工程学报,2009,(12):161-166.]

[6]Zhang Fei,Tashpolat Tiyip,Ding Jianli,et al.Correspondence Analysis of Relationship between Characteristics and Spectral of Soil Salinization[J].Acta Pedologica Sinica,2009,46(3):513-519.[张飞,塔西甫拉提·特依拜,丁建丽,等.基于对应分析的土壤盐渍化现状特征及其与光谱关系研究[J].土壤学报,2009,46(3):513-519.] 

[7]Yao Yuan,Ding Jianli,Ardak·Kelimu,et al.Research on Remote Sensing Monitoring of Soil Salinization based on Measured Hyperspectral and EM38 Data[J].Spectroscopy and Spectral Analysis,2013,(7):1917-1921.[姚远,丁建丽,阿尔达克·克里木,等.基于实测高光谱和电磁感应数据的区域土壤盐渍化遥感监测研究[J].光谱学与光谱分析,2013,(7):1917-1921.]

[8]Csillag F,Psillag L,Biehl L L.Spectral Band Selection for the 〖HJ2mm〗Characterization of Salinity Status of Soils[J].Remote Sensing of Environment,1993,43(3):231-242.

[9]Weng Yongling,Qi Haoping,Fang Hongbin,et al.PLSR-bas-ed Hyperspectral Remote Sensing Retrieval of Soil Salinity of Chaka-Gonghe Basin in Qinghai Province[J].Acta Pedologica Sinica,2010,47(6):1255-1263.[翁永玲,戚浩平,方洪宾,等.基于PLSR方法的青海茶卡—共和盆地土壤盐分高光谱遥感反演[J].土壤学报,2010,47(6):1255-1263.]

[10]Goldshleger N,Chudnovsky A,Ben-Binyamin R.Predicting Salinity in Tomato Using Soil Reflectance Spectra[J].International Journal of Remote Sensing,2013,34(17):6079-6093.

[11]Atzberger C,Guzber M,Baret,et al.Comparative Analysis of Three Chemometric Techniques for the Spectroradiometric Assessment of Canopy Chlorophyll Content in Winter Wheat[J].Computers and Electronics in Agriculture,2010,73(2):165-173.

[12]Song K,Li L,Li S,et al.Hyperspectral Retrieval of Phycocyanin in Potable Water Sources Using Genetic Algorithment of Canopy Chlorophyll Content in Winter[J].International Journal of Applied Earth Observation and Geoinformation,2012,18:368-385.

[13]Prieto N,Oliveri P,Leardi R,et al.Application of a GAal of Applied Earth Observation and Geoinformation Genetic Algorithm[J].Sensors and Actuators B:Chemical,2013,183:52-57.

[14]Wu Yueru,Wang Weizhen,Wang Haibing,et al.Analysis of Variation of Soil Salt with New Electric Conductivity Index[J].Acta Pedologica Sinica,2011,48(4):869-873.[吴月茹,王维真,王海兵,等.采用新电导率指标分析土壤盐分变化规律[J].土壤学报,2011,48(4):869-873.]

[15]Liu Qingsheng,Liu Gaohuan.The Simple Analysis on water-Salt and Field Spectral Characteristics of Calcaric Fluvisols in the Yellow River Delta[J].Chinese Agricultural Science Bulletin,2008,24(3):253-257.[刘庆生,刘高焕.现代黄河三角洲潮土水盐与野外光谱特征浅析[J].中国农学通报,2008,24(3):253-257.]

[16]Leardi R,Nrgaard L.Sequential Application of Backward Interval Partial Least Squares and Genetic Algorithms for the Selection of Relevant Spectral Regions[J].Journal of Chemometrics,2004,18(11):486-497.

[17]Kruger U,Xie L,“Partial Least Squares”in Statistical Monitoring of Complex Multivariate Processes[M].Singapore:John Wiley & Sons,Ltd,2012:375-409.

[18]Leardi R,Lupidi A.Genetic Algorithms Applied to Feature Selection in PLS Regression:How and When to Use Them[J].Chemometrics and Intelligent Laboratory Systems,1998,41(2):195-207.

[19]Pope R M,Fry E S.Absorption Spectrum(380~700 nm)of Pure Water Ⅱ:Integrating Cavity Measurements[J].Applied Optics,1997,36:8710-8723.

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