典型半干旱区土壤盐分高光谱特征反演
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摘要
选取陕北典型半干旱区为研究对象,利用土壤高光谱特征对盐分进行反演研究。在研究区域选取样点,采集土壤样品测定土壤光谱特征,以土壤反射率(R)、反射率对倒数(Log(1/R))及去包络线的反射率(R_(Cr))三个光谱特征进行土壤盐分反演研究,分析其与土壤盐分的相关性,遴选特征波段,并通过Matlab编程利用最小偏二乘回归方法(partial least squares regression,PLSR)建立土壤盐分定量反演模型,然后利用检验样点进行精度检验和比较。结果表明,利用经包络线去除后光谱反射率进行定量反演的均方根预测误差最小(1.253<1.367<1.575),其预测精度最高;利用土壤高光谱特征进行盐分反演的预测值与实测值相关性良好(r~2=0.761),趋势线接近于y=x。总之,研究发现,土壤反射率经过包络线去除后,利用偏最小二乘回归方法建立的反演模型具有良好的精度,这将有利于提高土壤盐渍化的监测效率。
Hysperspectral inversion of soil salinity was researched in the present paper with the chosen study object of typical semiarid area in North Shaanxi Province.The studying sites were selected,the hy perspectral data were collected,and the soil samples were taken back for experiment analysis.The reflectance of soils(R),the logarithm of the reciprocal of the reflectance(LogG/R)) and the continual removed reflectance(R_(cr)) were used to research the soil salinity.The correlations between the hyperspectral character and soil salinity was studied to filter the characteristics bands.Then the partial least squares regression(PLSR) was used to study the inversion model of soil salinity with Matlab program,and the precision was compared with the verifying sites.The research result showed that the root mean square error(RMSE) of the inversion with R_(cr) was the least(1.253<1.367<1.575),and its precision was the best) the correlation between the predicted value and the measured value was well(r~2 =0.761),the trend line was near y=x.In conclusion,the quantificational inversion model with the variables of Rcr establised by PLSR was well,which will improve the survey efficiency of soil salinity.
Hysperspectral inversion of soil salinity was researched in the present paper with the chosen study object of typical semiarid area in North Shaanxi Province.The studying sites were selected,the hy perspectral data were collected,and the soil samples were taken back for experiment analysis.The reflectance of soils(R),the logarithm of the reciprocal of the reflectance(LogG/R)) and the continual removed reflectance(R_(cr)) were used to research the soil salinity.The correlations between the hyperspectral character and soil salinity was studied to filter the characteristics bands.Then the partial least squares regression(PLSR) was used to study the inversion model of soil salinity with Matlab program,and the precision was compared with the verifying sites.The research result showed that the root mean square error(RMSE) of the inversion with R_(cr) was the least(1.253<1.367<1.575),and its precision was the best) the correlation between the predicted value and the measured value was well(r~2 =0.761),the trend line was near y=x.In conclusion,the quantificational inversion model with the variables of Rcr establised by PLSR was well,which will improve the survey efficiency of soil salinity.
引文
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[4]WANGJing,HE Ting,LI Yu-huan(王静,何挺,李玉环).Journal of Remote Sensing(遥感学报):2005,9(4):438.
[5]HU Wen-sheng,REN Hong-yan,ZHUANG Da-fang,et al(胡文生,任红艳,庄大方.等).Journal of Geo-information Science(信息科学学报),2012.14(2):258.
[6]JI Wen-jun,SHI Zhou.ZHOU Qing,et al(纪文君,史舟,周清,等).J.Infrared MiUim.Waves(红外与毫米波学报),2012,31(3):277.
[7]ZHANG Juan-juan,TIAN Yong-chao,YAOXia,et al(张娟娟.田永超,姚霞,等).Acta Pedologica Sinica(土壤学报),2012,49(1):50.
[8]CHEN Zhen(陈祯).Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2012,28(4):76.
[9]GU Yan,ZHANG Ying,LI Huan(顾燕,张鹰,李欢).Wetland Science(湿地科学),2013,11(2):167.
[10]Dehaan R L,Taylor G R Remote Sensing of Environment,2002,80(3):406.
[11]Farifteh J,Farshad A,George R J.Geoderma,2006,130(3-4):191.
[12]Farifteh J,Van der Meer F D,Atzberger C,et al.Remote Sensing of Environment,2007,110(1),59.
[13]LIU Huan-jun,ZHANG Bai,WANG Zong-ming,et al(刘焕军,张柏,王宗明,等).J.Infrared MiUim.Waves(红外与毫米波学报),2008,27(2):138.
[14]LU Ru-kun(鲁如坤).Analysis Method for Soil Agricultural Chemistry(土壤农业化学分析方法).Nanjing:Hohai University Press(南京:河海大学出版社),2000.
[15]XIEBo-cheng,XUE Xu-zhang,LIU Wei-dong,et al(谢伯承,薛绪掌,刘伟东.等).Acta Pedologica Sinica(土壤学报),2005.42(1):171.
[16]WENG Yong-ling,QI Hao-ping,FANG Hong-bin,et al(翁水玲,戚浩平,方洪宾,等).Acta Pedologica Sinica(土壤学报),2010,47(6):1255.