基于高光谱的民勤土壤盐分定量分析

庞国锦; 王涛; 孙家欢; 李森

doi:10.7522/j.issn.1000-694X.2014.00045

中国沙漠 >

2014 , Vol. 34 >Issue 4: 1073 - 1079

DOI: https://doi.org/10.7522/j.issn.1000-694X.2014.00045

生物与土壤

基于高光谱的民勤土壤盐分定量分析

庞国锦 ,
王涛 ,
孙家欢 ,
李森

展开

1. 中国科学院寒区旱区环境与工程研究所沙漠与沙漠化重点实验室, 甘肃兰州 730000;
2. 中国科学院大学, 北京 100049

庞国锦（1985-），女，河南平顶山人，博士研究生，研究方向为遥感与GIS应用。Email：pangguojin@163.com

收稿日期: 2014-02-27

修回日期: 2014-04-04

网络出版日期: 2014-07-20

基金资助

国家重点基础研究发展计划项目（2011CB403306）；青年人才成长基金项目（Y251C01001）；“西部之光”项目（29Y329951）资助

收起

Quantitative Analysis of Soil Salinity Content with Hyperspectra Data in Minqin, Gansu, China

Pang Guojin ,
Wang Tao ,
Sun Jiahuan ,
Li Sen

Expand

1. Key laboratory of Desert and Desertification, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2014-02-27

Revised date: 2014-04-04

Online published: 2014-07-20

Fold

摘要

土壤盐渍化是重要的生态环境问题，严重影响着干旱、半干旱区的农牧业及经济发展。高光谱遥感技术能够提供地物的连续光谱信息，易于分析细微差别，在定量研究土壤盐分含量方面具有较大优势。民勤县位于甘肃省石羊河流域下游，水力资源匮乏，盐渍化问题十分严峻。本研究基于实验室光谱数据，通过建立模型定量分析土壤盐分含量。首先对原始数据进行连续统去除（cn）预处理，然后分别建立了土壤盐分含量的高光谱指数模型（NDSI）、偏最小二乘回归模型（PLS）、间隔偏最小二乘法模型（iPLS）和反向间隔偏最小二乘法模型（BiPLS），考察各种模型对土壤盐分的预测能力。对比分析发现，使用全部波段信息建模的PLS模型优于仅使用两个波段信息的NDSI模型，而iPLS和BiPLS模型通过选择特征波段进行建模，结果均好于全谱PLS模型。其中，BiPLS模型波段选择的能力优于iPLS模型，得出的模型结果最好，预测相对偏差RPD达到2.02，决定系数R²和模拟值与预测值线性回归的斜率分别为0.76和0.92，模型可以近似地预测土壤盐分含量。结果说明特征波段选择方法能够从大量数据中提取有效信息，简化模型，并获取比NDSI模型和全谱PLS模型更优的预测结果。这些研究对于使用高光谱数据定量分析土壤盐渍化有一定的意义。

关键词： 土壤盐渍化; 高光谱; NDSI; PLS; iPLS; BiPLS; 民勤

本文引用格式

庞国锦 , 王涛 , 孙家欢 , 李森 . 基于高光谱的民勤土壤盐分定量分析[J]. 中国沙漠, 2014 , 34(4) : 1073 -1079 . DOI: 10.7522/j.issn.1000-694X.2014.00045

Abstract

Soil salinization is a severe environmental issue, which has already restricted the development in arid and semi-arid regions. Hyperspectra remote sensing has an advantage in the quantitative study of soil salt content (SSC) because it includes continuous spectrum information, which is easily to recognize slight characteristics in different objects. Minqin County is located in the downstream of the Shiyanghe River in Gansu province, where the water resources is shortage and soil salinization is very serious. In this paper, we quantitatively analyzed the SSC by establishing models based on laboratory spectral data. Firstly, the original spectra were transformed by continuum removal (cn) method. Then, normalized difference salinity index (NDSI), partial least square regression (PLS), interval partial least squares (iPLS) and backward interval partial least squares (BiPLS) based on the spectrum were used for modeling, in order to study the prediction ability of different models for SSC. The results showed that the PLS model based on the full spectra was better than NDSI model only based on two spectra data, while iPLS and BiPLS models built by using the spectrum after band selection both were superior to the PLS model. Meanwhile, BiPLS model had better ability of band selection than the iPLS, which was the best model. The RPD is 2.02, R² is 0.76 and slope is 0.92, respectively, in BiPLS, which could make approximate predictions of SSC. These results showed that band selection method was able to remove redundant information, simplify the calibration model and improve the predictive ability. Therefore, these studies were meaningful of quantitative monitoring soil salinization.

Key words： soil salinization; hyperspectra; NDSI; PLS; iPLS; BiPLS; Minqin

参考文献

[1] 翁永玲,宫鹏.土壤盐渍化遥感应用研究进展[J].地理科学,2006,26(3):369-375.
[2] Sreenivas K,Venkataratnam L,Rao P N.Dielectric properties of salt-affected soils[J].International Journal of Remote Sensing,1995,16(4):641-649.
[3] 王涛.干旱区绿洲化、荒漠化研究的进展与趋势[J].中国沙漠,2009,29(1):1-9.
[4] 张建明,齐文文.民勤绿洲土壤盐分组成与光谱特征[J].生态学杂志,32(10):2620-2626.
[5] 齐文文.基于高光谱数据的民勤绿洲土壤含盐量预测[D].兰州:兰州大学,2011.
[6] Goetz A F H,Vane G,Solomon J E,et al.Imaging spectrometry for earth remote sensing[J].Science,1985,228(4704):1147-1153.
[7] Csillag F,Pásztor L,Biehl L L.Spectral band selection for the characterization of salinity status of soils[J].Remote Sensing of Environment,1993,43(3):231-242.
[8] 刘焕军,张柏,王宗明,等.基于反射光谱特征的土壤盐碱化评价[J].红外与毫米波学报,2008,27(2):138-142.
[9] 戚浩平,翁永玲,赵福岳,等.茶卡-共和盆地土壤盐分与光谱特征研究[J].国土资源遥感,2010,86:4-8.
[10] Dehaan R,Taylor G.Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization[J].Remote Sensing of Environment,2002,80(3):406-417.
[11] Farifteh J,Van Der Meer F,Van Der Meijde M,et al.Spectral characteristics of salt-affected soils:a laboratory experiment[J].Geoderma,2008,145(3):196-206.
[12] Wang Q,Li P,Chen X.Modeling salinity effects on soil reflectance under various moisture conditions and its inverse application:a laboratory experiment[J].Geoderma,2012,170:103-111.
[13] Weng Y L,Gong P,Zhu Z L.A spectral index for estimating soil salinity in the Yellow River Delta Region of China using EO-1 hyperion data[J].Pedosphere,2010,20(3):378-388.
[14] 蒲智,于瑞德,尹昌应,等.干旱区典型盐碱土壤含盐量估算的最佳高光谱指数研究[J].水土保持通报,2012,32(6):129-133.
[15] 丁建丽,伍漫春,刘海霞,等.基于综合高光谱指数的区域土壤盐渍化监测研究[J].光谱学与光谱分析,2012,32(7):1918-1922.
[16] 张芳,熊黑钢,栾福明,等.土壤碱化的实测光谱响应特征[J].红外与毫米波学报,2011,30(1):55-60.
[17] 陈东强,王让会.准噶尔盆地人工林地土壤全盐的高光谱反演[J].干旱区研究,2013,30(3):444-448.
[18] Shi Z,Cheng J L,Huang M X,et al.Assessing reclamation levels of coastal saline lands with integrated stepwise discriminant analysis and laboratory hyperspectral data[J].Pedosphere,2006,16(2):154-160.
[19] Zhang F,Tiyip T,Ding J,et al.Studies on the reflectance spectral features of saline soil along the middle reaches of Tarim River:a case study in Xinjiang Autonomous Region,China[J].Environmental Earth Sciences,2013,69(8):2743-2761.
[20] 雷磊,塔西甫拉提·特依拜,丁建丽,等.基于HJ-1A高光谱影像的盐渍化土壤信息提取——以渭干河-库车河绿洲为例[J].中国沙漠,2013,33(4):1104-1109.
[21] 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.
[22] 屈永华,段小亮,高鸿永,等.内蒙古河套灌区土壤盐分光谱定量分析研究[J].光谱学与光谱分析,2009,29(5):1362-1366.
[23] 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,2012,32(8):2075-2079.
[24] 扶卿华,倪绍祥,王世新,等.土壤盐分含量的遥感反演研究[J].农业工程学报,2007(01):48-54.
[25] 刘丹丹,王强.土壤含盐量BP神经网络反演模型[J].东北林业大学学报,2009,37(12):88-90.
[26] Farifteh J,Van Der Meer F,Atzberger C,et al.Quantitative analysis of salt-affected soil reflectance spectra:a comparison of two adaptive methods (PLSR and ANN)[J].Remote Sensing of Environment,2007,110(1):59-78.
[27] Janik L,Forrester S,Rawson A.The prediction of soil chemical and physical properties from mid-infrared spectroscopy and combined partial least-squares regression and neural networks (PLS-NN) analysis[J].Chemometrics and Intelligent Laboratory Systems,2009,97(2):179-188.
[28] Mouazen A,Kuang B,De Baerdemaeker J,et al.Comparison among principal component,partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy[J].Geoderma,2010,158(1):23-31.
[29] Mashimbye Z,Cho M,Nell J,et al.Model-based integrated methods for quantitative estimation of soil salinity from hyperspectral remote sensing data:a case study of selected South African soils[J].Pedosphere,2012,22(5):640-649.
[30] 黄维,田丰玲,刘振尧,等.基于不同 PLS 算法的方竹笋中蛋白质分析的近红外光谱特征波段选择[J].食品科学,2013,32卷(期):页码.
[31] 王立琦,孔庆明,李贵滨,等.基于iPLS的油脂过氧化值近红外光谱特征波段选择[J].食品科学,2011,32(9):97-100.
[32] 黄铃凌,王平,刘淑英,等.民勤绿洲农田生态系统能值空间分异特征[J].中国沙漠,2014,34(1):291-298.
[33] 尤全刚,薛娴,黄翠华.地下水深埋区咸水灌溉对土壤盐渍化影响的初步研究——以民勤绿洲为例[J].中国沙漠,2011,31(2):302-308.
[34] 刘普幸.近54年民勤绿洲气候变化趋势与周期特征[J].干旱区研究,2009,26(04):471-476.
[35] 黄翠华,王涛,薛娴,等.民勤咸水灌溉及SWAP模型模拟研究[J].中国沙漠,2011,31(2):288-294.
[36] 黄翠华.民勤不同水质灌溉水对土壤性质的影响及模型模拟[D].兰州:中国科学院寒区旱区环境与工程研究所,2009.
[37] 廖杰,文星.基于遥感的近30年来河西地区盐碱地动态变化研究[J].干旱区资源与环境,2011,25(9):96-101.
[38] 鲍士旦.土壤农化分析[M].北京:中国农业出版社,2000.
[39] Clark R N,Roush T L.Reflectance spectroscopy:quantitative analysis techniques for remote sensing applications[J].Journal of Geophysical Research:Solid Earth (1978-2012),1984,89(B7):6329-6340.
[40] 王惠文.偏最小二乘回归方法及其应用[M].北京:国防工业出版社,1999.
[41] Norgaard L,Saudland A,Wagner J,et al.Interval partial least-squares regression (iPLS):a comparative chemometric study with an example from near-infrared spectroscopy[J].Applied Spectroscopy,2000,54(3):413-419.
[42] 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.
[43] Chang C-W,Laird D A,Mausbach M J,et al.Near-infrared reflectance spectroscopy principal components regression analyses of soil properties[J].Soil Science Society of America Journal,2001,65(2):480-490.

Options

文章导航

模态框（Modal）标题

摘要

本文引用格式

Abstract

参考文献