基于高光谱数据和MODIS影像的土壤特性的定量估算
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摘要
土地作为一种不可再生的自然资源,是人类社会生存和发展的最重要条件之一,由于人类对土壤资源认识的不全面性,森林肆意砍伐、土地盲目开发,使的近年来土地耕地面积不断锐减,土壤质量迅速下降,生态环境受到严重破坏。高光谱遥感以其多波段且连续性、高分辨率等特点,及时、准确的获取大面积的土壤环境信息提供了依据,这对土壤质量监测、农业生产、生态环境维护与治理具有现实意义。以黑龙江省大庆地区土壤为研究对象,采用野外调查取样与室内高光谱(350-2500mn)数据测定、MODIS影像相结合的方法,采集126个土壤样本,构建基于偏最小二乘法(PLSR)和BP神经网络(BPNN)建立的土壤有机质SOM、全氮N、全磷P、全钾K、重金属(HM)、盐碱含量的光谱估算模型。同时利用MODIS影像对土壤各成分信息专题制图。研究结果显示：
1)按照土壤类型和土地利用方式的不同,采集土样并分析土壤有机质SOM、全氮N、全磷P、全钾K、重金属(Co、Cd、F、Hg、V、Se、Cr、Cu、As、Pb、Ni、Mn、 Zn)、全盐量、总碱度和碱化度的含量。
2)土壤机械组成,土壤水分,土壤有机质等是影响土壤光谱曲线特征的重要因素。当土壤中所含水分达到70%1临近饱和状态时,土壤的反射率极低,且1400nm和1900nm的两个水分吸收带随着土壤水分的增加,吸收峰随之变宽。
3)土壤光谱预处理对于模型的建立起到决定性的作用,文本采取标准正态变量校正(Standard Normal Variate Transformation,SNV),多元散射校正(Multiplicative Scatter Correction,MSC)、数学运算组合、去包络线法(Continuum Removed)及衍生值等19种处理方式,土壤各指标含量与处理后的光谱指数的相关性显著提高。
4)利用偏最小二乘PLSR和BP神经网络建立土壤有机质SOM、全氮N、全磷P、全钾K、重金属(Co、Cd、F、Hg、V、Se、Cr、Cu、As、Pb、Ni、Mn、Zn)全盐量、总碱度,碱化度的高光谱估算模型精度较高,RMSE较低,土壤各指标含量精确的估算是可行的。
5)利用高光谱波段模拟MODIS多波段,并建立土壤SOM、全氮N、全磷P、全钾K、重金属(Co、Cd、F、Hg、V、Se、Cr、Cu、As、Pb、Ni、Mn、Zn)盐碱含量的偏最小二乘模型,大多数土壤指标估算精度在0.7以上,可实现精确的估算,全碱度、Hg和P模拟R2较低,只能用于粗略估算。
6)根据偏最小二乘模型结果结合MODIS影像,对大庆地区土壤SOM、全氮N、全磷P、全钾K、重金属(Co、Cd、F、Hg、V, Se、Cr、Cu、As、Pb、Ni、Mn、Zn)、全盐量、总碱度和碱化度专题制图,建立较为精细的土壤信息空间分布图。
As a non-renewable natural resource, soil is one of the crucial conditions that supports the survival and development of human society. Recent years, over deforesting and over developing with incomplete understanding of soil conditions caused a series of problems such as the declining of farmland, the soil degradation, and environment deterioration. Hyper spectral remote sensing technique with special advantages of high spectral resolution and strong band continuity. It can monitor and analyze crops vigor and soil environmental factors that affect crops production. These possess practical significance for agricultural production and soil quality monitoring, eco-environment management and maintenance.
This research is taken place in Daqing of Heilongjiang province. The soil condition in this area is studied by integrating soil spectral characteristic (indoor reflectance measurements among350nm-2500nm and MODIS image. In this research,126soil samples was collected, SOM, total phosphorus (P), potassium (K), nitrogen (N),Heavy Metal(Co、Cd、F、Hg、 V、Se、Cr、C、As、Pb、Ni、Mn、Zn),Total salt,Total Alkalinity,Exchange Sodium Percentage(ESP) concentration were estimated by using the Partial Least Squares Regression (PLSR) and Back-Propagation Neural Network (BPNN) model. After that, MODIS images were used to create spatial distribution maps for soil contents. The results showed:
(1) According to the different soil types and land use types, collected soil samples, analysis of SOM, N, P, K Heavy Metals (Co、Cd、F、Hg、V、Se、Cr、Cu、As、Pb、 Ni、Mn、Zn), Total Salt Total Alkalinity and ESP content.
(2) Soil mechanical composition and soil moisture, soil organic matter and so on are the important factors that affect soil spectral curve features. When soil moisture content of70%is near saturation, The two absorption peaks of the moisture band width than before in band1400nm and1900nm.
(3) Soil spectral preprocess play a decisive role for the establishing model. In the research we choice19kinds of preprocess.including Standard Normal variables, Multiplicative Scatter Correction, Mathematics, Continuum Removed and it's the derivative value.The correlation index significantly increased between the index content and soil spectral after treatment.
(4)Estimated SOM.Total N P K, Heavy Metal(Co、Cd、F、Hg、V、Se、Cr、Cu、 As、Pb、Ni、Mn、Zn),Total Salt, Total Alkalinity, ESP concentration by using the Partial Least Squares Regression (PLSR) and back-propagation neural network (BPNN) model.Model is high precision, low RMSE, it s feasible of accurate estimates for soil content.
(5) Using hyper spectral bands to simulate MODIS band,then established SOM, Total N, P K, Heavy Metals (Co、Cd、F、Hg、V、Se、Cr、Cu、As、Pb、Ni、Mn、Zn). Total salt, Total Alkalinity, ESP by PLSR model.Most soil index estimation accuracy above0.7, Total alkalinity, Hg and P estimation accuracy is low, just only be used as a rough estimating.
(6)Based on MODIS images and result of SMLR model, creating spatial distribution maps for s SOM, Total NPK, Heavy Metals (Co、Cd、F、Hg、V、Se、Cr、Cu、As、 Pb、Ni、Mn、Zn),Total Salt, Total Alkalinity and ESP in Daqing region.
1)按照土壤类型和土地利用方式的不同,采集土样并分析土壤有机质SOM、全氮N、全磷P、全钾K、重金属(Co、Cd、F、Hg、V、Se、Cr、Cu、As、Pb、Ni、Mn、 Zn)、全盐量、总碱度和碱化度的含量。
2)土壤机械组成,土壤水分,土壤有机质等是影响土壤光谱曲线特征的重要因素。当土壤中所含水分达到70%1临近饱和状态时,土壤的反射率极低,且1400nm和1900nm的两个水分吸收带随着土壤水分的增加,吸收峰随之变宽。
3)土壤光谱预处理对于模型的建立起到决定性的作用,文本采取标准正态变量校正(Standard Normal Variate Transformation,SNV),多元散射校正(Multiplicative Scatter Correction,MSC)、数学运算组合、去包络线法(Continuum Removed)及衍生值等19种处理方式,土壤各指标含量与处理后的光谱指数的相关性显著提高。
4)利用偏最小二乘PLSR和BP神经网络建立土壤有机质SOM、全氮N、全磷P、全钾K、重金属(Co、Cd、F、Hg、V、Se、Cr、Cu、As、Pb、Ni、Mn、Zn)全盐量、总碱度,碱化度的高光谱估算模型精度较高,RMSE较低,土壤各指标含量精确的估算是可行的。
5)利用高光谱波段模拟MODIS多波段,并建立土壤SOM、全氮N、全磷P、全钾K、重金属(Co、Cd、F、Hg、V、Se、Cr、Cu、As、Pb、Ni、Mn、Zn)盐碱含量的偏最小二乘模型,大多数土壤指标估算精度在0.7以上,可实现精确的估算,全碱度、Hg和P模拟R2较低,只能用于粗略估算。
6)根据偏最小二乘模型结果结合MODIS影像,对大庆地区土壤SOM、全氮N、全磷P、全钾K、重金属(Co、Cd、F、Hg、V, Se、Cr、Cu、As、Pb、Ni、Mn、Zn)、全盐量、总碱度和碱化度专题制图,建立较为精细的土壤信息空间分布图。
As a non-renewable natural resource, soil is one of the crucial conditions that supports the survival and development of human society. Recent years, over deforesting and over developing with incomplete understanding of soil conditions caused a series of problems such as the declining of farmland, the soil degradation, and environment deterioration. Hyper spectral remote sensing technique with special advantages of high spectral resolution and strong band continuity. It can monitor and analyze crops vigor and soil environmental factors that affect crops production. These possess practical significance for agricultural production and soil quality monitoring, eco-environment management and maintenance.
This research is taken place in Daqing of Heilongjiang province. The soil condition in this area is studied by integrating soil spectral characteristic (indoor reflectance measurements among350nm-2500nm and MODIS image. In this research,126soil samples was collected, SOM, total phosphorus (P), potassium (K), nitrogen (N),Heavy Metal(Co、Cd、F、Hg、 V、Se、Cr、C、As、Pb、Ni、Mn、Zn),Total salt,Total Alkalinity,Exchange Sodium Percentage(ESP) concentration were estimated by using the Partial Least Squares Regression (PLSR) and Back-Propagation Neural Network (BPNN) model. After that, MODIS images were used to create spatial distribution maps for soil contents. The results showed:
(1) According to the different soil types and land use types, collected soil samples, analysis of SOM, N, P, K Heavy Metals (Co、Cd、F、Hg、V、Se、Cr、Cu、As、Pb、 Ni、Mn、Zn), Total Salt Total Alkalinity and ESP content.
(2) Soil mechanical composition and soil moisture, soil organic matter and so on are the important factors that affect soil spectral curve features. When soil moisture content of70%is near saturation, The two absorption peaks of the moisture band width than before in band1400nm and1900nm.
(3) Soil spectral preprocess play a decisive role for the establishing model. In the research we choice19kinds of preprocess.including Standard Normal variables, Multiplicative Scatter Correction, Mathematics, Continuum Removed and it's the derivative value.The correlation index significantly increased between the index content and soil spectral after treatment.
(4)Estimated SOM.Total N P K, Heavy Metal(Co、Cd、F、Hg、V、Se、Cr、Cu、 As、Pb、Ni、Mn、Zn),Total Salt, Total Alkalinity, ESP concentration by using the Partial Least Squares Regression (PLSR) and back-propagation neural network (BPNN) model.Model is high precision, low RMSE, it s feasible of accurate estimates for soil content.
(5) Using hyper spectral bands to simulate MODIS band,then established SOM, Total N, P K, Heavy Metals (Co、Cd、F、Hg、V、Se、Cr、Cu、As、Pb、Ni、Mn、Zn). Total salt, Total Alkalinity, ESP by PLSR model.Most soil index estimation accuracy above0.7, Total alkalinity, Hg and P estimation accuracy is low, just only be used as a rough estimating.
(6)Based on MODIS images and result of SMLR model, creating spatial distribution maps for s SOM, Total NPK, Heavy Metals (Co、Cd、F、Hg、V、Se、Cr、Cu、As、 Pb、Ni、Mn、Zn),Total Salt, Total Alkalinity and ESP in Daqing region.
引文
[1]梅安新,彭望琭,秦其明,等.遥感导论.北京：高等教育出版社,2001：2-3
[2]陈彭述,童庆禧,郭东华.遥感信息机理研究.北京：科学出版社,1988：35-36
[3]刘伟东.高光谱遥感土壤信息提取与挖掘研究.北京：中国科学院博士论文.2002.25
[4]张立福,张良培,村松加奈子等.利用MODIS数据计算陆地植被指数VIUPD.武汉大学学报[信息科学版].2005,30(7)：371-376
[5]童庆禧,张兵,郑兰芬.高光谱遥感的多学科应用.北京电子工业出版社.2006：2-4
[6]浦瑞良,宫鹏.高光谱遥感及其应用.北京：高等教育出版社,2000：1-228
[7]陆婉珍,袁洪福编著现代近红外光谱分析技术.北京：中国石化出版社,2000年4月第一版
[8]Welzel,D.L. Near Infrared Reflectance Analysis-Sleeper Among Spectroscopic Techniques. Analytical Chemistry.1983,55(12):1165A～1176A
[9]何绪生,近红外反射光谱分析在土壤学的应用及前景.中国农业科技导报.2004,6(4)：71～75
[10]Condit H.R. The spectral reflectance of American soil. Photogramm.Eng.1970,36:955-966
[11]Stone E R,and Baungardner M F.Characteristic variations in reflectance of surface soils. Soil Science Society of America,1981,45:1161～1165
[12]戴昌达.中国主要土壤光谱反射特性分类与数据处理的初步研究.遥感文集.北京：科学出版社,1981：315～323
[13]王人潮,苏海萍,王深法.浙江省主要土壤光谱反射特性及其模糊分类在土壤分类中的应用研究.浙江农业大学学报,1986,12(4)：464-471
[14]黄应丰,刘腾辉.华南主要土壤类型的光谱特性与土壤分类.土壤学报,1995,32(1)：58～68
[15]季耿善,徐彬彬.土壤主要粘土矿物的近红外反射特性.中国科学院南京土壤研究所.土壤专报(第40号).北京：科学出版社,1987：59-65
[16]吴豪翔,王人潮.土壤光谱特征及其定量分析在土壤分类上的应用研究.土壤学报,1991,28(2)：178-185
[17]Lyon.R.J.P.风化及其它类荒漠河漆表面层对高光谱分辨率遥感的影响(一).环境遥感,1996,11(2)：138-150
[18]Lyon.R.J.P,风化及其它类荒漠河漆表面层对高光谱分辨率遥感的影响(二).环境遥感,1996,11(3)：146～194
[19]周萍.高光谱土壤成分信息的量化反演.北京：中国地质大学博士论文.2006：65-71
[20]程彬.松辽平原黑土有机质及相关元素遥感定量反演研究.长春：吉林大学博士论 文.2007：10-16
[21]Bedidi A,Cervelle B,Maderira J.Moisture effects on spectral character sties (visible) of lateritic soils.Soil Science 1992,153:129-141
[22]张晋.土壤光谱特性的研究.杨凌：西北农林科技大学硕士论文.2008：6～10
[23]Bowers S,A, Hanks R,J. Refection of radiant energy from soil. Soil Science,1965,100: 130-138
[24]Gerbermann A H. Reflectance of varying mixtures of clay soil and sand.Photogrammetric Engineering and Remote Sensing,1979,45:1145-1150
[25]徐彬彬,耿继善.土壤光谱反射特性与理化性状的相关分析.土壤专报.1987,41：66-75
[26]陈怀亮,冯定原,邹春晖,等.用遥感资料估算深层土壤水分的方法和模型.应用气象学报.1999,10(2).232～237
[27]彭玉魁,张建新,何绪生,等.土壤水分、有机质和总氮含量的近红外光谱分析研究.土壤学报,1998,35(4)：553～559
[28]Krishnan P., JD Alexander., BJ Butler. etal. Reflectance technique for predicting soil organic matter.Soil Science,1980,44:1282-1285
[29]Liu W D, Baret F,Gu X F, etal. Relating soil surface moisture to reflectance.Remote Sens.Environ.,2002,81:238-246
[30]BowersS.A,HanksR.J.Refiection of radiant energy from soil.Soil Science,1965,100: 130-138
[31]Stoner E R,Baumgardner M F.Characteristic variations in reflectance of surface soils.Soil Sci.Soc.Am.J.,1981,45:1161-1165
[32]Bowers S,A, Smith S,J. Spectrophotometric determination of soil water content.Soil Sci.Soc.Am.Proc.,1972,36:978-980
[33]Dalal R C,Henry R J.Simultaneous determination of moisture,organic carbon,and total nitrogen by infrared reflectance spectrometry.Soil Sci.Soc.Am.J.,1986,50:120-123
[34]Hepherd K D,Walsh M G. Development of reflectance spectral libraries for characterization of soil properties.Soil Science Society of America Journal,2002,66:988-998
[35]李朝杏.利用遥感资料进行旱情检测研究.卫星应用.1996,4(4)：39-43
[36]陈怀亮,冯定原,邹春晖,关文雅.用遥感资料估算深层土壤水分的方法和模型.应用气象学报.1999,10(2).232-237
[37]彭玉魁,张建新,何绪生,等.土壤水分、有机质和总氮含量的近红外光谱分析研究.土壤学报,1998,35(4)：553-559
[38]Toner E R,et al.Atlas of soil reflectance properties.Purdue University,Indiana,1980
[39]Obulhov A I,and Orlov D S.Spectral reflectivity of major soil groups and the possibility of using diffuse reflection in soil investigtion.Sov Soil Sci,1964,2:174～184
[40]季耿善,徐彬彬.土壤粘土矿物反射特性及其在土壤学上的应用.土壤学报,1987,24(1)：67～76
[41]彭杰,张杨珠,周清,等.去除有机质对土壤光谱特性的影响.土壤学报,2006,38(4)：453-458
[42]何挺,王静,程烨,等.土壤氧化铁光谱特征研究.地理与地理信息科学,2006,22(2)：30～34
[43]周萍.高光谱土壤成分信息的量化反演.北京：中国地质大学博士论文.2006：65-71
[44]Krishnan P, JD Alexander, BJ Butler, etal. Reflectance technique for predicting soil organic matter.Soil Science,1980,44:1282～1285
[45]刘伟东,张兵.高光谱遥感土壤湿度信息提取研究.土壤学报,2004,41(50)：700-706
[46]Henderson T L, BaumgardnerM F, Franzmeier D P, etal. High dimensional reflectance analysis of soil organic matter. Soil Science Society of America,1992,56:865～872
[47]Sudduth K A, Hpmmel J W. soil organic matter CEC and moisture sensing with portable NIR spect rophptometer. Transaction of the ASAE,1993,36(6):1571-1582
[48]Ben-Dor E, Banin A. Near-infrared analysis as a rapid method to Simultaneously evaluate several soil properties. Soil Science Society of America,1995,59:364～372
[49]Chang C W, Laird D A. Near-infrared reflectance spectro-scopic analysis of soil C and N. Soil Science.2002,167(2):110～116
[50]Mecarty.G.W,Reeves.J.B,Reeves.V.B,etal.Mid-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Science Society of America,2002.66(2):640～646
[51]Baumgardner F, Stoner E R, Silval L F, Biehl L L. Reflective Properties of soils. In Brady N.(Ed), Advances in Agronomy,38. Academia Press. New York,1985:1～44
[52]Galvao L S, Vitorello I. Role of Organic Matter in Obliterating the Effects of Iron on Spectral Reflectance and Colour of Brazilian Tropical Soils. Int. J. Remote Sens, 1998(19):1969-1979
[53]Henderson T L, Szilagyi A, Baumgardner M F, et al. Spectral Band Se lection for Classification of So il Organic Matter Content. Soil Sei. Soc. Am. J.1989(53): 1778-1784
[54]AI-Abbas A H, Swain P H, Baumgardner M F, Relating of matter and clay content to the content to the multispectral radiance of soils. Soil Science.1972,114(6):477～485
[55]Viscarra Rossel R A, Walvoort D J, McBratney A B, etal. Visible,near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma,2006,131:59～75
[56]沙晋明,陈鹏程,陈松林.土壤有机质光谱响应特性研究.水土保持研究,2003,10(2)：21～24
[57]谢伯承,薛绪掌,王纪华,等.褐潮土的光谱特性及用土壤反射率估算有机质含量的研究.土壤通报,2004,35(4)：391-395
[58]刘焕军,等.黑土有机质含量高光谱模型研究.土壤学报,2007.40(11)：27-32
[59]卢艳丽.基于高光谱的土壤有机质含量预测模型的建立与评价.中国农业科学,2007,40(9)：1989～1995
[60]程彬,姜琦刚.利用可见光/近红外-短波红外光谱预测土壤总氮含量的研究.安徽农业科学.2007.35(10)：3009～3013
[61]乔璐,陈立新,张杰.哈尔滨市土壤有机质高光谱模型研究.东北林业大学学报,2010,38(7)：116-118
[62]贺军亮,蒋建军,周生路,等.土壤有机质含量高光谱特性及其反演.中国农业科学.2007,40(3)：638～643
[63]沈润平,丁国香,魏国栓,等.基于人工神经网络的土壤有机质含量高光谱反演.土壤学报,2009,46(3)：391～397
[64]郑立华,李民赞,潘娈,等.基于近红外光谱技术的土壤参数BP神经网络预测.光谱学与光谱分析,2008,28(5)：1160-1164
[65]黄明祥.海涂土壤高光谱特性及其沙粒含量预测研究.土壤学报,2009,46(5)：181～186
[66]R.A.V. Rossel, D.J.J.Walvoort, A.B. Mc Bratney, etal. Visible near infrared mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma,2006,131(122):59～75
[67]A M.Mouazen, M.R.Maleki,J.De, Baerdemaeker, etal. On-line measurement of some selected soil properties using a VIS-NIR sensor. Soil&Tillage Research,2007,93:13～27
[68]M.R. Maleki, L Van Holm, H Ramon,etal. Phosphorus Sensing for Fresh Soils using Visible and Near Infrared Spectroscopy. Biosystems Engineering,2006,95 (3):425-436
[69]A.M. Mouazen, B. Kuang, J. De Baerdemaeker etal. 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. Geoderma,2010,158:23～31
[70]Shao Yong-ni, He Yong. Nitrogen, phosphorus, and potassium prediction in soils, using infrared spectroscopy. Soil Research,2011,49(2):166～172
[71]徐永明,蔺启忠,黄秀华,等.利用可见光/近红外反射光谱估算土壤总氮含量的实验研究.地理与地理信息科学,2005,21(1)：19-22
[72]袁石林,马天云,宋韬,等.土壤中总氮与总磷含量的近红外光谱实时检测方法.农业机械学报,2009,40：150-153
[73]宋海燕,何勇.近红外光谱法分析土壤中磷,钾含量及pH值的研究.山西农业大学学报自然科学版,2008.,28(3)：275～178
[74]高洪智,卢启鹏,丁海泉,等.基于连续投影算法的土壤总氮近红外特征波长的选取.光谱学与光谱分析,2009,29(11)：2951-2954
[75]张娟娟,田永超,姚霞,等.基于高光谱的土壤全氮含量估测.自然资源学报,2011,26(5)：881～889
[76]陈鹏飞,刘良云,王纪华,等.近红外光谱技术实时测定土壤中总氮及磷含量的初步研究.光谱学与光谱分析,2008,28(2)：295-298
[77]蒋璐璐,张瑜,王艳艳,等.基于光谱技术的土壤养分快速测试方法研究.浙江大学学报(农业与生命科)2010,36(4)：445-450
[78]申艳,张晓平,梁爱珍,等.近红外光谱分析法测定东北黑土有机碳和全氮含量.应用生态学报.2010,21(1)：109～114
[79]Kemper T, Sommer S.Estimate of Heavy Met al C on examination in S oils after a Mining Accident Using Reflectance Spectroscopy.Environmental Science and Technology, 2002,36(12);2742～2747
[80]Kooistra L,Leuven R S E W,Wehrens R,et al. A Comparison of Methods to Relate Grass Reflectance to Soil Metal Contamination. International Journal of Remote Sensing,2003,24 (24):4995～5010
[81]Kooistra L, Wehrens R,Leuven R S E W,et al. Possibilities of Visible Near Inf rared Spectroscopy for t he Assessment of Soil Contamination in River Flood Plains.Analytica Chimica Acta,2001,446:97-105
[82]Thomas K,Stefan S. Estimate of Heavy Metal Contamination in Soils after a Mining Accident Using Reflectance Spectroscopy.Environmental Science & Technology,2002, 36(12)：2742－2747
[83]Wu Y Z, Chen J, Ji J F, etal. Feasibility of Reflectance Spectroscopy for the assessment of Soil Mercury Contamination.Environmental Science and Technology,2005, 39(3):873-878
[84]黄长平,刘波,张霞,童庆禧.土壤重金属Cu含量遥感反演的波段选择与最佳光谱分辨率研究遥.感技术与应用,2010,25(3)：353-357
[85]王璐,蔺启忠,贾东,等.基于反射光谱预测土壤重金属元素含量的研究.遥感学报,2007,11(6)：906～912
[86]龚绍琦,王鑫,沈润平,等.滨海盐土重金属含量高光谱遥感研究.遥感技术与应,2010,25(2)：170～176
[87]Siebielec Grzegorz, Mccarty Gregory, Stuczynski, T., and Reeves, J. Predicting the Metal Contents of Diverse Soils Using NIRS Analysis.2002
[88]Malley, D. F, L. Yesmin, D. W ray. and S. Edwards. Application of near-infrared spectroscopy in analysis of soil mineral nutrients. Communications in Soil Science and PlantAnalysis 1999,30(7/8):999～1012
[89]黄福荣,潘涛,张甘霖,等.应用近红外漫反射光谱快速测定土壤锌含量.2010,18(3)：586-591
[90]亢庆,于嵘,张增祥,等土壤盐碱化遥感应用研究进展.遥感技术与应用.200520(4)：447～452
[91]Singh A N,Kristof S J,Baumgardner M R.Delineating salt-affected Soils in the Gangetic Plain India by Digital Analysis of Landsat Data.Purdue University Laboratory for Applications of Remote Sensing.Technical report 1977,111～477
[92]王耿明.基于BP神经网络的松辽平原盐碱土含盐量的遥感反演研究.长春：吉林大学硕士论文.2007：5-15
[93]Hunt G, Salisbury J, Lenhoff C. Visible and Near Infrared Spectra of Minerals and Rocks:B. Haides, Phosphates,Arsenates, Venadates and Borates. Modern Geology, 1972,3:121～132
[94]Metternicht G I, Zink J A. Remote Sensing of Soil Salinity:Potentials and Constraints. Remote Sensing of/Environment.2003,85:1～20
[95]Mulders M. Remote Sensing in Soil Science. Development in Soil Science. Amsterdam, The Netherlands.Elsevier,1987
[96]Siegal B,Gillespie. Remote Sensing in Geology.New York:Wiley.1980
[97]Csillag F,Pasztor L,BieM.Spectral Band Selection for the Characterization of Salinity Status of Soils. Remote Sensing of Environment,1993,43:231～242
[98]Dehaan R L,Taylor G R.Field-derived Spectra of Salinized Soils and Vegetation as Indicators of Irrigation-induced Soil Salinization.Remote Sensing of Environment.2002, 80:406-417
[99]Rao B R M,Sankar T R,Dwivedi R S,etal.Spectral behaviour of salt-affected soils.International Journal of Remote Sensing,1995,16(12):2125～2136
[100]Talor G R,Mah A H,etal.Characterization of Saline Soils Using Airborne Radar Imagery. Remote Sensing of Environment,1996,57(3):127-142
[101]D.WANG,C. WILSON and M.C.SHANNON.Interpretation of salinity and irrigation effects on soybean canopy reflectance in visible and near-infrared spectrum domain.International Journal of Remote Sensing,2002,23(5):811～824
[102]J Farifteh, F Vander Meer, etal. Quantitative analysis of salt-affected soil reflectance spectra:A comparison of two adaptive methods(PLSR and ANN).Remote Sensing of Environmental,2006,7(2):1～20
[103]关元秀.刘高焕.区域土壤盐渍化遥感监测研究综述.遥感技术与应用,2001,16(1)：40-44
[104]吴景坤,章兆兴,王爱军.库尔勒盐渍土的遥感图像处理.遥感信息,1987,(1)：26
[105]彭望琭,李天杰.TM数据的Kauth-Thomas变换在盐渍土分析中的作用-以阳高盆地 为例.环境遥感,1989,4(3)：183-190.
[106]彭望录.土壤盐碱化量化的遥感与GIS实验.遥感学报,1997,1(3)：237-240
[107]骆玉霞.GIS支持下的TM图像土壤盐渍化分级.遥感信息.2001(4)：12-15
[108]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.光谱学与光谱分析.2009,29(5)：1362-1366
[109]翁永玲,宫鹏.黄河三角洲盐渍土盐分特征研究.南京大学学报(自然科学),2006,42(6)：602-610
[110]扶卿华,倪绍祥,王世新,等.土壤盐分含量的遥感反演研究.农业工程学报,2007,23(1)：48-54
[111]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红外与毫米波学报.2008,27(2)：138-142
[112]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.光谱学与光谱分析.2012,32(7)：1918～1922
[113]乔璐.哈尔滨城区土壤高光谱特性与TM遥感的定量反演.哈尔滨：东北林业大学硕士论文.2010.12～45
[114]王遵亲.中国盐演土.北京：科学出版社,1993：50-70
[115]陈建军,张树文,陈静,等.大庆市土地盐碱化遥感监测与动态分析.干旱区资源与环境.2003 17(4)：101～106
[116]潘保原,宫伟光,张子峰,等大庆苏打盐渍土壤的分类与评价.东北林业大学学报,2006,34(2)：57-59
[117]鲁如坤.土壤农业化学分析方法.北京：中国农业科技出版社,2000,149-171.
[118]周清.土壤有机质含量高光谱预测模型及其差异性研究.杭州：浙江大学博士论文.2004,23～79
[119]Barnes R J, Dhanoa M S. Appl. Spectrosc.1989,43:772～777
[120]Isaksson T, Naes T. Appl. Spectrosc.1988,42:1273-1284
[121]Chen J Y, Iyo C, Teradab F. J. Near Infrared Spectrosc.2002,10:301-307
[122]褚小立,袁洪福,陆婉珍.近红外分析中光谱预处理及波长选择方法进展与应用.化学进展,2004,16(4)：528-539
[123]李翔,俞乐,董传万,等.新昌地区典型火成岩发射光谱特征分析.国土资源遥感.2010,82(2)：68～72
[124]沈掌泉,王珂,Xue wen Huang.用近红外光谱预测土壤碳含量的研究.红外与毫米波学报2010,29(1)：32-37
[125]杨萍.基于实验室高光谱反射数据的土壤成分含量估算研究.南京：南京农业大学硕士论文.2007：14-27
[126]李宗坤,陈乐意,孙颖章.偏最小二乘回归在渗流监控模型中的应用.郑州大学学报(工学版)2006,27(2)：117～123
[127]李兴旺,冯宝平.基于BP神经网络的土壤含水量预测.水土保持学报,2002,16(5)：117～119
[128]徐振东.人工神经网络的数学模型建立及成矿预测BP的实现.长春：吉林大学硕士论文2002.13～15.
[129]陈立新.土壤实验教程.哈尔滨：东北林业大学出版社,2005,50-101
[130]黄昌勇.土壤学.北京：中国农业出版社.2000,30-70
[2]陈彭述,童庆禧,郭东华.遥感信息机理研究.北京：科学出版社,1988：35-36
[3]刘伟东.高光谱遥感土壤信息提取与挖掘研究.北京：中国科学院博士论文.2002.25
[4]张立福,张良培,村松加奈子等.利用MODIS数据计算陆地植被指数VIUPD.武汉大学学报[信息科学版].2005,30(7)：371-376
[5]童庆禧,张兵,郑兰芬.高光谱遥感的多学科应用.北京电子工业出版社.2006：2-4
[6]浦瑞良,宫鹏.高光谱遥感及其应用.北京：高等教育出版社,2000：1-228
[7]陆婉珍,袁洪福编著现代近红外光谱分析技术.北京：中国石化出版社,2000年4月第一版
[8]Welzel,D.L. Near Infrared Reflectance Analysis-Sleeper Among Spectroscopic Techniques. Analytical Chemistry.1983,55(12):1165A～1176A
[9]何绪生,近红外反射光谱分析在土壤学的应用及前景.中国农业科技导报.2004,6(4)：71～75
[10]Condit H.R. The spectral reflectance of American soil. Photogramm.Eng.1970,36:955-966
[11]Stone E R,and Baungardner M F.Characteristic variations in reflectance of surface soils. Soil Science Society of America,1981,45:1161～1165
[12]戴昌达.中国主要土壤光谱反射特性分类与数据处理的初步研究.遥感文集.北京：科学出版社,1981：315～323
[13]王人潮,苏海萍,王深法.浙江省主要土壤光谱反射特性及其模糊分类在土壤分类中的应用研究.浙江农业大学学报,1986,12(4)：464-471
[14]黄应丰,刘腾辉.华南主要土壤类型的光谱特性与土壤分类.土壤学报,1995,32(1)：58～68
[15]季耿善,徐彬彬.土壤主要粘土矿物的近红外反射特性.中国科学院南京土壤研究所.土壤专报(第40号).北京：科学出版社,1987：59-65
[16]吴豪翔,王人潮.土壤光谱特征及其定量分析在土壤分类上的应用研究.土壤学报,1991,28(2)：178-185
[17]Lyon.R.J.P.风化及其它类荒漠河漆表面层对高光谱分辨率遥感的影响(一).环境遥感,1996,11(2)：138-150
[18]Lyon.R.J.P,风化及其它类荒漠河漆表面层对高光谱分辨率遥感的影响(二).环境遥感,1996,11(3)：146～194
[19]周萍.高光谱土壤成分信息的量化反演.北京：中国地质大学博士论文.2006：65-71
[20]程彬.松辽平原黑土有机质及相关元素遥感定量反演研究.长春：吉林大学博士论 文.2007：10-16
[21]Bedidi A,Cervelle B,Maderira J.Moisture effects on spectral character sties (visible) of lateritic soils.Soil Science 1992,153:129-141
[22]张晋.土壤光谱特性的研究.杨凌：西北农林科技大学硕士论文.2008：6～10
[23]Bowers S,A, Hanks R,J. Refection of radiant energy from soil. Soil Science,1965,100: 130-138
[24]Gerbermann A H. Reflectance of varying mixtures of clay soil and sand.Photogrammetric Engineering and Remote Sensing,1979,45:1145-1150
[25]徐彬彬,耿继善.土壤光谱反射特性与理化性状的相关分析.土壤专报.1987,41：66-75
[26]陈怀亮,冯定原,邹春晖,等.用遥感资料估算深层土壤水分的方法和模型.应用气象学报.1999,10(2).232～237
[27]彭玉魁,张建新,何绪生,等.土壤水分、有机质和总氮含量的近红外光谱分析研究.土壤学报,1998,35(4)：553～559
[28]Krishnan P., JD Alexander., BJ Butler. etal. Reflectance technique for predicting soil organic matter.Soil Science,1980,44:1282-1285
[29]Liu W D, Baret F,Gu X F, etal. Relating soil surface moisture to reflectance.Remote Sens.Environ.,2002,81:238-246
[30]BowersS.A,HanksR.J.Refiection of radiant energy from soil.Soil Science,1965,100: 130-138
[31]Stoner E R,Baumgardner M F.Characteristic variations in reflectance of surface soils.Soil Sci.Soc.Am.J.,1981,45:1161-1165
[32]Bowers S,A, Smith S,J. Spectrophotometric determination of soil water content.Soil Sci.Soc.Am.Proc.,1972,36:978-980
[33]Dalal R C,Henry R J.Simultaneous determination of moisture,organic carbon,and total nitrogen by infrared reflectance spectrometry.Soil Sci.Soc.Am.J.,1986,50:120-123
[34]Hepherd K D,Walsh M G. Development of reflectance spectral libraries for characterization of soil properties.Soil Science Society of America Journal,2002,66:988-998
[35]李朝杏.利用遥感资料进行旱情检测研究.卫星应用.1996,4(4)：39-43
[36]陈怀亮,冯定原,邹春晖,关文雅.用遥感资料估算深层土壤水分的方法和模型.应用气象学报.1999,10(2).232-237
[37]彭玉魁,张建新,何绪生,等.土壤水分、有机质和总氮含量的近红外光谱分析研究.土壤学报,1998,35(4)：553-559
[38]Toner E R,et al.Atlas of soil reflectance properties.Purdue University,Indiana,1980
[39]Obulhov A I,and Orlov D S.Spectral reflectivity of major soil groups and the possibility of using diffuse reflection in soil investigtion.Sov Soil Sci,1964,2:174～184
[40]季耿善,徐彬彬.土壤粘土矿物反射特性及其在土壤学上的应用.土壤学报,1987,24(1)：67～76
[41]彭杰,张杨珠,周清,等.去除有机质对土壤光谱特性的影响.土壤学报,2006,38(4)：453-458
[42]何挺,王静,程烨,等.土壤氧化铁光谱特征研究.地理与地理信息科学,2006,22(2)：30～34
[43]周萍.高光谱土壤成分信息的量化反演.北京：中国地质大学博士论文.2006：65-71
[44]Krishnan P, JD Alexander, BJ Butler, etal. Reflectance technique for predicting soil organic matter.Soil Science,1980,44:1282～1285
[45]刘伟东,张兵.高光谱遥感土壤湿度信息提取研究.土壤学报,2004,41(50)：700-706
[46]Henderson T L, BaumgardnerM F, Franzmeier D P, etal. High dimensional reflectance analysis of soil organic matter. Soil Science Society of America,1992,56:865～872
[47]Sudduth K A, Hpmmel J W. soil organic matter CEC and moisture sensing with portable NIR spect rophptometer. Transaction of the ASAE,1993,36(6):1571-1582
[48]Ben-Dor E, Banin A. Near-infrared analysis as a rapid method to Simultaneously evaluate several soil properties. Soil Science Society of America,1995,59:364～372
[49]Chang C W, Laird D A. Near-infrared reflectance spectro-scopic analysis of soil C and N. Soil Science.2002,167(2):110～116
[50]Mecarty.G.W,Reeves.J.B,Reeves.V.B,etal.Mid-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Science Society of America,2002.66(2):640～646
[51]Baumgardner F, Stoner E R, Silval L F, Biehl L L. Reflective Properties of soils. In Brady N.(Ed), Advances in Agronomy,38. Academia Press. New York,1985:1～44
[52]Galvao L S, Vitorello I. Role of Organic Matter in Obliterating the Effects of Iron on Spectral Reflectance and Colour of Brazilian Tropical Soils. Int. J. Remote Sens, 1998(19):1969-1979
[53]Henderson T L, Szilagyi A, Baumgardner M F, et al. Spectral Band Se lection for Classification of So il Organic Matter Content. Soil Sei. Soc. Am. J.1989(53): 1778-1784
[54]AI-Abbas A H, Swain P H, Baumgardner M F, Relating of matter and clay content to the content to the multispectral radiance of soils. Soil Science.1972,114(6):477～485
[55]Viscarra Rossel R A, Walvoort D J, McBratney A B, etal. Visible,near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma,2006,131:59～75
[56]沙晋明,陈鹏程,陈松林.土壤有机质光谱响应特性研究.水土保持研究,2003,10(2)：21～24
[57]谢伯承,薛绪掌,王纪华,等.褐潮土的光谱特性及用土壤反射率估算有机质含量的研究.土壤通报,2004,35(4)：391-395
[58]刘焕军,等.黑土有机质含量高光谱模型研究.土壤学报,2007.40(11)：27-32
[59]卢艳丽.基于高光谱的土壤有机质含量预测模型的建立与评价.中国农业科学,2007,40(9)：1989～1995
[60]程彬,姜琦刚.利用可见光/近红外-短波红外光谱预测土壤总氮含量的研究.安徽农业科学.2007.35(10)：3009～3013
[61]乔璐,陈立新,张杰.哈尔滨市土壤有机质高光谱模型研究.东北林业大学学报,2010,38(7)：116-118
[62]贺军亮,蒋建军,周生路,等.土壤有机质含量高光谱特性及其反演.中国农业科学.2007,40(3)：638～643
[63]沈润平,丁国香,魏国栓,等.基于人工神经网络的土壤有机质含量高光谱反演.土壤学报,2009,46(3)：391～397
[64]郑立华,李民赞,潘娈,等.基于近红外光谱技术的土壤参数BP神经网络预测.光谱学与光谱分析,2008,28(5)：1160-1164
[65]黄明祥.海涂土壤高光谱特性及其沙粒含量预测研究.土壤学报,2009,46(5)：181～186
[66]R.A.V. Rossel, D.J.J.Walvoort, A.B. Mc Bratney, etal. Visible near infrared mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma,2006,131(122):59～75
[67]A M.Mouazen, M.R.Maleki,J.De, Baerdemaeker, etal. On-line measurement of some selected soil properties using a VIS-NIR sensor. Soil&Tillage Research,2007,93:13～27
[68]M.R. Maleki, L Van Holm, H Ramon,etal. Phosphorus Sensing for Fresh Soils using Visible and Near Infrared Spectroscopy. Biosystems Engineering,2006,95 (3):425-436
[69]A.M. Mouazen, B. Kuang, J. De Baerdemaeker etal. 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. Geoderma,2010,158:23～31
[70]Shao Yong-ni, He Yong. Nitrogen, phosphorus, and potassium prediction in soils, using infrared spectroscopy. Soil Research,2011,49(2):166～172
[71]徐永明,蔺启忠,黄秀华,等.利用可见光/近红外反射光谱估算土壤总氮含量的实验研究.地理与地理信息科学,2005,21(1)：19-22
[72]袁石林,马天云,宋韬,等.土壤中总氮与总磷含量的近红外光谱实时检测方法.农业机械学报,2009,40：150-153
[73]宋海燕,何勇.近红外光谱法分析土壤中磷,钾含量及pH值的研究.山西农业大学学报自然科学版,2008.,28(3)：275～178
[74]高洪智,卢启鹏,丁海泉,等.基于连续投影算法的土壤总氮近红外特征波长的选取.光谱学与光谱分析,2009,29(11)：2951-2954
[75]张娟娟,田永超,姚霞,等.基于高光谱的土壤全氮含量估测.自然资源学报,2011,26(5)：881～889
[76]陈鹏飞,刘良云,王纪华,等.近红外光谱技术实时测定土壤中总氮及磷含量的初步研究.光谱学与光谱分析,2008,28(2)：295-298
[77]蒋璐璐,张瑜,王艳艳,等.基于光谱技术的土壤养分快速测试方法研究.浙江大学学报(农业与生命科)2010,36(4)：445-450
[78]申艳,张晓平,梁爱珍,等.近红外光谱分析法测定东北黑土有机碳和全氮含量.应用生态学报.2010,21(1)：109～114
[79]Kemper T, Sommer S.Estimate of Heavy Met al C on examination in S oils after a Mining Accident Using Reflectance Spectroscopy.Environmental Science and Technology, 2002,36(12);2742～2747
[80]Kooistra L,Leuven R S E W,Wehrens R,et al. A Comparison of Methods to Relate Grass Reflectance to Soil Metal Contamination. International Journal of Remote Sensing,2003,24 (24):4995～5010
[81]Kooistra L, Wehrens R,Leuven R S E W,et al. Possibilities of Visible Near Inf rared Spectroscopy for t he Assessment of Soil Contamination in River Flood Plains.Analytica Chimica Acta,2001,446:97-105
[82]Thomas K,Stefan S. Estimate of Heavy Metal Contamination in Soils after a Mining Accident Using Reflectance Spectroscopy.Environmental Science & Technology,2002, 36(12)：2742－2747
[83]Wu Y Z, Chen J, Ji J F, etal. Feasibility of Reflectance Spectroscopy for the assessment of Soil Mercury Contamination.Environmental Science and Technology,2005, 39(3):873-878
[84]黄长平,刘波,张霞,童庆禧.土壤重金属Cu含量遥感反演的波段选择与最佳光谱分辨率研究遥.感技术与应用,2010,25(3)：353-357
[85]王璐,蔺启忠,贾东,等.基于反射光谱预测土壤重金属元素含量的研究.遥感学报,2007,11(6)：906～912
[86]龚绍琦,王鑫,沈润平,等.滨海盐土重金属含量高光谱遥感研究.遥感技术与应,2010,25(2)：170～176
[87]Siebielec Grzegorz, Mccarty Gregory, Stuczynski, T., and Reeves, J. Predicting the Metal Contents of Diverse Soils Using NIRS Analysis.2002
[88]Malley, D. F, L. Yesmin, D. W ray. and S. Edwards. Application of near-infrared spectroscopy in analysis of soil mineral nutrients. Communications in Soil Science and PlantAnalysis 1999,30(7/8):999～1012
[89]黄福荣,潘涛,张甘霖,等.应用近红外漫反射光谱快速测定土壤锌含量.2010,18(3)：586-591
[90]亢庆,于嵘,张增祥,等土壤盐碱化遥感应用研究进展.遥感技术与应用.200520(4)：447～452
[91]Singh A N,Kristof S J,Baumgardner M R.Delineating salt-affected Soils in the Gangetic Plain India by Digital Analysis of Landsat Data.Purdue University Laboratory for Applications of Remote Sensing.Technical report 1977,111～477
[92]王耿明.基于BP神经网络的松辽平原盐碱土含盐量的遥感反演研究.长春：吉林大学硕士论文.2007：5-15
[93]Hunt G, Salisbury J, Lenhoff C. Visible and Near Infrared Spectra of Minerals and Rocks:B. Haides, Phosphates,Arsenates, Venadates and Borates. Modern Geology, 1972,3:121～132
[94]Metternicht G I, Zink J A. Remote Sensing of Soil Salinity:Potentials and Constraints. Remote Sensing of/Environment.2003,85:1～20
[95]Mulders M. Remote Sensing in Soil Science. Development in Soil Science. Amsterdam, The Netherlands.Elsevier,1987
[96]Siegal B,Gillespie. Remote Sensing in Geology.New York:Wiley.1980
[97]Csillag F,Pasztor L,BieM.Spectral Band Selection for the Characterization of Salinity Status of Soils. Remote Sensing of Environment,1993,43:231～242
[98]Dehaan R L,Taylor G R.Field-derived Spectra of Salinized Soils and Vegetation as Indicators of Irrigation-induced Soil Salinization.Remote Sensing of Environment.2002, 80:406-417
[99]Rao B R M,Sankar T R,Dwivedi R S,etal.Spectral behaviour of salt-affected soils.International Journal of Remote Sensing,1995,16(12):2125～2136
[100]Talor G R,Mah A H,etal.Characterization of Saline Soils Using Airborne Radar Imagery. Remote Sensing of Environment,1996,57(3):127-142
[101]D.WANG,C. WILSON and M.C.SHANNON.Interpretation of salinity and irrigation effects on soybean canopy reflectance in visible and near-infrared spectrum domain.International Journal of Remote Sensing,2002,23(5):811～824
[102]J Farifteh, F Vander Meer, etal. Quantitative analysis of salt-affected soil reflectance spectra:A comparison of two adaptive methods(PLSR and ANN).Remote Sensing of Environmental,2006,7(2):1～20
[103]关元秀.刘高焕.区域土壤盐渍化遥感监测研究综述.遥感技术与应用,2001,16(1)：40-44
[104]吴景坤,章兆兴,王爱军.库尔勒盐渍土的遥感图像处理.遥感信息,1987,(1)：26
[105]彭望琭,李天杰.TM数据的Kauth-Thomas变换在盐渍土分析中的作用-以阳高盆地 为例.环境遥感,1989,4(3)：183-190.
[106]彭望录.土壤盐碱化量化的遥感与GIS实验.遥感学报,1997,1(3)：237-240
[107]骆玉霞.GIS支持下的TM图像土壤盐渍化分级.遥感信息.2001(4)：12-15
[108]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.光谱学与光谱分析.2009,29(5)：1362-1366
[109]翁永玲,宫鹏.黄河三角洲盐渍土盐分特征研究.南京大学学报(自然科学),2006,42(6)：602-610
[110]扶卿华,倪绍祥,王世新,等.土壤盐分含量的遥感反演研究.农业工程学报,2007,23(1)：48-54
[111]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红外与毫米波学报.2008,27(2)：138-142
[112]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.光谱学与光谱分析.2012,32(7)：1918～1922
[113]乔璐.哈尔滨城区土壤高光谱特性与TM遥感的定量反演.哈尔滨：东北林业大学硕士论文.2010.12～45
[114]王遵亲.中国盐演土.北京：科学出版社,1993：50-70
[115]陈建军,张树文,陈静,等.大庆市土地盐碱化遥感监测与动态分析.干旱区资源与环境.2003 17(4)：101～106
[116]潘保原,宫伟光,张子峰,等大庆苏打盐渍土壤的分类与评价.东北林业大学学报,2006,34(2)：57-59
[117]鲁如坤.土壤农业化学分析方法.北京：中国农业科技出版社,2000,149-171.
[118]周清.土壤有机质含量高光谱预测模型及其差异性研究.杭州：浙江大学博士论文.2004,23～79
[119]Barnes R J, Dhanoa M S. Appl. Spectrosc.1989,43:772～777
[120]Isaksson T, Naes T. Appl. Spectrosc.1988,42:1273-1284
[121]Chen J Y, Iyo C, Teradab F. J. Near Infrared Spectrosc.2002,10:301-307
[122]褚小立,袁洪福,陆婉珍.近红外分析中光谱预处理及波长选择方法进展与应用.化学进展,2004,16(4)：528-539
[123]李翔,俞乐,董传万,等.新昌地区典型火成岩发射光谱特征分析.国土资源遥感.2010,82(2)：68～72
[124]沈掌泉,王珂,Xue wen Huang.用近红外光谱预测土壤碳含量的研究.红外与毫米波学报2010,29(1)：32-37
[125]杨萍.基于实验室高光谱反射数据的土壤成分含量估算研究.南京：南京农业大学硕士论文.2007：14-27
[126]李宗坤,陈乐意,孙颖章.偏最小二乘回归在渗流监控模型中的应用.郑州大学学报(工学版)2006,27(2)：117～123
[127]李兴旺,冯宝平.基于BP神经网络的土壤含水量预测.水土保持学报,2002,16(5)：117～119
[128]徐振东.人工神经网络的数学模型建立及成矿预测BP的实现.长春：吉林大学硕士论文2002.13～15.
[129]陈立新.土壤实验教程.哈尔滨：东北林业大学出版社,2005,50-101
[130]黄昌勇.土壤学.北京：中国农业出版社.2000,30-70