基于BP神经网络的干旱区盐碱土盐分遥感反演模型研究
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摘要
土壤盐碱化是干旱、半干旱农业区主要的土地退化问题,及时、精准、动态地获取盐碱土盐分信息,对治理盐碱土、防止其进一步退化以及农业可持续发展至关重要。传统土壤盐碱化监测采用野外土壤定点调查方式,不仅费时、费力,而且测点少,代表性差,无法实现大面积实时监测。遥感由于能大面积、重复获取多波段、多时相的信息,为大面积实时动态监测盐碱土状况提供了可能。
本论文以渭—库三角洲绿洲作为研究区,利用盐碱土光谱数据和非遥感因子构建盐碱土盐分遥感反演模型。利用土壤溶液测定法得到土壤含盐量,利用CI700便携式野外光谱仪野外采集光谱数据,通过分析不同盐分盐碱土的光谱数据得出盐碱土光谱曲线特征。利用单波段相关分析及多元线性逐步回归分析方法,讨论了不同盐分盐碱土光谱数据与盐分的关系,精选出能够表征盐碱土盐分信息的最佳波段组合,即890nm、800nm、680nm、590nm、470nm该波段组合可以作为本模型的输入变量。除遥感因子外、地下水埋深、地下水矿化度和表层土壤矿化度三个非遥感因子也是建立盐碱土盐分遥感反演模型重要的输入变量。选用BP人工神经网络模型——智能计算的重要分支,在建模过程中经过反复试验与结果比较,不断调整隐含层数目和神经元的个数,使得模型结果不断优化。最终,选择BP神经网络反演盐分信息模型。该模型奠定了利用卫星影像反演盐碱土盐分的基础,有可能为各地区盐碱土盐分信息的提取提供方法支持。
论文共分5章,第1章主要阐述了本论文的研究背景、目标、意义,盐碱土遥感监测的国内外研究进展及应用3S技术对土壤盐渍化研究存在的问题。第2章介绍了研究区——渭—库三角洲绿洲的自然、人文地理概况、土壤盐碱化现状,研究区数据源及野外考察内容。第3章介绍了本研究的主要研究方法、研究内容、工作路线并定量分析了实验采集的盐碱土光谱数据与盐分、TM影像数据的关系,利用多元线性逐步回归和相关分析方法确定了表征盐碱土盐分信息的最佳波段组合。第4章介绍了BP神经网络的基本原理、影响盐碱土盐分的关键因子,然后基于BP神经网络模型构建了盐碱土盐分遥感反演模型、并进行模型精度检验。第5章对论文所做的工作进行了简单的总结,并并讨论了本模型现存问题及改进之处。
Soil salinization is a major environmental issue in the world, and it is more serious in arid and semi-arid area. Acquiring accurate salinity information timely is important for monitoring and evaluating soil salinization. Traditionally, soil salinization monitoring selects fixed points to investigate in field, which wastes not only time but also manpower and can’t show representative areas. It is impossible to realize large-area, real-time inspection. Remote sensing technique shows huge excellence in these aspects. Spectral technique is a new and effective approach in studying soil attributes. Spectral data in ground which is the base of band selection, validation and evaluation can build the links of ground, aviation and satellite remote sensing data.
Taking Weigan—Kucha Oasis as the example area, this paper aims to explore saline-alkali soils salinity information remote sensing inversion model based on BP neural network in this semi-arid area. Firstly, the salinity of soil specimens is measured with soil solution method in laboratory and spectral data is gotten by CI700 in field. Analyzing the saline-alkali soils spectral data characteristic, this paper discusses the relations between saline-alkali soils spectral data and salinity, then selects the best band combination which can represent saline-alkali soils salinity spectral characteristic by means of multi-variables linear stepwise regression analysis and correlation analysis methods. That is spectral reflectance of 890nm, 800nm, 680nm, 590nm, 470nm, except remote sensing factors, the degree of groundwater mineralization, buried groundwater depth and the degree of topsoil mineralization are the three major factors which influence saline-alkali soils salinity, and these three factors are important input variables for this model.
Being an important branch of intelligence computation, neural network model is a nonlinear mathematical model. This method may realize the mapping from eight dimensions variables to salinity information through training specimen data and adjusting the weights. Using neural network method to retrieve saline-alkali soils salinity is beneficial and can show the potentials of geography computation techniques in analyzing high quality data.This model contains two hidden layers, the first hidden layer including five nodes and the second hidden layer including three nodes. It has only one output layer, that is salinity information. The recycling model training makes this model approach to real mapping relation of datasets infinitely.
This paper was divided into five chapters as follows: The first chapter expresses the main study background, goal, significance, then dissertates the evolvement of saline-alkali soils remote sensing inspection in the world, and some existing problems of the research on the saline-alkali soils using the 3S technology. The second chapter introduces the situations of the study area which cause soil salinization, including its nature environment, social economic situations and the status of soil salinization, The data source of the study area and the investigate in field.
The third chapter dissertates the study method, content, technique route in this paper, and quantitively analyzes the relations among the collected spectral data in experiment, salinity and TM image data, and make use of multi-variables linear stepwise regression and correlation analysis method to select the best band combination which can show saline-alkali soils salinity characteristic.The forth chapter introduced the fundamental of NN model、the main factors to effecting saline-alkali soils salinity, then designs saline-alkali soils salinity information remote sensing inversion model based on back propagation neural network, and then this chapter gives the method of precision validation. In the last chapter, the results and the defects about this paper are discussed.
本论文以渭—库三角洲绿洲作为研究区,利用盐碱土光谱数据和非遥感因子构建盐碱土盐分遥感反演模型。利用土壤溶液测定法得到土壤含盐量,利用CI700便携式野外光谱仪野外采集光谱数据,通过分析不同盐分盐碱土的光谱数据得出盐碱土光谱曲线特征。利用单波段相关分析及多元线性逐步回归分析方法,讨论了不同盐分盐碱土光谱数据与盐分的关系,精选出能够表征盐碱土盐分信息的最佳波段组合,即890nm、800nm、680nm、590nm、470nm该波段组合可以作为本模型的输入变量。除遥感因子外、地下水埋深、地下水矿化度和表层土壤矿化度三个非遥感因子也是建立盐碱土盐分遥感反演模型重要的输入变量。选用BP人工神经网络模型——智能计算的重要分支,在建模过程中经过反复试验与结果比较,不断调整隐含层数目和神经元的个数,使得模型结果不断优化。最终,选择BP神经网络反演盐分信息模型。该模型奠定了利用卫星影像反演盐碱土盐分的基础,有可能为各地区盐碱土盐分信息的提取提供方法支持。
论文共分5章,第1章主要阐述了本论文的研究背景、目标、意义,盐碱土遥感监测的国内外研究进展及应用3S技术对土壤盐渍化研究存在的问题。第2章介绍了研究区——渭—库三角洲绿洲的自然、人文地理概况、土壤盐碱化现状,研究区数据源及野外考察内容。第3章介绍了本研究的主要研究方法、研究内容、工作路线并定量分析了实验采集的盐碱土光谱数据与盐分、TM影像数据的关系,利用多元线性逐步回归和相关分析方法确定了表征盐碱土盐分信息的最佳波段组合。第4章介绍了BP神经网络的基本原理、影响盐碱土盐分的关键因子,然后基于BP神经网络模型构建了盐碱土盐分遥感反演模型、并进行模型精度检验。第5章对论文所做的工作进行了简单的总结,并并讨论了本模型现存问题及改进之处。
Soil salinization is a major environmental issue in the world, and it is more serious in arid and semi-arid area. Acquiring accurate salinity information timely is important for monitoring and evaluating soil salinization. Traditionally, soil salinization monitoring selects fixed points to investigate in field, which wastes not only time but also manpower and can’t show representative areas. It is impossible to realize large-area, real-time inspection. Remote sensing technique shows huge excellence in these aspects. Spectral technique is a new and effective approach in studying soil attributes. Spectral data in ground which is the base of band selection, validation and evaluation can build the links of ground, aviation and satellite remote sensing data.
Taking Weigan—Kucha Oasis as the example area, this paper aims to explore saline-alkali soils salinity information remote sensing inversion model based on BP neural network in this semi-arid area. Firstly, the salinity of soil specimens is measured with soil solution method in laboratory and spectral data is gotten by CI700 in field. Analyzing the saline-alkali soils spectral data characteristic, this paper discusses the relations between saline-alkali soils spectral data and salinity, then selects the best band combination which can represent saline-alkali soils salinity spectral characteristic by means of multi-variables linear stepwise regression analysis and correlation analysis methods. That is spectral reflectance of 890nm, 800nm, 680nm, 590nm, 470nm, except remote sensing factors, the degree of groundwater mineralization, buried groundwater depth and the degree of topsoil mineralization are the three major factors which influence saline-alkali soils salinity, and these three factors are important input variables for this model.
Being an important branch of intelligence computation, neural network model is a nonlinear mathematical model. This method may realize the mapping from eight dimensions variables to salinity information through training specimen data and adjusting the weights. Using neural network method to retrieve saline-alkali soils salinity is beneficial and can show the potentials of geography computation techniques in analyzing high quality data.This model contains two hidden layers, the first hidden layer including five nodes and the second hidden layer including three nodes. It has only one output layer, that is salinity information. The recycling model training makes this model approach to real mapping relation of datasets infinitely.
This paper was divided into five chapters as follows: The first chapter expresses the main study background, goal, significance, then dissertates the evolvement of saline-alkali soils remote sensing inspection in the world, and some existing problems of the research on the saline-alkali soils using the 3S technology. The second chapter introduces the situations of the study area which cause soil salinization, including its nature environment, social economic situations and the status of soil salinization, The data source of the study area and the investigate in field.
The third chapter dissertates the study method, content, technique route in this paper, and quantitively analyzes the relations among the collected spectral data in experiment, salinity and TM image data, and make use of multi-variables linear stepwise regression and correlation analysis method to select the best band combination which can show saline-alkali soils salinity characteristic.The forth chapter introduced the fundamental of NN model、the main factors to effecting saline-alkali soils salinity, then designs saline-alkali soils salinity information remote sensing inversion model based on back propagation neural network, and then this chapter gives the method of precision validation. In the last chapter, the results and the defects about this paper are discussed.
引文
[1]刘兴土.松嫩平原退化土地整治与农业发展[M].北京:科学出版社,2001.
[2]李凤全,吴樟荣.半干旱地区土地盐碱化预警研究以吉林省西部土地盐碱化预警为例[J].水土保持通报,2002,22(1):57-59.
[3] Szabolcs I. Salination of soil and water and its relation to desertification. Desertification Control Bulletin, 1992, 21:32-37.
[4]水利部计划司,农水司.全国灌溉面积发展“九五”计划及2010年规划.中国水利水电科学研究院,1997,1-3.
[5]田长彦,周宏飞,刘国庆.21世纪新疆土壤盐渍化调控与农业持续发展研究建议[J].干旱区地理,2000,23:178-181.
[6]许志坤.新疆盐渍土的形成、分类、特点和利用改良[M].国际盐渍土改良学术讨论会论文集,1985,5:109-112.
[7]许英.土壤水盐时空变异的稳健性分析和条件模拟研究[D].内蒙古农业大学博士学位论文. 2002:1-7.
[8]李述刚,程心俊,王州琼.干旱区绿洲农业持续发展战略[J].干旱区研究,1997,14(2):537-544.
[9]潘晓玲.干旱区绿洲生态系统动态稳定性的初步研究[J].第四纪研究,2001,21(4):345-352.
[10]何祺胜.星载雷达图像在干旱区盐渍地信息提取中的应用研究[D].新疆大学硕士学位论文,2007年7月:17-19.
[11]朱鹤健,何宜庚.土壤地理学[M].北京:高等教育出版社.2003:189-199.
[12]赵可夫,李法曾.中国盐生殖物[M].科学出版社.1999:1-10.
[13]N.C.布雷迪.土壤的本质与性状[M].科学出版社.1982:184-216.
[14]张士功,邱建军,张华.我国盐渍土资源及其综合治理[J].中国农业资源与区划,2000,21(1):52-56.
[15]梅安心,彭望禄,秦其明,等.遥感导论[M].高等教育出版社,2001:46-47.
[16] G.I. Metternicht. Remote sensing of soil salinity-potentials and constraints[J].Remote Sensing of Environment,2003,85:1-20.
[17] Harri Kiiveri, Peter Caccetta. Image Fusion with Conditional Probability Networks for Monitoring the Salinization of Farmland[J]. Digital Signal Processing, 1998 , 8: 225-230.
[18] S. Mehtaa, A.E. Fryar, Modeling regional salinization of the Ogallala aquifer[J]. Southern High Plains,TX, USA, Journal of Hydrology, 2000, 238:44-64.
[19] M.K. Berdimuratov, S.I.Lyashko, D.A.Nomirovskii. Modeling and optimization of salinization of irrigated soil[J]. Journal of Mathematical Sciences.1999,96:20-30.
[20] Grant R. Cramer. Differential effects of salinity on leaf elongation kinetics of three grass species[J]. Plant and Soil 253: 233–244, 2003.
[21] LAMBERT K. SMEDEMA & KARIM SHIATI. Irrigation and salinity: a perspective review of the salinity hazards of irrigation development in the arid zone[J]. Irrigation and Drainage Systems 16: 161–174, 2002.
[22] CHRISTOPHER J. CLARKE,RICHARD W. BELL. Incorporating Geological Effects in Modeling of Revegetation Strategies for Salt-Affected Landscapes[J]. Environmental Management Vol. 24, No. 1, pp. 99–109.
[23] Dennis Wichelns. An economic model of waterlogging and salinization in arid regions[J]. Ecological Economics 30 (1999) 475–491.
[24]关元秀,王劲峰,刘高焕.黄河三角洲土地盐碱化遥感监测、预测和治理研究[M].中国科学院地理科学与资源研究所博士学位论文2001:178-181.
[25]霍东民,张景雄,张家柄.利用CBERS-1卫星数据进行盐碱地专题信息提取研究[J].国土资源遥感,2001(2): 48-52.
[26] Singh AN, Kristof S J, BaumgardnerM R. Delineating salt affected Soils in the Gangetic Plain India by Digital Analysis of Landsat Data[R]. Purdue University Laboratory for Applications of Remote Sensing. Technical report, 111-477.
[27] Sommerfeldt T G, Thompson M D, Pront N A. Delineation and Mapping of Soil Salinity in Southern Alberta from Landsat Data[J]. Canadian Journal of Remote Sensing, 1985, 10:104-118.
[28]R.L. Dehaan, G.R. Taylor. Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil-salinization[J]. Remote Sensing of Environment,2002,80:406-417.
[29]S.D Kirkby. Integrating a GIS with an expert system to identify and manage dryland salinization[J]. Applied Geograph,1996,4(16):289-303.
[30]Bao B R M , Sankar T R, Dwivedi R S, et al. Spectral Behavior of salt-affected soils[J]. International Journal of Remote Sensing, 1995, 16(12):2125-2136.
[31]Taylor G R, Mah A H, et al. Characterization of saline soils using Airborne Radar Imagery [J]. Remote Sensing of Environment, 1996, 57(3):127-142.
[32]Dwivedi R S, Rao B R M. The selection of the best possible Landsat TM band combination for delineating salt-affected soils[J]. Int. Journal of Remote Sensing, 1992, 13(11):2051-2058.
[33]Bui E N, Henderson B L .Vegetation indicators of salinity in northern Queensland [J]. Austral Ecology, 2003,(28) : 539-552.
[34]Fouad Al-Khaier. Soil Salinity Detection using satellite remote sensing[M]. Master' s thesis International institute for Geo-information science and earth observation, enschede, the Nether lands, 2003.3.
[35]Ali Kerem Saysel. A dynamic model of salinization on irrigated lands[J]. Ecological Modeling, 2001,139:177-199.
[36]S.Chiba,T.Hirawake. An overview of the biological/oceanographic survey by the RTV UmitakaMaruIII Adelie Land[J]. Antarctica in January-February 1996, Deep-Sea Research II,2000, 47:2 589-2613.
[37]曾志远.卫星图像土壤类型自动识别与制图研究:计算机分类及其结果的光谱学分析[J].土壤学报,1984,21(2):183-193.
[38]戴昌达,扬瑜,石晓日.黄淮海平原地产土壤的遥感清查[J].环境遥感,1986,1(2):81-91.
[39]王杰生,戴昌达,胡德永.土地利用变化的卫星遥感监测—以河北省南皮县为例[J].环境遥感,1989,4(4):243-248.
[40]王西川.豫东平原盐渍土的遥感分析[J].遥感信息,1992,(4):24-27.
[41]张恒云,尚淑招.NOAA.AVNRR资料在监测土壤盐渍化程度中的应用[J].遥感信息,1992(1):24-26.
[42]李海涛,P.Brunner ,李文鹏,等. ASTER遥感影像数据在土壤盐渍化评价中的应用[J].水文地质工程地质,2006(5):75-79.
[43]刘庆生,骆剑承,刘高焕.资源一号卫星数据在黄河三角洲地区的应用潜力初探[J].地球信息科学,2000,2(2):56-57.
[44]江红南.基于3S技术的干旱区土壤盐渍化时空演变研究[D].新疆大学,2007年7月:13-15.
[45]庞治国,吕宪国,李取生.3S技术支持下的盐碱化土地现状评价与发展对策研究—以吉林省西部大安市为例[J].国土与自然资源研究,2000,(4):25-27.
[46]何祺胜,塔西甫拉提·特依拜,丁建丽.基于决策树方法的干旱区盐渍地信息提取研究—以渭干河-库车河三角洲绿洲为例[J].资源科学,2006,28(6):134-140.
[47]牛博,倪萍,塔西甫拉提·特依拜.遥感技术在干旱区盐渍化动态变化分析中的应用以新疆于田县为例[J].地质灾害与环境保护,2004,15(4):78-82.
[48]关元秀,刘高焕.区域土壤盐渍化遥感监测研究综述[J].遥感技术与应用,2001,1(16):40-44.
[49]许迪,王少丽.利用NDVI指数识别作物及土壤盐碱分布的应用研究[J].灌溉排水学报,2003, 22(6):5-8.
[50]李晓燕,张树文.吉林省大庆市近50年土地盐碱化时空动态及成因分析[J].资源科学,2005,27(3):92-97.
[51]张飞,塔西甫拉提·特依拜,丁建丽.渭干河—库车河三角洲绿洲盐渍化土壤特征研究[J].干旱地区农业研究,2007,25(2):146-161.
[52]李凤全,卞建民,张殿发.半干旱地区土壤盐渍化预报研究——以吉林省西部洮儿河流域为例[J].水土保持通报,2000,2:2-4.
[53]骆玉霞,陈焕伟.GIS支持下的TM图像土壤盐渍化分级[J].遥感信息理论研究,2001.(4):12-15.
[54]关元秀,刘高焕,刘庆生,等.黄河三角洲盐碱地遥感调查研究[J].遥感学报,2001,1(5):46-53.
[55]徐东瑞,高广惠.盐渍化土壤遥感动态监测及人工调控[J].华北农学报,1995,10:175-180.
[56]戚隆溪,陈启生,将春浩.土壤盐渍化的监测和预报研究[J].土壤学报,1997,34(2):189-199.
[57]宋长春,邓伟,李取生.松嫩平原土壤次生盐渍化过程模型研究[J].水土保持学报,2002,5(16):23-42.
[58] Dw ivedi R S. Monitoring and the Study of the Effects of Image Scale on Delineation of Salt2affected Soils in the Indo Gangetic Plains[J]. International Journal of Remote Sensing, 1992, 13 (8): 1527-1536.
[59]乔玉良.彩红外航片在忻定盆地水浇地清查及盐碱地动态监测中的应用[J].国土资源遥感, 1993, (2): 17-21.
[60] Dwivedi R S, Sreenivas K. Delineation of Salt affected So island Waterlogged Areas in the Indo Gangetic Plains U singers 21C L ISS2?〔J. International Journal of Remote Sensing, 1998, 19 (14) : 2739-2751.
[61] Dwivedi R S, Sreenivas K. Image Transforms as a Tool for the Study of Soil Salinity and Alkalinity Dynamics[J]. International Journal of Remote Sensing, 1998, 19 ( 4) : 605-619.
[62] Sreenivas K, Venkataratnam L, Narasimha Rao P V. Dielectric properties of salt-affected soils[J]. International Journal of Remote Sensing, 1995, 16 (4) : 641 - 649.
[63] Metternicht G, Zinck J A. Spatial discrimination of salt and sodium - affected soil surfaces[J]. International Journal of Remote Sensing, 1997, 18 (12) : 2571 - 2586.
[64]关元秀,刘高焕,王劲峰.基于GIS的黄河三角洲盐碱地改良分区[J].地理学报,2001,56(2):118-205.
[65]史晓霞.基于CA模型的长岭县土壤盐渍化时空演变可视化模拟[J].硕士毕业论文,2005.5.
[66]田庆久,宫鹏.地物波谱数据库研究现状与发展趋势[J].遥感信息,2002,(3):2-6.
[67]杜华强.荒漠化地区高光谱遥感数据预处理及地物光谱重建的研究[D].东北林业大学,2002.
[68]Bowers S A,Hanks R J.Reflection of radiant energy from soils[J]. Soil Sci,1965,100(2):130-138.
[69]朱永豪,邓仁达,卢亚非等.不同湿度条件下黄棕壤光谱反射率的变化特征及其遥感意义[J].土壤学报,1984,21(2):194-201.
[70]张文群,蒋光润,商铁兰等.定西遥感试验场土壤光谱特征分析[J].遥感技术与应用,1992,7(2):25-31.
[71] Hummel J W,Sudduth K A,Hollinger S E. Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soils sensor[J]. Computers and Electronics in Agriculture, 2001(32):149-165.
[72]Krishnan P,Alexander J D,Butler B J et al. Reflectance technique for predicting soil organic matter[J]. Soil Sci Soc Am J,1980(44): 1282-1285.
[73]Stoner E R, Baumgardner M F. Characteristic variations in reflectance of surface soil[J]. Soil Sci. Soc. Am. J, 1981(45):1161-1165.
[74]Henderson T L, Baumgardner M F, Franzmeier D P, et al. High dimensional reflectance analysis of soil organic matter[J]. Soil Sci, Soc Am J, 1992(56):865-872.
[75]Chang C W,Laird D A. Near-infrared reflectance spectroscopic analysis of soil C and N[J]. Soil Sci, 2002, 167(2):110-116.
[76]Mccarty G W,Reeves III J B, Reeves V B,et al.Mid-infrared diffuse reflectance spectroscopy for soil carbon measurement[J]. Soil Sci Soc Am J, 2002, 66(2):640-646.
[77]Al-Abbas A H,Swain P H, Baumgardner M F. Relating organic matter and clay content to themultispectral radiance of soils[J].Soil, Sci, 1972, 114(6):477-485.
[78]Dematte J AM, Garcia G J. Alteration of soil proper tier through a weathering sequence as evaluated by spectral reflectance[J].Soil, Sci , Am J,1999(63): 327-342.
[79]曾志远.土壤肥力的卫星遥感探测[J].土壤,1987(2):73-78.
[80]汪周伟,钱淑苹.东北主要土壤的光谱反射特性[J].土壤通报,1984(1):209-211.
[81]沙明晋.利用土壤黑度值实现土壤肥力遥感监测的机理[J].山西农业大学学报,1996,16(4):372-376.
[82]满苏尔·沙比提,热合漫·玉苏甫,阿布拉江·苏莱曼.渭干河——库车河三角洲绿洲土地资源合理利用对策分析[J].干旱区资源与环境,2004,18(1):111-116.
[83]库车县志编纂委员会编.库车县志[Z].乌鲁木齐:新疆大学出版社,1991:1-7.
[84]沙雅县史志编纂委员会.沙雅县志[Z].乌鲁木齐:新疆人民出版社,1995:1-7.
[85]新和县地方志编纂委员会,新和县志[Z].乌鲁木齐:新疆人民出版社,1997:1-5.
[86]苟新华.新疆维吾尔自治区塔里木盆地水资源评价及其开发利用[D].吉林大学, 2001.5.
[87]库车县水利志编纂委员会编.库车县水利志[M].乌鲁木齐:新疆科技卫生出版社:1993,22-23.
[88]关欣,张凤荣,李巧云.新疆土地资源的持续利用与开发[J].干旱地区农业研究,2002(1):95-101.
[89]刘立诚.塔里木盆地北部土壤盐渍化特征的初步研究[J].土壤通报,1994,6(12):196～200.
[90]库车县志编纂委员会编.库车县志[Z].乌鲁木齐:新疆大学出版社,2005.
[91]沙雅县史志编纂委员会.沙雅县志[Z].乌鲁木齐:新疆人民出版社,2004.
[92]新和县地方志编纂委员会.新和县志[Z].乌鲁木齐:新疆人民出版社,2005.
[93]张飞,渭干河—库车河三角洲绿洲盐渍化土壤特征研究[D].新疆大学,2007年7月:76-78.
[94]满苏尔·沙比提,阿布拉江·苏来曼.渭干河一库车河绿洲生态环境变化特征与防治对策[J].新疆师范大学学报(自然科学版),2003,22 (3): 81~85.
[95]李德仁.论全球定位系统(GPS)、数字摄影测量系统(DPS)、遥感(RS)、地理信息系统(GIS)和专家系统(ES)的结合[J].测绘通报,1994,55(1):3-8.
[96]陈述彭等.地学的探索第三卷,遥感应用[M].北京:科学出版社,1990.39-41.
[97]陈述彭,鲁学军,周成虎.地理信息系统导论[M].北京:科学出版社,2000.3-11.
[98]徐绍铨,张华海,等.GPS测量原理及应用[M].武汉:武汉测绘科技大学出版社,1998,1-13.
[99]罗名海.3S技术的发展趋势与在城市规划中的应用前景[J].地理空间信息,2004,2(4):24-28.
[100]王亚飞,钱乐祥,刘含海.地物光谱曲线特征点的提取和应用[J].河南大学学报(自然科学版),2006,36(4):67-70.
[101]许卫东,尹球,匡定波.地物光谱匹配模型比较研究[J].红外与毫米波学报,2005,24(4):296-300.
[102]Baumgardner M F, Silva LF, Biehl L L et al. Reflectance properties of soils. Advances inAgronomy,1985(38): 1-44.
[103]Madeira Netto J.Spectral reflectance properties of soils.Photo-interpretation, 1996(2): 59-76.
[104]楼琇林,黄韦艮,基于人工神经网络的赤潮卫星遥感方法研究[J],遥感学报,2003(3):125-129.
[105]黄文梅,杨勇,雄桂林,等.系统仿真分析与设计[M].长沙:国防科技大学出版社,2001.
[106]张亭禄等.基于人工神经网络的一类水域叶绿素-a浓度反演方法[J].遥感学报.2002(1): 40-45.
[107]石元春,李保国等.区域水盐运动监测预报[M].河北科学技术出版社,1991:1-7.
[108]娄薄礼.土壤积盐与地下水的关系[J].水利学报,1964,(3):1-12.
[109]苏里坦,宋郁东,张展羽.新疆渭干河流域地下水含盐量的时空变异特征[J].地理学报,2003, 58(6):854-860.
[110]汤国安,赵牡丹,李天文等. DEM提取黄土高原地面坡度的不确定性[J].地理学报2003, 58(6) 824-830.
[111]丛爽.面向MATLAB工具箱的神经网络理论与应用[M].合肥.中国科学技术大学出版社, 1998.45-60.
[2]李凤全,吴樟荣.半干旱地区土地盐碱化预警研究以吉林省西部土地盐碱化预警为例[J].水土保持通报,2002,22(1):57-59.
[3] Szabolcs I. Salination of soil and water and its relation to desertification. Desertification Control Bulletin, 1992, 21:32-37.
[4]水利部计划司,农水司.全国灌溉面积发展“九五”计划及2010年规划.中国水利水电科学研究院,1997,1-3.
[5]田长彦,周宏飞,刘国庆.21世纪新疆土壤盐渍化调控与农业持续发展研究建议[J].干旱区地理,2000,23:178-181.
[6]许志坤.新疆盐渍土的形成、分类、特点和利用改良[M].国际盐渍土改良学术讨论会论文集,1985,5:109-112.
[7]许英.土壤水盐时空变异的稳健性分析和条件模拟研究[D].内蒙古农业大学博士学位论文. 2002:1-7.
[8]李述刚,程心俊,王州琼.干旱区绿洲农业持续发展战略[J].干旱区研究,1997,14(2):537-544.
[9]潘晓玲.干旱区绿洲生态系统动态稳定性的初步研究[J].第四纪研究,2001,21(4):345-352.
[10]何祺胜.星载雷达图像在干旱区盐渍地信息提取中的应用研究[D].新疆大学硕士学位论文,2007年7月:17-19.
[11]朱鹤健,何宜庚.土壤地理学[M].北京:高等教育出版社.2003:189-199.
[12]赵可夫,李法曾.中国盐生殖物[M].科学出版社.1999:1-10.
[13]N.C.布雷迪.土壤的本质与性状[M].科学出版社.1982:184-216.
[14]张士功,邱建军,张华.我国盐渍土资源及其综合治理[J].中国农业资源与区划,2000,21(1):52-56.
[15]梅安心,彭望禄,秦其明,等.遥感导论[M].高等教育出版社,2001:46-47.
[16] G.I. Metternicht. Remote sensing of soil salinity-potentials and constraints[J].Remote Sensing of Environment,2003,85:1-20.
[17] Harri Kiiveri, Peter Caccetta. Image Fusion with Conditional Probability Networks for Monitoring the Salinization of Farmland[J]. Digital Signal Processing, 1998 , 8: 225-230.
[18] S. Mehtaa, A.E. Fryar, Modeling regional salinization of the Ogallala aquifer[J]. Southern High Plains,TX, USA, Journal of Hydrology, 2000, 238:44-64.
[19] M.K. Berdimuratov, S.I.Lyashko, D.A.Nomirovskii. Modeling and optimization of salinization of irrigated soil[J]. Journal of Mathematical Sciences.1999,96:20-30.
[20] Grant R. Cramer. Differential effects of salinity on leaf elongation kinetics of three grass species[J]. Plant and Soil 253: 233–244, 2003.
[21] LAMBERT K. SMEDEMA & KARIM SHIATI. Irrigation and salinity: a perspective review of the salinity hazards of irrigation development in the arid zone[J]. Irrigation and Drainage Systems 16: 161–174, 2002.
[22] CHRISTOPHER J. CLARKE,RICHARD W. BELL. Incorporating Geological Effects in Modeling of Revegetation Strategies for Salt-Affected Landscapes[J]. Environmental Management Vol. 24, No. 1, pp. 99–109.
[23] Dennis Wichelns. An economic model of waterlogging and salinization in arid regions[J]. Ecological Economics 30 (1999) 475–491.
[24]关元秀,王劲峰,刘高焕.黄河三角洲土地盐碱化遥感监测、预测和治理研究[M].中国科学院地理科学与资源研究所博士学位论文2001:178-181.
[25]霍东民,张景雄,张家柄.利用CBERS-1卫星数据进行盐碱地专题信息提取研究[J].国土资源遥感,2001(2): 48-52.
[26] Singh AN, Kristof S J, BaumgardnerM R. Delineating salt affected Soils in the Gangetic Plain India by Digital Analysis of Landsat Data[R]. Purdue University Laboratory for Applications of Remote Sensing. Technical report, 111-477.
[27] Sommerfeldt T G, Thompson M D, Pront N A. Delineation and Mapping of Soil Salinity in Southern Alberta from Landsat Data[J]. Canadian Journal of Remote Sensing, 1985, 10:104-118.
[28]R.L. Dehaan, G.R. Taylor. Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil-salinization[J]. Remote Sensing of Environment,2002,80:406-417.
[29]S.D Kirkby. Integrating a GIS with an expert system to identify and manage dryland salinization[J]. Applied Geograph,1996,4(16):289-303.
[30]Bao B R M , Sankar T R, Dwivedi R S, et al. Spectral Behavior of salt-affected soils[J]. International Journal of Remote Sensing, 1995, 16(12):2125-2136.
[31]Taylor G R, Mah A H, et al. Characterization of saline soils using Airborne Radar Imagery [J]. Remote Sensing of Environment, 1996, 57(3):127-142.
[32]Dwivedi R S, Rao B R M. The selection of the best possible Landsat TM band combination for delineating salt-affected soils[J]. Int. Journal of Remote Sensing, 1992, 13(11):2051-2058.
[33]Bui E N, Henderson B L .Vegetation indicators of salinity in northern Queensland [J]. Austral Ecology, 2003,(28) : 539-552.
[34]Fouad Al-Khaier. Soil Salinity Detection using satellite remote sensing[M]. Master' s thesis International institute for Geo-information science and earth observation, enschede, the Nether lands, 2003.3.
[35]Ali Kerem Saysel. A dynamic model of salinization on irrigated lands[J]. Ecological Modeling, 2001,139:177-199.
[36]S.Chiba,T.Hirawake. An overview of the biological/oceanographic survey by the RTV UmitakaMaruIII Adelie Land[J]. Antarctica in January-February 1996, Deep-Sea Research II,2000, 47:2 589-2613.
[37]曾志远.卫星图像土壤类型自动识别与制图研究:计算机分类及其结果的光谱学分析[J].土壤学报,1984,21(2):183-193.
[38]戴昌达,扬瑜,石晓日.黄淮海平原地产土壤的遥感清查[J].环境遥感,1986,1(2):81-91.
[39]王杰生,戴昌达,胡德永.土地利用变化的卫星遥感监测—以河北省南皮县为例[J].环境遥感,1989,4(4):243-248.
[40]王西川.豫东平原盐渍土的遥感分析[J].遥感信息,1992,(4):24-27.
[41]张恒云,尚淑招.NOAA.AVNRR资料在监测土壤盐渍化程度中的应用[J].遥感信息,1992(1):24-26.
[42]李海涛,P.Brunner ,李文鹏,等. ASTER遥感影像数据在土壤盐渍化评价中的应用[J].水文地质工程地质,2006(5):75-79.
[43]刘庆生,骆剑承,刘高焕.资源一号卫星数据在黄河三角洲地区的应用潜力初探[J].地球信息科学,2000,2(2):56-57.
[44]江红南.基于3S技术的干旱区土壤盐渍化时空演变研究[D].新疆大学,2007年7月:13-15.
[45]庞治国,吕宪国,李取生.3S技术支持下的盐碱化土地现状评价与发展对策研究—以吉林省西部大安市为例[J].国土与自然资源研究,2000,(4):25-27.
[46]何祺胜,塔西甫拉提·特依拜,丁建丽.基于决策树方法的干旱区盐渍地信息提取研究—以渭干河-库车河三角洲绿洲为例[J].资源科学,2006,28(6):134-140.
[47]牛博,倪萍,塔西甫拉提·特依拜.遥感技术在干旱区盐渍化动态变化分析中的应用以新疆于田县为例[J].地质灾害与环境保护,2004,15(4):78-82.
[48]关元秀,刘高焕.区域土壤盐渍化遥感监测研究综述[J].遥感技术与应用,2001,1(16):40-44.
[49]许迪,王少丽.利用NDVI指数识别作物及土壤盐碱分布的应用研究[J].灌溉排水学报,2003, 22(6):5-8.
[50]李晓燕,张树文.吉林省大庆市近50年土地盐碱化时空动态及成因分析[J].资源科学,2005,27(3):92-97.
[51]张飞,塔西甫拉提·特依拜,丁建丽.渭干河—库车河三角洲绿洲盐渍化土壤特征研究[J].干旱地区农业研究,2007,25(2):146-161.
[52]李凤全,卞建民,张殿发.半干旱地区土壤盐渍化预报研究——以吉林省西部洮儿河流域为例[J].水土保持通报,2000,2:2-4.
[53]骆玉霞,陈焕伟.GIS支持下的TM图像土壤盐渍化分级[J].遥感信息理论研究,2001.(4):12-15.
[54]关元秀,刘高焕,刘庆生,等.黄河三角洲盐碱地遥感调查研究[J].遥感学报,2001,1(5):46-53.
[55]徐东瑞,高广惠.盐渍化土壤遥感动态监测及人工调控[J].华北农学报,1995,10:175-180.
[56]戚隆溪,陈启生,将春浩.土壤盐渍化的监测和预报研究[J].土壤学报,1997,34(2):189-199.
[57]宋长春,邓伟,李取生.松嫩平原土壤次生盐渍化过程模型研究[J].水土保持学报,2002,5(16):23-42.
[58] Dw ivedi R S. Monitoring and the Study of the Effects of Image Scale on Delineation of Salt2affected Soils in the Indo Gangetic Plains[J]. International Journal of Remote Sensing, 1992, 13 (8): 1527-1536.
[59]乔玉良.彩红外航片在忻定盆地水浇地清查及盐碱地动态监测中的应用[J].国土资源遥感, 1993, (2): 17-21.
[60] Dwivedi R S, Sreenivas K. Delineation of Salt affected So island Waterlogged Areas in the Indo Gangetic Plains U singers 21C L ISS2?〔J. International Journal of Remote Sensing, 1998, 19 (14) : 2739-2751.
[61] Dwivedi R S, Sreenivas K. Image Transforms as a Tool for the Study of Soil Salinity and Alkalinity Dynamics[J]. International Journal of Remote Sensing, 1998, 19 ( 4) : 605-619.
[62] Sreenivas K, Venkataratnam L, Narasimha Rao P V. Dielectric properties of salt-affected soils[J]. International Journal of Remote Sensing, 1995, 16 (4) : 641 - 649.
[63] Metternicht G, Zinck J A. Spatial discrimination of salt and sodium - affected soil surfaces[J]. International Journal of Remote Sensing, 1997, 18 (12) : 2571 - 2586.
[64]关元秀,刘高焕,王劲峰.基于GIS的黄河三角洲盐碱地改良分区[J].地理学报,2001,56(2):118-205.
[65]史晓霞.基于CA模型的长岭县土壤盐渍化时空演变可视化模拟[J].硕士毕业论文,2005.5.
[66]田庆久,宫鹏.地物波谱数据库研究现状与发展趋势[J].遥感信息,2002,(3):2-6.
[67]杜华强.荒漠化地区高光谱遥感数据预处理及地物光谱重建的研究[D].东北林业大学,2002.
[68]Bowers S A,Hanks R J.Reflection of radiant energy from soils[J]. Soil Sci,1965,100(2):130-138.
[69]朱永豪,邓仁达,卢亚非等.不同湿度条件下黄棕壤光谱反射率的变化特征及其遥感意义[J].土壤学报,1984,21(2):194-201.
[70]张文群,蒋光润,商铁兰等.定西遥感试验场土壤光谱特征分析[J].遥感技术与应用,1992,7(2):25-31.
[71] Hummel J W,Sudduth K A,Hollinger S E. Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soils sensor[J]. Computers and Electronics in Agriculture, 2001(32):149-165.
[72]Krishnan P,Alexander J D,Butler B J et al. Reflectance technique for predicting soil organic matter[J]. Soil Sci Soc Am J,1980(44): 1282-1285.
[73]Stoner E R, Baumgardner M F. Characteristic variations in reflectance of surface soil[J]. Soil Sci. Soc. Am. J, 1981(45):1161-1165.
[74]Henderson T L, Baumgardner M F, Franzmeier D P, et al. High dimensional reflectance analysis of soil organic matter[J]. Soil Sci, Soc Am J, 1992(56):865-872.
[75]Chang C W,Laird D A. Near-infrared reflectance spectroscopic analysis of soil C and N[J]. Soil Sci, 2002, 167(2):110-116.
[76]Mccarty G W,Reeves III J B, Reeves V B,et al.Mid-infrared diffuse reflectance spectroscopy for soil carbon measurement[J]. Soil Sci Soc Am J, 2002, 66(2):640-646.
[77]Al-Abbas A H,Swain P H, Baumgardner M F. Relating organic matter and clay content to themultispectral radiance of soils[J].Soil, Sci, 1972, 114(6):477-485.
[78]Dematte J AM, Garcia G J. Alteration of soil proper tier through a weathering sequence as evaluated by spectral reflectance[J].Soil, Sci , Am J,1999(63): 327-342.
[79]曾志远.土壤肥力的卫星遥感探测[J].土壤,1987(2):73-78.
[80]汪周伟,钱淑苹.东北主要土壤的光谱反射特性[J].土壤通报,1984(1):209-211.
[81]沙明晋.利用土壤黑度值实现土壤肥力遥感监测的机理[J].山西农业大学学报,1996,16(4):372-376.
[82]满苏尔·沙比提,热合漫·玉苏甫,阿布拉江·苏莱曼.渭干河——库车河三角洲绿洲土地资源合理利用对策分析[J].干旱区资源与环境,2004,18(1):111-116.
[83]库车县志编纂委员会编.库车县志[Z].乌鲁木齐:新疆大学出版社,1991:1-7.
[84]沙雅县史志编纂委员会.沙雅县志[Z].乌鲁木齐:新疆人民出版社,1995:1-7.
[85]新和县地方志编纂委员会,新和县志[Z].乌鲁木齐:新疆人民出版社,1997:1-5.
[86]苟新华.新疆维吾尔自治区塔里木盆地水资源评价及其开发利用[D].吉林大学, 2001.5.
[87]库车县水利志编纂委员会编.库车县水利志[M].乌鲁木齐:新疆科技卫生出版社:1993,22-23.
[88]关欣,张凤荣,李巧云.新疆土地资源的持续利用与开发[J].干旱地区农业研究,2002(1):95-101.
[89]刘立诚.塔里木盆地北部土壤盐渍化特征的初步研究[J].土壤通报,1994,6(12):196～200.
[90]库车县志编纂委员会编.库车县志[Z].乌鲁木齐:新疆大学出版社,2005.
[91]沙雅县史志编纂委员会.沙雅县志[Z].乌鲁木齐:新疆人民出版社,2004.
[92]新和县地方志编纂委员会.新和县志[Z].乌鲁木齐:新疆人民出版社,2005.
[93]张飞,渭干河—库车河三角洲绿洲盐渍化土壤特征研究[D].新疆大学,2007年7月:76-78.
[94]满苏尔·沙比提,阿布拉江·苏来曼.渭干河一库车河绿洲生态环境变化特征与防治对策[J].新疆师范大学学报(自然科学版),2003,22 (3): 81~85.
[95]李德仁.论全球定位系统(GPS)、数字摄影测量系统(DPS)、遥感(RS)、地理信息系统(GIS)和专家系统(ES)的结合[J].测绘通报,1994,55(1):3-8.
[96]陈述彭等.地学的探索第三卷,遥感应用[M].北京:科学出版社,1990.39-41.
[97]陈述彭,鲁学军,周成虎.地理信息系统导论[M].北京:科学出版社,2000.3-11.
[98]徐绍铨,张华海,等.GPS测量原理及应用[M].武汉:武汉测绘科技大学出版社,1998,1-13.
[99]罗名海.3S技术的发展趋势与在城市规划中的应用前景[J].地理空间信息,2004,2(4):24-28.
[100]王亚飞,钱乐祥,刘含海.地物光谱曲线特征点的提取和应用[J].河南大学学报(自然科学版),2006,36(4):67-70.
[101]许卫东,尹球,匡定波.地物光谱匹配模型比较研究[J].红外与毫米波学报,2005,24(4):296-300.
[102]Baumgardner M F, Silva LF, Biehl L L et al. Reflectance properties of soils. Advances inAgronomy,1985(38): 1-44.
[103]Madeira Netto J.Spectral reflectance properties of soils.Photo-interpretation, 1996(2): 59-76.
[104]楼琇林,黄韦艮,基于人工神经网络的赤潮卫星遥感方法研究[J],遥感学报,2003(3):125-129.
[105]黄文梅,杨勇,雄桂林,等.系统仿真分析与设计[M].长沙:国防科技大学出版社,2001.
[106]张亭禄等.基于人工神经网络的一类水域叶绿素-a浓度反演方法[J].遥感学报.2002(1): 40-45.
[107]石元春,李保国等.区域水盐运动监测预报[M].河北科学技术出版社,1991:1-7.
[108]娄薄礼.土壤积盐与地下水的关系[J].水利学报,1964,(3):1-12.
[109]苏里坦,宋郁东,张展羽.新疆渭干河流域地下水含盐量的时空变异特征[J].地理学报,2003, 58(6):854-860.
[110]汤国安,赵牡丹,李天文等. DEM提取黄土高原地面坡度的不确定性[J].地理学报2003, 58(6) 824-830.
[111]丛爽.面向MATLAB工具箱的神经网络理论与应用[M].合肥.中国科学技术大学出版社, 1998.45-60.