河套灌区土壤水溶性盐基离子高光谱综合反演模型
|
摘要
为了提高野外高光谱反演土壤水溶性盐基离子的精度,以河套灌区永济灌域盐渍化土壤为研究对象,构建了基于光谱变换、特征波段、特征光谱指数筛选以及支持向量机(SVM)的机器学习相结合的高光谱综合反演模型。结果表明,经预处理的原始光谱反射率与土壤离子相关性总体较低,最大相关系数仅为0.18,原始光谱反射率与土壤离子的相关系数由大到小依次为Ca~(2+)、SO_4~(2-)、Mg~(2+)、全盐量、Na~++K~+、Cl~-。全盐量、Na~++K~+、Cl~-、SO_4~(2-)、Ca~(2+)、Mg~(2+)的光谱最优变换形式分别为(1/R)″、(1/R)″、(lnR)'、(lnR)″、R'、(lnR)″,敏感波段(P<0.01)数分别为41、7、9、65、76、28个,利用逐步回归法在敏感波段中筛选出特征波段,基于特征波段建立的回归模型中各离子的决定系数R~2平均值为0.35,均方根误差RMSE平均值为0.87 g/kg,其中SO_4~(2-)拟合精度最高,R~2为0.52,Ca~(2+)拟合精度最低,R~2仅为0.20。将特征波段代入光谱指数中,结合逐步回归法确定了Mg~(2+)特征光谱指数为3个,全盐量特征光谱指数为2个,Na~++K~+、SO_4~(2-)、Ca~(2+)特征光谱指数分别为1个,与仅考虑特征波段的回归模型相比,特征波段+特征光谱指数结合后各离子回归模型的R~2平均提高了58.67%,RMSE降低了24.60%,其中SO_4~(2-)拟合精度最高,R~2为0.74,RMSE为0.47 g/kg。考虑了特征波段+特征光谱指数的SVM模型相比仅考虑特征波段的SVM模型,其预测能力有了明显提高,各离子相对分析误差(RPD)平均提高了110.27%,训练集R~2平均提高了37.54%,RMSE平均降低了40.12%,验证集R~2平均提高了56.04%,RMSE平均降低了39.39%。SO_4~(2-)的RPD达到3.000,模拟效果最优,具备很好的预测能力;全盐量模型具有很好的定量预测能力,Mg~(2+)模型可用于评估或相关性方面的预测,Na~++K~+、Ca~(2+)的模型具有区别高低值的能力。
It is significant to take best agricultural measures and improve salinization to rapidly and accurately determinate the composition and content of soil salt. The hyperspectral integrated inversion model based on transformation of hyperspectral,characteristic bands,characteristic spectral indices screening and support vector machine( SVM) was established to improve the accuracy of water-soluble salt ions content by taking the saline soil of Yongji irrigation area of Hetao Irrigation District. The results showed that the correlation between the original spectral reflectance by pretreatment and water-soluble salt ions content was relatively low and the maximum correlation coefficient was 0. 18,and the sequence of them from big to small was as follows: Ca~( 2+),SO_4~(2-),Mg~(2+),the content of salt,Na++ K+and Cl-. The optional transformation forms of salt content,Na~++ K~+,Cl~-,SO_4~(2-),Ca~(2+)and Mg~(2+)were( 1/R) ″,( 1/R) ″,( lnR) ',( lnR) ″,R' and( lnR) ″,respectively. The numbers of sensitive bands( P < 0. 01)were 41,7,9,65,76 and 28,respectively. Stepwise regression method was used to filtrate the characteristic bands from sensitive bands,and the average of determination coefficient( R~2) and the average of root mean square error( RMSE) of each ion in the regression model based on the characteristic band were 0. 35 and 0. 87 g/kg,of which R~2 was the largest and the smallest were SO_4~(2-)( 0. 52) and Ca~(2+)( 0. 20),respectively. Combined with the stepwise regression method,the characteristic bands were substituted into the spectral index to determine that there were three characteristic spectral indices for Mg~(2+),there were two characteristic spectral indices for salt content,and there were one characteristic spectral index for Na++ K+,SO_4~(2-) and Ca~(2+),respectively. The R~2 of model for water-soluble salt ions content based on the characteristic bands and characteristic spectral indices was increased by 58. 67%,and the RMSE was decreased by 24. 60%,of which the maximum R~2 was SO_4~(2-)( 0. 74),RMSE was 0. 47 g/kg.The model of SVM based on the characteristic bands and characteristic spectral indices combined had a significant improvement in the prediction than that merely based on the characteristic bands,for example,the average relative analysis error( RPD) was increased by 110. 27%,the R~2 was increased by 37. 54%and the RMSE was decreased by 40. 12% in the training set,the R~2 was increased by 56. 04% and the RMSE was decreased by 39. 39% in the verification set. The results showed that the RPD of SO_4~(2-) reached 3. 000,which showed a good prediction ability. The model of salt content and Mg~(2+)had good quantitative prediction ability which can be used for assessment or correlation prediction,respectively.The SVM models of Na++ K+and Ca~(2+)had the ability to distinguish between high and low values.
It is significant to take best agricultural measures and improve salinization to rapidly and accurately determinate the composition and content of soil salt. The hyperspectral integrated inversion model based on transformation of hyperspectral,characteristic bands,characteristic spectral indices screening and support vector machine( SVM) was established to improve the accuracy of water-soluble salt ions content by taking the saline soil of Yongji irrigation area of Hetao Irrigation District. The results showed that the correlation between the original spectral reflectance by pretreatment and water-soluble salt ions content was relatively low and the maximum correlation coefficient was 0. 18,and the sequence of them from big to small was as follows: Ca~( 2+),SO_4~(2-),Mg~(2+),the content of salt,Na++ K+and Cl-. The optional transformation forms of salt content,Na~++ K~+,Cl~-,SO_4~(2-),Ca~(2+)and Mg~(2+)were( 1/R) ″,( 1/R) ″,( lnR) ',( lnR) ″,R' and( lnR) ″,respectively. The numbers of sensitive bands( P < 0. 01)were 41,7,9,65,76 and 28,respectively. Stepwise regression method was used to filtrate the characteristic bands from sensitive bands,and the average of determination coefficient( R~2) and the average of root mean square error( RMSE) of each ion in the regression model based on the characteristic band were 0. 35 and 0. 87 g/kg,of which R~2 was the largest and the smallest were SO_4~(2-)( 0. 52) and Ca~(2+)( 0. 20),respectively. Combined with the stepwise regression method,the characteristic bands were substituted into the spectral index to determine that there were three characteristic spectral indices for Mg~(2+),there were two characteristic spectral indices for salt content,and there were one characteristic spectral index for Na++ K+,SO_4~(2-) and Ca~(2+),respectively. The R~2 of model for water-soluble salt ions content based on the characteristic bands and characteristic spectral indices was increased by 58. 67%,and the RMSE was decreased by 24. 60%,of which the maximum R~2 was SO_4~(2-)( 0. 74),RMSE was 0. 47 g/kg.The model of SVM based on the characteristic bands and characteristic spectral indices combined had a significant improvement in the prediction than that merely based on the characteristic bands,for example,the average relative analysis error( RPD) was increased by 110. 27%,the R~2 was increased by 37. 54%and the RMSE was decreased by 40. 12% in the training set,the R~2 was increased by 56. 04% and the RMSE was decreased by 39. 39% in the verification set. The results showed that the RPD of SO_4~(2-) reached 3. 000,which showed a good prediction ability. The model of salt content and Mg~(2+)had good quantitative prediction ability which can be used for assessment or correlation prediction,respectively.The SVM models of Na++ K+and Ca~(2+)had the ability to distinguish between high and low values.
引文
[1]EMTSEV V T,SOKOLOVA A Y,SELITSKAYA O V.Protective effect of Klebsiella bacteria on lawn grasses under conditions of soil salinization[J].Eurasian Soil Science,2010,43(7):771-776.
[2]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.QU Yonghua,DUAN Xiaoliang,GAO Hongyong,et al.Quantitative retrieval of soil salinity using hyperspectral data in the region of Inner Mongolia Hetao irrigation district[J].Spectroscopy and Spectral Analysis,2009,29(5):1362-1366.(in Chinese)
[3]雷磊,塔西甫拉提·特依拜,丁建丽,等.实测高光谱和HSI影像的区域土壤盐渍化遥感监测研究[J].光谱学与光谱分析,2014,34(7):1948-1953.LEI L,TASHPOLAT T,DING J L,et al.Study on the soil salinization monitoring based on measured hyperspectral and HSIdata[J].Spectroscopy and Spectral Analysis,2014,34(7):1948-1953.(in Chinese)
[4]BULENT A,CHIMAN K.Application of deep belief network to land cover classification using hyperspectral images[C]∥Advances in neural networks-ISNN 2017.Proceedings of 14th International Symposium,ISNN 2017,Sapporo,Hakodate,and Murcran,Hokkaido,2017::269-276.
[5]曹引,冶运涛,赵红莉,等.基于离散粒子群和偏最小二乘的水源地浊度高光谱反演[J/OL].农业机械学报,2018,49(1):173-182.CAO Yin,YE Yuntao,ZHAO Hongli,et al.Satellite hyperspectral retrieval of turbidity for water source based on discrete particle swarm and partial least squares[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2018,49(1):173-182.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20180122&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2018.01.022.(in Chinese)
[6]韩兆迎,朱西存,房贤一,等.基于SVM与RF的苹果树冠LAI高光谱估测[J].光谱学与光谱分析,2016,36(3):800-805.HAN Z Y,ZHU X C,FANG X Y,et al.Hyperspectral estimation of apple tree canopy LAI based on SVM and RF regression[J].Spectroscopy and Spectral Analysis,2016,36(3):800-805.(in Chinese)
[7]岳学军,凌康杰,洪添胜,等.基于高光谱图像的龙眼叶片叶绿素含量分布模型[J/OL].农业机械学报,2018,49(8):18-25.YUE Xuejun,LING Kangjie,HONG Tiansheng,et al.Distribution model of chlorophyll content for longan leaves based on hyperspectral imaging technology[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2018,49(8):18-25.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20180802&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2018.08.002.(in Chinese)
[8]李粉玲,常庆瑞.基于连续统去除法的冬小麦叶片全氮含量估算[J/OL].农业机械学报,2017,48(7):174-179.LI Fenling,CHANG Qingrui.Estimation of winter wheat leaf nitrogen content based on continuum removed spectra[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(7):174-179.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20170722&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.07.022.(in Chinese)
[9]黄双萍,洪添胜,岳学军,等.基于高光谱的柑橘叶片磷含量估算模型实验[J/OL].农业机械学报,2013,44(4):202-207.HUANG Shuangping,HONG Tiansheng,YUE Xuejun,et al.Hyperspectral estimation model of total phosphorus content for citrus leaves[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2013,44(4):202-207.http:∥www.j.csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20130435&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2013.04.035.(in Chinese)
[10]刘冰峰,李军,贺佳.玉米叶片全磷含量高光谱遥感监测诊断模型研究[J/OL].农业机械学报,2015,46(8):252-258.LIU Bingfeng,LI Jun,HE Jia.Total phosphorus content estimation models of summer maize leaves based on hyperspectral remote sensing[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2015,46(8):252-258.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20150835&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2015.08.035.(in Chinese)
[11]李岚涛,任涛,汪善勤,等.基于角果期高光谱的冬油菜产量预测模型研究[J/OL].农业机械学报,2017,48(3):221-229.LI Lantao,REN Tao,WANG Shanqin,et al.Prediction models of winter oilseed rape yield based on hyperspectral data at pod-filling stage[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(3):221-229.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20170328&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.03.028.(in Chinese)
[12]张秋霞,张合兵,张会娟,等.粮食主产区耕地土壤重金属高光谱综合反演模型[J/OL].农业机械学报,2017,48(3):148-155.ZHANG Qiuxia,ZHANG Hebing,ZHANG Huijuan,et al.Hybrid inversion model of heavy metals with hyperspectral reflectance in cultivated soils of main grain producing areas[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(3):148-155.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20170319&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.03.019.(in Chinese)
[13]王敬哲,塔西甫拉提·特依拜,张东.基于分数阶微分的荒漠土壤铬含量高光谱检测[J/OL].农业机械学报,2017,48(5):152-158.WANG Jingzhe,TASHPOLAT·Tiyip,ZHANG Dong.Spectral detection of chromium content in desert soil based on fractional differential[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(5):152-158.http:∥www.jcsam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20170518&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.05.018.(in Chinese)
[14]洪永胜,朱亚星,苏学平,等.高光谱技术联合归一化光谱指数估算土壤有机质含量[J].光谱学与光谱分析,2017,37(11):3537-3542.HONG Yongsheng,ZHU Yaxing,SU Xueping,et al.Estimation of soil organic matter content using hyperspectral techniques combined with normalized difference spectral index[J].Spectroscopy&Spectral Analysis,2017,37(11):3537-3542.(in Chinese)
[15]张智韬,韩文霆.基于岭回归的土壤含水率高光谱反演研究[J/OL].农业机械学报,2018,49(5):240-248.ZHANG Zhitao,HAN Wenting.Inversion of soil moisture content from hyperspectra based on ridge regression[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2018,49(5):240-248.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20180528&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2018.05.028.(in Chinese)
[16]阿尔达克·克里木,塔西甫拉提·特依拜,张东,等.基于高光谱的ASTER影像土壤盐分模型校正及验证[J].农业工程学报,2016,32(12):144-150.ARDAK·Kelimu,TASHPOLAT·Tiyip,ZHENG Dong,et al.Calibration and validation of soil salinity estimation model based on measured hyperspectral and ASTER image[J].Transactions of the CSAE,2016,32(12):144-150.(in Chinese)
[17]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.ZHANG Xiaoguang,HUANG Biao,JI Junfeng,et al.Quantitative prediction of soil salinity content with visible-near infrared hyper-spectra in northeast China[J].Spectroscopy and Spectral Analysis,2012,32(8):2075-2079.(in Chinese)
[18]韩霁昌,李晓明.盐碱地利用障碍因子高光谱遥感反演研究[J].光谱学与光谱分析,2013,33(7):1932-1935.HAN Jichang,LI Xiaoming.Research on hyperspectral remote sensing inversion of barrier factors in saline-alkaline land use[J].Spectroscopy and Spectral Analysis,2013,33(7):1932-1935.(in Chinese)
[19]厉彦玲,赵庚星,常春艳,等.OLI与HSI影像融合的土壤盐分反演模型[J].农业工程学报,2017,33(21):173-180.LI Yanling,ZHAO Gengxing,CHANG Chunyan,et al.Soil salinity retrieval model based on OLI and HSI image fusion[J].Transactions of the CSAE,2017,33(21):173-180.(in Chinese)
[20]AN D,ZHAO G,CHANG C,et al.Hyperspectral field estimation and remote-sensing inversion of salt content in coastal saline soils of the Yellow River Delta[J].International Journal of Remote Sensing,2016,37(2):455-470.
[21]ROCHA N,ODILIO C,TEIXEIRA A,et al.Hyperspectral remote sensing for detecting soil salinization using Pro Spec TIR-VSaerial imagery and sensor simulation[J].Remote Sensing,2017,9(1):42.
[22]YAO Y,DING J L,KELIMUL A,et al.Research on remote sensing monitoring of soil salinization based on measured hyperspectral and EM38 data[J].Spectroscopy and Spectral Analysis,2013,33(7):1917-1921.
[23]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,2013,33(7):1917-1921.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,2013,33(7):1917-1921.(in Chinese)
[24]韩阳,秦伟超,王野乔.吉林省西部典型盐渍化土壤偏振反射高光谱特征与模型研究[J].光谱学与光谱分析,2014,34(6):1640-1644.HAN Yang,QIN Weichao,WANG Yeqiao.Study on the polarized reflectance hyperspectral characteristics and models of typical saline soil in the west of Jilin Province,China[J].Spectroscopy and Spectral Analysis,2014,34(6):1640-1644.(in Chinese)
[25]徐文茹,韩阳,秦艳,等.盐渍化土壤偏振高光谱信息与土壤线的关系初探[J].光谱学与光谱分析,2015,35(10):2856-2861.XU Wenru,HAN Yang,QIN Yan,et al.Study on the relationship between hyperspectral polarized information of soil salinization and soil line[J].Spectroscopy and Spectral Analysis,2015,35(10):2856-2861.(in Chinese)
[26]彭杰,王家强,向红英,等.土壤含盐量与电导率的高光谱反演精度对比研究[J].光谱学与光谱分析,2014,34(2):510-514.PENG Jie,WANG Jiaqiang,XIANG Hongying,et al.Comparative study on hyperspectral inversion accuracy of soil salt content and electrical conductivity[J].Spectroscopy and Spectral Analysis,2014,34(2):510-514.(in Chinese)
[27]张贤龙,张飞,张海威,等.基于光谱变换的高光谱指数土壤盐分反演模型优选[J].农业工程学报,2018,34(1):110-117.ZHANG Xianlong,ZHANG Fei,ZHANG Haiwei,et al.Optimization of soil salt inversion model based on spectral transformation from hyperspectral index[J].Transactions of the CSAE,2018,34(1):110-117.(in Chinese)
[28]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.DING Jianli,WU Manchun,LIU Haixia,et al.Study on the soil salinization monitoring based on synthetical hyperspectral index[J].Spectroscopy and Spectral Analysis,2012,32(7):1918-1922.(in Chinese)
[29]张建锋,宋玉民,邢尚军,等.盐碱地改良利用与造林技术[J].东北林业大学学报,2002,30(6):124-129.ZHANG Jianfeng,SONG Yumin,XING Shangjun,et al.Saline soil amelioration and forestation techniques[J].Journal of Northeast Forestry University,2002,30(6):124-129.(in Chinese)
[30]代希君,张艳丽,彭杰,等.土壤水溶性盐基离子的高光谱反演模型及验证[J].农业工程学报,2015,31(22):139-145.DAI Xijun,ZHANG Yanli,PENG Jie,et al.Prediction and validation of water-soluble salt ions content using hyperspectral data[J].Transactions of the CSAE,2015,31(22):139-145.(in Chinese)
[31]王海江,蒋天池,YUNGER J A,等.基于支持向量机的土壤主要盐分离子高光谱反演模型[J/OL].农业机械学报,2018,49(5):263-270.WANG Haijiang,JIANG Tianchi,YUNGER J A,et al.Hyperspectral inverse model for soil salt ions based on support vector machine[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2018,49(5):263-270.http:∥www.jcsam.org/jcsam/ch/r eader/view_abstract.aspx?flag=1&file_no=20180531&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2018.05.031.(in Chinese)
[32]鲍士旦.土壤农化分析[M].3版.北京:中国农业出版社,2000.
[33]雷志栋.土壤水动力学[M].北京:清华大学出版社,1988.
[34]李志婷,王昌昆,潘贤章,等.基于模拟Landsat-8 OLI数据的小麦秸秆覆盖度估算[J].农业工程学报,2016,32(增刊1):145-152.LI Zhiting,WANG Changkun,PAN Xianzhang,et al.Estimation of wheat residue cover using simulated Landsat-8 OLI datas[J].Transactions of the CSAE,2016,32(Supp.1):145-152.(in Chinese)
[35]SAID N.Estimation of soil salinity using three quantitative methods based on visible and near-infrared reflectance spectroscopy:a case study from Egypt[J].Arabian Journal of Geosciences,2015,8(7):5127-5140.
[36]DOUAOUI A E K,HERVNICOLAS,WALTER C.Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data[J].Geoderma,2006,134(1-2):217-230.
[37]张录,张芳,熊黑钢,等.不同季节强碱土土壤呼吸影响因子分析与模型预测[J].干旱地区农业研究,2017,35(1):71-78.ZHANG Lu,ZHANG Fang,XIONG Heigang,et al.Analysis of soil respiration factors and model prediction of alkaline soil in different seasons[J].Agricultural Research in Arid Regions,2017,35(1):71-78.(in Chinese)
[38]ROSSEL R A V,TAYLOR H J,MCBRATNEY A B.Multivariate calibration of hyperspectralγ-ray energy spectra for proximal soil sensing[J].European Journal of Soil Science,2010,58(1):343-353.
[39]张东辉,赵英俊,秦凯,等.光谱变换方法对黑土养分含量高光谱遥感反演精度的影响[J].农业工程学报,2018,34(20):141-147.ZHANG Donghui,ZHAO Yingjun,QIN Kai,et al.Influence of spectral transformation methods on nutrient content inversion accuracy by hyperspectral remote sensing in black soil[J].Transactions of the CSAE,2018,34(20):141-147.(in Chinese)
[40]陈弈云,赵瑞瑛,齐天赐,等.结合光谱变换和Kennard-Stone算法的水稻土全氮光谱估算模型校正集构建策略研究[J].光谱学与光谱分析,2017,37(7):2133-2139.CHEN Yiyun,ZHAO Ruiying,QI Tianci,et al.Constructing representative calibration dataset based on spectral transformation an Kennard-Stone algorithm for VNIR modeling of soil total nitrogen in paddy soil[J].Spectroscopy and Spectral Analysis,2017,37(7):2133-2139.(in Chinese)
[41]彭杰,刘焕军,史舟,等.盐渍化土壤光谱特征的区域异质性及盐分反演[J].农业工程学报,2014,30(17):167-174.PENG Jie,LIU Huanjun,SHI Zhou,et al.Regional heterogeneity of hyperspectral characteristics of salt-affected soil and salinity inversion[J].Transactions of the CSAE,2014,30(17):167-174.(in Chinese)
[42]SRIVASTAVA R,SETHI M,YADAV R K,et al.Visible-near infrared reflectance spectroscopy for rapid characterization of salt-affected soil in the Indo-Gangetic Plains of Haryana,India[J].Journal of the Indian Society of Remote Sensing,2017,45(2):307-315.
[43]刘亚秋,陈红艳,王瑞燕,等.基于可见/近红外光谱的黄河口区土壤盐分及其主要离子的定量分析[J].中国农业科学,2016,49(10):1925-1935.LIU Yaqiu,CHEN Hongyan,WANG Ruiyan,et al.Quantitative analysis of soil salt and its main ions based on visible/near infrared spectroscopy in Estuary Area of Yellow[J].Scientia Agricultura Sinica,2016,49(10):1925-1935.(in Chinese)
[2]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.QU Yonghua,DUAN Xiaoliang,GAO Hongyong,et al.Quantitative retrieval of soil salinity using hyperspectral data in the region of Inner Mongolia Hetao irrigation district[J].Spectroscopy and Spectral Analysis,2009,29(5):1362-1366.(in Chinese)
[3]雷磊,塔西甫拉提·特依拜,丁建丽,等.实测高光谱和HSI影像的区域土壤盐渍化遥感监测研究[J].光谱学与光谱分析,2014,34(7):1948-1953.LEI L,TASHPOLAT T,DING J L,et al.Study on the soil salinization monitoring based on measured hyperspectral and HSIdata[J].Spectroscopy and Spectral Analysis,2014,34(7):1948-1953.(in Chinese)
[4]BULENT A,CHIMAN K.Application of deep belief network to land cover classification using hyperspectral images[C]∥Advances in neural networks-ISNN 2017.Proceedings of 14th International Symposium,ISNN 2017,Sapporo,Hakodate,and Murcran,Hokkaido,2017::269-276.
[5]曹引,冶运涛,赵红莉,等.基于离散粒子群和偏最小二乘的水源地浊度高光谱反演[J/OL].农业机械学报,2018,49(1):173-182.CAO Yin,YE Yuntao,ZHAO Hongli,et al.Satellite hyperspectral retrieval of turbidity for water source based on discrete particle swarm and partial least squares[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2018,49(1):173-182.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20180122&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2018.01.022.(in Chinese)
[6]韩兆迎,朱西存,房贤一,等.基于SVM与RF的苹果树冠LAI高光谱估测[J].光谱学与光谱分析,2016,36(3):800-805.HAN Z Y,ZHU X C,FANG X Y,et al.Hyperspectral estimation of apple tree canopy LAI based on SVM and RF regression[J].Spectroscopy and Spectral Analysis,2016,36(3):800-805.(in Chinese)
[7]岳学军,凌康杰,洪添胜,等.基于高光谱图像的龙眼叶片叶绿素含量分布模型[J/OL].农业机械学报,2018,49(8):18-25.YUE Xuejun,LING Kangjie,HONG Tiansheng,et al.Distribution model of chlorophyll content for longan leaves based on hyperspectral imaging technology[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2018,49(8):18-25.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20180802&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2018.08.002.(in Chinese)
[8]李粉玲,常庆瑞.基于连续统去除法的冬小麦叶片全氮含量估算[J/OL].农业机械学报,2017,48(7):174-179.LI Fenling,CHANG Qingrui.Estimation of winter wheat leaf nitrogen content based on continuum removed spectra[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(7):174-179.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20170722&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.07.022.(in Chinese)
[9]黄双萍,洪添胜,岳学军,等.基于高光谱的柑橘叶片磷含量估算模型实验[J/OL].农业机械学报,2013,44(4):202-207.HUANG Shuangping,HONG Tiansheng,YUE Xuejun,et al.Hyperspectral estimation model of total phosphorus content for citrus leaves[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2013,44(4):202-207.http:∥www.j.csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20130435&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2013.04.035.(in Chinese)
[10]刘冰峰,李军,贺佳.玉米叶片全磷含量高光谱遥感监测诊断模型研究[J/OL].农业机械学报,2015,46(8):252-258.LIU Bingfeng,LI Jun,HE Jia.Total phosphorus content estimation models of summer maize leaves based on hyperspectral remote sensing[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2015,46(8):252-258.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20150835&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2015.08.035.(in Chinese)
[11]李岚涛,任涛,汪善勤,等.基于角果期高光谱的冬油菜产量预测模型研究[J/OL].农业机械学报,2017,48(3):221-229.LI Lantao,REN Tao,WANG Shanqin,et al.Prediction models of winter oilseed rape yield based on hyperspectral data at pod-filling stage[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(3):221-229.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20170328&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.03.028.(in Chinese)
[12]张秋霞,张合兵,张会娟,等.粮食主产区耕地土壤重金属高光谱综合反演模型[J/OL].农业机械学报,2017,48(3):148-155.ZHANG Qiuxia,ZHANG Hebing,ZHANG Huijuan,et al.Hybrid inversion model of heavy metals with hyperspectral reflectance in cultivated soils of main grain producing areas[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(3):148-155.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20170319&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.03.019.(in Chinese)
[13]王敬哲,塔西甫拉提·特依拜,张东.基于分数阶微分的荒漠土壤铬含量高光谱检测[J/OL].农业机械学报,2017,48(5):152-158.WANG Jingzhe,TASHPOLAT·Tiyip,ZHANG Dong.Spectral detection of chromium content in desert soil based on fractional differential[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(5):152-158.http:∥www.jcsam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20170518&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.05.018.(in Chinese)
[14]洪永胜,朱亚星,苏学平,等.高光谱技术联合归一化光谱指数估算土壤有机质含量[J].光谱学与光谱分析,2017,37(11):3537-3542.HONG Yongsheng,ZHU Yaxing,SU Xueping,et al.Estimation of soil organic matter content using hyperspectral techniques combined with normalized difference spectral index[J].Spectroscopy&Spectral Analysis,2017,37(11):3537-3542.(in Chinese)
[15]张智韬,韩文霆.基于岭回归的土壤含水率高光谱反演研究[J/OL].农业机械学报,2018,49(5):240-248.ZHANG Zhitao,HAN Wenting.Inversion of soil moisture content from hyperspectra based on ridge regression[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2018,49(5):240-248.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20180528&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2018.05.028.(in Chinese)
[16]阿尔达克·克里木,塔西甫拉提·特依拜,张东,等.基于高光谱的ASTER影像土壤盐分模型校正及验证[J].农业工程学报,2016,32(12):144-150.ARDAK·Kelimu,TASHPOLAT·Tiyip,ZHENG Dong,et al.Calibration and validation of soil salinity estimation model based on measured hyperspectral and ASTER image[J].Transactions of the CSAE,2016,32(12):144-150.(in Chinese)
[17]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.ZHANG Xiaoguang,HUANG Biao,JI Junfeng,et al.Quantitative prediction of soil salinity content with visible-near infrared hyper-spectra in northeast China[J].Spectroscopy and Spectral Analysis,2012,32(8):2075-2079.(in Chinese)
[18]韩霁昌,李晓明.盐碱地利用障碍因子高光谱遥感反演研究[J].光谱学与光谱分析,2013,33(7):1932-1935.HAN Jichang,LI Xiaoming.Research on hyperspectral remote sensing inversion of barrier factors in saline-alkaline land use[J].Spectroscopy and Spectral Analysis,2013,33(7):1932-1935.(in Chinese)
[19]厉彦玲,赵庚星,常春艳,等.OLI与HSI影像融合的土壤盐分反演模型[J].农业工程学报,2017,33(21):173-180.LI Yanling,ZHAO Gengxing,CHANG Chunyan,et al.Soil salinity retrieval model based on OLI and HSI image fusion[J].Transactions of the CSAE,2017,33(21):173-180.(in Chinese)
[20]AN D,ZHAO G,CHANG C,et al.Hyperspectral field estimation and remote-sensing inversion of salt content in coastal saline soils of the Yellow River Delta[J].International Journal of Remote Sensing,2016,37(2):455-470.
[21]ROCHA N,ODILIO C,TEIXEIRA A,et al.Hyperspectral remote sensing for detecting soil salinization using Pro Spec TIR-VSaerial imagery and sensor simulation[J].Remote Sensing,2017,9(1):42.
[22]YAO Y,DING J L,KELIMUL A,et al.Research on remote sensing monitoring of soil salinization based on measured hyperspectral and EM38 data[J].Spectroscopy and Spectral Analysis,2013,33(7):1917-1921.
[23]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,2013,33(7):1917-1921.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,2013,33(7):1917-1921.(in Chinese)
[24]韩阳,秦伟超,王野乔.吉林省西部典型盐渍化土壤偏振反射高光谱特征与模型研究[J].光谱学与光谱分析,2014,34(6):1640-1644.HAN Yang,QIN Weichao,WANG Yeqiao.Study on the polarized reflectance hyperspectral characteristics and models of typical saline soil in the west of Jilin Province,China[J].Spectroscopy and Spectral Analysis,2014,34(6):1640-1644.(in Chinese)
[25]徐文茹,韩阳,秦艳,等.盐渍化土壤偏振高光谱信息与土壤线的关系初探[J].光谱学与光谱分析,2015,35(10):2856-2861.XU Wenru,HAN Yang,QIN Yan,et al.Study on the relationship between hyperspectral polarized information of soil salinization and soil line[J].Spectroscopy and Spectral Analysis,2015,35(10):2856-2861.(in Chinese)
[26]彭杰,王家强,向红英,等.土壤含盐量与电导率的高光谱反演精度对比研究[J].光谱学与光谱分析,2014,34(2):510-514.PENG Jie,WANG Jiaqiang,XIANG Hongying,et al.Comparative study on hyperspectral inversion accuracy of soil salt content and electrical conductivity[J].Spectroscopy and Spectral Analysis,2014,34(2):510-514.(in Chinese)
[27]张贤龙,张飞,张海威,等.基于光谱变换的高光谱指数土壤盐分反演模型优选[J].农业工程学报,2018,34(1):110-117.ZHANG Xianlong,ZHANG Fei,ZHANG Haiwei,et al.Optimization of soil salt inversion model based on spectral transformation from hyperspectral index[J].Transactions of the CSAE,2018,34(1):110-117.(in Chinese)
[28]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.DING Jianli,WU Manchun,LIU Haixia,et al.Study on the soil salinization monitoring based on synthetical hyperspectral index[J].Spectroscopy and Spectral Analysis,2012,32(7):1918-1922.(in Chinese)
[29]张建锋,宋玉民,邢尚军,等.盐碱地改良利用与造林技术[J].东北林业大学学报,2002,30(6):124-129.ZHANG Jianfeng,SONG Yumin,XING Shangjun,et al.Saline soil amelioration and forestation techniques[J].Journal of Northeast Forestry University,2002,30(6):124-129.(in Chinese)
[30]代希君,张艳丽,彭杰,等.土壤水溶性盐基离子的高光谱反演模型及验证[J].农业工程学报,2015,31(22):139-145.DAI Xijun,ZHANG Yanli,PENG Jie,et al.Prediction and validation of water-soluble salt ions content using hyperspectral data[J].Transactions of the CSAE,2015,31(22):139-145.(in Chinese)
[31]王海江,蒋天池,YUNGER J A,等.基于支持向量机的土壤主要盐分离子高光谱反演模型[J/OL].农业机械学报,2018,49(5):263-270.WANG Haijiang,JIANG Tianchi,YUNGER J A,et al.Hyperspectral inverse model for soil salt ions based on support vector machine[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2018,49(5):263-270.http:∥www.jcsam.org/jcsam/ch/r eader/view_abstract.aspx?flag=1&file_no=20180531&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2018.05.031.(in Chinese)
[32]鲍士旦.土壤农化分析[M].3版.北京:中国农业出版社,2000.
[33]雷志栋.土壤水动力学[M].北京:清华大学出版社,1988.
[34]李志婷,王昌昆,潘贤章,等.基于模拟Landsat-8 OLI数据的小麦秸秆覆盖度估算[J].农业工程学报,2016,32(增刊1):145-152.LI Zhiting,WANG Changkun,PAN Xianzhang,et al.Estimation of wheat residue cover using simulated Landsat-8 OLI datas[J].Transactions of the CSAE,2016,32(Supp.1):145-152.(in Chinese)
[35]SAID N.Estimation of soil salinity using three quantitative methods based on visible and near-infrared reflectance spectroscopy:a case study from Egypt[J].Arabian Journal of Geosciences,2015,8(7):5127-5140.
[36]DOUAOUI A E K,HERVNICOLAS,WALTER C.Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data[J].Geoderma,2006,134(1-2):217-230.
[37]张录,张芳,熊黑钢,等.不同季节强碱土土壤呼吸影响因子分析与模型预测[J].干旱地区农业研究,2017,35(1):71-78.ZHANG Lu,ZHANG Fang,XIONG Heigang,et al.Analysis of soil respiration factors and model prediction of alkaline soil in different seasons[J].Agricultural Research in Arid Regions,2017,35(1):71-78.(in Chinese)
[38]ROSSEL R A V,TAYLOR H J,MCBRATNEY A B.Multivariate calibration of hyperspectralγ-ray energy spectra for proximal soil sensing[J].European Journal of Soil Science,2010,58(1):343-353.
[39]张东辉,赵英俊,秦凯,等.光谱变换方法对黑土养分含量高光谱遥感反演精度的影响[J].农业工程学报,2018,34(20):141-147.ZHANG Donghui,ZHAO Yingjun,QIN Kai,et al.Influence of spectral transformation methods on nutrient content inversion accuracy by hyperspectral remote sensing in black soil[J].Transactions of the CSAE,2018,34(20):141-147.(in Chinese)
[40]陈弈云,赵瑞瑛,齐天赐,等.结合光谱变换和Kennard-Stone算法的水稻土全氮光谱估算模型校正集构建策略研究[J].光谱学与光谱分析,2017,37(7):2133-2139.CHEN Yiyun,ZHAO Ruiying,QI Tianci,et al.Constructing representative calibration dataset based on spectral transformation an Kennard-Stone algorithm for VNIR modeling of soil total nitrogen in paddy soil[J].Spectroscopy and Spectral Analysis,2017,37(7):2133-2139.(in Chinese)
[41]彭杰,刘焕军,史舟,等.盐渍化土壤光谱特征的区域异质性及盐分反演[J].农业工程学报,2014,30(17):167-174.PENG Jie,LIU Huanjun,SHI Zhou,et al.Regional heterogeneity of hyperspectral characteristics of salt-affected soil and salinity inversion[J].Transactions of the CSAE,2014,30(17):167-174.(in Chinese)
[42]SRIVASTAVA R,SETHI M,YADAV R K,et al.Visible-near infrared reflectance spectroscopy for rapid characterization of salt-affected soil in the Indo-Gangetic Plains of Haryana,India[J].Journal of the Indian Society of Remote Sensing,2017,45(2):307-315.
[43]刘亚秋,陈红艳,王瑞燕,等.基于可见/近红外光谱的黄河口区土壤盐分及其主要离子的定量分析[J].中国农业科学,2016,49(10):1925-1935.LIU Yaqiu,CHEN Hongyan,WANG Ruiyan,et al.Quantitative analysis of soil salt and its main ions based on visible/near infrared spectroscopy in Estuary Area of Yellow[J].Scientia Agricultura Sinica,2016,49(10):1925-1935.(in Chinese)
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |