黄河三角洲土壤含盐量定量遥感反演

doi:10.7523/j.issn.1002-1175.2013.02.012

中国科学院大学学报 ›› 2013, Vol. 30 ›› Issue (2): 220-227.DOI: 10.7523/j.issn.1002-1175.2013.02.012

黄河三角洲土壤含盐量定量遥感反演

张成雯^1,2, 唐家奎^1,2, 于新菊^1,2, 王春磊³, 米素娟^1,2

1. 中国科学院海岸带环境过程重点实验室, 山东省海岸带环境过程重点实验室, 中国科学院烟台海岸带研究所, 山东烟台 264003;
2. 中国科学院研究生院, 北京 100049;
3. 河北联合大学, 河北唐山 063009

收稿日期:2012-02-17 修回日期:2012-04-23 发布日期:2013-03-15
通讯作者: 唐家奎
基金资助:
国家自然科学基金(40801124)、山东省中青年科学家科研奖励基金(2010BSA06013)、中国科学院创新团队国际合作伙伴计划、中国科学院数字地球重点实验室开放基金(2011LDE015)和中国科学院研究生院院长基金资助

Quantitative retrieval of soil salt content based on remote sensing in the Yellow River delta

ZHANG Cheng-Wen^1,2, TANG Jia-Kui^1,2, YU Xin-Ju^1,2, WANG Chun-Lei³, MI Su-Juan^1,2

1. Key Laboratory of Coastal Zone Environmental Processes, Chinese Academy of Sciences; Shandong Provincial Key Laboratory of Coastal Zone Environmental Processes, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, Shandong, China;
2. Graduate University, Chinese Academy of Sciences, Beijing 100049, China;
3. Hebei United University, Tangshan 063009, Hebei, China

Received:2012-02-17 Revised:2012-04-23 Published:2013-03-15

摘要/Abstract

摘要：

以黄河三角洲地区为实验区域,利用实测的土壤全盐含量数据,结合中国产的中巴地球资源卫星02B(CBERS-02B)多光谱遥感影像,分别应用传统的多元线性回归模型和BP人工神经网络模型,对其进行含盐量反演建模,并对2种模型的精度进行比较.实验表明,应用BP人工神经网络建模,明显改善了反演精度;且该反演模型更适宜于高盐度区域(全盐含量>1%)土壤含盐量反演制图,具有较好的应用前景.

关键词: 盐渍化, 黄河三角洲, CBERS-02B, 遥感定量反演

Abstract:

The Yellow River delta is rich in land resource, but serious soil salinization affects local agricultural production and poses a threat to stability of ecological environment. The traditional multiple linear regression model and the BP artificial neural network model were both used to derive the soil salinity in the Yellow River delta based on the home-made CBERS-02B multispectral images. It is found that the BP artificial neural network model performs much better than the multiple linear regression model in inversing soil salinity, especially for heavy saline soil area.

Key words: salinity, Yellow River delta, CBERS-02B, quantitative remote sensing inversion

中图分类号:

S156.4
X833

张成雯, 唐家奎, 于新菊, 王春磊, 米素娟. 黄河三角洲土壤含盐量定量遥感反演[J]. 中国科学院大学学报, 2013, 30(2): 220-227.

ZHANG Cheng-Wen, TANG Jia-Kui, YU Xin-Ju, WANG Chun-Lei, MI Su-Juan. Quantitative retrieval of soil salt content based on remote sensing in the Yellow River delta[J]. , 2013, 30(2): 220-227.

参考文献

[1] Kang Q,Yu R,Zhang Z X,et al.Advanced in study on remote sensing application of soil salinization[J]. Remote Sensing Technology and Application, 2005, 20(4):447-454(in Chinese).亢庆,于嵘,张增祥,等.土壤盐碱化遥感应用研究进展[J].遥感技术与应用,2005,20(4):447-454.

[2] Weng Y L,Gong P. A review on remote sensing technique for salt-affected soils[J].Scientia Geographica Sinica, 2006, 26(3):369-375(in Chinese).翁永玲,宫鹏.土壤盐渍化遥感应用研究进展[J].地理科学,2006,26(3):369-375.

[3] Dehaan R L, Taylor G R. Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization[J]. Remote Sensing of Environment, 2002, 80:406-418.

[4] Verma K S, Saxena R K, Barthwal, et al. Remote sensing technique for mapping salt affected soils[J].International Journal of Remote Sensing,1994,15(9):1901-1914.

[5] Szabolcs I. Salinization of soil and water and its relation to desertification[J]. Desertification Control Bulletin, 1992, 21: 32-37.

[6] 水利部计划司, 农水司,中国水利水电科学研究院. 全国灌溉面积发展"九五"计划及2010年规划[R]. 1997.

[7] Fu Q H, Ni S X, Wang S X,et al. Retrieval of soil salt content based on remote sensing[J]. Transactions of the CSAE, 2007, 23(1):48-54(in Chinese).扶卿华,倪绍祥,王世新,等.土壤盐分含量的遥感反演研究[J].农业工程学报,2007,23(1):48-54.

[8] Everitt J, Escobar D, Gerbermann A, et al. Detecting saline soils with video imagery[J]. Photogrammetric Engineering and Remote Sensing, 1998, 54:1283-1287.

[9] Wiegand D L, Everitt J H, Richardson A J. Comparison of multispectral video and SPOT-1 HRV observations for cotton affected by soil salinity[J]. International Journal of Remote Sensing, 1992, 13:1511-1525.

[10] Dwivedi R S,Rao B R M.The selection of the best possible Landsat TM band combination for delineating salt-affected soils[J]. International Journal of Remote Sensing, 1992, 13(11): 2051-2058.

[11] Rao 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.

[12] Bowers S S, Hanks R J. Reflectance of radiant energy from soils[J]. Soil Science, 1965, 100:130-138.

[13] Csillag F, Pa'sztor L, Biehl L. Spectral band selection for the characterization of salinity status of soils[J]. Remote Sensing of Environment, 1993(43):231-242.

[14] Wang D, Wilson C,Shannon M C. Interpretation of salinity and irrigation effects on soybean canopy reflectance in visible and near-infrared spectrum domain[J]. International Journal of Remote Sensing,2002,23(5):811-824.

[15] Dehaan R L, Taylor G R. Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization[J]. Remote Sensing of Environment,2002(80): 406-417.

[16] Mougenot B, Epema G F, Pouget M. Remote sensing of salt affected soils[J]. Remote Sens- Rev, 1993, 7:241-259.

[17] Dwivedi R S, Sreenivas K, et al. Inventory of salt-affect-ed soils and waterlogged areas: a remote sensing approach[J]. International Journal of Remote Sensing,1999, 20(8):1589-1599.

[18] Peng W L, Li T J. The effect of Kauth-Thomas transformation of TM data in the analysis of saline soil-An example in Yang Gao basin[J]. Remote Sensing of Environment China, 1989, 4(4):183-190(in Chinese).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[J].环境遥感,1989,4(3):183-190.

[19] 陈述彭.国土普查卫星资料应用研究(第二集)[M].北京:科学出版社,1989.

[20] Deng X L, Yu R, Kang Q, et al. Monitoring of the saline-alkali land distribution in Yellow River delta based on remote sensing[J]. Remote Sensing Information, 2006, 5:34-36(in Chinese).邓小炼,于嵘,亢庆,等.基于遥感的黄河三角洲地区盐碱地分布监测[J].遥感信息应用技术,2006,5:34-36.

[21] Kang Q, Yu R, Zhang Z X, et al. Rapid survey of land salinization based on multi-source remote sensing data[J].Remote Sensing Information,2005,6:42-45(in Chinese).亢庆,于嵘,张曾祥,等.基于多源数据的土地盐碱化遥感快速监测[J].遥感信息应用技术,2005,6:42-45.

[22] Guan Y X, Liu G H, Wang J F. Regionalization of salt-affected land for amelioration in the Yellow River delta based on GIS[J]. Acta Geographica Sinca, 2002, 56(2):198-205(in Chinese).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):198-205.

[23] 刘高焕,叶庆华,刘庆生,等.黄河三角洲生态环境动态监测与数字模拟[M].北京:科学出版社,2003:52-74.

[24] Hou C L. Research on remote sensing monitoring in the typical salt-affected zone of the Yellow River delta based on BP neural net[D].Qingdao: College of Geo-resources & Information China University of Petroleum (East China), 2010(in Chinese).侯春玲.基于BP神经网络的黄河三角洲典型盐渍区遥感监测研究[D].青岛:中国石油大学(华东),2010.

[25] He Y H, Shi L S, Zhang R H, et al. Application research of CBERS-02 data in land- use survey[J].China Land Science, 2007, 21(2):51-57(in Chinese).何宇华,史良树,张荣慧,等. 中巴资源卫星数据(CBERS-02)在土地调查中的研究[J].中国土地科学,2007,21(2):51-57.

[26] Zhang C W, Tang J K, Mi S J, et al. Research on new LBV data transformation method for CBERS-02B multi-spectral images[J]. Geography and Geo- Information Science, 2011, 27(3):21-25(in Chinese).张成雯,唐家奎,米素娟,等.中巴02B卫星多光谱影像中LBV数据变换方法研究[J].地理与地理信息科学,2011,27(3):21-25

[27] Zhang W W. The study of paddy rice coverage monitoring based on multi-temporal CBRES CCD data[D].Chongqing: Southwest University, 2008(in Chinese).张微微.基于多时相CBERS CCD图像的水稻种植面积监测[D].重庆:西南大学,2008.

[28] Lin B. Multiple linear regression analysis and its application[J].China Science and Technology Information, 2010, 9:60-61(in Chinese).林彬.多元线性回归分析及其应用[J].中国科技信息,2010,9:60-61.

[29] Fu Q H. Soil salt content inversion using remote sensing and its application in study on oriental migratory locust[D]. Nanjing: Nanjing Normal University, 2005(in Chinese).扶卿华.土壤盐分含量的遥感反演及其在东亚飞蝗研究中的应用[D].南京:南京师范大学,2005.

[30] Gao D Q. On structures of supervised linear basis function feedforvard three-lyered neural networks[J]. Chinese J Computers, 1998, 21(1):80-86(in Chinese).高大启.有教师的线性基本函数前向三层神经网络结构研究[J].计算机学报,1998,21(1):80-86.

[31] Liao N F, Gao Z Y. The most suitable architecture if hidden-layer in BP neural networks for function approximation[J].Journal of Beijing Institute of Technology, 1998, 18(4):476-480(in Chinese).廖宁放,高稚允. BP神经网络用于函数逼近的最佳隐层结构[J]. 北京理工大学学报,1998,18(4):476-480.

[32] 飞思科技产品研发中心.神经网络理论与MATLAB 2007实现[M].北京:电子工业出版社,2007.

[33] 苏金明,王永利. MATLAB 7.0 实用指南[M].北京:电子工业出版社,2004.

[34] Nils J Nilsson著. 郑扣根, 庄越挺译. 人工智能[M].北京:机械工业出版社,2000.

[35] Shi Y N. Study of the hyperspectral remote sensing of shallow waters bathymetry with artificial neural network technology[D].Qingdao: Ocean University of China, 2005(in Chinese).

黄河三角洲土壤含盐量定量遥感反演

Quantitative retrieval of soil salt content based on remote sensing in the Yellow River delta

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