Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2015, Vol. 27 Issue (2) : 100-104     DOI: 10.6046/gtzyyg.2015.02.16
Technology and Methodology |
Research on remote sensing monitoring model of soil salinization based on spectrum characteristic analysis
GUAN Hong1, JIA Keli1,2, ZHANG Zhinan1, MA Xin1
1. College of Resource and Environment, Ningxia University, Yinchuan 750021, China;
2. Ningxia Key Laboratory of Intelligent Sensing for Desert Information, Yinchuan 750021, China
Download: PDF(3556 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

To establish the remote sensing monitoring model for soil salinization, the authors chose the typical soil salinization area in Pingluo County of Ningxia as the study area, and measured the spectral data in the field. These data, together with the values of pH and salinity measured in the laboratory, were taken as the basic data. Hyperspectral data processing method was used to analyze the spectral characteristics of different levels of soil salinization. Spectral data were transformed with 11 different approaches, such as reciprocal, logarithm, root mean square and their first order differentials. After the transformation, the correlation analysis was carried out between the obtained soil spectra and soil salinity. The most sensitive band was selected, and the field spectral sensitive band and soil salinity were used and the multiple linear regression was employed to establish the spectral quantitative models for evaluating the soil salinization degrees. The results show that the reciprocal first order differential of measured soil spectral is most sensitive to soil salinization degrees. The spectral quantitative models based on the wavelengths of 940 nm and 1 094 nm are the best.

Keywords remote sensing image      GPS      navigation     
:  TP751.1  
  P237  
Issue Date: 02 March 2015
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
YANG Qiyong
JIANG Zhongcheng
MA Zulu
SHEN Lina
Cite this article:   
YANG Qiyong,JIANG Zhongcheng,MA Zulu, et al. Research on remote sensing monitoring model of soil salinization based on spectrum characteristic analysis[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(2): 100-104.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2015.02.16     OR     https://www.gtzyyg.com/EN/Y2015/V27/I2/100

[1] 中国土壤学会.中国土壤学在前进[M].北京:中国农业科技出版社,1995. Soil Science Society of China.Chinese Soil Science in Advance[M].Beijing:China of Agricultural Science and Technology Press,1995.

[2] 李新国,李和平,任云霄,等.开都河流域下游绿洲土壤盐渍化特征及其光谱分析[J].土壤通报,2012,43(1):166-170. Li X G,Li H P,Ren Y X,et al.Analysis on the characteristics of the oasis soil salinization and soil spectrum in the lower reaches of Kaidu River Basin,Xinjiang[J].Chinese Journal of Soil Science,2012,43(1):166-170.

[3] Clark R N,Roush T L.Reflectance spectroscopy:Quantitative analysis techniques for remote sensing application[J].Journal of Geographical Research,1984,89(7):6329-6340.

[4] 孙毅,林培.盐渍土土壤光谱反射率与表土含盐量关系[J].陕西农业科学,1991(3):19-20. Sun Y,Lin P.The soil spectrum and the soil salinization[J].Shaanxi Journal of Agricultural Sciences,1991(3):19-20.

[5] Sandholt I,Rasmussen K,Andersen J.A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J].Remote Sensing of Environment,2001,79(2/3):213-224.

[6] 丁建丽,瞿娟,孙永猛,等.基于MSAVI-WI特征空间的新疆渭干河-库车河流域绿洲土壤盐渍化研究[J].地理研究,2013,32(2):223-232. Ding J L,Qu J,Sun Y M,et al.The retrieval model of soil salinization information in arid region based on MSAVI-WI feature space:A case study of the delta oasis in Weigan-Kuqa Watershed[J].Geographical Research,2013,32(2):223-232.

[7] 张成雯,唐家奎,于新菊,等.黄河三角洲土壤含盐量定量遥感反演[J].中国科学院研究生院学报,2013,30(2):220-227. Zhang C W,Tang J K,Yu X J,et al.Quantitative retrieval of soil salt content based on remote sensing in the Yellow River delta[J].Journal of Graduate University of Chinese Academy of Sciences,2013,30(2):220-227.

[8] 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 J L,Wu M C,Liu H X,et al.Study on the soil salinization monitoring based on synthetical hyperspectral index[J].Spectroscopy and Spectral Analysis,2012,32(7):1918-1922.

[9] 李晓松,李增元,高志海,等.基于NDVI与偏最小二乘回归的荒漠化地区植被覆盖度高光谱遥感估测[J].中国沙漠,2011,31(1):162-167. Li X S,Li Z Y,Gao Z H,et al.Estimation of vegetation cover in desertified regions from hyperion imageries using NDVI and partial least squares regression[J].Journal of Desert Research,2011,31(1):162-167.

[10] 张芳,熊黑钢,栾福明,等.土壤碱化的实测光谱响应特征[J].红外与毫米波学报,2011,30(1):55-60. Zhang F,Xiong H G,Luan F M,et al.Characteristics of field-measured spectral response to alkalinization soil[J].Journal of Infrared and Millimeter Waves,2011,30(1):55-60.

[11] 姚远,丁建丽,张芳,等.基于高光谱指数和电磁感应技术的区域土壤盐渍化监测模型[J].光谱学与光谱分析,2013,33(6):1658-1664. Yao Y,Ding J L,Zhang F,et al.Research on model of soil salinization monitoring based on hyperspectral index and EM38[J].Spectroscopy and Spectral Analysis,2013,33(6):1658-1664.

[12] 王艳,王正祥,廉晓娟,等.天津滨海地区土壤电导率的测定及其与含盐量的关系[J].天津农业科学,2011,17(2):18-21. Wang Y,Wang Z X,Lian X J,et al.Measurement of soil electric conductivity and relationship between soluble salt content and electrical conductivity in Tianjin coastal area[J].Tianjin Agricultural Sciences,2011,17(2):18-21.

[13] 王遵亲,祝寿泉,俞仁培,等.中国盐渍土[M].北京:科学出版社,1993. Wang Z Q,Zhu S Q,Yu R P,et al.Saline Soil in China[M].Beijing:Science Press,1993.

[14] Savitzky A,Golay M J E.Smoothing and differentiation of data by simplified least squares procedures[J].Analytical Chemistry,1964,36(8):1627-1639.

[15] Csillag F,Pásztor L,Biehl L L.Spectral band selection for the characterization of salinity status of soils[J].Remote Sensing of Environment,1993,43(3):231-242.

[16] 吴昀昭,田庆久,季峻峰,等.土壤光学遥感的理论、方法及应用[J].遥感信息,2003(1):40-47. Wu Y Z,Tian Q J,Ji J F,et al.Soil remote sensing research theory method and application[J].Remote Sensing Information,2003(1):40-47.

[17] 吴亚坤,杨劲松,李晓明.基于光谱指数与EM38的土壤盐分空间变异性研究[J].光谱学与光谱分析,2009,29(4):1023-1027. Wu Y K,Yang J S,Li X M.Study on spatial variability of soil salinity based on spectral indices and EM38 readings[J].Spectroscopy and Spectral Analysis,2009,29(4):1023-1027.

[18] 李美婷,武红旗,蒋平安,等.利用土壤的近红外光谱特征测定土壤含水量[J].光谱学与光谱分析,2012,32(8):2117-2121. Li M T,Wu H Q,Jiang P A,et al.Measuring soil water content by using near infrared spectral characteristics of soil[J].Spectroscopy and Spectral Analysis,2012,32(8):2117-2121.

[19] 赵振亮.基于高光谱数据的盐渍化土壤光谱特征研究及信息提取[D].乌鲁木齐:新疆大学,2013. Zhao Z L.The Study on Spectrum Characteristics and Information Extraction of Salinization Soil based on Hyper Spectral Data[D].Urumqi:Xinjiang University,2013.

[20] 朱高飞,盛建东,范燕敏,等.典型盐渍化土壤光谱特征及土壤含盐量反演建模[J].农业网络信息,2013(6):20-25. Zhu G F,Sheng J D,Fan Y M,et al.Spectral characteristics of typical saline soil and inversion modeling of soil salt content in northern Xinjiang[J].Agriculture Network Information,2013(6):20-25.

[21] 雷磊,塔西甫拉提·特依拜,丁建丽,等.基于HJ-1A高光谱影响的盐渍化土壤信息提取——以渭干河-库车河绿洲为例[J].中国沙漠,2013,33(4):1104-1109. Lei L,Tiyip T,Ding J L,et al.Soil salinization information extraction by using hyperspectral date of HJ-1A HIS:A case study in the Oasis of Ugan and Kuqa,Xinjiang,China[J].Journal of Desert Research,2013,33(4):1104-1109.

[1] ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[2] XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[3] SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[4] LIU Zhizhong, SONG Yingxu, YE Runqing. An analysis of rainstorm-induced landslides in northeast Chongqing on August 31, 2014 based on interpretation of remote sensing images[J]. Remote Sensing for Natural Resources, 2021, 33(4): 192-199.
[5] ZHANG Chengye, XING Jianghe, LI Jun, SANG Xiao. Recognition of the spatial scopes of tailing ponds based on U-Net and GF-6 images[J]. Remote Sensing for Natural Resources, 2021, 33(4): 252-257.
[6] LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.
[7] SANG Xiao, ZHANG Chengye, LI Jun, ZHU Shoujie, XING Jianghe, WANG Jinyang, WANG Xingjuan, LI Jiayao, YANG Ying. Application of intensity analysis theory in the land use change in Yijin Holo Banner under the background of coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(3): 148-155.
[8] WANG Yiuzhu, HUANG Liang, CHEN Pengdi, LI Wenguo, YU Xiaona. Change detection of remote sensing images based on the fusion of co-saliency difference images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 89-96.
[9] LIU Wanjun, GAO Jiankang, QU Haicheng, JIANG Wentao. Ship detection based on multi-scale feature enhancement of remote sensing images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 97-106.
[10] LU Qi, QIN Jun, YAO Xuedong, WU Yanlan, ZHU Haochen. Buildings extraction of GF-2 remote sensing image based on multi-layer perception network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 75-84.
[11] HU Suliyang, LI Hui, GU Yansheng, HUANG Xianyu, ZHANG Zhiqi, WANG Yingchun. An analysis of land use changes and driving forces of Dajiuhu wetland in Shennongjia based on high resolution remote sensing images: Constraints from the multi-source and long-term remote sensing information[J]. Remote Sensing for Land & Resources, 2021, 33(1): 221-230.
[12] LIU Zhao, ZHAO Tong, LIAO Feifan, LI Shuai, LI Haiyang. Research and comparative analysis on urban built-up area extraction methods from high-resolution remote sensing image based on semantic segmentation network[J]. Remote Sensing for Land & Resources, 2021, 33(1): 45-53.
[13] ZHENG Zhiteng, FAN Haisheng, WANG Jie, WU Yanlan, WANG Biao, HUANG Tengjie. An improved double-branch network method for intelligently extracting marine cage culture area[J]. Remote Sensing for Land & Resources, 2020, 32(4): 120-129.
[14] WANG Xiaobing. Denoising algorithm based on the fusion of lifting wavelet thresholding and multidirectional edge detection of remote sensing image of mining area[J]. Remote Sensing for Land & Resources, 2020, 32(4): 46-52.
[15] WEI Hongyu, ZHAO Yindi, DONG Jihong. Cooling tower detection based on the improved RetinaNet[J]. Remote Sensing for Land & Resources, 2020, 32(4): 68-73.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech