Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2014, Vol. 26 Issue (2) : 105-111     DOI: 10.6046/gtzyyg.2014.02.18
Technology and Methodology |
Prediction of soil organic matter content based on ground measured spectra
GUAN Xiao1, ZHOU Ping1, CHEN Shengbo2
1. China University of Geosciences (Beijing) School of Earth Sciences and Resources, Beijing 100083, China;
2. College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China
Download: PDF(1900 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

For the purpose of better predicting the soil organic matter content in the study area, the soil near the tailings dam of the Dexing copper mine was chosen as the study object. Using the ASD device in the laboratory, the authors measured 68 groups of soil samples and, by studying soil reflectance spectral characteristics, took the logarithmic differential transformation of the selected reflectance spectra as the dependent variable of the soil organic matter prediction model. The correlation analysis of soil organic matter and soil spectra showed that the first derivative of logarithm of 402 nm and 2 312 nm wavelength reflectance was the best. Finally, from the multiple regression analysis and fuzzy mathematics, two models of organic matter content prediction were established. The results demonstrate that the research method based on fuzzy mathematics is better than multiple linear regressions, with the correlation coefficient up to 89.3% and the error relatively smaller. Studies have shown that using ground measured spectra to predict soil organic matter content has such advantages as short cycle and low cost.

Keywords forest disturbance      remote sensing      Landsat      monitoring      progress     
:  TP79  
Issue Date: 28 March 2014
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
ZHU Shanyou
ZHANG Ying
ZHANG Hailong
CAO Yun
ZHANG Guixin
Cite this article:   
ZHU Shanyou,ZHANG Ying,ZHANG Hailong, et al. Prediction of soil organic matter content based on ground measured spectra[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 105-111.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2014.02.18     OR     https://www.gtzyyg.com/EN/Y2014/V26/I2/105

[1] Baumgardner M F,Silva L F,Biehl L L,et al.Reflectance properties of soils[J].Advances in Agronomy,1985,38:1-44.

[2] Krishnan P,Alexander J D,Butler B J,et al.Reflectance technique for predicting soil organic matter[J].Soil Science Society of America Journal,1980,44(6):1282-1285.

[3] 周萍.高光谱土壤成分信息的量化反演[D].北京:中国地质大学(北京),2006. Zhou P.Quantitative retrieval of soil constituents using hyperspectrum[D].Beijing:China University of Geosciences(Beijing),2006.

[4] Wang J,He T,Lv C Y,et al.Mapping soil organic matter based on land degradation spectral response units using Hyperion images[J].International Journal of Applied Earth Observation and Geoinformation,2010,12:171-180.

[5] 杨修,高林.德兴铜矿矿山废弃地植被恢复与重建研究[J].生态学报,2001,21(11):1932-1940. Yang X,Gao L.A study on re-vegetation in mining wasteland of Dexing Copper Mine,China[J].Acta Ecologica Sinica,2001,21(11):1932-1940.

[6] 姜文显.德兴县科学技术志[Z].德兴:德兴市科学技术委员会编,1992. Jiang W X.Dexing county annals of science and technology[Z].Dexing:Dexing Municipal Science and Technology Committees,1992.

[7] 张海星,姚丽文,熊报国,等.德兴铜矿1号尾矿库废弃土地生态恢复试验研究[J].环境与开发,1999,14(1):10-11,45. Zhang H X,Yao L W,Xiong B G,et al.Study on the eco-recover test of waste land of 1# tailings bank in Dexing copper mine[J].Environment and Exploitation,1999,14(1):10-11,45.

[8] 刘炜,常庆瑞,郭曼,等.小波变换在土壤有机质含量可见/近红外光谱分析中的应用[J].干旱地区农业研究,2010,28(5):241-246. Liu W,Chang Q R,Guo M,et al.Application of wavelet transformation in detection of organic matter content based on visible/near infrared reflectance spectroscopy[J].Agricultural Research in the Arid Areas,2010,28(5):241-246.

[9] 胡芳,蔺启忠,王钦军,等.土壤钾含量高光谱定量反演研究[J].国土资源遥感,2012,24(4):157-162. Hu F,Lin Q Z,Wang Q J,et al.Quantitative inversion of soil potassium content by using hyperspectral reflectance[J].Remote Sensing for Land and Resources,2012,24(4):157-162.

[10] 何挺,王静,程烨,等.土壤氧化铁光谱特征研究[J].地理与地理信息科学,2006,22(2):30-34. He T,Wang J,Cheng Y,et al.Study on spectral features of soil Fe2O3[J].Geography and Geo-information Science,2006,22(2):30-34.

[11] 刘磊,沈润平,丁国香.基于高光谱的土壤有机质含量估算研究[J].光谱学与光谱分析,2011,31(3):762-766. Liu L,Chen R P,Ding G X.Studies on the estimation of soil organic matter content based on hyper-spectrum[J].Spectroscopy and Spectral Analysis,2011,31(3):762-766.

[12] 黄应丰,刘腾辉.华南主要土壤类型的光谱特性与土壤分类[J].土壤学报,1995,32(1):58-68. Huang Y F,Liu T H.Spectral characteristics of main types of soils in Southern China and soil classification[J].Acta Pedologica Sinica,1995,32(1):58-68.

[13] 周萍,王润生,阎柏琨,等.高光谱遥感土壤有机质信息提取研究[J].地理科学进展,2008,27(5):27-34. Zhou P,Wang R S,Yan B K,et al.Extraction of soil organic matter information by hyperspectral remote sensing[J].Progress in Geography,2008,27(5):27-34.

[14] 徐彬彬.土壤剖面的反射光谱研究[J].土壤,2000,32(6):281-287. Xu B B.Reflectance spectra of soil profile[J].Soils,2000,32(6):281-287.

[15] 张娟娟,余华,乔红波,等.基于高光谱特征的土壤有机质含量估测研究[J].中国生态农业学报,2012,20(5):566-572. Zhang J J,Yu H,Qiao H B,et al.Soil organic matter content estimation based on hyperspectral properties[J].Chinese Journal of Eco-agriculture,2012,20(5):566-572.

[16] 李希灿,宗学才,李军,等.模糊综合分析预测模式与应用[J].山东农业大学学报(自然科学版),2003,34(2):267-271. Li X C,Zong X C,Li J,et al.Fuzzy comprehensive prediction model and its application[J].Journal of Shandong Agricultural University:Natural Science,2003,34(2):267-271.

[17] Wang J.Development of geographic image cognition approach for land degradation assessment with Hyperion images[DB/OL].http://www.lib.polyu.edu.hk,2011-07-18.

[18] 李希灿,刘桂生,张波,等.水文中长期预报成因模糊综合分析预测模式[J].黑龙江水专学报,1998,25(3):67-71. Li X C,Liu R P,Zhang B.Prediction model of fuzzy comprehensive causation analysis for middle and long term hydrological forecast[J].Journal of Heilongjiang Hydraulic Engineering College,1998,25(3):67-71.

[19] 李勇志,唐家奎,王德强,等.莱州湾海岸带土壤光谱分析与有机质反演研究[J].地理与地理信息科学,2012,28(4):79-82. Li Y Z,Tang J K,Wang D Q,et al.Spectral analysis and retrieval of soil organic matter in coastal zone of Laizhou Bay[J].Geography and Geo-information Science,2012,28(4):79-82.

[1] LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[2] WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[3] LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[4] 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.
[5] 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.
[6] 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.
[7] LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[8] DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[9] GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[10] QIN Dahui, YANG Ling, CHEN Lunchao, DUAN Yunfei, JIA Hongliang, LI Zhenpei, MA Jianqin. A study on the characteristics and model of drought in Xinjiang based on multi-source data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 151-157.
[11] BO Yingjie, ZENG Yelong, LI Guoqing, CAO Xingwen, YAO Qingxiu. Impacts of floating solar parks on spatial pattern of land surface temperature[J]. Remote Sensing for Natural Resources, 2022, 34(1): 158-168.
[12] YANG Wang, HE Yi, ZHANG Lifeng, WANG Wenhui, CHEN Youdong, CHEN Yi. InSAR monitoring of 3D surface deformation in Jinchuan mining area, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 177-188.
[13] LIU Mingxing, LIU Jianhong, MA Minfei, JIANG Ya, ZENG Jingchao. Monitoring of Zanthoxylum bungeanum Maxim planting using GF-2 PMS images and the random forest algorithm: A case study of Linxia, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 218-229.
[14] ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[15] HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
Viewed
Full text


Abstract

Cited

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