光谱学与光谱分析 |
|
|
|
|
|
Estimating Soil Iron Content Based on Reflectance Spectra |
XIONG Jun-feng1, 2, ZHENG Guang-hui1*, LIN Chen2 |
1. School of Geography and Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China 2. Key Laboratory of Watershed Geographic Sciences, Institute of Geography and Limnology, Chinese Academy Sciences, Nanjing 210008, China |
|
|
Abstract In recent decades, the application of spectral technology in soil science is getting more and more attention. Soil information can be obtained quickly by using soil reflectance spectra to understand the physical and chemical properties of soil and to estimate soil iron content. In previous studies, the surface soil always is selected for the estimation of soil iron content by using spectroscopy. It needs to estimate total iron and, the different forms of soil iron is ignored, therefore, the estimation result is not ideal. In order to gets a different form of soil iron processing method of optimal model to evaluate the accuracy of models, as well as discuss the organic matter content and soil depth on the influence of different forms of soil iron estimation accuracy. A total of 160 soil samples were collected from 20 sites in Dongtai city, Jiangsu province. These samples were ground to 10 meshes and 100 meshes. In the use of 8 different methods for the pretreatment of the same time each method will be selected by a variety of parameters, using partial least squares regression method to model the total reflection band and the total iron, free iron, amorphous iron content in the soil respectively, then evaluation model precision. The results showed that: (1) the optimal model of three kinds of soil iron was all ground to 100 meshes and the best pretreatment method was MSC. The prediction accuracy of total iron was acceptable and R2 was less than 0.6. The results of free iron and amorphous iron inversion were better and the R2 was 0.77 and 0.69, respectively. The errors were small and the models were stable. (2) Because the ferric metasilicate in total iron is easily affected by external environment, the organic matter and soil depth are of great influence on the estimate precision of total iron the most. But the estimation accuracy of free iron is the least affected. Because of the low content of amorphous iron, the estimated model is also susceptible to the influence of organic matter and soil depth.
|
Received: 2015-09-28
Accepted: 2016-01-25
|
|
Corresponding Authors:
ZHENG Guang-hui
E-mail: zgh@nuist.edu.cn
|
|
[1] Viscarral Rossel R A, Bui E N, Caritat P de. Journal of Geophysical Research, 2010, 115:F04031. [2] Viscarral Rossel R A, Walvoort D J J, McBratney A B, et al. Geoderma, 2006, 131(1-2): 59. [3] Reeves J B. Geoderma, 2010, 158: 3. [4] Viscarral Rossel R A, Adamchuk V I, Sudduth K A, et al. Advances in Agronomy, 2011, 113: 243. [5] Ben-Dor E. Advances in Agronomy, 2002, 75: 173. [6] Stenberg B, Viscarral Rossel R A, Mouazen A M, et al. Advances in Agronomy, 2010, 107: 163. [7] Kuang B Y, Mahmood H S, Quraishi M Z, et al. Advances in Agronomy, 2012, 114: 155. [8] Song Y X, Li F L, Yang Z F, et al. Applied Clay Science, 2012, 64: 75. [9] Ben-Dor E, Banin A. Soil Science Society of America Journal, 1995b, (59): 364. [10] HE Ting, WANG Jing, CHENG Ye, et al(何 挺,王 静,程 烨,等). Geography and Geo-Information Science(地理与地理信息科学), 2006, 22(2): 30. [11] PENG Jie, ZHANG Yang-zhu, ZHOU Qing(彭 杰,张杨珠,周 清). Journal of Tarim University(塔里木大学学报), 2006, 18(2): 18. [12] XIA Xue-qi, JI Jun-feng, CHEN Jun, et al(夏学齐,季峻峰,陈 骏,等). Earth Science Frontiers(地学前缘), 2009, 16(4): 354. [13] Dematte J A M, Sousa A A, Alves M C, et al. Geoderma, 2006, 135: 179. [14] Demate J A M, Garcia G J. Soil Science Society of America Journal, 1999, 63: 327. [15] WEI Chang-long, ZHAO Yu-guo, WU Deng-wei, et al(魏昌龙,赵玉国,邬登巍,等). Soils(土壤), 2014, 46(4): 678. |
[1] |
ZHENG Hong-quan, DAI Jing-min*. Research Development of the Application of Photoacoustic Spectroscopy in Measurement of Trace Gas Concentration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 1-14. |
[2] |
CHENG Jia-wei1, 2,LIU Xin-xing1, 2*,ZHANG Juan1, 2. Application of Infrared Spectroscopy in Exploration of Mineral Deposits: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 15-21. |
[3] |
FAN Ping-ping,LI Xue-ying,QIU Hui-min,HOU Guang-li,LIU Yan*. Spectral Analysis of Organic Carbon in Sediments of the Yellow Sea and Bohai Sea by Different Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 52-55. |
[4] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
[5] |
BAI Xi-lin1, 2, PENG Yue1, 2, ZHANG Xue-dong1, 2, GE Jing1, 2*. Ultrafast Dynamics of CdSe/ZnS Quantum Dots and Quantum
Dot-Acceptor Molecular Complexes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 56-61. |
[6] |
XU Tian1, 2, LI Jing1, 2, LIU Zhen-hua1, 2*. Remote Sensing Inversion of Soil Manganese in Nanchuan District, Chongqing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 69-75. |
[7] |
WANG Fang-yuan1, 2, HAN Sen1, 2, YE Song1, 2, YIN Shan1, 2, LI Shu1, 2, WANG Xin-qiang1, 2*. A DFT Method to Study the Structure and Raman Spectra of Lignin
Monomer and Dimer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 76-81. |
[8] |
YANG Cheng-en1, 2, LI Meng3, LU Qiu-yu2, WANG Jin-ling4, LI Yu-ting2*, SU Ling1*. Fast Prediction of Flavone and Polysaccharide Contents in
Aronia Melanocarpa by FTIR and ELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 62-68. |
[9] |
LIU Zhen1*, LIU Li2*, FAN Shuo2, ZHAO An-ran2, LIU Si-lu2. Training Sample Selection for Spectral Reconstruction Based on Improved K-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 29-35. |
[10] |
YANG Chao-pu1, 2, FANG Wen-qing3*, WU Qing-feng3, LI Chun1, LI Xiao-long1. Study on Changes of Blue Light Hazard and Circadian Effect of AMOLED With Age Based on Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 36-43. |
[11] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[12] |
ZHENG Pei-chao, YIN Yi-tong, WANG Jin-mei*, ZHOU Chun-yan, ZHANG Li, ZENG Jin-rui, LÜ Qiang. Study on the Method of Detecting Phosphate Ions in Water Based on
Ultraviolet Absorption Spectrum Combined With SPA-ELM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 82-87. |
[13] |
XU Qiu-yi1, 3, 4, ZHU Wen-yue3, 4, CHEN Jie2, 3, 4, LIU Qiang3, 4 *, ZHENG Jian-jie3, 4, YANG Tao2, 3, 4, YANG Teng-fei2, 3, 4. Calibration Method of Aerosol Absorption Coefficient Based on
Photoacoustic Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 88-94. |
[14] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
[15] |
XING Hai-bo1, ZHENG Bo-wen1, LI Xin-yue1, HUANG Bo-tao2, XIANG Xiao2, HU Xiao-jun1*. Colorimetric and SERS Dual-Channel Sensing Detection of Pyrene in
Water[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 95-102. |
|
|
|
|