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中红外光谱法测定土壤碳的研究进展

王君玮 袁会敏 江荣风 王雁峰 武良 王盛锋

王君玮, 袁会敏, 江荣风, 王雁峰, 武 良, 王盛锋. 中红外光谱法测定土壤碳的研究进展[J]. 土壤通报, 2021, 52(1): 233 − 241 doi: 10.19336/j.cnki.trtb.2020042501
引用本文: 王君玮, 袁会敏, 江荣风, 王雁峰, 武 良, 王盛锋. 中红外光谱法测定土壤碳的研究进展[J]. 土壤通报, 2021, 52(1): 233 − 241 doi: 10.19336/j.cnki.trtb.2020042501
WANG Jun-wei, YUAN Hui-min, JIANG Rong-feng, WANG Yan-feng, WU Liang, WANG Sheng-feng. Research Advance in the Determination of Soil Carbon by Mid-infrared Spectroscopy[J]. Chinese Journal of Soil Science, 2021, 52(1): 233 − 241 doi: 10.19336/j.cnki.trtb.2020042501
Citation: WANG Jun-wei, YUAN Hui-min, JIANG Rong-feng, WANG Yan-feng, WU Liang, WANG Sheng-feng. Research Advance in the Determination of Soil Carbon by Mid-infrared Spectroscopy[J]. Chinese Journal of Soil Science, 2021, 52(1): 233 − 241 doi: 10.19336/j.cnki.trtb.2020042501

中红外光谱法测定土壤碳的研究进展

doi: 10.19336/j.cnki.trtb.2020042501
基金项目: 国家重点研发计划项目(2016YFD0200403,2016YFE0101100,2017YFD0200201,2016YFD0200401)资助
详细信息
    作者简介:

    王君玮(1996−),女,山东青岛人,硕士研究生,主要从事土壤养分快速测定的研究。E-mail: WJW00223830@163.com

    通讯作者:

    E-mail: hmyuan@cau.edu.cn

  • 中图分类号: S-1

Research Advance in the Determination of Soil Carbon by Mid-infrared Spectroscopy

  • 摘要: 土壤碳不仅是影响土壤肥力和农业可持续发展的重要因子,也对减缓温室效应有重要意义。因此,土壤碳分析是农业、环境等学科领域的重要研究内容。传统化学分析方法存在测定效率低、实时性差、有污染等缺点,无法满足现代绿色农业快速测定土壤碳的需求。近年来,中红外光谱(mid-infrared spectroscopy, MIR)技术以其操作简便、重现性好、测试速度快、样品用量少、绿色环保和适合批量样品测定的特点,逐渐成为获取土壤碳信息的有效方法。本文简要介绍了中红外光谱分析基本原理,重点论述了该技术在预测土壤碳方面的国内外研究进展及影响因素,并对MIR技术在我国土壤碳定量研究方面的应用前景进行了展望。目前,MIR技术已成功应用于土壤总碳、有机碳、无机碳、炭、水溶性有机碳、微生物量碳、生物量碳、颗粒有机碳、矿物结合有机碳、惰性有机碳等土壤碳组分的预测,为土壤碳分析提供了快速无损的测定手段,也为绿色农业和精准农业的发展提供了先进的技术支撑。
  • 表  1  近十年以来MIR技术在土壤总碳、有机碳及无机碳定量分析中的应用情况

    Table  1.   Application of MIR technology in the quantitative analysis of soil total carbon, organic carbon and inorganic carbon in the past ten years

    参考文献
    Ref.
    仪器
    Ins.
    光谱采集
    Spectral Measurements
    样本信息
    Sample
    指标
    Ind.
    模型
    Model
    评价参数
    Performance parameters
    范围 (cm−1)
    Range
    分辨率 (cm−1
    Resolution
    扫描次数
    Scan time
    R2RMSEp
    (%)
    RPD/
    RPIQb
    [13] Tensor 27 4000 ~ 602 4 32 971s./18c.
    V = 30%
    OC 21数据点间隔平滑 + 一阶导数-PLSR 0.77 0.91a 2.04
    [22] Scimitar2000 6000 ~ 400 4 32 305s./5i.USA
    V = 30%
    TC 均值中心化+Savitzky-Golay卷积平滑+ 一阶导数-PLSR 0.94 3.08 3.91
    Savitzky-Golay卷积平滑 + 一阶导数-RF 0.96 2.28 5.28
    [18] Nicolet 6700 4000 ~ 400 4 32 2086s./FRA
    V = 80%
    OC 5数据点间隔平滑-PLSR 0.89 3.00
    IC 0.97 5.60
    [19] Nicolet 6700 8000 ~ 400 8 60 18005s./AUS
    V = 33%
    TC 基线校正 + 均值中心化-PLSR 0.91 0.431 3.38
    19766s./AUS
    V = 33%
    OC 0.92 0.417 3.62
    2454s./AUS
    V = 33%
    IC 0.93 0.344 4.43
    [23] Scimitar 2000 6000 ~ 400 4 32 1014s./Florida
    V = 30%
    TC 吸光度转化-PLSR 0.95 0.24 4.60
    5窗口Savitzky-Golay卷积平滑 + 一阶导数-RF 0.94 0.28 3.90
    OC 吸光度转化-PLSR 0.96 0.23 4.70
    5窗口Savitzky-Golay卷积平滑 + 一阶导数-RF 0.94 0.28 3.90
    [20] Vertex 70 7498 ~ 600 4 32 20102s./NSSC
    V = 50%
    TC 10波段窗口平均-PLSR 0.95 1.90 4.44
    10波段窗口平均-ANN 0.97 1.34 6.32
    17298s./NSSC
    V = 50%
    OC 10波段窗口平均-PLSR 0.95 1.89 4.55
    10波段窗口平均-ANN 0.99 0.75 11.46
    7861s./NSSC
    V = 50%
    IC 10波段窗口平均-PLSR 0.97 0.26 5.61
    10波段窗口平均-ANN 0.99 0.13 11.23
    [24] Vertex 70 7498 ~ 600 4 32 14594s./KSSL
    V=25%
    TC 11窗口Savitzky-Golay卷积平滑 + 标准正态变量变换-PLSR 0.95 0.37 1.83b
    11窗口Savitzky-Golay卷积平滑 + 标准正态变量变换-Cubist 0.97 0.28 2.41 b
    标准正态变量变换-CNN 0.98 0.21 3.12 b
    OC 11窗口Savitzky-Golay卷积平滑 + 标准正态变量变换-PLSR 0.94 0.35 1.37 b
    11窗口Savitzky-Golay卷积平滑 + 标准正态变量变换-Cubist 0.97 0.24 2.02 b
    标准正态变量变换-CNN 0.98 0.20 2.37 b
    [21] Tensor 27 7498 ~ 600 4 32 285s./20c.SSA
    V=30%
    OC 吸光度转化 + Savitzky-Golay卷积一阶导数-PLSR 0.80 0.52 1.94 b
    [25] ExoScan 4100 6000 ~ 650 8 15 458s./CNSA
    V=25%
    TC 去趋势法-BAG 0.98 0.22 6.68
    去趋势法-RF 0.96 0.28 5.25
    去趋势法-DT 0.68 0.84 1.76
    去趋势法-Bagging 0.94 0.38 3.94
    去趋势法- RR 0.97 0.25 5.92
    去趋势法-PLSR 0.94 0.30 4.13
    [26] Agilent 4300 4000 ~ 650 8 64 90s./GER
    V=33%
    OC 吸光度转化 + 多元散射校正-PLSR 0.63 0.17 1.65
      注:s.:样本数;c.:国家;i.:岛屿;V:验证集样本量占总样本量百分比;a:单位转化,由g kg−1转化为%;-:未知;TC:总碳;OC:有机碳;IC:无机碳;b:RPIQ值;NSSC:USDA–NRCS美国土壤调查中心;KSSL:美国Kellogg土壤调查实验室;SSA:撒哈拉沙漠以南的非洲地区;CNSA:澳大利亚CSIRO国家土壤档案馆;PLSR:偏最小二乘回归;RF:随机森林;ANN:人工神经网络;CNN:卷积神经网络;GPR:高斯回归过程;DT:决策树;RR:回归规则。
    下载: 导出CSV
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  • 收稿日期:  2020-04-25
  • 修回日期:  2020-07-30
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