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土壤游离氧化铁高光谱特征与定量反演

阳洋, 黄伟濠, 卢瑛, 李博, 欧锦琼, 唐贤, 王超, 陈勇

阳洋, 黄伟濠, 卢瑛, 等. 土壤游离氧化铁高光谱特征与定量反演[J]. 华南农业大学学报, 2020, 41(1): 91-99. DOI: 10.7671/j.issn.1001-411X.201901032
引用本文: 阳洋, 黄伟濠, 卢瑛, 等. 土壤游离氧化铁高光谱特征与定量反演[J]. 华南农业大学学报, 2020, 41(1): 91-99. DOI: 10.7671/j.issn.1001-411X.201901032
YANG Yang, HUANG Weihao, LU Ying, et al. Spectral characteristics and quantitative retrieval of free iron content in soil[J]. Journal of South China Agricultural University, 2020, 41(1): 91-99. DOI: 10.7671/j.issn.1001-411X.201901032
Citation: YANG Yang, HUANG Weihao, LU Ying, et al. Spectral characteristics and quantitative retrieval of free iron content in soil[J]. Journal of South China Agricultural University, 2020, 41(1): 91-99. DOI: 10.7671/j.issn.1001-411X.201901032

土壤游离氧化铁高光谱特征与定量反演

基金项目: 国家自然科学基金(41271233);国家科技基础性工作专项重点项目(2014FY110200)
详细信息
    作者简介:

    阳洋(1993—),女,硕士研究生,E-mail: 835821911@qq.com

    通讯作者:

    卢 瑛(1966—),男,教授,博士,E-mail: luying@scau.edu.cn

  • 中图分类号: S151

Spectral characteristics and quantitative retrieval of free iron content in soil

  • 摘要:
    目的 

    建立基于可见−近红外光谱的土壤游离铁精确预测模型,简单、快速、经济地预测土壤游离铁,有助于研究土壤发生和分类。

    方法 

    采集广西壮族自治区的铁铝土、富铁土、淋溶土和雏形土等82个旱地土壤剖面的B层土壤,进行室内土壤化学分析、光谱测定,分析不同光谱变换后的光谱反射率与土壤游离铁含量的相关性。基于特征波段利用偏最小二乘回归(PLSR)和逐步多元线性回归(SMLR)法建立土壤游离铁含量光谱预测模型,通过决定系数(R2)、均方根误差(RMSE)和相对预测偏差(PRD)确定最优模型。

    结果 

    土壤光谱曲线分别在457、800和900 nm波段附近有明显的游离铁吸收和反射峰特征;土壤游离铁含量与原始光谱反射率呈负相关;原始光谱经过微分变换后,游离铁含量与光谱反射率相关性显著提高;基于400~580和760~1 300 nm特征波段和一阶微分光谱变换的SMLR模型预测精度最高,其验证集的R2和RPD分别为0.85和2.62,RMSE为8.41 g·kg−1

    结论 

    将可见近红外光谱技术应用于土壤游离铁含量高效快速地预测具有良好的可行性。广西旱地土壤光谱反射率与土壤游离铁含量具有高度的相关性,应用逐步多元线性回归方法可以很好地建立土壤游离铁含量反演模型。

    Abstract:
    Objective 

    To establish an accurate predicted model for free iron in soil based on visible and near infrared (vis-NIR) reflectance spectroscopy, provide a simple, rapid and economical method for soil free iron determination, and facilitate the pedogenesis and classification of soil.

    Method 

    Soil samples in B horizon were collected from eighty-two upland soil profiles in Guangxi including ferralosols, ferrosols, argosols and cambosols. Chemical and spectral properties of soil samples were analyzed under laboratory condition. The correlation between spectral reflectance after transformation and free iron content in soils was analyzed. The predicted models of soil free iron were established by the method of partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) based on characteristic bands. The optimal model was determined by evaluating the coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation(RPD).

    Result 

    Soil spectral curves had obvious characteristics of free iron absorption and reflection peaks near 457, 800 and 900 nm bands respectively. Free iron content in soils negatively correlated with the raw spectral reflectance. The correlation coefficient between spectral reflectance and free iron content in soils increased significantly after differential transformation of the raw spectrum. The predicted model of free iron content in soils established by the first-order differential spectral transformation and SMLR based on characteristic bands of 400−580 and 760−1 300 nm had the highest accuracy, R2 and RPD of the verification set were 0.85, and 2.62 respectively, and RMSE was 8.41 g·kg−1.

    Conclusion 

    It is feasible to rapidly and cost-effectively predict free iron content in soils using vis-NIR spectral technology. Soil spectral reflectance of upland in Guangxi has a high correlation with soil free iron content. SMLR is a good method to establish the predicted model of soil free iron content.

  • 图  1   广西采样点分布图

    Figure  1.   Distribution of sampling sites in Guangxi

    图  2   土壤游离铁含量箱型散点图

    I: 全部样本 Whole set; II: 建模样本 Calibration set; III: 验证样本 Validation set

    Figure  2.   Boxplot and scatter plot of free iron content in soil

    图  3   不同游离铁含量的土壤光谱曲线

    Figure  3.   Reflectivity curves of soils with different free iron contents

    图  4   不同光谱变换形式反射率与土壤游离铁含量的相关性分析

    **表示相关系数达0.01显著水平;** represents 0.01 level of significance test of correlation coefficient

    Figure  4.   Correlation analysis between reflectance with different spectral transformation and soil free iron content

    图  5   不同建模波段最佳模型的土壤游离铁含量

    a:全波段倒数一阶微分逐步多元线性回归模型; b:特征波段一阶微分逐步多元线性回归模型; c:敏感波段倒数一阶微分偏最小二乘回归模型

    Figure  5.   Soil free iron content of the optimal model in different modeling band

    a: Reciprocal first differential method using the SMLR model in the full bands; b: First differential method using the SMLR model in the feature bands; c: Reciprocal first differential method using the PLSR model in the sensitive bands

    表  1   敏感波段共线性检验1)

    Table  1   Collinearity test of sensitive band

    光谱变换
    Pectral transformation
    λ(敏感)/nm
    Sensitive band
    皮尔逊相关系数
    Pearson correlation coefficient
    容差
    Tolerance
    方差膨胀因子
    Variance inflation factor
    一阶微分
    First order differential
    735 0.43** 1.00 1.00
    847 −0.49** 1.00 1.00
    去包络线
    Continuum removal
    456 0.55** 0.17 5.96
    1 102 −0.43** 0.99 1.01
    倒数 Reciprocal 400 0.64** 0.19 5.18
    510 0.41** 0.19 5.18
    倒数一阶微分
    Reciprocal first order differential
    498 −0.78** 0.54 1.84
    2 191 0.63** 0.54 1.84
    平方根一阶微分
    Square root first order differential
    457 −0.64** 0.59 1.69
    848 −0.48** 0.65 1.54
    1 028 0.49** 0.92 1.08
    1 759 −0.50** 0.90 1.11
     1)**表示0.01水平显著相关
     1) ** represents 0.01 level of significant correlation
    下载: 导出CSV

    表  2   土壤游离铁含量预测模型1)

    Table  2   Calibration and validation of the model for predicting soil free iron content

    光谱变换
    Spectral transformation
    SMLR PLSR
    建模样本
    Calibration set
    验证样本
    Validation set
    建模样本
    Calibration set
    验证样本
    Validation set
    R2 RMSE/
    (g·kg−1)
    R2 RMSE/
    (g·kg−1)
    RPD 主成分
    个数
    R2 RMSE/
    (g·kg−1)
    R2 RMSE/
    (g·kg−1)
    RPD
    全波段 Full band
      FD 0.85 8.92 0.28 19.53 1.13 10 0.99 2.25 0.64 14.27 1.54
      CR 0.76 12.16 0.62 17.86 1.23 3 0.51 16.10 0.69 15.56 1.42
      RT 0.87 8.28 0.80 10.34 2.20 4 0.61 14.52 0.78 12.26 1.68
      RTFD 0.89 7.61 0.82 9.97 2.21 9 0.96 2.85 0.69 13.80 1.60
      SRFD 0.89 7.65 0.45 19.77 1.11 12 1.00 0.91 0.67 13.22 1.67
    特征波段 Characteristic band
      FD 0.89 7.68 0.85 8.41 2.62 7 0.96 4.42 0.64 13.15 1.68
      CR 0.83 9.77 0.78 10.00 2.20 3 0.84 9.44 0.68 11.81 1.87
      RT 0.63 14.11 0.75 10.97 2.01 8 0.90 9.77 0.77 11.14 1.98
      RTFD 0.89 7.73 0.40 17.08 1.29 3 0.71 12.61 0.71 13.13 1.68
      SRFD 0.89 7.77 0.80 10.74 2.05 5 0.91 7.11 0.71 13.26 1.66
    敏感波段 Sensitive band
      FD 0.56 15.34 0.59 14.22 1.55 2 0.64 13.89 0.64 14.09 1.56
      CR 0.74 11.90 0.71 13.75 1.60 4 0.74 11.67 0.69 11.75 1.88
      RT 0.70 12.70 0.48 15.36 1.43 4 0.63 13.96 0.61 13.13 1.68
      RTFD 0.81 10.04 0.60 13.62 1.62 3 0.83 10.10 0.79 10.31 2.20
      SRFD 0.65 13.71 0.65 12.52 1.76 2 0.68 13.13 0.67 12.75 1.73
     1) FD为一阶微分;CR为去包络线;RT为倒数;RTFD为倒数一阶微分;SRFD为平方根一阶微分;R2为决定系数;RMSE为均方根误差;RPD为相对分析误差;全波段是400~2 400 nm;特征波段是400~580和760~1 300 nm;敏感波段是400、457、510、735、848 nm等12个波段
     1) FD is first order differential; CR is continuum removal; RT is reciprocal; RTFD is reciprocal first order differential; SRFD is square root first order differential; R2 is determination coefficient; RMSE is root mean squared error; RPD is relative percent deviation; Full bands are 400−2 400 nm; Characteristic bands are 400−580 and 760−1 300 nm; Sensitive bands are twelve bands including 400, 457, 510, 735, 848 nm, et al
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-01-17
  • 网络出版日期:  2023-05-17
  • 刊出日期:  2020-01-09

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