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    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
    shu
    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
    shu

    土壤游离氧化铁高光谱特征与定量反演

    Spectral characteristics and quantitative retrieval of free iron content in soil

      摘要
    • 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.

       

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