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中国农学通报 ›› 2011, Vol. 27 ›› Issue (12): 117-123.

• 农学 农业基础科学 • 上一篇    下一篇

土壤养分的可见光谱研究

丁文广 陈劲松   

  • 收稿日期:2010-11-23 修回日期:2010-12-10 出版日期:2011-05-25 发布日期:2011-05-25
  • 基金资助:

    国家自然科学基金

A Study of Visible Spectrum to Soil Nutrient Parameters

  • Received:2010-11-23 Revised:2010-12-10 Online:2011-05-25 Published:2011-05-25

摘要:

本研究尝试在可见光波段运用高光谱技术综合反映土壤养分状况,明确其主要参考因子和技术要点。对黄土高原地区63个土壤样本进行采样并在实验室测定其14个养分相关因子的含量和可见光波段的光谱反射率。选取与光谱反射率相关性较好的五个因子建立土壤养分的预测模型并进行反演检验。结果表明:光谱反射率一阶导数R′ 值与土壤各养分相关因子的相关性明显优于原光谱反射率R值和反射率倒数的对数A值。用R′ 值与土壤有机碳、阳离子交换量、全氮、速效氮、速效钾的实测值建立各因子的预测模型的相关系数均在0.74以上。其中有机碳、阳离子交换量预测模型为y = -24.19x + 4.049和y = -126.3x + 20.54,相关系数r分别为0.9282、0.9273,最佳预测波段为560nm和520nm。相应的反演模型r值为0.7886、0.5401,标准差SD为0.1443和0.627。土壤可见光光谱具有综合表征土壤养分状况的潜力,多因子光谱分析能使土壤养分信息得到全面、准确的反映。

关键词: GC/MS, GC/MS

Abstract:

This study tried to characterize comprehensively on soil nutrient status by spectral reflectance, define its main parameters and technical points. We collected 63 soil samples from Loess Plateau, measured its 14 nutrient-related soil parameters and visual band spectral reflectance in laboratory. And then, choose five categories of nutrient parameters which have large correlation coefficient with spectral reflectivity to made predictive models. At last, fit the predicted value and measured value to test its predictive effect. Results indicated that: The correlation coefficient between the First Derivative of spectral reflectivity (R′ value) and soil nutrients parameters is much larger than spectral reflectivity (R value) and spectral reflectivity’s Logarithm of Reciprocal (A value). The correlation coefficients of the predictive models to SOC, CEC, TN, AN, AK are over 0.74. The prediction equation of SOC, CEC are y = -24.19x + 4.049, y = -126.3x + 20.54, respectively. Corresponding correlation coefficients (r) are 0.9282, 0.9273, and best predictive wavelengths are 560nm, 520nm; The r value in inversion test models are 0.7886, 0.5401, while standard deviation are 0.1443, 0.627. Soil visible spectrum maintains the potential of characterizing soil nutrient comprehensively, and multiple factors analysis make the information of soil nutrients reflected fully and accurately.