中国农业科学 ›› 2007, Vol. 40 ›› Issue (9): 1989-1995 .

• 土壤肥料·节水灌溉 • 上一篇    下一篇

基于高光谱的土壤有机质含量预测模型的建立与评价

卢艳丽,白由路,杨俐苹,王红娟   

  1. 中国农业科学院农业资源与农业区划研究所/农业部植物营养与养分循环重点开放实验室
  • 收稿日期:2007-01-15 修回日期:1900-01-01 出版日期:2007-09-10 发布日期:2007-09-10
  • 通讯作者: 白由路

Prediction and validation of soil organic matter content based on hyperspectrum

  

  1. 中国农业科学院农业资源与农业区划研究所/农业部植物营养与养分循环重点开放实验室
  • Received:2007-01-15 Revised:1900-01-01 Online:2007-09-10 Published:2007-09-10

摘要: 【目的】土壤有机质含量是反映土壤肥力的重要特征,利用高光谱技术对有机质(OM)含量进行定量化反演为土壤信息化管理和资源评价提供了重要的依据。【方法】利用ASD2500高光谱仪在室内条件下测定了风干土壤样品的可见—近红外光谱,分析了不同区域范围土壤光谱反射率曲线形状变化和土壤有机质含量的变化特点,并针对东北地区以黑土为主的土样光谱反射率不同变换形式与有机质含量进行了相关性分析。【结果】结果表明,有机质含量较高的黑土的光谱曲线与其它土壤类型的光谱曲线在形状上有很大差异,即在600~900 nm附近,以黑龙江土样为代表的东北黑土表现为直缓上升,而河南和山东的潮土则表现为曲陡上升。相关分析结果表明,土壤有机质含量与原始光谱反射率在545~830 nm呈显著负相关,其中在580~738 nm波段范围内达到极显著负相关。与一阶导数光谱相关性进一步增强,在481~598 nm呈现极显著负相关,而在816~932 nm和1 039~1 415 nm波段范围内具有极显著的正相关性。土壤有机质含量与部分波段处的吸收深度和反射峰高度也表现为不同程度的相关性。【结论】利用570~590 nm波段的一阶导数光谱和1 280 nm处反射峰高度P_Depth1280可以较好地预测东北主要土壤类型有机质含量。在此基础上建立了土壤有机质含量的高光谱反演模型并进行了验证。

关键词: 光谱, 有机质, 模型

Abstract: NIR-Visible spectral reflectance of soil samples were measured using ASD2500 hyperspectral meter. The results indicated that the spectral reflectance curve shape of north-east black soil of Heilongjiang is different from fluvo-aquic soil of Henan and Shandong province. The former shows ascending in a slow curve but the latter in a steep curve in 600-900nm region. The correlation analysis indicated that organic matter (OM) content has negative correlation with spectral reflectance in 545-830nm at 0.05 significant level and in 580-738nm at 0.01 significant level. OM has positive correlation with first derivative spectral reflectance in 458-1069nm and 1166-1306nm at 0.01 significant level. Furthermore, the degree of correlation is stronger than original spectral reflectance. Different spectral characteristic parameters are selected including reflectance and its first derivation, reflected height and absorbed depth. The analysis results show that OM can be predicted using first derivative reflectance in 570-590nm and reflected height in 1280nm (P_Depth1280). By validating, the prediction models are practical and feasible. Different models can be made reference to each other in practice.

Key words: Spectral, Organic matter, Model