亚热带典型区域水稻土氧化铁高光谱反演——以珠江三角洲为例

氧化铁是土壤中铁元素的主要存在形式,亚热带土壤中高含量的氧化铁形成了该区域重要的土壤附色成分针铁矿和赤铁矿等矿物,使得土壤颜色明显区别于其他气候带.以亚热带典型地区珠江三角洲为例,分析不同光谱形式与土壤氧化铁含量的相关性,提取特征光谱波段建立土壤氧化铁的反演模型.结果表明: 土壤氧化铁含量与反射光谱之间呈负相关,且敏感波段主要位于404、574、784、854和1204 nm等可见近红外区域.微分处理后的光谱与土壤氧化铁的相关性明显提高.在相关性显著波段的基础上采用逐步多元线性回归以及主成分分析剔除共线性波段,最后选择特征光谱波段作为反演模型的输入参数.比较反演模型的结果,得出该地区土壤氧化铁含量的最佳反演模型为BP人工神经网络(RMSEC=0.22,RMSEP=0.81,R²=0.93,RPD=12.20),该模型具有非常好的稳定性,适用于快速估测土壤中氧化铁含量,并且能够为测度土壤氧化铁的空间分布提供研究基础.

Hyperspectral inversion of paddy soil iron oxide in typical subtropical area with Pearl River Delta,China as illustration

Iron oxide is the main form of iron element existing in the soil. In subtropical areas, the high-content iron oxide constitutes the soil’s important coloring components, or its mineral substances, such as goethite and hematite, making the soil color apparently different from that in other climatic zones. The present paper, with the Pearl River Delta, a typical subtropical area, as illustration, and through analysis of the correlation between different spectral forms and the content of soil iron oxide, created inversion models of soil iron oxide by extracting characteristic spectral bands. The findings showed that there was a negative correlation between the content of soil iron oxide and the reflection spectrum, and the sensitive bands were mainly found in such visible near-infrared regions such as 404, 574, 784, 854 and 1204 nm. The correlation between the spectrum through differential processing and the soil iron oxide was significantly improved. On the basis of the correlation-prominent bands, the methods of both multiple linear regression and principal component analysis were adopted so as to remove collinear bands, and finally, characteristic bands were selec-ted to serve as the input parameters of inversion models. A comparison of the results revealed that the best inversion model of soil iron oxide content in the Pearl River Delta was BP artificial neural network (i.e., RMSEC=0.22, RMSEP=0.81, R²=0.93, RPD=12.20). It was applicable with excellent stability to the fast estimation of the iron oxide content in the soil and could hopefully serve as the research basis for the measure of the spatial distribution of the soil iron oxide.