摘要
氧化铁是土壤中含铁矿物的主体,是土壤发育和土壤分类最明显和最有用的指标之一。本文以湖南省大围山森林土壤为研究对象,通过实验室化学成分测定和光谱采集,在光谱预处理及组合变换基础上,采用相关性分析筛选土壤氧化铁全量的敏感波段,并分别建立多元逐步回归和偏最小二乘回归反演模型。结果表明:不同土壤光谱曲线趋势基本一致,均形似陡坎,且在420~580 nm波段,土壤氧化铁全量与光谱反射率呈负相关关系;不同的光谱数据变换方式可以提高光谱与氧化铁全量的相关性,Savitzky-Golay(S-G)平滑和去包络线相结合优于其他预处理方法;土壤氧化铁全量的特征波段主要为392、427、529、523、549、559、565、570、994和1040 nm,偏最小二乘回归模型比多元逐步回归模型具有更好的稳定性,适合于快速估算红黄壤区森林土壤氧化铁全量。
Iron oxide is the main body of iron-bearing minerals in the soil and is one of the most obvious and useful indicators of soil development and soil classification.In this paper,forest soils in Dawei Mountain of Hunan Province were collected,iron oxide contents in soils were determined respectively by conventional chemical method and by hyperspectral inversion with models of multiple stepwise regression and partial least squares regression inversion established by screening sensitive bands after spectral preprocessing and combinatorial transformation.The results showed that soil spectral curves with different iron oxide contents all were in steep-hill shape in the whole band,iron oxide content was negatively correlated with spectral reflectance within 420-580 nm,different spectral transformation could improve the correlation,and the combination of Savitzky-Golay(S-G)smoothing and de-embedding lines was superior to other pretreatment methods in inversion.The characteristic bands of iron oxides were 392,427,529,523,549,559,565,570,994 and 1040 nm.Partial least squares regression model had better stability than multiple stepwise regression model,and is suitable for rapid estimation of iron oxide contents in forest red and yellow soils.
作者
谭洁
陈严
周卫军
崔浩杰
刘沛
TAN Jie;CHEN Yan;ZHOU Weijun;CUI Haojie;LIU Pei(College of Landscape Architecture and Art Design,Hunan Agricultural University,Changsha 410128,China;College of Resources and Environment,Hunan Agricultural University,Changsha 410128,China)
出处
《土壤》
CAS
CSCD
北大核心
2021年第4期858-864,共7页
Soils
基金
国家自然科学基金项目(41771272)资助。
关键词
土壤光谱
氧化铁
多元逐步回归
偏最小二乘回归
Soil spectra
Iron oxide
Multiple stepwise regression
Partial least squares regression