基于高光谱的复垦农田土壤有机质含量估测

为了快速准确估算矿区复垦土地土壤有机质含量,以永城矿区复垦农田为例,在土样有机质含量测定和高光谱数据测量的基础上,对土壤高光谱数据进行多种预处理并与有机质实测含量进行相关性分析,利用相关系数进行P=0.01水平显著检验,确定敏感波段,建立一元线性回归、多元逐步回归和偏最小二乘回归等多种有机质含量与高光谱估测模型。结果表明:经过数学变换的土壤光谱反射率与土壤有机质含量相关性显著提高,复垦区土壤光谱经过多元散射校正和一元微分处理并利用偏最小二乘回归模型建模预测效果最好。当前较少有研究对矿区复垦农田土壤有机质进行高光谱估测,本研究成果可为有效利用高光谱遥感技术,快速、有效地测定复垦农田土壤有机质含量提供技术支撑。

Estimation of SOM Content in Reclaimed Cropland Based on Hyperspectra

33 soil samples were collected from the reclaimed farmland in the coal mining areas in Yongcheng in order to estimate quickly and accurately SOM content by hyperspectra, in which 23 samples and 10 samples were used to establish and verify the hyperspectral models respectively. SOM contents and hyperspectra of the soil samples were measured, the correlations were analyzed between SOM content and the multi-pretreated hyperspectral data, correlation coefficients at P= 0.01 level were used to test to determine the sensitive bands, and models of simple linear regression, multiple stepwise regression (MSR) and partial least squares regression (PLSR) were established. The results showed that transformed soil spectral reflectance promoted the correlation coefficients, and PLSR models of the differential spectra were optimal after the spectral noise were reduced by multiple scattering correction. Due to little information is available now on hyperspectral inversion of SOM content in reclaimed farmland, the above results provide technical support for the efficient and rapid determination of SOM content in reclaimed farmland by using hyperspectral remote sensing.