摘要
土壤退化是草地退化的更深层次指示,运用遥感手段大面积测定土壤有机碳进而评估草地土壤状况有助于对草地退化状态的正确认识。以甘南州高寒草地土壤为研究对象,使用ASD地物光谱仪,在室内条件下对土壤样品进行可见光/近红外光谱测量,分析8种光谱变换形式与土壤有机碳含量的相关性并选取特征波段,利用3种多元回归方法(逐步多元线性回归、主成分回归、偏最小二乘回归),通过验证样本的决定系数(R_v^2)、均方根误差(RMSE)和剩余估计偏差(RPD)来评价模型,进而确定高寒草地土壤有机碳的最佳估测模型。结果表明,微分变换方法可以显著提高光谱特征与土壤有机碳含量的相关性,在所有变换形式中以光谱反射率的一阶微分与土壤有机碳含量相关性最好,最大相关系数绝对值为0.865;基于光谱反射率一阶微分变换形式的3种多元回归方法对土壤有机碳均有极好的预测能力,表明对于土壤有机碳的稳定监测来说光谱反射率的一阶微分是非常有效的变换形式;综合考虑基于所有光谱变换形式的3种多元回归方法的预测结果,偏最小二乘回归法具有高的R_v^2和RPD,同时具有低的RMSE值,是研究区土壤有机碳估测的最优回归方法;基于光谱反射率对数的一阶微分变换形式所建立的偏最小二乘回归模型具有相对较高的预测集决定系数(R_v^2=0.878)、最大剩余估计偏差(RPD=2.946)和最小均方根误差(RMSE=7.520),因此该模型为甘南高寒草地土壤有机碳的最优估测模型,最优模型的RPD大于2.5说明该模型有足够的稳定性可以应用于其他地区土壤有机碳的估测。
Soil degradation is often reflects grassland degradation.Monitoring soil organic carbon （SOC） content over large areas using remote sensing technology can help assess soil condition allowing better understanding of grassland degradation.Alpine grassland in the Gannan Prefecture was selected for this research.NIR-Visible spectral reflectance of grassland soil samples was measured using ASD （analytical spectral devices） spectroradiometer under laboratory conditions.Correlation analyses between eight transformations of soil spectral reflectance and SOC content were undertaken and sensitive wavebands selected.Three multivariate regression techniques （stepwise multiple linear regression,SMLR,principal components regression,PCR,partial least squares regression,PLSR）were compared with the aim of identifying the best inversion model to estimate alpine grassland SOC.The determination coefficient of validation dataset（R_v^2）,the root mean square error （RMSE）,and the residual prediction deviation （RPD） were used to evaluate the models.The result indicated that differential transformation could improve the correlation between spectral characteristics and SOC content.The first derivative of reflectance had the best correlation with SOC content during transformation,the maximum correlation coefficient value was 0.865;Three multivariate regression models based on the first derivative of reflectance had excellent SOC prediction capability and recommended as a good spectral transformation for reliably estimating SOC.Comparing the multivariate regression techniques based on all transformations,PLSR performed best （high R_v^2 and RPD,low RMSE）,which suggests that PLSR is the most suitable method for estimating SOC content in this study.The optimal SOC estimation model of Gannan alpine grassland was the combination of PLSR and the first derivative of log reflectance spectra[（lgR）′],providing a relatively high coefficient of determination for the validation set （R_v^2=0.878）,the highest residual prediction deviation （RPD=2.946） and the lowest root mean square error （RMSE=7.520）.The RPD of the optimal model was higher than 2.5,which suggested that the model was robust and stable enough to be applied for estimation of SOC in other areas.
出处
《草业学报》
CSCD
北大核心
2017年第10期20-29,共10页
Acta Prataculturae Sinica
基金
国家自然科学基金(41401472)
兰州大学中央高校基本科研业务费专项资金(lzujbky-2015-140)资助
关键词
高光谱
光谱预处理
多元逐步线性回归
主成分回归
偏最小二乘回归
hyperspectral
spectral pre-processing
stepwise multiple linear regression
principal components regression
partial least squares regression
