草业学报2017，Vol.26Issue(10)：20-29,10.DOI:10.11686/cyxb2016509
基于高光谱数据的高寒草地土壤有机碳预测模型研究
Estimation of soil organic carbon content in alpine grassland using hyperspectral data
摘要
Abstract
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 (Rv2),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 Rv2 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 (Rv2 =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.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Key words
hyperspectral/spectral pre-processing/stepwise multiple linear regression/principal components regression/partial least squares regression引用本文复制引用
崔霞,宋清洁,张瑶瑶,胥刚,孟宝平,高金龙..基于高光谱数据的高寒草地土壤有机碳预测模型研究[J].草业学报,2017,26(10):20-29,10.基金项目
国家自然科学基金(41401472)和兰州大学中央高校基本科研业务费专项资金(lzujbky-2015-140)资助. （41401472）
