Estimation and Mapping of Soil Organic Matter Based on Vis-NIR Reflectance Spectroscopy
GUO Yan1, JI Wen-jun1, WU Hong-hai2*, SHI Zhou1, 3
1. Institute of Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China 2. Department of Earth Sciences, Zhejiang University, Hangzhou 310027, China 3. Cyrus Tang Center for Sensor Materials and Applications, Zhejiang University, Hangzhou 310058, China
摘要： 利用野外实时快速获取的土壤光谱进行土壤有机质(SOM)预测与制图是精确农业与土壤遥感制图的必然需要，利用ASD FieldSpec Pro FR野外型光谱仪实时快速获取的光谱数据，去除噪声较大的边缘波段后，进行倒数的对数转换(Log(1/R))为吸收光谱。在分析吸收光谱和光谱指数与SOM关系的基础上，采用偏最小二乘回归法进行SOM的建模预测并借助地统计学方法进行SOM空间变异制图研究。结果表明，建模效果好的指标分别为特征波段(R2=0.91，RPD=3.28)，归一化光谱指数(R2=0.90，RPD=3.08)，特征波段与3个光谱指数组合(R2=0.87，RPD=2.67)，全波段(R2=0.95，RPD=4.36)。光谱指标的克里格制图与实测SOM制图表现出相同的空间变异趋势，不同的指标均达到了较好的预测效果。
Abstract：Visible-near infrared (Vis-NIR) reflectance spectroscopy, which is rapid, cost-effective, in-situ, nondestructive and without hazardous chemicals, is increasingly being used for prediction and digital soil mapping of soil organic matter (SOM). This method is the inevitable demand for precision agriculture and soil remote sensing mapping. In the present study, the Vis-NIR (350~2 500 nm) diffuse reflectance spectral collected by ASD FieldSpec Pro FR spectrometer was truncated by removing the noisy edge values below 400 nm and above 2 450 nm and then was transformed into apparent absorbance spectral using log(1/R). Based on the relationship analysis between absorbance spectral, spectral indices and SOM, partial least squares regression (PLSR) model was applied to predict SOM, and finally the spatial variability of SOM was characterized by geostatistics method. The results indicated that good model was modeling from the characteristic bands (CB, R2=0.91，RPD=3.28) of correlation coefficient more than 0.5, the spectral index (SI) of normalized difference index (NDI, R2=0.90，RPD=3.08), CB integrating SI with which a correlation coefficient was more than 0.5 (R2=0.87，RPD=2.67), and total bands (TA, 400~2 450 nm, R2=0.95，RPD=4.36). While the digital mapping of SOM produced by kriging and cokriging interpolation methods implied a better prediction result, showing similar spatial distribution with the measured SOM, indicating that it is feasible and reliable to use these spectral indices to predict and map the spatial variability.
Key words：Visible-near infrared(Vis-NIR) reflectance spectroscopy;ASD FieldSpec Pro FR spectrometer;Soil organic matter(SOM);Prediction and mapping;Partial Least Squares Regression(PLSR);Geostatistics
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