基于光谱指数的土壤重金属Zn的定量预测与空间分布研究

Quantitative Prediction and Spatial Distribution of Soil Heavy Metal Zn Based on Spectral Indices

高光谱技术在反演土壤重金属含量方面具有独特优势,而高光谱中存在大量冗余信息,需要采用相应方法来减少冗余信息对反演精度的影响,以实现对土壤Zn含量的准确预测。以云南省墨江哈尼族自治县农田采集的土壤Zn含量与高光谱数据为数据源,将获取的高光谱数据利用Savitzky-Golay平滑处理后,使用R′、(1/R)′、(√R)′、(logR)′四种不同数学形式的变换对光谱进行处理并构建了五种指数,即归一化指数(NDI)、差值指数(DI)、比值指数(RI)、和指数(SI)、倒数差值指数(IDI),从中挑选出与土壤Zn含量相关系数绝对值最大的光谱指数值作为模型输入量,结合偏最小二乘法(PLSR)与多元回归分析法(MLR)建立土壤Zn含量的最优反演模型,结果表明:(1)在不同数学变换形式下所得到的优化光谱指数值与土壤Zn含量均表现出较高的相关性,优化光谱指数能有效增强光谱与土壤Zn含量的敏感性,相关系数绝对值可达到0.7以上。(2)基于优化光谱指数得到的最佳预测模型(1/R)′~PLSR其验证集R2为0.77,RMSE为5.07mg·kg-1,RPD达到了2.09,较于相同变量的MLR模型R2提高了0.04,RMSE降低了0.47,RPD提高了0.18,具有较好的预测能力,可作为研究区土壤Zn含量的最优估测模型。(3)由最优估算模型结合空间插值方法,绘制出研究区土壤Zn含量空间分布图可知,土壤Zn含量的空间分布在图中部含量较高,且随着地形高程的增加而降低。基于优化光谱指数结合PLSR建模方法在估测土壤Zn含量方面具有一定的可行性,可为农田土壤Zn含量的估测提供参考。

Hyperspectral technology has unique advantages in the inversion of soil heavy metal content.Still,there is a large amount of redundant information in the hyperspectral data,and corresponding methods are needed to reduce the influence of redundant information on the inversion accuracy to realize the accurate prediction of soil Zn content.In this study,we used the soil Zn content and hyperspectral data collected from the farmland of Mojiang Hani Autonomous County,Yunnan Province,as the data source,and Savitzky-Golay smoothed the acquired hyperspectral data,and then four different mathematical transformations,R′,(1/R)′,(√R)′and(logR)′were used to process the spectra.Five indexes are constructed,namely normalized index(NDI),difference index(DI),ratio index(RI),sum index(SI),and inverse difference index(IDI).The spectral index with the largest absolute value of the correlation coefficient with the soil Zn content was selected as the input to the model and combined with the partial least squares method(PLSR)and multiple regression(MLR)to establish an optimal inversion model for soil Zn content.The results show that(1)the optimized spectral indices exhibit high correlations with soil zinc content under various mathematical transformations.These indices effectively enhance the sensitivity of spectral measurements to variations in zinc levels,with correlation coefficients achieving absolute values of 0.7or higher.(2)The best prediction model(1/R)′PLSR based on the optimized spectral index has a validation set of R~(2)of 0.77,RMSE of 5.07mg·kg~(-1),and RPD of 2.09.Compared with the MLR model with the same variable,the R~(2)increased by 0.04,the RMSE decreased by 0.47,and the RPD increased by 0.18,which has better predictive ability and can be used as an optimal estimation model for soil Zn content in the study area.(3)the spatial distribution map of soil Zn content in the study area was drawn based on the optimal estimation model combined with the spatial interpolation method.It can be seen that the spatial distribution of soil Zn content is higher in the middle of the map and decreases with the increase of terrain elevation.It is feasible to estimate soil Zn content based on an optimized spectral index combined with the PLSR modeling method,which can provide a reference for estimating Zn content in farmland soil.