土壤重金属Cu含量遥感反演的波段选择与最佳光谱分辨率研究

高光谱数据以其高光谱分辨率和多而连续的光谱波段为预测土壤重金属污染提供了有力工具,但波段选择方法与光谱分辨率的影响不容忽视。利用实验室测定的181个土壤光谱样本数据,利用逐步回归法进行土壤Cu含量反演的波段选择,进而利用偏最小二乘方回归PLSR方法建模,分析了波段数对Cu含量反演的影响;此外,采用高斯响应函数重采样方法,探讨了光谱分辨率降低对反演精度的影响。实验表明,预测重金属元素Cu含量的最佳波段数为10个,模型可决系数R2=0.7523,拟合均方根误差RMSE=0.4699;预测Cu含量的最佳光谱采样间隔为32 nm,R2=0.7028,RMSE=0.5147。该结果可能为将来设计低廉实用的高光谱卫星传感器提供指标论证,为模拟卫星传感器波段预测土壤重金属含量提供理论依据。

Study on Band Selection and Optimal Spectral Resolution for Prediction of Cu Contamination in Soils

Hyper-spectral data offers a powerful tool for predicting soil heavy metal contamination due to its high spectral resolution and many continuous bands.Band selection and spectral resolution,however,are the prerequisite of heavy metal inversion by  hyper-spectral data.In this study,soil reflectance spectra and their Cu contents were measured for 181 soil samples in the laboratory.Based on these dataset,band selection was conducted to inverse Cu content using stepwise regression approach,and prediction accuracies of Cu based on partial least-squares regression (PLSR) model with different selected bands were analyzed.In addition,the influences of spectral resolution on prediction results of Cu were discussed by a Gaussian re-sampling function.It demonstrated that the optimal band number was 10 for Cu inversion and the corresponding model prediction accuracy was R^2₌0.7523 and RMSE of 0.4699.The optimal spectral resolution was 32 nm and the model on this basis had an accuracy of R²=0.7028 and RMSE=0.5147.Results of this paper may provide scientific verification for designing low-cost and practical hyper-spectral space-borne sensors and provide theoretical bases for simulating space-borne sensors to predict soil heavy metals content in the future.