SPECTRAL PREPROCESSING FOR HYPERSPECTRAL REMOTE SENSING OF HEAVY METALS IN WATER

• Main Authors: M. Lee, X.-Y. Chen, H.-C. Lee
• Format: Article
• Language: English
• Published: Copernicus Publications 2019-06-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W13/1869/2019/isprs-archives-XLII-2-W13-1869-2019.pdf

This study aims to investigate the feasibility of using hyperspectral remote sensing technique by visible-near infrared spectroradiometer (VNIR, FieldSpec HandHeld 2) for rapid water monitoring of heavy metal, followed by comparison of different spectral preprocessing methods for the development of quantitative predictive model. The water samples evaluated in this study were prepared in our laboratory by dilution of stock solutions. Heavy metals of lead (Pb), zinc (Zn) and copper (Cu) in the range of concentration between 100 to 2000 mg/L were selected as the target samples in this study. The sensitive bands for the target metals were characterized in the range from 800 nm to 1075 nm, based on the reflectance spectral data. Spectral data for developing of the quantitative predictive model was first preprocessed with first derivative and logarithm transformation, followed by establishing of the prediction model using multivariate linear regression (MLR). It was observed that increase in the number of sensitive bands for the MLR can significantly improve the adjusted R² for the model. The prediction model for Cu was found to have the highest adjusted R² of 0.92 and least normalized root mean square error (NRMSE) of 0.065, while using the reflectance values of 7 sensitive bands. This result could be attributed to the blue color characteristic of the solution, whereas the others remain clear. Additionally, the first derivative transformation was determined as the best method for predicting Pb, whereas the logarithm transformation provided the best outcomes for predicting Cu and Zn.