Reflectance spectroscopy for assessing heavy metal pollution indices in mangrove sediments using XGBoost method and physicochemical properties

• Published in: Catena (Giessen) Vol. 211; p. 105967
• Main Authors: Zhao, Demei, Wang, Junjie, Jiang, Xiapeng, Zhen, Jianing, Miao, Jing, Wang, Jingzhe, Wu, Guofeng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2022
• Subjects: Heavy metal pollution index; Mangrove; Reflectance spectroscopy; Standard normal variate; XGBoost; Reflectance spectroscopy; Standard normal variate; Heavy metal pollution index; Mangrove; XGBoost
• ISSN: 0341-8162; 1872-6887
• DOI: 10.1016/j.catena.2021.105967

[Display omitted] •Single and composite heavy metal pollution indices of mangrove sediment were explored.•Four types of spectral transformation methods were compared.•XGBoost and physicochemical properties were integrated to estimate pollution indices.•Composite pollution index was more accurately estimated than the other indices.•Fe and organic carbon significantly affected estimation accuracy of pollution indices. Single and composite pollution indices (SPI and CPI) of heavy metals are calculated with background heavy metal concentrations. They are efficient evaluation indicators of sediment pollution levels and could be directly compared among different regions and habitat types, however, they are rarely investigated in mangrove sediment with visible and near-infrared spectroscopy (VNIRS). With mangrove sediment samples collected from five regions and four habitat types, this study aimed to develop estimation models of heavy metal (Cr, Ni, Cu, Zn and Pb) pollution indices using XGBoost (extreme gradient boosting) method, and further to explore the effect of physicochemical properties (Fe, organic carbon (OC), clay and salinity) on the estimation accuracy. Kruskal-Walls test showed that regions and habitat types were significant factors affecting the six pollution indices. Compared with first derivative, second derivative and continuum removal reflectance, standard normal variate (SNV) reflectance was more accurate for estimating SPIs and CPI using XGBoost model. The XGBoost models with sensitive wavelengths of SNV reflectance outperformed that with whole spectrum (410 bands) in estimating the six pollution indices, and the ranking of estimation accuracy was CPI > SPICu > SPIZn > SPIPb > SPINi > SPICr. Compared with the XGBoost model with sensitive wavelengths alone, the model with additional physicochemical properties could increase R2 and RPD (the ratio of standard deviation to RMSE of validation in the test subset) values by 0.795–7.282% and 1.199–8.780%, and decrease RMSE values by 4.101–26.866% in estimating SPICr, SPINi and SPICu. Moreover, Fe and OC had greater effect on the estimation accuracy than clay and salinity. We conclude the integration of XGBoost and physicochemical properties holds potentials for accurately assessing heavy metal pollution indices in mangrove sediment with VNIRS, which can provide methodological basis for evaluating environment quality and ecological risk in mangrove ecosystem at the plot or landscape scale.