VNIR estimation of heavy metals concentrations in suburban soil with multi-scale geographically weighted regression

• Published in: Catena (Giessen) Vol. 219; p. 106585
• Main Authors: Zhang, Shuangyin, Chen, Yiyun, Zhang, Zheyue, Wang, Siying, Wu, Zihao, Hong, Yongsheng, Wang, Yan, Hou, Haobo, Hu, Zhongzheng, Fei, Teng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-12-2022
• Subjects: GWR; Heavy metals contamination; MGWR; Spatial heterogeneity; Spatial heterogeneity; MGWR; Heavy metals contamination; GWR
• ISSN: 0341-8162; 1872-6887
• DOI: 10.1016/j.catena.2022.106585

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•Spatial relationships between soil chromophores and heavy metals were explored.•Spatial correlations with TFe2O3 were main reason that Cr and Ni can be estimated.•MGWR outperformed traditional regression methods for VNIR estimations of Cr and Ni.•Varied scales and bandwidths contribute to improving model performance.
Visible and near-infrared reflectance (VNIR) spectroscopy serves as a promising alternative to detect heavy metal contamination in suburban soils. However, the traditional regression method assumes a global rather than a non-stationary relationship between soil spectra and heavy metals. This study explored the spatially varying relationships between soil chromophores (e.g., SOM and TFe2O3) and heavy metals, and further investigated its implication for heavy metal estimation with VNIR. Specifically, we (i) explore the spatial patterns and covariation of soil chromophore and heavy metals, (ii) verify the feasibility of using multi-scale geographically weighted regression (MGWR) in the VNIR estimation of heavy metals, and (iii) investigate the potential mechanism underlying the models. Specifically, continuum removal and derivation were used to preprocess the reflectance spectra, and then a random frog algorithm was performed to select candidate feature wavelengths, followed by the removal of wavelengths with a correlation higher than 0.80 to avoid collinearity. Standard errors were calculated to further eliminate the variables with a high variance of coefficients. The remaining wavelengths were retained as inputs of the estimation models. The results demonstrated that MGWR outperformed its competitors, resulting in an increase in the coefficient of determination and a decrease in the residual sum of squares. Our study revealed the spatial heterogeneities of the relationships between heavy metals concentrations and soil spectra, and found that the spatial correlation between heavy metals and TFe2O3 (p < 0.01) was the main reason that Cr and Ni can be estimated by using VNIR. The MGWR method not only improves the capability of VNIR in the estimation of heavy metal concentrations, but promotes our understanding of the estimation mechanism.