Multisource spectral-integrated estimation of cadmium concentrations in soil using a direct standardization and Spiking algorithm.

Benefiting from the advantages of a wide spatial sampling range and strong continuity, hyperspectral analysis provides a potential way to detect heavy metals in soil. However, it is still a great challenge to identify the spectral response characteristics of heavy metals from naturally polluted soil samples. This paper innovatively produces near standard soil samples for exploring the exact spectral response of cadmium (Cd) in soil and presents a novel method by combining the direct standardization (DS) and Spiking algorithms for integrating multisource spectra to improve the accuracy of Cd concentration estimation. A total of 46 naturally polluted soil samples were collected from a known Cd-contaminated mining area in Xiangjiang River Basin, China. The soil spectra of the naturally polluted soil samples were synchronously measured in the field. Moreover, clean soils with low heavy metal contaminants were collected to produce 65 near standard soil samples with known Cd levels. Then, the spectra and Cd concentrations of all 111 soil samples were measured under laboratory conditions. The principle component stepwise regression (PCSR) analysis results illustrated that the reflectance at all the wavelengths (380-2460 nm) is indicative of the differences in the soil Cd concentrations. Among these, the sensitivity of the spectral reflectance is the strongest at approximately 400 nm, 1000 nm and above 2300 nm. Additionally, the integrated multisource spectra significantly improved the accuracy of soil Cd concentration estimation (coefficient of determination, R2 = 0.96; root mean square error, RMSE = 0.29; ratio of prediction to deviation, RPD = 1.21) when 30 transfer samples and 15 training samples were simultaneously implemented in the combined DS and Spiking algorithm. This will provide a feasible scheme for exploration of spectral response characteristics of multiple soil heavy metals, and highlight the potential of developing low-level and satellite remote sensing on a large scale.