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.

A transferable spectroscopic diagnosis model for predicting arsenic contamination in soil.

Visible and near-infrared reflectance (VNIR) spectroscopy is considered to be a potential and efficient means for monitoring soil arsenic (As) contamination. While current studies mainly focus on the evaluation of models' performance when training and verification samples are collected from the same region, whether the model developed at a specific region can be transferred to other regions is still unclear. To answer this question, this study collected a total of 247 samples for training and verification from regions with different geographical conditions, which are Yuanping and Baoding in northern China, Chenzhou and Hengyang in southern China. Afterward, we proposed a transfer component analysis (TCA) based spectroscopic diagnosis model, which aims at adapting a model learned from one region to other regions. This model was compared with the traditional modeling method in terms of the prediction accuracy by four experiments. The results show that: (1) The traditional modeling method trained by specific regional samples has no transfer capability to different regions, since the coefficient of determination (R2) and the ratio of prediction to deviation (RPD) were 0.02 and 0.65 for the first pair of study areas, 0.01 and 1.01 for the second pair of study areas; (2) A transfer model with favorable predictability can be constructed with the aid of TCA spectral transformation and a small amount off-site samples (R2 and RPD were improved to 0.68 and 1.54 for the first pair of study areas, 0.64 and 1.66 for the second pair of study areas). Results suggest that it is promising to develop potential implementations of transferable spectroscopic diagnosis models for estimating soil As concentrations in large area with lower cost.