Possibilities of visible–near-infrared spectroscopy for the assessment of soil contamination in river floodplains

• Published in: Analytica chimica acta Vol. 446; no. 1; pp. 97 - 105
• Main Authors: Kooistra, L, Wehrens, R, Leuven, R.S.E.W, Buydens, L.M.C
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier B.V 19.11.2001; Elsevier
• Subjects: Analytical chemistry; Animal, plant and microbial ecology; Applied ecology; Biological and medical sciences; Chemistry; Contaminated sediment; Ecotoxicology, biological effects of pollution; Exact sciences and technology; Floodplains; Fundamental and applied biological sciences. Psychology; Multivariate calibration; Remote sensing; River Rhine; Spectrometric and optical methods; Terrestrial environment, soil, air; VNIR spectroscopy; Rhine Basin; Europe; Contaminated sediment; Remote sensing; River Rhine; VNIR spectroscopy; Multivariate calibration; Floodplains; Cadmium; Data analysis; Chemical analysis; Ecotoxicology; Derivative spectrometry; Visible spectrometry; Transition metal; Multivariate analysis; Sediments; Zinc; Environmental control; Chemical pollution; PLS regression; Near infrared spectrometry; Flood plain; Multicomponent analysis; Environmental monitoring; Chemometrics; Quantitative analysis
• DOI: 10.1016/S0003-2670(01)01265-X
• ISSN: 0003-2670

During the past decades, large amounts of diffuse cadmium (Cd) and zinc (Zn) contaminated soil material have been deposited in the floodplains of the river Rhine in the Netherlands. As spatial information on soil quality is required at different scale levels covering the whole area, characterisation exclusively based on soil sampling and analysis is time-consuming and very expensive. A quicker method is developed based on a multivariate calibration procedure using partial least squares (PLS) regression to establish a relationship between reflectance spectra in the visible–near-infrared (VNIR) region and spectrally active soil characteristics (organic matter and clay content) that are inter-correlated with concentration levels of Cd and Zn. Several spectral pre-processing methods (normalisation, multiplicative scatter correction (MSC), derivation, standard normal variate (SNV) transform) were employed to improve the robustness and performance of the calibration models. No pre-processing gave the best results for Cd and Zn with RMSECV equal to 0.676 and 80.97 mg kg −1, respectively. Application of the calibration models for soil quality characterisation in river floodplains is promising. The future possibilities of multivariate calibration and pre-processing in remote sensing have to be explored.