Quantitative Estimation of Tobacco Copper Ion Content from Hyperspectral Data by Inverting a Modified Radiative Transfer Model: Algorithm and Preliminary Validation - Yonghua Qu; Sihong Jiao (Journal of Spectroscopy)
Excess heavy metal, for example, copper, in vegetation will depress the normal plant growth, and the yield of such plant will be harmful if they are loaded into the food chain. Spectroscopy is thought as an efficient noncontact method on detecting the heavy metal in vegetation. This paper is aimed at retrieving the copper ion content in copper-stressed tobacco leaves from hyperspectral data by inverting a modified radiative transfer (RT) model. The dataset regarding the reflectance spectra, biochemical components, and copper ion contamination of copper-treated leaves was obtained from a laboratory experiment on spectral data from copper-treated tobacco. A simultaneous inversion on multiple parameters was conducted to explore the difficulties in estimating copper ion concentrations without considering the correlation between input parameters. This simultaneous inversion produced an unsatisfactory result, with the correlation coefficient (R) and root-mean-squared error (RMSE) being 0.12 and 0.021, respectively. Then, the sensitivity of the input parameters of the RT model was analyzed. Based on the sensitivity bands and the RT model, a concrete procedure for a multiobjective and multistage decision-making method was defined to perform the inversion of the copper ion content. The accuracy of the inversion results was improved significantly, and the values of the R and RMSE were 0.60 and 0.015, respectively. The proposed method fully considers the correlativity among the model parameters. Additionally, the method promises to provide a theoretical basis and technical support for heavy metal monitoring using the spectroscopy method.