Using the Hilbert–Huang spectrum transformation to estimate soil lead concentration

Abstract

ABSTRACT Hyperspectral remote sensing provides apromising solution for estimation of heavy metal concentration in soil. However, few studies have focused on the detection of soil heavy metal concentration by the frequency domain analysis. In this letter, the Hilbert–Huang transform (HHT) is introduced to fully explore the hidden information in the spectrum. Based on experimentally acquired spectra of soil contaminated by lead (Pb) and chemical data, HHT was employed to obtain the Hilbert energy spectra (HES) and intrinsic model function (IMF) component of spectra. Then, characteristic spectral bands of Pb could be fully mined through these components, and random forest was utilized to retrieve Pb concentration. The following conclusions are drawn: (1) the components of HHT has good correlation with Pb concentration in the 340–1400 nm, and they can better highlight response of Pb concentration in the 1440–2450 nm; (2) characteristic bands extracted by the IMF components and HES are quite effective as input variables, and its correlation coefficient (r) and root mean square error (RMSE) for random forest is 0.9676 and 0.0741, respectively. Compared with the variables of original spectral reflectance and spectral domain transformation, the proposed spectral HHT variables achieve the highest estimation accuracy.