Heavy Metal Detection in Crops and Soil Clay Mineral Abundance Mapping using Hyperspectral Data

Abstract

Presence of heavy metal in crops is an indicator of environmental pollution. Theheavy metals found in the plant indicate that the specific metal exists in the terrestrialenvironment. These metals affect leaves� spectral characteristics and interferewith plants� biochemical features, such as chlorophyll concentration and photosynthesis.Accurate detection of heavy metals in plants is necessary for agriculturalmanagement and to preserve ecological balance. Field spectroscopy techniquesare used to measure the spectral changes triggered due to contaminationwith heavy metals. The advantage of these remote sensing approaches to assessheavy metal contamination is that they can frequently collect data across a widegeographic area.The study mainly focuses on detecting different levels of heavy metal pollutionfrom airborne hyperspectral data using reference data from in situ controlledpot experiments. We constructed a training set spectrum from a controlled experimenton cotton and tobacco for two important heavy metals, Lead (Pb) andCadmium (Cd). Cotton and tobacco crops were grown in pots after artificiallycontaminating the soil with four Pb and Cd heavy metal treatments. The hyperspectraland biochemical data generated spectra of heavy metal concentrations atdifferent crop growth stages. Standard reflectance spectra at different contaminationlevels do not show significant changes at different wavelengths due to thepresence of heavy metal. These spectra were further decomposed using wavelettransform at different levels to capture the subtle changes in spectra using thedetailed component of wavelets. The reconstructed detailed wavelet reflectanceat the third level of decomposition was found to be significant with heavy metalstress. The correlation analysis established that the wavelength range of 651-742nano meter (nm) in cotton was sensitive to Pb stress, and 631-802 nm was sensitiveto Cd stress in tobacco. The reconstructed detail reflectance at a particularwavelength was then further used as reference spectra with different heavy metallevels to map heavy metal pollution.The AVIRIS-NG data obtained for the study area was first classified to identifythe tobacco crop in the Anand region and the cotton crop in the Surendranagarregion using a combination of Autoencoder (AE) for feature extraction followedby an artificial neural network for classification. The training data obtained fromthe pot experiment were utilized to map Pb and Cd pollution from classified airbornehyperspectral data from Airborne Visible InfraRed Imaging Spectrometer- Next Generation (AVIRIS-NG) using a spectral matching algorithm known asDynamic Spectral Warping (DSW). The results confirm the efficiency of the developedalgorithm in estimating Cd content in tobacco and Pb content in cottoncrops. The model was validated by collecting the exact field points and heavymetal concentration, which shows a promising result for this algorithm.Diverse soil minerals may be easily identified through modern hyperspectraltechnology for remote sensing. The aerial hyperspectral sensor�s enhanced spatialand spectral resolution can identify the abundance of several clay minerals, suchas Kaolinite, Montmorillonite, and Illite. This study maps the clay mineral distributionin the Udaipur area of Rajasthan and the Ambaji region of Gujarat usinghyperspectral data acquired by the AVIRIS-NG sensor on an airborne platform.The representative soil sampling sites were selected from hyperspectral datausing the Spectral Feature Fitting (SFF) algorithm. X-ray Diffraction (XRD) analysiswas carried out to find different clay minerals in the samples. Then the regressionanalysis was carried out to find the relation between Absorption PeakDepth (APD) extracted from hyperspectral data corresponding to the actual locationof sampling sites and the corresponding clay percentage obtained from XRDanalysis. Regression analysis between absorption peak depth values estimatedfrom hyperspectral data at 2205 nm � 2214 nm spectral region of soil samplingsites and corresponding clay content value showed a significant relationship. Theregression line obtained for the known pixel is used to prepare the mineral abundance map over the study area. The study over the Udaipur region shows thedominance of montmorillonite clay minerals, and the Ambaji region showed anabundance of kaolinite.