Feasibility of estimating heavy metal concentrations in Phragmites australis using laboratory-based hyperspectral data—A case study along Le'an River, China

Monitoring stress levels on rice with heavy metal pollution from hyperspectral reflectance data using wavelet-fractal analysis

“…(ii) Normalized spectral absorption depth, which is the standard transform in spectroscopy through continuum removal to enhance the spectral absorption features based on metal binding mechanisms. It includes variations in absorption features, such as the peak depth and peak area at specific wavebands (van der Meer, 2006;Liu et al, 2010c). (iii) Integrated spectral indices, which combine two or more spectral bands to enhance the vegetative signal while minimizing background effects and are commonly used to measure the sensitivity of vegetation to heavy metal stress Gallagher et al, 2008).…”

“…However, the pollution level in the real world ecosystems is relatively low, which means that no visible symptoms exist in leaf reflectance spectra. Other researchers have developed spectral analysis methods, such as derivative transform (Gitelson et al, 1996;Ren et al, 2008), continuum removal (van der Meer, 2006;Liu et al, 2010c), in order to enhance the vegetative stress signals through minimizing the effects of background materials. Several studies using hyperspectral data of vegetation have already demonstrated the benefits of wavelet transform for spectral smoothing and noise removal (Bruce and Li, 2001;Schmidt and Skidmore, 2004).…”

A Wavelet-Based Area Parameter for Indirectly Estimating Copper Concentration in Carex Leaves from Canopy Reflectance

“…Numerous studies have focused on the retrieval of the spectrally active biochemical properties of plants (e.g., water, chlorophyll, and nitrogen) using physical or statistical models [8][9][10][11][12][13][14][15][16][17][18][19][20]. In contrast, much less progress has been made to estimate foliar heavy metal concentrations [21][22][23][24][25], which may be due to their very low concentrations in plant leaves (e.g., normally 5−30 mg•kg −1 for Cu) [26] and absence of evident physical absorption features existing for major constituents (e.g., water, chlorophyll, and starch) [27]. This makes it impossible to predict heavy metal concentrations directly from hyperspectral data.…”

An improved assimilation method with stress factors incorporated in the WOFOST model for the efficient assessment of heavy metal stress levels in rice

“…Remote sensing, as an alternative method for monitoring heavy metal contamination, has been developed in recent years (Kopackova, 2014;Liu et al, 2012;Schuerger et al, 2003). Previous studies mainly focused on building models based on the relationship between spectral feature parameters and the heavy metal concentrations or physiological parameters of crops, such as the chlorophyll content, nutrient elements content and cellular structure (Dunagan et al, 2007;Liu et al, 2011aLiu et al, , 2010Rosso et al, 2005;Wilson et al, 2004). All of the selected physiological parameters mainly come from the aboveground parts of plants, whereas roots below ground have rarely been considered.…”