Estimation of heavy metal concentration in the Pearl River estuarine waters from remote sensing data

Abstract

With the increase of population and the development of light industry in the Pearl River Delta area, a great deal of industrial and household waste waters with heavy metals are discharged to the ocean via the river channels. The heavy metals cannot be decomposed but can be transferred and accumulated with food chains [1,2]. Many heavy metals are toxic to human beings. It is very important to measure the heavy metal concentration in the coastal waters for water quality investigation, environmental management and for security of aquaculture products. The remote sensing technology has been successfully applied for estimation of many parameters of water quality, such as the suspended sediments [3], chlorophyll a, [4][5][6], CDOM (colored dissolved organic matter) [7-8], eutrophication [9-10], salinity [11], and water quality grades [12]. The remote sensing technology has many advantages over the conventional investigation of water quality for its high spatial and temporal resolution, low cost, big coverage of data and synchronization. With its development, the remote sensing technique is expected to retrieve more parameters of water quality. As the heavy metals in water exist in three forms [13][14], each of which is respectively controlled by optically-significant parameters, suspended sediments, dissolved organic matters and phytoplankton. Previous work showed the possibility of detecting heavy metal concentration from remote sensing data [15].Eight cruises were conducted for in-situ data collection in the Pearl An above-water method was used for the measurement of remote sensing reflectance (Rrs). The water-leaving radiance, the radiance reflected by a reference panel with 25% reflectance and the sky radiance were measured in turn for 3 times using the Ocean Optics USB4000 spectrometer (wavelength range from 346 to 1037 nm with a spectral resolution of 0.22 nm) at 128 sampling points, where the water samples were synchronously collected for lab analysis of concentration for three heavy metals (Cu, Pb and Zn) and other water components (suspended sediments, chlorophyll-a and CDOM). The in-situ measured Rrs data were calculated to match the bands of EO-1 Hyperion and the Landsat-TM band 1 to 4. The relationships between the concentration of the three heavy metals (Cu, Pb and Zn) and the remote sensing reflectance were analysed. The results showed that all the three heavy metals have good correlative relation with Rrs data. The correlative relation could be improved if it was analyzed respectively from data collected in dry season or wet season, and the correlative relation could be improved with the band ratio of Rrs. Algorithms for estimation of heavy metal concentration were developed using the ratio band combination, which has the maximum correlative coefficient with heavy metals. Then the algorithms were applied to Landsat TM data for estimation of the heavy metals (Cu, Pb, Zn) concentration. The results showed that the distribution of the three heavy metals' 2575 978-1-4673-1159-5/12/$31.00 ©2012 IEEE IGARSS 20...

A Robust Fuzzy Neural Network Model for Soil Lead Estimation from Spectral Features

“…In addition, the visible spectral areas centered on 375 nm, 500 nm and 610 nm, which are proposed in [3] and/or [7], are also considered as the most probable informative bands for spectral feature design.…”

“…For this reason Oi (3) (i.e., the output of the normalization layer) are determined based on current values of aij, bij for all the training samples. Then weight parameters are determined to give the least square error in the output layer.…”

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