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Review
. 2016 Aug 16;16(8):1298.
doi: 10.3390/s16081298.

A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques

Mohammad Haji Gholizadeh  1 , Assefa M Melesse  2 , Lakshmi Reddi  3
Affiliations
Review

A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques

Mohammad Haji Gholizadeh et al. Sensors (Basel). .

Abstract

Remotely sensed data can reinforce the abilities of water resources researchers and decision makers to monitor waterbodies more effectively. Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies (i.e., suspended sediments, colored dissolved organic matter (CDOM), chlorophyll-a, and pollutants). A large number of different sensors on board various satellites and other platforms, such as airplanes, are currently used to measure the amount of radiation at different wavelengths reflected from the water's surface. In this review paper, various properties (spectral, spatial and temporal, etc.) of the more commonly employed spaceborne and airborne sensors are tabulated to be used as a sensor selection guide. Furthermore, this paper investigates the commonly used approaches and sensors employed in evaluating and quantifying the eleven water quality parameters. The parameters include: chlorophyll-a (chl-a), colored dissolved organic matters (CDOM), Secchi disk depth (SDD), turbidity, total suspended sediments (TSS), water temperature (WT), total phosphorus (TP), sea surface salinity (SSS), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD).

Keywords: airborne sensors; remote sensing; spaceborne sensors; water quality indicators.

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Figures

Figure 1
Figure 1
The Absorption Spectrum of both the chlorophyll-a and the Chlorophyll-b pigments.
Figure 2
Figure 2
Spectral band positioning of Landsat7/ETM+ on ASD spectroradiometer spectrum [95].
Figure 3
Figure 3
Two different kinds of Secchi disks [183].
Figure 4
Figure 4
A suggested remote sensing based framework to predict and assessment of water quality variables.
See this image and copyright information in PMC

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