Operational algorithm for the retrieval of water quality in the Great Lakes

Pozdnyakov, Dmitry; Shuchman, Robert A.; Korosov, Anton; Hatt, Charles R.

doi:10.1016/j.rse.2005.04.018

Cited by 80 publications

(54 citation statements)

References 27 publications

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“…As a direct consequence to their proximity to land masses, waters in the coastal zone (case II waters) are optically complex (Bukata et al, 2001). The optical properties of case II waters are influenced not only by phytoplankton and the substances originating from the phytoplankton's life cycle, but also by other matters independent of phytoplankton (Pozdnyakov et al, 2005). The multiple water constituents, together with the eventually influence of the seafloor, makes the mapping of coastal waters more difficult than to case I waters.…”

Section: Current Remote Sensing Techniquesmentioning

confidence: 99%

“…The atmospheric aerosols over case I waters contain mostly marine particulate matter, generally homogenously spatially and chemically. Its optical properties are fairly well established and, hence, can be adequately assessed (Pozdnyakov et al, 2005). On the contrary, due to their proximity to land masses, the aerosols over case II waters reveal a pronounced spatial and compositional heterogeneity.…”

Section: Current Remote Sensing Techniquesmentioning

confidence: 99%

“…On the contrary, due to their proximity to land masses, the aerosols over case II waters reveal a pronounced spatial and compositional heterogeneity. Aerosols over coastal areas originate from a wealth of different sources, often anthropogenic in nature (Pozdnyakov et al, 2005). Consequently, the atmospheric correction over such areas is complicated and the resulting water spectra of lower quality.…”

Section: Current Remote Sensing Techniquesmentioning

confidence: 99%

“…The various existing water colours show that the backscattered flux contains information about the optical properties and composition of the water column. Hence, satellite sensors that measure the reflected flux at multiple wavelengths are, in principle, able to observe in-water parameters and associated processes (Kester et al, 1996;Dey & Singh, 2003;Pozdnyakov et al, 2005). Although broad-band Landsat-TM, SPOT HVR, or Linear Imaging Self-Scanning Sensor (LISS-III) satellite data can not be analysed for retrieval of specific absorption characteristics due to the broad bandwidths, they offer the advantage of large area coverage and multi-temporal use.…”

Section: Mapping Sediment Concentrations and Light Climatementioning

confidence: 99%

“…In the case of mid-oceanic (case I) waters, simple algorithms are effective due to the optical simplicity of such waters (Pozdnyakov et al, 2005). Case I waters are generally only loaded with phytoplankton and accompanying and covarying products of their life cycle as well as some microscopic organisms (e.g., flagellates, bacteria, viruses) which are also native to off-shore waters.…”

Section: Mapping Sediment Concentrations and Light Climatementioning

confidence: 99%

See 4 more Smart Citations

Satellite-based monitoring of tropical seagrass vegetation: current techniques and future developments

Ferwerda¹,

Leeuw

Atzberger³

et al. 2007

Hydrobiologia

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Decline of seagrasses has been documented in many parts of the world. Reduction in water clarity, through increased turbidity and increased nutrient concentrations, is considered to be the primary cause of seagrass loss. Recent studies have indicated the need for new methods that will enable early detection of decline in seagrass extent and productivity, over large areas. In this review of current literature on coastal remote sensing, we examine the ability of remote sensing to serve as an information provider for seagrass monitoring. Remote sensing offers the potential to map the extent of seagrass cover and monitor changes in these with high accuracy for shallow waters. The accuracy of mapping seagrasses in deeper waters is unclear. Recent advances in sensor technology and radiometric transfer modelling have resulted in the ability to map suspended sediment, sea surface temperature and below-surface irradiance. It is therefore potentially possible to monitor the factors that cause the decline in seagrass status. When the latest products in remote sensing are linked to seagrass production models, it may serve as an early-warning system for seagrass decline and ultimately allow a better management of these susceptible ecosystems.

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Section: Current Remote Sensing Techniquesmentioning

confidence: 99%

Section: Current Remote Sensing Techniquesmentioning

confidence: 99%

Section: Current Remote Sensing Techniquesmentioning

confidence: 99%

Section: Mapping Sediment Concentrations and Light Climatementioning

confidence: 99%

Section: Mapping Sediment Concentrations and Light Climatementioning

confidence: 99%

See 3 more Smart Citations

Satellite-based monitoring of tropical seagrass vegetation: current techniques and future developments

Ferwerda¹,

Leeuw

Atzberger³

et al. 2007

Hydrobiologia

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Basics of Remote Sensing Techniques Applicable in Wetlands Ecosystems

Chakraborty

Sanyal

Ray³

2023

Wetlands Ecology

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A bio-optical model based method of estimating total suspended matter of Lake Taihu from near-infrared remote sensing reflectance

Zhang

Shen

et al. 2007

Environ Monit Assess

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Total suspended matter is an important water quality parameter, and plays a key role in water quality evaluation, especially of inland waters. Many different methods have been developed to estimate TSM from remote sensing data, in which empirical methods and model-based methods are two types of commonly used methods. Compared with empirical methods, model-based methods have the advantages of definite physical meanings, high robustness and retrieval accuracy. In model-based methods, matrix inversion method is commonly used in monitoring water qualities of inland waters. However, matrix inversion method has to predetermine some optical parameters by empirical values or simplified optical model, which may introduce some errors in retrieved water quality parameters. In order to overcome the shortcomings of matrix inversion method and increase the estimating accuracy of total suspended mater, in this paper, a bio-optical model based method is developed, which estimates total suspended mater by using remote sensing reflectance of two near-infrared bands. This method is validated by in-situ experiment data measured in Lake Taihu, a big turbid lake in eastern China. The results show that this method has better performance than matrix inversion method. The average relative error of the estimated total suspended matter by this method is only 13.0%, which is much smaller than the errors by matrix inversion method (32.7%). This method has the advantages of definite physical meaning, easiness to carry out, and high estimating accuracy. However, the applicable scope of this method has limitations: it can only be applied to optically deep waters with high concentrations of total suspended matter.

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Operational algorithm for the retrieval of water quality in the Great Lakes

Cited by 80 publications

References 27 publications

Satellite-based monitoring of tropical seagrass vegetation: current techniques and future developments

Satellite-based monitoring of tropical seagrass vegetation: current techniques and future developments

Basics of Remote Sensing Techniques Applicable in Wetlands Ecosystems

A bio-optical model based method of estimating total suspended matter of Lake Taihu from near-infrared remote sensing reflectance

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