scholarly journals A grid enabled Monte Carlo hyperspectral synthetic image remote sensing model (GRID-MCHSIM) for coastal water quality algorithm

2006 ◽  
Author(s):  
Gen-Tao Chiang ◽  
Martin Dove ◽  
Stuart Ballard ◽  
Charles Bostater ◽  
Ian Frame
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Monte Carlo ◽  
Coastal Water ◽  
Synthetic Image ◽  
Coastal Water Quality ◽  
Remote Sensing Model ◽  
Sensing Model

Hyperspectral remote sensing for estimating coastal water quality: case study on the coast of Black Sea, Romania

2014 ◽  
Author(s):  
S. G. Ghezehegn ◽  
Peters Steef ◽  
Annelies Hommersom ◽  
De Reus Nils ◽  
Oana Culcea ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Black Sea ◽  
Coastal Water ◽  
Hyperspectral Remote Sensing ◽  
Coastal Water Quality

scholarly journals A Semi-Analytical Optical Remote Sensing Model to Estimate Suspended Sediment and Dissolved Organic Carbon in Tropical Coastal Waters Influenced by Peatland-Draining River Discharges off Sarawak, Borneo

2020 ◽  
Vol 13 (1) ◽  
pp. 99
Author(s):  
Nagur Cherukuru ◽  
Patrick Martin ◽  
Nivedita Sanwlani ◽  
Aazani Mujahid ◽  
Moritz Müller
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Organic Carbon ◽  
Dissolved Organic Carbon ◽  
Suspended Sediment ◽  
Coastal Water ◽  
Coastal Waters ◽  
Suspended Sediments ◽  
Optical Remote Sensing ◽  
Coastal Water Quality

Coastal water quality degradation is a global challenge. Marine pollution due to suspended sediments and dissolved matter impacts water colour, biogeochemistry, benthic habitats and eventually human populations that depend on marine resources. In Sarawak (Malaysian Borneo), peatland-draining river discharges containing suspended sediments and dissolved organic carbon influence coastal water quality at multiple locations along the coast. Optical remote sensing is an effective tool to monitor coastal waters over large areas and across remote geographic locations. However, the lack of regional optical measurements and inversion models limits the use of remote sensing observations for water quality monitoring in Sarawak. To overcome this limitation, we have (1) compiled a regional spectral optical library for Sarawak coastal waters, (2) developed a new semi-analytical remote sensing model to estimate suspended sediment and dissolved organic carbon in coastal waters, and (3) demonstrated the application of our remote sensing inversion model on satellite data over Sarawak. Bio-optical data analysis revealed that there is a clear spatial variability in the inherent optical properties of particulate and dissolved matter in Sarawak. Our optical inversion model coupled with the Sarawak spectral optical library performed well in retrieving suspended sediment (bias = 3% and MAE = 5%) and dissolved organic carbon (bias = 3% and MAE = 8%) concentrations. Demonstration products using MODIS Aqua data clearly showed the influence of large rivers such as the Rajang and Lupar in discharging suspended sediments and dissolved organic carbon into coastal waters. The bio-optical parameterisation, optical model, and remote sensing inversion approach detailed here can now help improve monitoring and management of coastal water quality in Sarawak.

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