Statistical Techniques for Correlating Total Suspended Matter Concentration with Seawater Reflectance Using Multispectral Satellite Data

Teodoro, Ana Cláudia; Gomes, Fernando Veloso; Gonçalves, Hernâni

doi:10.2112/06-0770.1

Cited by 16 publications

(7 citation statements)

References 24 publications

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“…Three different statistical techniques were used to establish correlations between a range of SSC values (0-100 mg/L) and satellite reflectance values (Teodoro et al 2008) -linear regression, multivariate regression and ANN regression. For linear regressions, the band that produced the best statistical correlation for each sensor was determined and then used in subsequent calculations.…”

Section: Discussionmentioning

confidence: 99%

Estuarine sediment deposition during wetland restoration: A GIS and remote sensing modeling approach

Newcomer

Kuss

Ketron

et al. 2013

Geocarto International

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Restoration is currently underway in the industrial salt flats of San Francisco Bay, California. Remote sensing of suspended sediment concentration and other geographical information system predictor variables were used to model sediment deposition within recently restored ponds. Suspended sediment concentrations were calibrated to reflectance values from Landsat TM 5 and ASTER satellite image data using three statistical techniques -linear regression, multivariate regression and artificial neural network (ANN) regression. Multivariate and ANN regressions using ASTER proved to be the most accurate methods, yielding r 2 values of 0.88 and 0.87, respectively. Predictor variables such as sediment grain size and tidal frequency were used in the marsh sedimentation (MARSED) model for predicting deposition rates. MARSED results show a root-mean-square deviation of 66.8 mm (<1σ) between modelled and field observations. This model was applied to a pond breached in November 2010 and indicated that the pond will reach sediment equilibrium levels after 60 months of tidal inundation.

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Section: Discussionmentioning

confidence: 99%

Estuarine sediment deposition during wetland restoration: A GIS and remote sensing modeling approach

Newcomer

Kuss

Ketron

et al. 2013

Geocarto International

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“…Doxaran et al [ 32 ] determined the composition of water in terms of turbidity using visible and near-infrared (NIR) wavelength satellite data. Teodoro et al [ 33 ] also studied the TSS concentration in sea water using multispectral satellite data. Analyses have proven a non-linear correlation for TSS concentration and sea water reflectivity.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Sensing for Turbid Water Quality Monitoring in Freshwater Rivers: Empirical Relationship between Reflectance and Turbidity and Total Solids

Huang

et al. 2014

Sensors

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Total suspended solid (TSS) is an important water quality parameter. This study was conducted to test the feasibility of the band combination of hyperspectral sensing for inland turbid water monitoring in Taiwan. The field spectral reflectance in the Wu river basin of Taiwan was measured with a spectroradiometer; the water samples were collected from the different sites of the Wu river basin and some water quality parameters were analyzed on the sites (in situ) as well as brought to the laboratory for further analysis. To obtain the data set for this study, 160 in situ sample observations were carried out during campaigns from August to December, 2005. The water quality results were correlated with the reflectivity to determine the spectral characteristics and their relationship with turbidity and TSS. Furthermore, multiple-regression (MR) and artificial neural network (ANN) were used to model the transformation function between TSS concentration and turbidity levels of stream water, and the radiance measured by the spectroradiometer. The value of the turbidity and TSS correlation coefficient was 0.766, which implies that turbidity is significantly related to TSS in the Wu river basin. The results indicated that TSS and turbidity are positively correlated in a significant way across the entire spectrum, when TSS concentration and turbidity levels were under 800 mg·L−1 and 600 NTU, respectively. Optimal wavelengths for the measurements of TSS and turbidity are found in the 700 and 900 nm range, respectively. Based on the results, better accuracy was obtained only when the ranges of turbidity and TSS concentration were less than 800 mg·L−1 and less than 600 NTU, respectively and used rather than using whole dataset (R2 = 0.93 versus 0.88 for turbidity and R2 = 0.83 versus 0.58 for TSS). On the other hand, the ANN approach can improve the TSS retrieval using MR. The accuracy of TSS estimation applying ANN (R2 = 0.66) was better than with the MR approach (R2 = 0.58), as expected due to the nonlinear nature of the transformation model.

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“…Moreover, various studies have been carried out combining in situ measurements and satellite data in order to relate spectral properties of seawater and TSM concentration (e.g. Chen et al 2014;Teodoro et al 2008). Many TSM models based on empirical methods have been used in operational satellite RS systems.…”

Section: Quantification Of the Total Suspended Matter (Tsm) Concentramentioning

confidence: 99%

“…It was very difficult get water samples and simultaneous radiometric measurements in the breaking zone. Therefore an evaluation of the range of TSM values, typically found in this area, need to be obtained through simulations on different beaches of the study area (Teodoro et al 2007a(Teodoro et al , 2008Teodoro and Veloso-Gomes 2007). In these simulations the bubbles and turbulence presented in the breaking zone were considered.…”

Section: Quantification Of the Total Suspended Matter (Tsm) Concentramentioning

confidence: 99%

Remote Sensing Technologies for the Assessment of Marine and Coastal Ecosystems

Gutierres

Teodoro

Reis

et al. 2016

Seafloor Mapping Along Continental Shelves

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This chapter reviews the Remote Sensing (RS) technologies that are particularly appropriate for marine and coastal ecosystem research and management. RS techniques are used to perform analysis of water quality in coastal water bodies; to identify, characterize and analyze river plumes; to extract estuarine/ coastal sandy bodies; to identify beach features/patterns; and to evaluate the changes and integrity (health) of the coastal lagoon habitats. For effective management of these ecosystems, it is essential to have satellite data available and complementary accurate information about the current state of the coastal regions, in addition to well-informed forecasts about its future state. In recent years, the use of space, air and ground-based RS strategies has allowed for the rapid data collection, Image processing (Pixel-Based and Object-Based Image Analysis (OBIA) classification) and dissemination of such information to reduce vulnerability to natural hazards, anthropic pressures, and to monitoring essential ecological processes, life support systems and biological diversity.

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Statistical Techniques for Correlating Total Suspended Matter Concentration with Seawater Reflectance Using Multispectral Satellite Data

Cited by 16 publications

References 24 publications

Estuarine sediment deposition during wetland restoration: A GIS and remote sensing modeling approach

Estuarine sediment deposition during wetland restoration: A GIS and remote sensing modeling approach

Hyperspectral Sensing for Turbid Water Quality Monitoring in Freshwater Rivers: Empirical Relationship between Reflectance and Turbidity and Total Solids

Remote Sensing Technologies for the Assessment of Marine and Coastal Ecosystems

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