A Semi-Analytic Model for Estimating Total Suspended Sediment Concentration in Turbid Coastal Waters of Northern Western Australia Using MODIS-Aqua 250 m Data

Dorji, Passang; Fearns, Peter; Broomhall, M.

doi:10.3390/rs8070556

Cited by 43 publications

(31 citation statements)

References 81 publications

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“…True color image of the study site is reprinted from Figure 1 in Dorji et al [64] under a Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). The black polygon added in Fig 1 represents the area where Worldview 2 data were captured on June 13 th 2014.…”

Section: Methodsmentioning

confidence: 99%

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Impact of the spatial resolution of satellite remote sensing sensors in the quantification of total suspended sediment concentration: A case study in turbid waters of Northern Western Australia

Dorji

Fearns

2017

PLoS ONE

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The impact of anthropogenic activities on coastal waters is a cause of concern because such activities add to the total suspended sediment (TSS) budget of the coastal waters, which have negative impacts on the coastal ecosystem. Satellite remote sensing provides a powerful tool in monitoring TSS concentration at high spatiotemporal resolution, but coastal managers should be mindful that the satellite-derived TSS concentrations are dependent on the satellite sensor’s radiometric properties, atmospheric correction approaches, the spatial resolution and the limitations of specific TSS algorithms. In this study, we investigated the impact of different spatial resolutions of satellite sensor on the quantification of TSS concentration in coastal waters of northern Western Australia. We quantified the TSS product derived from MODerate resolution Imaging Spectroradiometer (MODIS)-Aqua, Landsat-8 Operational Land Image (OLI), and WorldView-2 (WV2) at native spatial resolutions of 250 m, 30 m and 2 m respectively and coarser spatial resolution (resampled up to 5 km) to quantify the impact of spatial resolution on the derived TSS product in different turbidity conditions. The results from the study show that in the waters of high turbidity and high spatial variability, the high spatial resolution WV2 sensor reported TSS concentration as high as 160 mg L-1 while the low spatial resolution MODIS-Aqua reported a maximum TSS concentration of 23.6 mg L-1. Degrading the spatial resolution of each satellite sensor for highly spatially variable turbid waters led to variability in the TSS concentrations of 114.46%, 304.68% and 38.2% for WV2, Landsat-8 OLI and MODIS-Aqua respectively. The implications of this work are particularly relevant in the situation of compliance monitoring where operations may be required to restrict TSS concentrations to a pre-defined limit.

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Section: Methodsmentioning

confidence: 99%

“…A brief description of the in situ R rs measurements using the DALEC and sampling of TSS concentration are provided below. Further details of the R rs and TSS concentration data used in this study, including the data collection procedure and data quality control measures, are discussed in depth in Dorji et al [64]. …”

Section: Methodsmentioning

confidence: 99%

Impact of the spatial resolution of satellite remote sensing sensors in the quantification of total suspended sediment concentration: A case study in turbid waters of Northern Western Australia

Dorji

Fearns

2017

PLoS ONE

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“…The validation was based on five statistical parameters, namely, Pearson correlation coefficient (R, Equation (7)), RMSE (Equation (8)) [37], mean absolute error (MAE, Equation (9)), Bias (ψ, Equation (10)) and scattering of data points (|ψ|, Equation (12)) [38], where R is a measurement of the correlation between the observed and the predicted datasets. RMSE measures the difference between observed and predicted values.…”

Section: Performance Evaluationmentioning

confidence: 99%

Evaluation of Empirical and Machine Learning Algorithms for Estimation of Coastal Water Quality Parameters

Nazeer

Bilal

Alsahli

et al. 2017

IJGI

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Abstract:Coastal waters are one of the most vulnerable resources that require effective monitoring programs. One of the key factors for effective coastal monitoring is the use of remote sensing technologies that significantly capture the spatiotemporal variability of coastal waters. Optical properties of coastal waters are strongly linked to components, such as colored dissolved organic matter (CDOM), chlorophyll-a (Chl-a), and suspended solids (SS) concentrations, which are essential for the survival of a coastal ecosystem and usually independent of each other. Thus, developing effective remote sensing models to estimate these important water components based on optical properties of coastal waters is mandatory for a successful coastal monitoring program. This study attempted to evaluate the performance of empirical predictive models (EPM) and neural networks (NN)-based algorithms to estimate Chl-a and SS concentrations, in the coastal area of Hong Kong. Remotely-sensed data over a 13-year period was used to develop regional and local models to estimate Chl-a and SS over the entire Hong Kong waters and for each water class within the study area, respectively. The accuracy of regional models derived from EPM and NN in estimating Chl-a and SS was 83%, 93%, 78%, and 97%, respectively, whereas the accuracy of local models in estimating Chl-a and SS ranged from 60-94% and 81-94%, respectively. Both the regional and local NN models exhibited a higher performance than those models derived from empirical analysis. Thus, this study suggests using machine learning methods (i.e., NN) for the more accurate and efficient routine monitoring of coastal water quality parameters (i.e., Chl-a and SS concentrations) over the complex coastal area of Hong Kong and other similar coastal environments.

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“…As part of a regional water monitoring program, in situ reflectance and TSS measurements were carried out for the waters off the coast of northern Western Australia to develop regional TSS models (see MOD-A1 and LAN-A1 in Appendix A) [67]. The details of the in situ measurements and regional TSS model developed using in situ data can be obtained from [67].…”

Section: Model Evaluation Using In Situ Datamentioning

confidence: 99%

“…The details of the in situ measurements and regional TSS model developed using in situ data can be obtained from [67]. A set of high scoring models (MOD-E10, MOD-A4, LAN-E9, and LAN-A5) and low scoring models (MOD-E1, MOD-E38, LAN-E6, and LAN-A3) were selected to compare with MOD-A1 and LAN-A1 in the context of in situ data comparisons.…”

Section: Model Evaluation Using In Situ Datamentioning

confidence: 99%

A Quantitative Comparison of Total Suspended Sediment Algorithms: A Case Study of the Last Decade for MODIS and Landsat-Based Sensors

Dorji

Fearns

2016

Remote Sensing

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A quantitative comparative study was performed to assess the relative applicability of Total Suspended Solids (TSS) models published in the last decade for the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat-based sensors. The quantitative comparison was performed using a suite of statistical tests and HydroLight simulated data for waters ranging from clear open ocean case-1 to turbid coastal case-2 waters. The quantitative comparison shows that there are clearly some high performing TSS models that can potentially be applied in mapping TSS concentration for regions of uncertain water type. The highest performing TSS models tested were robust enough to retrieve TSS from different water types with Mean Absolute Relative Errors (MARE) of 69.96%-481.82% for HydroLight simulated data. The models were also compared in regional waters of northern Western Australia where the highest performing TSS models yielded a MARE in the range of 43.11%-102.59%. The range of Smallest Relative Error (SRE) and Largest Relative Error (LRE) between the highest and the lowest performing TSS models spanned three orders of magnitude, suggesting users must be cautious in selecting appropriate models for unknown water types.

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A Semi-Analytic Model for Estimating Total Suspended Sediment Concentration in Turbid Coastal Waters of Northern Western Australia Using MODIS-Aqua 250 m Data

Cited by 43 publications

References 81 publications

Impact of the spatial resolution of satellite remote sensing sensors in the quantification of total suspended sediment concentration: A case study in turbid waters of Northern Western Australia

Impact of the spatial resolution of satellite remote sensing sensors in the quantification of total suspended sediment concentration: A case study in turbid waters of Northern Western Australia

Evaluation of Empirical and Machine Learning Algorithms for Estimation of Coastal Water Quality Parameters

A Quantitative Comparison of Total Suspended Sediment Algorithms: A Case Study of the Last Decade for MODIS and Landsat-Based Sensors

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