Spatio-Temporal Variability in a Turbid and Dynamic Tidal Estuarine Environment (Tasmania, Australia): An Assessment of MODIS Band 1 Reflectance

Fischer, Andrew M.; Pang, Daniel; Kidd, Ian M.; Moreno-Madriñán, Max J.

doi:10.3390/ijgi6110320

Cited by 10 publications

(8 citation statements)

References 50 publications

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“…Inland, coastal and estuarian shallow water area is dependent on the dynamics of sediments originated by wave erosion, tidal currents, hydrological parameters, biological and chemical constituent of water. For that this region presents a complex optical proprieties of case 2 waters, they are influenced by turbidity TU, Total suspended sediments TSS, dissolved matter, organic matter, microorganism, inorganic particulates matter from minerals [21].…”

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confidence: 99%

Estimation of Chlorophyll and Turbidity Using Sentinel 2A and EO1 Data in Kneiss Archipelago Gulf of Gabes, Tunisia

Katlane¹,

Dupouy²,

Kilani³

et al. 2020

IJG

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Multispectral and hyperspectral sensor data of the bio-optical parameters with a high spatial resolution are important for monitoring and mapping of the coastal ecosystems and estuarine areas, such as the Kneiss Islands in the Gulf of Gabes. Sentinel 2 S2A and Hyperion Earth observing-1 (EO1) imaging sensors reflectance data have been used for water quality determination and mapping of turbidity TU and chlorophyll Chl-a in shallow waters. First, we applied a tidal swing area mask based on uncorrelated pixel via 2D scatter plot between 665 nm and 865 nm to eliminate the overestimation of the concentration of water quality parameters due to the effect of the bottom reflection. The processing for mapping and validating Chl-a, Turbidity S2A, and EO1 were performed using a relation between reflectance bands and in situ measurements. Therefore, we were able to validate the performance of the case 2 regional coast colour processor (C2RCC) as well as our region-adapted empirical optical remote sensing algorithms. Turbidity was mapped based on the reflectance of 550 nm band for EO1 (R 2 = 0.63) and 665 nm band for S2A (R 2 = 0.70). Chlorophyll was mapped based on (457/528 nm) reflectance ratio (R 2 = 0.57) for EO1 and (705/665 nm) reflectance ratio (R 2 = 0.72) for the S2A.

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confidence: 99%

Estimation of Chlorophyll and Turbidity Using Sentinel 2A and EO1 Data in Kneiss Archipelago Gulf of Gabes, Tunisia

Katlane¹,

Dupouy²,

Kilani³

et al. 2020

IJG

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“…Generally, the water reflectance in the red part of the spectra is well correlated to the light backscattering by suspended sediments in the water column for low to moderate sediment concentrations [43]. Previous studies have demonstrated that RS atmospheric correction algorithms are successfully applied to highly turbid waters [44][45][46][47]. However, this has not been tested with Landsat imagery yet.…”

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confidence: 99%

Long-Term High-Resolution Sediment and Sea Surface Temperature Spatial Patterns in Arctic Nearshore Waters Retrieved Using 30-Year Landsat Archive Imagery

et al. 2019

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The Arctic is directly impacted by climate change. The increase in air temperature drives the thawing of permafrost and an increase in coastal erosion and river discharge. This leads to a greater input of sediment and organic matter into coastal waters, which substantially impacts the ecosystems, the subsistence economy of the local population, and the climate because of the transformation of organic matter into greenhouse gases. Yet, the patterns of sediment dispersal in the nearshore zone are not well known, because ships do not often reach shallow waters and satellite remote sensing is traditionally focused on less dynamic environments. The goal of this study is to use the extensive Landsat archive to investigate sediment dispersal patterns specifically on an exemplary Arctic nearshore environment, where field measurements are often scarce. Multiple Landsat scenes were combined to calculate means of sediment dispersal and sea surface temperature under changing seasonal wind conditions in the nearshore zone of Herschel Island Qikiqtaruk in the western Canadian Arctic since 1982. We use observations in the Landsat red and thermal wavebands, as well as a recently published water turbidity algorithm to relate archive wind data to turbidity and sea surface temperature. We map the spatial patterns of turbidity and water temperature at high spatial resolution in order to resolve transport pathways of water and sediment at the water surface. Our results show that these pathways are clearly related to the prevailing wind conditions, being ESE and NW. During easterly wind conditions, both turbidity and water temperature are significantly higher in the nearshore area. The extent of the Mackenzie River plume and coastal erosion are the main explanatory variables for sediment dispersal and sea surface temperature distributions in the study area. During northwesterly wind conditions, the influence of the Mackenzie River plume is negligible. Our results highlight the potential of high spatial resolution Landsat imagery to detect small-scale hydrodynamic processes, but also show the need to specifically tune optical models for Arctic nearshore environments.

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“…For images covered by clouds, the methodology based on band 2 was applied to remove cloud contamination [2,3,12]. Many studies have tested this product and the reflectance was found to have significant correlation with in situ turbidity measurements and SSC in coastal areas [1,12,23,24].…”

Section: Modis Datamentioning

confidence: 99%

“…Many studies have developed algorithms to retrieve SSC and turbidity, using the correlation between in situ observations and the reflectance measurements obtained by MODIS and other ocean color sensors [1,11,23,31]. The algorithm used for this study is based on the semi-analytic sediment model (SASM), which was applied to MODIS-Aqua observations by Dorji et al [32].…”

Section: Turbidity Retrieval Algorithmmentioning

confidence: 99%

Using 250-M Surface Reflectance MODIS Aqua/Terra Product to Estimate Turbidity in a Macro-Tidal Harbour: Darwin Harbour, Australia

et al. 2018

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Turbidity is an indicator of the quality of water and usually exhibits variability associated with changing hydrodynamic conditions, which can be reflected in the sediment dynamics in coastal regions. Darwin Harbour is a typical macro-tidal, well mixed, and complex environment influenced by industries, human activities, and natural factors-including winds, currents, river discharges, waves, and tides. As a case study, hydrodynamics and sediment dynamics in Darwin Harbour are investigated using moderate resolution imaging spectroradiometer (MODIS) measurements. This study focuses on understanding the variability of turbidity, mechanisms that control the variations of turbidity and analyzing field data to determine the main factors that influence the sediment dynamics in Darwin Harbour. The results of this study illustrate the seasonal turbidity variation is mainly influenced by the wind waves. The dredging campaigns in 2013 and 2014 wet seasons contributed to the rise of turbidity in Darwin Harbour. The action of tidal currents appears to be the dominant factor controlling the turbidity pattern in a spring-neap cycle and the turbidity intra-tidal variation. In addition, the turbidity maximum zone (TMZ) near Charles Point is formed by the tidal current convergence based on the results of current modelling.

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Spatio-Temporal Variability in a Turbid and Dynamic Tidal Estuarine Environment (Tasmania, Australia): An Assessment of MODIS Band 1 Reflectance

Cited by 10 publications

References 50 publications

Estimation of Chlorophyll and Turbidity Using Sentinel 2A and EO1 Data in Kneiss Archipelago Gulf of Gabes, Tunisia

Estimation of Chlorophyll and Turbidity Using Sentinel 2A and EO1 Data in Kneiss Archipelago Gulf of Gabes, Tunisia

Long-Term High-Resolution Sediment and Sea Surface Temperature Spatial Patterns in Arctic Nearshore Waters Retrieved Using 30-Year Landsat Archive Imagery

Using 250-M Surface Reflectance MODIS Aqua/Terra Product to Estimate Turbidity in a Macro-Tidal Harbour: Darwin Harbour, Australia

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