Northern Gulf of Mexico estuarine coloured dissolved organic matter derived from MODIS data

Schaeffer, Blake A.; Conmy, Robyn N.; Duffy, Allyn; Aukamp, Jessica; Yates, Diane F.; Craven, George

doi:10.1080/01431161.2015.1035408

Cited by 18 publications

(10 citation statements)

References 41 publications

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“…Both studies used the red to blue-green spectral band approach to estimate CDOM absorption (Table 4). Results indicated that the goodnesss of fit was the same (R 2 = 0.78) for all three models, however Equation (9) results had the lowest %RMSE (34.9%) when compared to %RSME values derived from results produced by the and Schaeffer et al [43] (84.4%) and Twari and Shanmugam (118%) algorithms (Figure 8). Equation (9) also had a slope (1.1) closest to unity when compared with Twari and Shanmugam (1.6) and Schaeffer et al [43] (2.0) ( Table 5).…”

Section: Validation Of Cdom and Salinity Algorithm Performancementioning

confidence: 90%

“…Twari and Shanmugam [50] a cdom 412 = 0.00411 + 2.0ˆ(R rs 670/R rs 490) Schaeffer et al [43] a cdom 412 = 2.48ˆ(R rs 667/R rs 488)´0.82 Figure 8. Scatterplot of a cdom 412 absorptions derived from Equation (9) and published models using spectral data and measured absorptions from NB (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)2005), SJB (2010), and PB (2011).…”

Section: Methods Descriptionmentioning

confidence: 99%

“…Using in situ Rrs665/Rrs490 and laboratory measured CDOM absorption values from NB (1999)(2000), SJB (2010), and PB (2011), comparisons were made between aCDOM412 values derived from Equation (5) and aCDOM412 values derived from algorithms in Schaeffer et al [43] and Twari and Shanmugam [50] to validate algorithm performance. Both studies used the red to blue-green spectral band approach to estimate CDOM absorption (Table 4).…”

Section: Validation Of Cdom and Salinity Algorithm Performancementioning

confidence: 99%

“…CDOM absorption coefficients were determined using water filtered through Whatman 47 mm GF/F glass fiber filters (nominal pore size = 0.7 µm) and absorbance was measured using a 10 cm cuvette with a Shimadazu UV1700 dual-beam spectrophotometer (Schaeffer et al [43]). OD and absorption coefficients were derived using Equations (1) and (2).…”

Section: Determination Of Cdom Absorption Coefficientsmentioning

confidence: 99%

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Optical Models for Remote Sensing of Colored Dissolved Organic Matter Absorption and Salinity in New England, Middle Atlantic and Gulf Coast Estuaries USA

2016

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Ocean color algorithms have been successfully developed to estimate chlorophyll a and total suspended solids concentrations in coastal and estuarine waters but few have been created to estimate light absorption due to colored dissolved inorganic matter (CDOM) and salinity from the spectral signatures of these waters. In this study, we used remotely sensed reflectances in the red and blue-green portions of the visible spectrum retrieved from Medium Resolution Imaging Spectrometer (MERIS) and the International Space Station (ISS) Hyperspectral Imager for the Coastal Ocean (HICO) images to create a model to estimate CDOM absorption. CDOM absorption results were then used to develop an algorithm to predict the surface salinities of coastal bays and estuaries in New England, Middle Atlantic, and Gulf of Mexico regions. Algorithm-derived CDOM absorptions and salinities were successfully validated using laboratory measured absorption values over magnitudes of~0.1 to 7.0 m´1 and field collected CTD data from oligohaline to polyhaline (S less than 5 to 18-30) environments in Narragansett Bay (Rhode Island); the Neuse River Estuary (North Carolina); Pensacola Bay (Florida); Choctawhatchee Bay (Florida); St. Andrews Bay (Florida); St. Joseph Bay (Florida); and inner continental shelf waters of the Gulf of Mexico.

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Section: Validation Of Cdom and Salinity Algorithm Performancementioning

confidence: 90%

Section: Methods Descriptionmentioning

confidence: 99%

Section: Validation Of Cdom and Salinity Algorithm Performancementioning

confidence: 99%

Section: Determination Of Cdom Absorption Coefficientsmentioning

confidence: 99%

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Optical Models for Remote Sensing of Colored Dissolved Organic Matter Absorption and Salinity in New England, Middle Atlantic and Gulf Coast Estuaries USA

2016

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“…MODIS, MERIS, Landsat) that offer the necessary spatial resolution and/or sensor sensitivity for mapping and assessing spatial and temporal variation of water quality and sediment flux (e.g. Burrage et al, 2008;Doxaran et al, 2009;Elsner et al, 2012;Salama et al, 2012;Chen et al, 2014;Eleveld et al, 2014;Karageorkis et al, 2014;Keith, 2014;Loisel et al, 2014;Mendes et al, 2014;Tian et al, 2014;Cai et al, 2015;Schaeffer et al, 2015). The main parameters assessed by EO include the phytoplankton Chl-a concentration and SPM.…”

Section: Special Issue Science Of the Total Environmentmentioning

confidence: 99%

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

Tyler

Hunter

Spyrakos

et al. 2016

Science of The Total Environment

130

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The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production.Despite this, the Earth's surface waters are impacted by multiple natural and anthropogenic pressures and drivers of environmental change. The complex interaction between physical, chemical and biological processes in surface waters poses significant challenges for in situ monitoring and assessment and often limits our ability to adequately capture the dynamics of aquatic systems and our understanding of their status, functioning and response to pressures. Here we explore the opportunities that Earth observation (EO) has to offer to basin-scale monitoring of water quality over the surface water continuum comprising inland, transition and coastal water bodies, with a particular focus on the Danube and Black Sea region. This review summarises the technological advances in EO and the opportunities that the next generation satellites offer for water quality monitoring. We provide an overview of algorithms for the retrieval of water quality parameters and demonstrate how such models have been used for the assessment and monitoring of inland, transitional, coastal and shelfsea systems. Further, we argue that very few studies have investigated the connectivity between these systems especially in large river-sea systems such as the Danube-Black Sea. Subsequently, we describe current capability in operational processing of archive and near real-time satellite data. We conclude that while the operational use of satellites for the assessment and monitoring of surface waters is still developing for inland and coastal waters and more work is required on the development and validation of remote sensing algorithms for these optically complex waters, the potential that these data streams offer for developing an improved, potentially paradigm-shifting understanding of physical and biogeochemical processes across large scale river-sea continuum including the Danube-Black Sea is considerable.

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Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis

Lebrasse

Schaeffer

Coffer

et al. 2022

Estuaries and Coasts

Self Cite

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Seagrasses are globally recognized for their contribution to blue carbon sequestration. However, accurate quantification of their carbon storage capacity remains uncertain due, in part, to an incomplete inventory of global seagrass extent and assessment of its temporal variability. Furthermore, seagrasses are undergoing significant decline globally, which highlights the urgent need to develop change detection techniques applicable to both the scale of loss and the spatial complexity of coastal environments. This study applied a deep learning algorithm to a 30-year time series of Landsat 5 through 8 imagery to quantify seagrass extent, leaf area index (LAI), and belowground organic carbon (BGC) in St. Joseph Bay, Florida, between 1990 and 2020. Consistent with previous field-based observations regarding stability of seagrass extent throughout St. Joseph Bay, there was no temporal trend in seagrass extent (23 ± 3 km2, τ = 0.09, p = 0.59, n = 31), LAI (1.6 ± 0.2, τ = -0.13, p = 0.42, n = 31), or BGC (165 ± 19 g C m−2, τ = - 0.01, p = 0.1, n = 31) over the 30-year study period. There were, however, six brief declines in seagrass extent between the years 2004 and 2019 following tropical cyclones, from which seagrasses recovered rapidly. Fine-scale interannual variability in seagrass extent, LAI, and BGC was unrelated to sea surface temperature or to climate variability associated with the El Niño-Southern Oscillation or the North Atlantic Oscillation. Although our temporal assessment showed that seagrass and its belowground carbon were stable in St. Joseph Bay from 1990 to 2020, forecasts suggest that environmental and climate pressures are ongoing, which highlights the importance of the method and time series presented here as a valuable tool to quantify decadal-scale variability in seagrass dynamics. Perhaps more importantly, our results can serve as a baseline against which we can monitor future change in seagrass communities and their blue carbon.

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Northern Gulf of Mexico estuarine coloured dissolved organic matter derived from MODIS data

Cited by 18 publications

References 41 publications

Optical Models for Remote Sensing of Colored Dissolved Organic Matter Absorption and Salinity in New England, Middle Atlantic and Gulf Coast Estuaries USA

Optical Models for Remote Sensing of Colored Dissolved Organic Matter Absorption and Salinity in New England, Middle Atlantic and Gulf Coast Estuaries USA

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis

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