Seasonal Variation of Colored Dissolved Organic Matter in Barataria Bay, Louisiana, Using Combined Landsat and  Field Data

Joshi, Ishan D.; D’Sa, Eurico J.

doi:10.3390/rs70912478

Cited by 52 publications

(48 citation statements)

References 55 publications

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“…As such, relating in situ observations with remote sensing products will push the boundaries of our ability to resolve spatiotemporal dynamics Bauer et al, 2013). For example, chromophoric DOM (CDOM) is related to bulk DOC concentrations, and in some cases terrestriallyderived DOM, and can be visualized using ocean color sensors (Chaichitehrani et al, 2013;Tehrani et al, 2013;Joshi and D'Sa, 2015). Optical parameters can also prove useful for interpreting surface water quality in some cases (Singh et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Where Carbon Goes When Water Flows: Carbon Cycling across the Aquatic Continuum

et al. 2017

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The purpose of this review is to highlight progress in unraveling carbon cycling dynamics across the continuum of landscapes, inland waters, coastal oceans, and the atmosphere. Earth systems are intimately interconnected, yet most biogeochemical studies focus on specific components in isolation. The movement of water drives the carbon cycle, and, as such, inland waters provide a critical intersection between terrestrial and marine biospheres. Inland, estuarine, and coastal waters are well studied in regions near centers of human population in the Northern hemisphere. However, many of the world's large river systems and their marine receiving waters remain poorly characterized, particularly in the tropics, which contribute to a disproportionately large fraction of the transformation of terrestrial organic matter to carbon dioxide, and the Arctic, where positive feedback mechanisms are likely to amplify global climate change. There are large gaps in current coverage of environmental observations along the aquatic continuum. For example, tidally-influenced reaches of major rivers and near-shore coastal regions around river plumes are often left out of carbon budgets due to a combination of methodological constraints and poor data coverage. We suggest that closing these gaps could potentially alter global estimates of CO 2 outgassing from surface waters to the atmosphere by several-fold. Finally, in order to identify and constrain/embrace uncertainties in global carbon budget estimations it is important that we further adopt statistical and modeling approaches that have become well-established in the fields of oceanography and paleoclimatology, for example.

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Section: Discussionmentioning

confidence: 99%

Where Carbon Goes When Water Flows: Carbon Cycling across the Aquatic Continuum

et al. 2017

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“…Likewise, the relationship between turbidity and physical forcings, for example, could be strongly non-linear and involve complex interactions that could not be explained by commonly-used statistical modeling approaches. Classification (categorical dependent variable) and regression (numerical dependent variable) trees are the modern statistical techniques for exploring and modeling such a complexity in data [56], and have been widely used in a variety of fields such as agriculture, coastal environment [51], and freshwater and marine ecology [57,58]. Trees explain the variation of a single dependent variable corresponding to one or more explanatory variables by The validity of FLAASH-corrected satellite observations was evaluated using two approaches, namely, (1) comparing the aerosol optical thickness (AOT); and (2) relating water-leaving radiance (L w ) or normalized water-leaving reflectance ([ρ w ] normalized ) obtained from the in situ and satellite measurements.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, water surface reflectance (ρ) may have some effects of residual skylight and sun-glint. FLAASH-based image processing was used as described in Joshi and D'Sa (2015) [51]. After applying suitable quality-check criteria to remove the effects of outliers, average values of 5 × 5 and 7 × 7 pixels were calculated from the atmospherically-corrected Landsat images for turbidity algorithm and FLAASH-AERONET analysis, respectively, at each sampling site.…”

Section: Landsat Image Processingmentioning

confidence: 99%

Turbidity in Apalachicola Bay, Florida from Landsat 5 TM and Field Data: Seasonal Patterns and Response to Extreme Events

et al. 2017

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Synoptic monitoring of estuaries, some of the most bio-diverse and productive environments on Earth, is essential to study small-scale water dynamics and its role on spatiotemporal variation in water quality important to indigenous marine species and surrounding human settlements. We present a detailed study of turbidity, an optical index of water quality, in Apalachicola Bay, Florida (USA) using historical in situ measurements and Landsat 5 TM data archive acquired from 2004 to 2011. Data mining techniques such as time-series decomposition, principal component analysis, and classification tree-based models were utilized to decipher time-series for examining variations in physical forcings, and their effects on diurnal and seasonal variability in turbidity in Apalachicola Bay. Statistical analysis showed that the bay is highly dynamic in nature, both diurnally and seasonally, and its water quality (e.g., turbidity) is largely driven by interactions of different physical forcings such as river discharge, wind speed, tides, and precipitation. River discharge and wind speed are the most influential forcings on the eastern side of river mouth, whereas all physical forcings were relatively important to the western side close to the major inlet, the West Pass. A bootstrap-optimized and atmospheric-corrected single-band empirical relationship (Turbidity (NTU) = 6568.23 × (Reflectance (Band 3)) 1.95 ; R 2 = 0.77 ± 0.06, range = 0.50-0.91, N = 50) is proposed with seasonal thresholds for its application in various seasons. The validation of this relationship yielded R 2 = 0.70 ± 0.15 (range = −0.96-0.97; N = 38; RMSE = 7.78 ± 2.59 NTU; Bias (%) = −8.70 ± 11.48). Complex interactions of physical forcings and their effects on water dynamics have been discussed in detail using Landsat 5 TM-based turbidity maps during major events between 2004 and 2011. Promising results of the single-band turbidity algorithm with Landsat 8 OLI imagery suggest its potential for long-term monitoring of water turbidity in a shallow water estuary such as Apalachicola Bay.

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“…Comparison with the normalized difference water index (NDWI) indicated significantly better and more stable performance of the UWEM that generally minimized over-and under-estimation issues when mapping urban surface water under complex environmental conditions. Joshi and D'Sa, [9] studied the effects of meteorological and hydrological factors, such as wind speed, freshwater diversions, and river discharge, on the spatial and seasonal variability of colored dissolved organic matter (CDOM) in Barataria Bay, LA, USA using field and Landsat-5 TM imagery.…”

Section: Highlights Of Research Articlesmentioning

confidence: 99%

Preface: Remote Sensing of Water Resources

Mishra

D’Sa

Mishra³

2016

Remote Sensing

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Abstract:The Special Issue (SI) on "Remote Sensing of Water Resources" presents a diverse range of papers studying remote sensing tools, methods, and models to better monitor water resources which include inland, coastal, and open ocean waters. The SI is comprised of fifteen articles on widely ranging research topics related to water bodies. This preface summarizes each article published in the SI.Keywords: oil spill; super algal bloom; suspended sediment concentration; river network detection; satellite rainfall products; chlorophyll-a; inherent optical properties; urban surface waters; colored dissolved organic matter; decision support system; phytoplankton; optically complex waters; primary productivity Overview and ScopeWater resources remain the most abundant natural resource and the major driving force on our planet which supports numerous ecosystems, commercial, and cultural services-from maintaining biodiversity, nutrient cycling, and primary productivity, to recreation, fisheries, ecotourism, transport, and religious uses. The pressure on water resources has been on the rise and will continue to increase in the coming years because of increased frequency of drought, urbanization, urban population growth, deforestation, increased use of fertilizers and pesticides, and spread of invasive species. It is expected that the quality of surface water both inland and coastal will deteriorate with continued warming due to global climate change. Rising temperatures along with excessive nutrient, sediment, and pesticide pollution will exacerbate water quality degradation in many ways including triggering super algal blooms and ultimately making the water body toxic, hypoxic, and stratified. Therefore, accurate, inexpensive, and rapid monitoring tools and models using remote sensing are needed for timely implementation of conservation and restoration measures in problematic areas.The main goal of this Special Issue on "remote sensing of water resources" was to highlight some of the remote sensing-driven applied research currently being performed to solve some of the aforementioned problems in water resources. Manuscripts were invited covering a broad range of application of remote sensing in monitoring water resources, including model and algorithm development, analysis of data from new satellite sensors, long-term time series and phenological analysis of water quality and productivity parameters, and novel concepts for effective water resource management. The response to the Special Issue call was overwhelming and, at the end of the process, fifteen papers were incorporated in the issue covering a wide range of interesting topics. The section below provides a brief overview of each paper published in the Special Issue on "Water Resources".

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Seasonal Variation of Colored Dissolved Organic Matter in Barataria Bay, Louisiana, Using Combined Landsat and Field Data

Cited by 52 publications

References 55 publications

Where Carbon Goes When Water Flows: Carbon Cycling across the Aquatic Continuum

Where Carbon Goes When Water Flows: Carbon Cycling across the Aquatic Continuum

Turbidity in Apalachicola Bay, Florida from Landsat 5 TM and Field Data: Seasonal Patterns and Response to Extreme Events

Preface: Remote Sensing of Water Resources

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