An Optical Algorithm to Estimate Downwelling Diffuse Attenuation Coefficient in the Red Sea

Tiwari, Surya Prakash; Sarma, Y.V.B.; Kürten, Benjamin; Ouhssain, Mustapha; Jones, Burton H.

doi:10.1109/tgrs.2018.2849026

Cited by 11 publications

(14 citation statements)

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“…This parameter was also regarded as better than SST to represent the photosynthetic rate of VGPM model in the Southern Sea [100]. As a good indicator of phytoplankton growth, KD490 can show short-term phytoplankton blooms and physical processes (anticyclonic and cyclonic eddies) in the Red Sea [102]. The absorption-based models using remotely sensed data could minimize the impacts of pigment packaging, colored dissolved organic matter (CDOM), and non-algal matter, in order to reach both lower bias and higher standard deviation evaluated by in situ datasets in the Arctic Ocean [103].…”

Section: Discussionmentioning

confidence: 99%

Synergistic Use of Remote Sensing and Modeling for Estimating Net Primary Productivity in the Red Sea With VGPM, Eppley-VGPM, and CbPM Models Intercomparison

Tiwari

Qurban³

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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Primary Productivity (PP) has been recently investigated using remote sensing based models over quite limited geographical areas of the Red Sea. This work sheds light on how phytoplankton and primary production would react to the effects of global warming in the extreme environment of the Red Sea and, hence, illuminates how similar regions may behave in the context of climate variability. Our study focuses on using satellite observations to conduct an intercomparison of three net primary production (NPP) models-the VGPM (Vertically Generalized Production Model), the Eppley-VGPM and the CbPM (Carbonbased Production Model)-produced over the Red Sea domain for the 1998-2018 time period. A detailed investigation is conducted using multilinear regression analysis, multivariate visualization and moving averages correlative analysis to uncover the models' responses to various climate factors. Here we use the models' 8day composite and monthly averages compared with satellitebased variables including chlorophyll-a (Chla), mixed layer depth (MLD) and sea surface temperature (SST). Seasonal anomalies of NPP are analyzed against different climate indices, namely, the North Pacific Gyre Oscillation (NPGO), the Multivariate ENSO Index (MEI), the Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO) and the Dipole Mode Index (DMI). In our study, only the CbPM showed significant correlations with NPGO, MEI and PDO, with disagreements relative to the other two NPP models. This can be attributed to the models' connection to oceanographic and atmospheric parameters, as well as the Manuscript received.

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Section: Discussionmentioning

confidence: 99%

Synergistic Use of Remote Sensing and Modeling for Estimating Net Primary Productivity in the Red Sea With VGPM, Eppley-VGPM, and CbPM Models Intercomparison

Tiwari

Qurban³

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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“…variables including CHL, suspended particles, nutrients and oxygen demonstrate the coupling between various scales of physical forcing and variability with biogeochemical dynamics (e.g Mahadevan et al 2012;Mignot et al 2014). The use of bio-optical variables from both remote sensing and in-situ observations has yielded new insights into the biogeochemical functioning of Red Sea (Acker et al 2008;Brewin et al 2015;Gittings et al 2018;Racault et al 2015;Raitsos et al 2013;Tiwari et al 2018b;Zarokanellos et al 2017b;Kheireddine et al 2020) In a phytoplankton dominated regime a number of optical properties will covary with CHL concentration (Bricaud et al 1988;Siegel et al 2005), but other non-algal constituents are capable of absorbing and scattering light, such as detrital particles, CDOM, and suspended solids, which all contribute independently to ocean color (Loisel et al 2002(Loisel et al , 2007Maritorena and Siegel 2005;Tiwari et al 2018a). The present study aims to address the influence of wind mixing, mesoscale eddies, and lateral advection on the optical properties.…”

Section: Accepted Articlementioning

confidence: 99%

“…In a phytoplankton dominated regime, many optical properties will covary with CHL concentration (Bricaud et al., 1988; Siegel et al., 2005), but other nonalgal constituents are capable of absorbing and scattering light, such as detrital particles, CDOM, and suspended solids, which all contribute independently to ocean color (Loisel et al., 2002, 2007; Maritorena & Siegel, 2005; Tiwari, Sarma, et al., 2018). This study aims to address the influence of wind mixing, mesoscale eddies, and lateral advection on optical properties.…”

Section: Introductionmentioning

confidence: 99%

Influences of Physical and Biogeochemical Variability of the Central Red Sea During Winter

Zarokanellos

Jones

2021

JGR Oceans

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The Red Sea is a long, narrow basin spanning a distance of nearly 2,000 km from the strait of Bab al Mandab in the south to the southern tip of the Sinai peninsula in the north. Surrounded by hot deserts, its primary linkage to the global ocean is through the constricted strait of Bab el Mandeb, connecting it to the Arabian Sea through the Gulf of Aden. The Red Sea is characterized by negligible freshwater input from rainfall and terrestrial runoff, high temperatures and high salinity due to solar heating, and significant evaporation rates (Berumen et al., 2019; Smeed, 1997; Sofianos, 2002) and is considered to be a predominantly oligotrophic sea (Edwards & Head, 1987; Grasshoff, 1969). Besides, the Red Sea contains one of the most diverse marine ecosystems in the world with substantial economic and environmental impacts primarily due to its coral

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“…Following [66], accuracy assessments of the algorithms are presented through standard statistical errors (root mean square error (RMSE), bias (ψ), the mean absolute percentage difference (|ψ|)), and the linear regression (i.e., slope, intercept, and coefficient of determination (R 2 )). Mathematically, the value of ψ is derived from the following:…”

Section: Model Accuracy Assessmentmentioning

confidence: 99%

An Assessment of the Hydrological Trends Using Synergistic Approaches of Remote Sensing and Model Evaluations over Global Arid and Semi-Arid Regions

Thomas

Tiwari

et al. 2020

Remote Sensing

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Drylands cover about 40% of the world’s land area and support two billion people, most of them living in developing countries that are at risk due to land degradation. Over the last few decades, there has been warming, with an escalation of drought and rapid population growth. This will further intensify the risk of desertification, which will seriously affect the local ecological environment, food security and people’s lives. The goal of this research is to analyze the hydrological and land cover characteristics and variability over global arid and semi-arid regions over the last decade (2010–2019) using an integrative approach of remotely sensed and physical process-based numerical modeling (e.g., Global Land Data Assimilation System (GLDAS) and Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) models) data. Interaction between hydrological and ecological indicators including precipitation, evapotranspiration, surface soil moisture and vegetation indices are presented in the global four types of arid and semi-arid areas. The trends followed by precipitation, evapotranspiration and surface soil moisture over the decade are also mapped using harmonic analysis. This study also shows that some hotspots in these global drylands, which exhibit different processes of land cover change, demonstrate strong coherency with noted groundwater variations. Various types of statistical measures are computed using the satellite and model derived values over global arid and semi-arid regions. Comparisons between satellite- (NASA-USDA Surface Soil Moisture and MODIS Evapotranspiration data) and model (FLDAS and GLDAS)-derived values over arid regions (BSh, BSk, BWh and BWk) have shown the over and underestimation with low accuracy. Moreover, general consistency is apparent in most of the regions between GLDAS and FLDAS model, while a strong discrepancy is also observed in some regions, especially appearing in the Nile Basin downstream hyper-arid region. Data-driven modelling approaches are thus used to enhance the models’ performance in this region, which shows improved results in multiple statistical measures ((RMSE), bias (ψ), the mean absolute percentage difference (|ψ|)) and the linear regression coefficients (i.e., slope, intercept, and coefficient of determination (R2)).

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An Optical Algorithm to Estimate Downwelling Diffuse Attenuation Coefficient in the Red Sea

Cited by 11 publications

References 35 publications

Synergistic Use of Remote Sensing and Modeling for Estimating Net Primary Productivity in the Red Sea With VGPM, Eppley-VGPM, and CbPM Models Intercomparison

Synergistic Use of Remote Sensing and Modeling for Estimating Net Primary Productivity in the Red Sea With VGPM, Eppley-VGPM, and CbPM Models Intercomparison

Influences of Physical and Biogeochemical Variability of the Central Red Sea During Winter

An Assessment of the Hydrological Trends Using Synergistic Approaches of Remote Sensing and Model Evaluations over Global Arid and Semi-Arid Regions

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