Remote-sensing reflectance and true colour produced by a coupled hydrodynamic, optical, sediment, biogeochemical model of the Great Barrier Reef, Australia: Comparison with satellite data

Baird, Mark E.; Cherukuru, Nagur; Jones, Emlyn; Margvelashvili, Nugzar; Mongin, Mathieu; Oubelkheir, Kadija; Ralph, Peter J.; Rizwi, Farhan; Robson, Barbara; Schroeder, Thomas; Skerratt, Jennifer; Steven, Andrew D. L.; Wild-Allen, Karen

doi:10.1016/j.envsoft.2015.11.025

Cited by 73 publications

(99 citation statements)

References 57 publications

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“…Several studies have demonstrated that adding spectrally-resolved optics to biogeochemical models improves model skill (e.g., Dutkiewicz et al, 2015) as well as comparability to observed optical properties (e.g., Fujii et al, 2007;Baird et al, 2016). A minority of global numerical models resolve the bio-optical properties of different PG (Gregg and Casey, 2007;Dutkiewicz et al, 2015;Baird et al, 2016).…”

Section: Gap 2: Lack Of Traceability Of Uncertainties In Pg Algorithmsmentioning

confidence: 99%

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

et al. 2017

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To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit

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Section: Gap 2: Lack Of Traceability Of Uncertainties In Pg Algorithmsmentioning

confidence: 99%

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

et al. 2017

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“…The complex BGC model simulates optical, nutrient, plankton, benthic organisms (seagrass, macroalgae and coral), detritus, chemical and sediment dynamics across the whole GBR region, spanning estuarine systems to oligotrophic offshore reefs ( Fig. 3; Baird et al, 2016a). An expanded description of the BGC model is given in Appendix A, with a brief description of the optical model in Appendix B.…”

Section: The Ereefs Modelling Systemmentioning

confidence: 99%

“…The most spatially comprehensive dataset available for BGC assimilation is from Ocean Color (OC) remote sensing. The assimilation of remotely sensed data into marine BGC models has been problematic due to differences between the variables represented in models and the variables that are routinely observed (Baird et al, 2016a), typically referred to as "difference in kind" errors. Satellites measure the top of atmosphere radiance, not chlorophyll a concentration (Chl a; or other modelled variables) directly.…”

Section: Introductionmentioning

confidence: 99%

Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

et al. 2016

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Abstract. Skillful marine biogeochemical (BGC) models are required to understand a range of coastal and global phenomena such as changes in nitrogen and carbon cycles. The refinement of BGC models through the assimilation of variables calculated from observed in-water inherent optical properties (IOPs), such as phytoplankton absorption, is problematic. Empirically derived relationships between IOPs and variables such as chlorophyll-a concentration (Chl a), total suspended solids (TSS) and coloured dissolved organic matter (CDOM) have been shown to have errors that can exceed 100 % of the observed quantity. These errors are greatest in shallow coastal regions, such as the Great Barrier Reef (GBR), due to the additional signal from bottom reflectance. Rather than assimilate quantities calculated using IOP algorithms, this study demonstrates the advantages of assimilating quantities calculated directly from the less error-prone satellite remote-sensing reflectance (RSR). To assimilate the observed RSR, we use an in-water optical model to produce an equivalent simulated RSR and calculate the mismatch between the observed and simulated quantities to constrain the BGC model with a deterministic ensemble Kalman filter (DEnKF). The traditional assumption that simulated surface Chl a is equivalent to the remotely sensed OC3M estimate of Chl a resulted in a forecast error of approximately 75 %. We show this error can be halved by instead using simulated RSR to constrain the model via the assimilation system. When the analysis and forecast fields from the RSR-based assimilation system are compared with the non-assimilating model, a comparison against independent in situ observations of Chl a, TSS and dissolved inorganic nutrients (NO 3 , NH 4 and DIP) showed that errors are reduced by up to 90 %. In all cases, the assimilation system improves the simulation compared to the non-assimilating model. Our approach allows for the incorporation of vast quantities of remote-sensing observations that have in the past been discarded due to shallow water and/or artefacts introduced by terrestrially derived TSS and CDOM or the lack of a calibrated regional IOP algorithm.

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“…However, there are some models that have recently incorporated a more thorough treatment of the light field (e.g. Gregg and Casey, 2007;Mobley et al, 2009;Dutkiewicz et al, 2015), and some now include aspects such as reflectance or water-leaving irradiances that more directly relate to ocean colour Baird et al, 2016;Gregg and Rousseaux, 2017).…”

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confidence: 99%

“…However, it is only recently that models have directly included the treatment of ocean optics to allow for explicitly including diagnostics such as remotely sensed reflectance (e.g. Dutkiewicz et al, 2015;Baird et al, 2016). Here we use one of these models; a global three-dimensional biogeochemical, ecosystem and radiative transfer numerical model that can act as a virtual laboratory to explore the connections between satellite-derived products and the ecosystem variability that they are attempting to capture.…”

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confidence: 99%

Modelling ocean-colour-derived chlorophyll <i>a</i>

2018

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Abstract. This article provides a proof of concept for using a biogeochemical/ecosystem/optical model with a radiative transfer component as a laboratory to explore aspects of ocean colour. We focus here on the satellite ocean colour chlorophyll a (Chl a) product provided by the often-used blue/green reflectance ratio algorithm. The model produces output that can be compared directly to the real-world ocean colour remotely sensed reflectance. This model output can then be used to produce an ocean colour satellite-like Chl a product using an algorithm linking the blue versus green reflectance similar to that used for the real world. Given that the model includes complete knowledge of the (model) water constituents, optics and reflectance, we can explore uncertainties and their causes in this proxy for Chl a (called "derived Chl a" in this paper). We compare the derived Chl a to the "actual" model Chl a field. In the model we find that the mean absolute bias due to the algorithm is 22 % between derived and actual Chl a. The real-world algorithm is found using concurrent in situ measurement of Chl a and radiometry. We ask whether increased in situ measurements to train the algorithm would improve the algorithm, and find a mixed result. There is a global overall improvement, but at the expense of some regions, especially in lower latitudes where the biases increase. Not surprisingly, we find that regionspecific algorithms provide a significant improvement, at least in the annual mean. However, in the model, we find that no matter how the algorithm coefficients are found there can be a temporal mismatch between the derived Chl a and the actual Chl a. These mismatches stem from temporal decoupling between Chl a and other optically important water constituents (such as coloured dissolved organic matter and detrital matter). The degree of decoupling differs regionally and over time. For example, in many highly seasonal regions, the timing of initiation and peak of the spring bloom in the derived Chl a lags the actual Chl a by days and sometimes weeks. These results indicate that care should also be taken when studying phenology through satellite-derived products of Chl a. This study also reemphasizes that ocean-colourderived Chl a is not the same as the real in situ Chl a. In fact the model derived Chl a compares better to real-world satellite-derived Chl a than the model actual Chl a. Modellers should keep this is mind when evaluating model output with ocean colour Chl a and in particular when assimilating this product. Our goal is to illustrate the use of a numerical laboratory that (a) helps users of ocean colour, particularly modellers, gain further understanding of the products they use and (b) helps the ocean colour community to explore other ocean colour products, their biases and uncertainties, as well as to aid in future algorithm development.

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Remote-sensing reflectance and true colour produced by a coupled hydrodynamic, optical, sediment, biogeochemical model of the Great Barrier Reef, Australia: Comparison with satellite data

Cited by 73 publications

References 57 publications

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

Modelling ocean-colour-derived chlorophyll <i>a</i>

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Remote-sensing reflectance and true colour produced by a coupled hydrodynamic, optical, sediment, biogeochemical model of the Great Barrier Reef, Australia: Comparison with satellite data

Cited by 73 publications

References 57 publications

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

Modelling ocean-colour-derived chlorophyll &lt;i&gt;a&lt;/i&gt;

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Modelling ocean-colour-derived chlorophyll <i>a</i>