Atmospheric correction algorithm for MERIS above case‐2 waters

Schroeder, T.; Behnert, I.; Schaale, M.; Fischer, J.; Doerffer, R.

doi:10.1080/01431160600962574

Cited by 173 publications

(93 citation statements)

References 22 publications

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“…Typically errors in RSR as reported in Schroeder et al (2007) are in the order of 10-20 %, and we have used 0.2 for this experiment.…”

Section: Assimilation System Experiments and Configurationmentioning

confidence: 99%

“…Additionally, pure seawater both absorbs and scatters light. (Schroeder et al, 2007). Algorithm performance is described in detail in Goyens et al (2013) and King et al (2014).…”

Section: Modis Observationsmentioning

confidence: 99%

“…Analytical/sensor/processing errors (E A ), depending on the observational platform in use, can be small (e.g. Argo floats) or in the case of remote-sensing products, even with the ANN atmospheric correction, as large as 15-20 % (see Schroeder et al, 2007).…”

Section: Assimilation System Experiments and Configurationmentioning

confidence: 99%

“…The daily observations of RSR are obtained from MODISAqua and an atmospheric correction developed for the region is applied (Schroeder et al, 2007). The atmospheric correction applied an Artificial Neural Network (ANN) approach trained by a radiative transfer model to invert the topof-atmosphere signal measured by MODIS Aqua.…”

Section: Modis Observationsmentioning

confidence: 99%

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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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“…Typically errors in RSR as reported in Schroeder et al (2007) are in the order of 10-20 %, and we have used 0.2 for this experiment.…”

Section: Assimilation System Experiments and Configurationmentioning

confidence: 99%

“…Additionally, pure seawater both absorbs and scatters light. (Schroeder et al, 2007). Algorithm performance is described in detail in Goyens et al (2013) and King et al (2014).…”

Section: Modis Observationsmentioning

confidence: 99%

Section: Assimilation System Experiments and Configurationmentioning

confidence: 99%

Section: Modis Observationsmentioning

confidence: 99%

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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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“…As part of the MMP water quality program, regional algorithms were developed to provide better satellite retrieval of water quality concentrations in the optically complex coastal waters, or case II waters, of the GBR than the "NASA global algorithms" implemented in the SeaWiFS Data Analysis System (SeaDAS [72]) (the NASA's comprehensive image analysis package for the processing, display, analysis, and quality control of ocean colour data [36,37,57,58,60,72]). This work has provided regionally parameterised inversion algorithms, including (i) the artificial neural network atmospheric correction and (ii) the adaptive linear matrix (aLMI) inversion algorithm for deriving chlorophyll-a, TSS, and CDOM [58,72].…”

Section: Water Quality Productsmentioning

confidence: 99%

Water Quality and River Plume Monitoring in the Great Barrier Reef: An Overview of Methods Based on Ocean Colour Satellite Data

et al. 2015

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A strong driver of water quality change in the Great Barrier Reef (GBR) is the pulsed or intermittent nature of terrestrial inputs into the GBR lagoon, including delivery of increased loads of sediments, nutrients, and toxicants via flood river plumes (hereafter river plumes) during the wet season. Cumulative pressures from extreme weather with a high frequency of large scale flooding in recent years has been linked to the large scale reported decline in the health of inshore seagrass systems and coral reefs in the central areas of the GBR, with concerns for the recovery potential of these impacted ecosystems. Management authorities currently rely on remotely-sensed (RS) and in situ data for water quality monitoring to guide their assessment of water quality conditions in the GBR. The use of remotely-sensed satellite products provides a quantitative and accessible tool for scientists and managers. These products, coupled with in situ data, and more recently modelled data, are valuable for quantifying the influence of river plumes on seagrass and coral reef habitat in the GBR. This article reviews recent remote sensing techniques developed to monitor river plumes and water quality in the GBR. We also discuss emerging research that integrates hydrodynamic models with remote sensing and in situ OPEN ACCESS Remote Sens. 2015, 7 12910 data, enabling us to explore impacts of different catchment management strategies on GBR water quality.

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