A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic waters

Singh, Rajendra; Shanmugam, Palanisamy

doi:10.1016/j.rse.2013.12.001

Cited by 47 publications

(37 citation statements)

References 53 publications

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“…A major difference is that a biogenic optical model is used in the satellites for regulating the inter-band water reflectance rather than polynomial relationships as in GOCI's case. Atmospheric correction algorithms that assume inter-band relationship for aerosol reflectance [16,17] also seem vulnerable to this type of radiometric artefact that has band-specific effects. While the regulation of the shape of aerosol reflectance makes the estimation of aerosol reflectance less sensitive to the band-specific artefact, the effect of the artefact would be then more salient in the estimated water reflectance.…”

Section: Discussionmentioning

confidence: 99%

“…After a quality control process that screened the in situ measurements for poor-quality data [9], 26 match-up pairs between the in situ and the GOCI R rs were identified in the GOCI data over three days (31 July 2012, 16 October 2012, and 23 October 2013), which are dates that had strong SLRI in the corresponding L1B images. Since GOCI images were taken every hour, the maximum time difference between in situ measurements and the corresponding GOCI data is 30 min if the in situ data were collected between 00:16 UTC and 07:46 UTC.…”

Section: Study Area and Field Measurementsmentioning

confidence: 99%

“…Resuspension from tidal movement in the shallow water and the existence of a mud belt (Heuksan mud belt) in the offshore area cause high concentrations of suspended sediment of 10-100 g/m 3 . The acquisition times and the geographic coordinates of the in situ data are presented in Table 1 quality control process that screened the in situ measurements for poor-quality data [9], 26 match-up pairs between the in situ and the GOCI were identified in the GOCI data over three days (31 July 2012, 16 October 2012, and 23 October 2013), which are dates that had strong SLRI in the corresponding L1B images. Since GOCI images were taken every hour, the maximum time difference between in situ measurements and the corresponding GOCI data is 30 min if the in situ data were collected between 00:16 UTC and 07:46 UTC.…”

Section: Study Area and Field Measurementsmentioning

confidence: 99%

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Evaluation of Stray Light Correction for GOCI Remote Sensing Reflectance Using in Situ Measurements

et al. 2016

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Abstract:The Geostationary Ocean Color Imager (GOCI) is the world's first ocean color sensor in geostationary orbit. Although the GOCI has shown excellent radiometric performance with little long-term radiometric degradation and a high signal-to-noise ratio, there are radiometric artefacts in GOCI Level 1 products caused by stray light detected within the GOCI optics. To correct the radiometric bias, we developed an image-based correction algorithm called the correction of the interslot discrepancy using the minimum noise fraction transform (CIDUM) in a previous study and evaluated its performance with respect to the physical radiometric quantity stored in Level 1 products, i.e., top-of-atmosphere radiance. This study evaluated the performance of the CIDUM algorithm in terms of remote sensing reflectance, which is one of the most important products in ocean color remote sensing. The resultant CIDUM-corrected remote sensing reflectance products were validated using both relative (within the image) and absolute references (in situ measurements). Image validation showed that CIDUM corrected the bias in remote sensing reflectance (up to 20%) and reduced the bias to ď5% in the tested image. In situ validation showed that relative uncertainty was reduced by around 10% within the visible bands and the correlation between the in situ and GOCI radiometric data was enhanced.

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Section: Discussionmentioning

confidence: 99%

Section: Study Area and Field Measurementsmentioning

confidence: 99%

Section: Study Area and Field Measurementsmentioning

confidence: 99%

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Evaluation of Stray Light Correction for GOCI Remote Sensing Reflectance Using in Situ Measurements

et al. 2016

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“…This was achieved by calculating the aerosol reflectance and type from a cross-calibration process that makes use of a 5 × 5 grid of pixels from less turbid waters. A method developed for MODIS corrects for the contributions from suspended sediments in the Rayleigh-corrected radiance at 748 nm and then uses this value to estimate the aerosol contribution at 531 nm [219]. This technique might show some benefits over the turbid waters off the south coast of India [219].…”

Section: Black Pixel Approachesmentioning

confidence: 99%

“…A method developed for MODIS corrects for the contributions from suspended sediments in the Rayleigh-corrected radiance at 748 nm and then uses this value to estimate the aerosol contribution at 531 nm [219]. This technique might show some benefits over the turbid waters off the south coast of India [219]. A summary of atmospheric correction approaches that make the black pixel assumption can be seen in Table 2 [39,110,151,[206][207][208][209]217,218,220,221].…”

Section: Black Pixel Approachesmentioning

confidence: 99%

Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing

et al. 2015

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Accurate correction of the corrupting effects of the atmosphere and the water's surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi-and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio-Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges.

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An optical system for detecting and describing major algal blooms in coastal and oceanic waters around India

Gokul

Shanmugam

2016

JGR Oceans

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An optical system is developed with the aim to detect and monitor three major algal blooms (including harmful algal blooms ''HABs'') over ecologically relevant scales around India and to strengthen algal forecasting system. This system is designed to be capable of utilizing remote sensing, in situ, and radiative transfer techniques to provide species-specific data necessary for increasing capabilities of an algal forecasting system. With the ability to measure in-water optical properties by means of remote sensing and in situ observations, the optical system developed infers the desired phytoplankton signal from spectral distributions and utilize these data in a numerical classification technique to generate species-specific maps at given spatial and temporal scales. A simple radiative transfer model is adopted for this system to provide a means to optimally interpolate to regions with sparse in situ observation data and to provide a central component to generate in-water optical properties from remotely sensed data. For a given set of inherent optical properties along with surface and bottom boundary conditions, the optical system potentially provides researchers and managers coverage at different locations and depths for tracking algal blooms in the water column. Three major algal blooms focused here include Noctiluca scintillans/miliaris, Trichodesmium erythraeum, and Cochlodinium polykrikoides, which are recurring events in coastal and oceanic waters around India. Because satellite sensors provide a synoptic view of the ocean, both spatially and temporally, our initial efforts tested this optical system using several MODIS-Aqua images and ancillary data. Validation of the results with coincident in situ data obtained from either surface samples or depth samples demonstrated the robustness and potential utility of this approach, with an accuracy of 80-90% for delineating the presence of all three blooms in a heterogeneous phytoplankton community. Despite its limitation in detecting specific species during their prebloom phases and indicating whether a particular bloom is toxic or harmful, the proposed optical system will provide managers with the specific phytoplankton bloom maps to structure monitoring efforts and a powerful tool for studying the dynamics of algal blooms at various temporal and spatial scales.

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A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic waters

Cited by 47 publications

References 53 publications

Evaluation of Stray Light Correction for GOCI Remote Sensing Reflectance Using in Situ Measurements

Evaluation of Stray Light Correction for GOCI Remote Sensing Reflectance Using in Situ Measurements

Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing

An optical system for detecting and describing major algal blooms in coastal and oceanic waters around India

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