Sun glint estimation in marine satellite images: a comparison of results from calculation and radiative transfer modeling

Kay, Susan; Hedley, John D.; Lavender, Samantha

doi:10.1364/ao.52.005631

Cited by 11 publications

(10 citation statements)

References 16 publications

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“…This effect is called sun glint. More details on this effect may be found in Kay et al (2009Kay et al ( , 2013 and applications to spaceborne sensors are outlined in Zhang and Wang (2010), Hu (2011) and Steinmetz et al (2011). Since clouds in the visual appear bright, the sun glint will affect the OCRA cloud-fraction retrieval by mimicking an enhanced cloud fraction.…”

Section: Sun Glint Removalmentioning

confidence: 99%

OCRA radiometric cloud fractions for GOME-2 on MetOp-A/B

et al. 2016

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Abstract. This paper describes an approach for cloud parameter retrieval (radiometric cloud-fraction estimation) using the polarization measurements of the Global Ozone Monitoring Experiment-2 (GOME-2) onboard the MetOp-A/B satellites. The core component of the Optical Cloud Recognition Algorithm (OCRA) is the calculation of monthly cloud-free reflectances for a global grid (resolution of 0.2 • in longitude and 0.2 • in latitude) to derive radiometric cloud fractions. These cloud fractions will serve as a priori information for the retrieval of cloud-top height (CTH), cloud-top pressure (CTP), cloud-top albedo (CTA) and cloud optical thickness (COT) with the Retrieval Of Cloud Information using Neural Networks (ROCINN) algorithm. This approach is already being implemented operationally for the GOME/ERS-2 and SCIAMACHY/ENVISAT sensors and here we present version 3.0 of the OCRA algorithm applied to the GOME-2 sensors.Based on more than five years of GOME-2A data (April 2008 to June 2013), reflectances are calculated for ≈ 35 000 orbits. For each measurement a degradation correction as well as a viewing-angle-dependent and latitudedependent correction is applied. In addition, an empirical correction scheme is introduced in order to remove the effect of oceanic sun glint. A comparison of the GOME-2A/B OCRA cloud fractions with colocated AVHRR (Advanced Very High Resolution Radiometer) geometrical cloud fractions shows a general good agreement with a mean difference of −0.15 ± 0.20.From an operational point of view, an advantage of the OCRA algorithm is its very fast computational time and its straightforward transferability to similar sensors like OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument) on Sentinel 5 Precursor, as well as Sentinel 4 and Sentinel 5.In conclusion, it is shown that a robust, accurate and fast radiometric cloud-fraction estimation for GOME-2 can be achieved with OCRA using polarization measurement devices (PMDs).

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Section: Sun Glint Removalmentioning

confidence: 99%

OCRA radiometric cloud fractions for GOME-2 on MetOp-A/B

et al. 2016

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“…The specular reflection of the sun off ocean waves is termed sun glint. 1 Sun glint is typically much larger than signals taken from the water body, which makes retrieval of geophysical variables such as chlorophyll concentration and suspended sediment difficult or even impossible. Sun glint could cause serious problems in water environmental monitoring, underwater ecosystem research, and target detection on water surface.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the NIR reflection can be used to measure the amount of glint at various wavelengths. 1 Sun glint can also be used for cross calibration of various wavebands 10 or to provide certain information about the atmosphere. 11,12 It can be used to obtain the aerosol optical depth 13,14 and water vapor, to detect methane in the atmosphere, 15 to detect oil slicks, 16 or inner waves of ocean.…”

Section: Introductionmentioning

confidence: 99%

Versatile time-dependent spatial distribution model of sun glint for satellite-based ocean imaging

Zhou

Niu

et al. 2017

J. Appl. Remote Sens

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We propose a versatile model to describe the time-dependent spatial distribution of sun glint areas in satellite-based wave water imaging. This model can be used to identify whether the imaging is affected by sun glint and how strong the glint is. The observing geometry is calculated using an accurate orbit prediction method. The Cox-Munk model is used to analyze the bidirectional reflectance of wave water surface under various conditions. The effects of whitecaps and the reflectance emerging from the sea water have been considered. Using the moderate resolution atmospheric transmission radiative transfer model, we are able to effectively calculate the sun glint distribution at the top of the atmosphere. By comparing the modeled data with the medium resolution imaging spectrometer image and Feng Yun 2E (FY-2E) image, we have proven that the time-dependent spatial distribution of sun glint areas can be effectively predicted. In addition, the main factors in determining sun glint distribution and the temporal variation rules of sun glint have been discussed. Our model can be used to design satellite orbits and should also be valuable in either eliminating sun glint or making use of it.

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“…We reiterate, however, that the detectability via optical properties (i.e., oil spectral signatures) is not possible in glint regions, and that spills can be detected just as sea surface roughness anomalies. This is because the sun glint radiance, without ever entering the water, is so large compared to the entire water-leaving signal from the euphotic zone that the retrieval of bio-physical products, such as chlorophyll concentration, is compromised [19]. In general, the atmospheric correction procedure tends to flag the glint-contaminated regions of the images, so no optical products are retrievable (besides TOA radiances and products to which an atmospheric correction is not applied) in such conditions.…”

Section: Introductionmentioning

confidence: 99%

Oil Spill Detection in Glint-Contaminated Near-Infrared MODIS Imagery

2015

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Abstract:We present a methodology to detect oil spills using MODIS near-infrared sun glittered radiance imagery. The methodology was developed by using a set of seven MODIS images (training dataset) and validated using four other images (validation dataset). The method is based on the ratio image R = L'GN/LGN, where L'GN is the MODIS-retrieved normalized sun glint radiance image and LGN the same quantity, but obtained from the Cox and Munk isotropic (independent of wind direction) sun glint model. We show that in the R image, while clean water pixel values tend to one, oil spills stand out as anomalies. Moreover, we provide a criterion to distinguish between positive and negative oil-water contrast. A pixel in an R image is classified as a potential oil spill or water via a variable threshold Rs as a function of L'GN, where the threshold values are obtained from the slicks of our training dataset. Two different fitting curves are provided for Rs, according to the contrast sign. The selection of the correct fitting curve is based on the contrast type, resulting from the criterion above. Results indicate that the thresholding is able to isolate the spills and that the spills of the validation dataset are successfully detected. Spurious look-alike features, such as clouds, and other non-spill features, e.g., large areas at the glint region border, are also detected as oil spills and must be eliminated. We believe that our methodology represents a novel and promising, though preliminary, approach towards automatic oil spill detection in optical satellite images.

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Sun glint estimation in marine satellite images: a comparison of results from calculation and radiative transfer modeling

Cited by 11 publications

References 16 publications

OCRA radiometric cloud fractions for GOME-2 on MetOp-A/B

OCRA radiometric cloud fractions for GOME-2 on MetOp-A/B

Versatile time-dependent spatial distribution model of sun glint for satellite-based ocean imaging

Oil Spill Detection in Glint-Contaminated Near-Infrared MODIS Imagery

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