Image Transfer Through a Scattering Medium

Zege, Eleonora P.; Иванов, А. П.; Katsev, I. L.

doi:10.1007/978-3-642-75286-5

Cited by 286 publications

(222 citation statements)

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“…Equation (7) is similar to that derived by Rozenberg [1962]. It has been used successfully in various light-scattering media optics studies [see, e.g., Zege et al, 1991aZege et al, , 1991bKokhanovsky and Zege, 2004].…”

Section: Theorymentioning

confidence: 86%

Spectral reflectance of whitecaps

Kokhanovsky

2004

J. Geophys. Res.

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[1] Spectral properties of whitecaps are of importance for color ocean remote sensing and aerosol optical thickness probing from satellite-based instruments. They also influence planetary albedo and climate. In particular, whitecaps may affect the response of the climate system to changes in greenhouse gases and other atmospheric constituents. Several experimental measurements of whitecap spectral reflectance have been performed both in the surf zone and in the open ocean, which indicate that oceanic foam cannot be considered as a gray body (e.g., for satellite remote sensing techniques). This paper is devoted to the interpretation of experiments performed in terms of the radiative transfer theory. Only the case of a semi-infinite foam is studied in detail. However, results can be easily extended to the case of finite foamed media having large optical thickness. The model introduced is capable of explaining main features observed, like a sharp decrease of the foam spectral reflectance in the infrared as compared with the visible part of the electromagnetic spectrum and a high correlation of the foam reflectance R and the water absorption coefficient a. A simple method to retrieve the spectral dependence of a from the spectral foam reflectance R is proposed.

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Section: Theorymentioning

confidence: 86%

Spectral reflectance of whitecaps

Kokhanovsky

2004

J. Geophys. Res.

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“…Hence, snow surface albedo in the near-infrared mainly depends on the optical snow grain size. Zege et al (1991) showed that the plane albedo α p (θ 0 ) as a function of solar zenith angle θ 0 can be parameterized using an asymptotic solution of the radiative transfer theory by…”

Section: Instrumentmentioning

confidence: 99%

Comparison of different methods to retrieve optical-equivalent snow grain size in central Antarctica

et al. 2017

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Abstract. The optical-equivalent snow grain size affects the reflectivity of snow surfaces and, thus, the local surface energy budget in particular in polar regions. Therefore, the specific surface area (SSA), from which the optical snow grain size is derived, was observed for a 2-month period in central Antarctica (Kohnen research station) during austral summer 2013/14. The data were retrieved on the basis of ground-based spectral surface albedo measurements collected by the COmpact RAdiation measurement System (CORAS) and airborne observations with the Spectral Modular Airborne Radiation measurement sysTem (SMART). The snow grain size and pollution amount (SGSP) algorithm, originally developed to analyze spaceborne reflectance measurements by the MODerate Resolution Imaging Spectroradiometer (MODIS), was modified in order to reduce the impact of the solar zenith angle on the retrieval results and to cover measurements in overcast conditions. Spectral ratios of surface albedo at 1280 and 1100 nm wavelength were used to reduce the retrieval uncertainty. The retrieval was applied to the ground-based and airborne observations and validated against optical in situ observations of SSA utilizing an IceCube device. The SSA retrieved from CORAS observations varied between 27 and 89 m 2 kg −1 . Snowfall events caused distinct relative maxima of the SSA which were followed by a gradual decrease in SSA due to snow metamorphism and wind-induced transport of freshly fallen ice crystals. The ability of the modified algorithm to include measurements in overcast conditions improved the data coverage, in particular at times when precipitation events occurred and the SSA changed quickly. SSA retrieved from measurements with CORAS and MODIS agree with the in situ observations within the ranges given by the measurement uncertainties. However, SSA retrieved from the airborne SMART data slightly underestimated the ground-based results.

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“…The spectral dependencies of the optical characteristics of a layer are determined by the spectrum of the complex refractive index of ice by Warren and Brandt (2008) and spectral absorption of yellow pigments by Bricaud et al (1981). The used analytical approximation for R ice (θ , θ 0 , ϕ, λ) has been developed on the base of the asymptotic solution of the radiative transfer theory (Zege et al, 1991).…”

Section: Data Usedmentioning

confidence: 99%

Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 1: Validation against in situ, aerial, and ship cruise data

et al. 2015

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Abstract. The presence of melt ponds on the Arctic sea ice strongly affects the energy balance of the Arctic Ocean in summer. It affects albedo as well as transmittance through the sea ice, which has consequences for the heat balance and mass balance of sea ice. An algorithm to retrieve melt pond fraction and sea ice albedo from Medium Resolution Imaging Spectrometer (MERIS) data is validated against aerial, shipborne and in situ campaign data. The results show the best correlation for landfast and multiyear ice of high ice concentrations. For broadband albedo, R 2 is equal to 0.85, with the RMS (root mean square) being equal to 0.068; for the melt pond fraction, R 2 is equal to 0.36, with the RMS being equal to 0.065. The correlation for lower ice concentrations, subpixel ice floes, blue ice and wet ice is lower due to ice drift and challenging for the retrieval surface conditions. Combining all aerial observations gives a mean albedo RMS of 0.089 and a mean melt pond fraction RMS of 0.22. The in situ melt pond fraction correlation is R 2 = 0.52 with an RMS = 0.14. Ship cruise data might be affected by documentation of varying accuracy within the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol, which may contribute to the discrepancy between the satellite value and the observed value: mean R 2 = 0.044, mean RMS = 0.16. An additional dynamic spatial cloud filter for MERIS over snow and ice has been developed to assist with the validation on swath data.

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Image Transfer Through a Scattering Medium

Cited by 286 publications

References 0 publications

Spectral reflectance of whitecaps

Spectral reflectance of whitecaps

Comparison of different methods to retrieve optical-equivalent snow grain size in central Antarctica

Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 1: Validation against in situ, aerial, and ship cruise data

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