The polarized emissivity of a wind-roughened sea surface: A Monte Carlo model

Henderson, Bradley G.; Theiler, James; Villeneuve, Pierre V.

doi:10.1016/j.rse.2003.09.003

Cited by 48 publications

(17 citation statements)

References 27 publications

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“…In the infrared spectrum, Harris et al [1994] hypothesized the presence of some wind effects, and found that a decrease in IR emissivity for windy ocean (wind speed > 15 m/s) could lead to errors in retrieved SSTs up to 0.4°C for a dry atmosphere, but has small effects in the tropical atmosphere. More recent studies [ Watts et al , 1996; Wu and Smith , 1997; Henderson et al , 2003] included the effects of reflection of radiation emitted by a wind‐roughened sea surface in their models and found that these effects almost compensate the reduction of emissivity at high winds. Watts et al [1996] estimated that the assumptions of constant (zero‐wind speed) emissivity and specular reflection have a very small net effect on brightness temperatures (or SST), on the order of 0.1 K.…”

Section: Analysis Of Uncertainties In Sst Retrievalsmentioning

confidence: 99%

Uncertainties in sea surface temperature retrievals from space: Comparison of microwave and infrared observations from TRMM

Ricciardulli

2004

J. Geophys. Res.

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[1] A study of the uncertainties in infrared (IR) and microwave (MW) sea surface temperature (SST) retrievals by satellite is described here. The availability of both infrared (VIRS) and microwave (TMI) sensors on the Tropical Rainfall Measurement Mission (TRMM) provided an opportunity to perform this detailed SST intercomparison from two distinct but colocated sensors. Infrared retrievals provide good spatial resolution, but they are limited to clear-sky conditions. Microwave observations provide supplemental SST information in cloudy areas, but they are subject to other limitations such as poor spatial resolution and possible wind biases. One year of colocated VIRS and TMI SSTs are analyzed here, together with other colocated ancillary data sets including surface winds and water vapor, and potential sources of error are presented. The analysis shows some residual IR SST's dependence on atmospheric water vapor, despite corrective terms in the algorithm, with biases of a few tenths of a degree. The inclusion of a water vapor term in the IR algorithm reduces these biases. The results also show a higher uncertainty in MW SST retrievals for winds greater than 12 m/s, and a warm bias of MW SSTs of the order of 1°C within 50-100 km from land. Other potential sources of uncertainties discussed in the paper are undetected clouds, choice of IR SST algorithm, diurnal warming of the skin layer, and satellite maneuvers. The results of this intercomparison are valuable for the development of a future generation of a blended IR/MW SST data set, which combines the strengths of each observational method.

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Section: Analysis Of Uncertainties In Sst Retrievalsmentioning

confidence: 99%

Uncertainties in sea surface temperature retrievals from space: Comparison of microwave and infrared observations from TRMM

Ricciardulli

2004

J. Geophys. Res.

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“…Compared with traditional methods, multi-angle earth observation could obtain more specific and reliable three-dimensional structural parameters of a ground target. This would meet the requirement of a quantitative remote sensing inverting parameter, which would make quantitative remote sensing possible [12]. In recent research on polarized remote sensing [13][14][15], the importance of polarized information in recognizing surface features has been recognized.…”

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confidence: 99%

Study on the hyperspectral polarized reflection characteristics of oil slicks on sea surfaces

et al. 2011

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This study is concerned with the need for remote sensing techniques for the monitoring of oil-slick pollution on sea surfaces and the effects of oil-slick pollution on the sea. We used Daqing crude oil, Jilin crude oil, heavy oil and seawater from Dalian Bay to simulate oil-slick pollution on the sea surface and obtained multi-angle hyperspectral polarized reflectance information, from which we calculated the polarization for different kinds of crude oil, and oil-slicks of different thicknesses. By comparing the degree of polarization between oil-slicks of different thickness, and sea water, it was found that in the case of thin oil-slicks, the degree of polarization of seawater is higher than that of oil slicks with wavelengths of between 400-1000 nm. However, there was little difference at a wavelength of 785 nm. At angles of incident of 20° and 30° (the viewing angle equals the incident angle), it was easy to distinguish the changes of oil-slick thickness by the degree of polarization at 785 and 880 nm in the near-infrared band. The crude oil showed its inherent polarized characteristic as the oil-slick thickness increased to a certain degree. That is to say, the polarization of the seawater was higher than that of the oil-slick in the visible light range, but less than that in the near-infrared band. As the incident angle changed from 40° to 50°, the degree of polarization of seawater increased, and it was higher than that for the oil-sick between wavelengths of 400-1000 nm at an angle of incidence of 50°. This research on the polarized characteristics of an oil-slick on a sea surface brings new scientific techniques to the monitoring of sea-surface pollution by remote sensing. Oil from offshore spills is one of the common marine pollutants; it causes serious pollution of the marine environment and has harmful effects on marine life. Oil spills on the sea surface can obstruct the ocean-atmosphere heat exchange and influence the absorption, transmission and reflection of light waves by the sea surface [1]. When vast areas of the sea surface have been polluted, we can use remote sensing technology to do a real time, high speed, macro monitoring of the polluted regions. Studies on the condition and process of oil spills on sea surfaces are the subject of much attention worldwide [2][3][4].With the rapid development of remote sensing technology, researchers are focusing on using hyperspectral remote-*Corresponding authors

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“…The electric field E expressing light polarization [10,[19][20][21][22][23][24][25][26][27][28] is defined in an orthonormal basis V = {ι ι ι , ι ι ι ⊥ } as E = E ι ι ι + E ⊥ ι ι ι ⊥…”

Section: Polarized Lightmentioning

confidence: 99%

Effects of light polarization and waves slope statistics on the reflectance factor of the sea surface

D’Alimonte¹,

Kajiyama²

2016

Opt. Express

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Above-water radiometry depends on estimates of the reflectance factor ρ of the sea surface to compute the in situ water-leaving radiance. The Monte Carlo code for ocean color simulations MOX is used in this study to analyze the effect of different environmental components on ρ values. A first aspect is examining the reflectance factor without and by accounting for the sky-radiance polarization. The influence of the sea-surface statistics at discrete grid points is then considered by presenting a new scheme to define the variance of the waves slope. Results at different sun elevations and sensor orientations indicate that the light polarization effect on ρ simulations reduces from ∼17 to ∼10% when the wind speed increases from 0 to 14m s^-1. An opposite tendency characterizes the modeling of the sea-surface slope variance, with ρ differences up to ∼12% at a wind speed of 10m s^-1. The joint effect of polarization and the the sea-surface statistics displays a less systematic dependence on the wind speed, with differences in the range ∼13 to ∼18%. The ρ changes due to the light polarization and the variance of the waves slope become more relevant at sky-viewing geometries respectively lower and higher than 40° with respect to the zenith. An overall compensation of positive and negative offsets due to light polarization is finally documented when considering different sun elevations. These results address additional investigations which, by combining the modeling and experimental components of marine optics, better evaluate specific measurement protocols for collecting above-water radiometric data in the field.

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The polarized emissivity of a wind-roughened sea surface: A Monte Carlo model

Cited by 48 publications

References 27 publications

Uncertainties in sea surface temperature retrievals from space: Comparison of microwave and infrared observations from TRMM

Uncertainties in sea surface temperature retrievals from space: Comparison of microwave and infrared observations from TRMM

Study on the hyperspectral polarized reflection characteristics of oil slicks on sea surfaces

Effects of light polarization and waves slope statistics on the reflectance factor of the sea surface

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