Measuring the influence of aerosols and albedo on sky polarization

Kreuter, A.; Emde, Claudia; Blumthaler, M.

doi:10.1016/j.atmosres.2010.07.010

Cited by 22 publications

(7 citation statements)

References 28 publications

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“…Several investigators have shown modeled sensitivities of sky DoP values to surface albedo variation (e.g [11][12][13][14][15][16][17][18].). Specific observational studies have been dedicated to measuring changes in the DoP due to surface reflectance in environments with low aerosols [19], with high surface albedo [20], with a heterogeneous surface [21] and under a variety of aerosol and surface albedo conditions [14]. Important work by Coulson recognized that the sensitivity of the DoP to albedo varies with both solar zenith angle and wavelength.…”

Section: Surface Reflectionmentioning

confidence: 99%

Wavelength dependence of the degree of polarization in cloud-free skies: simulations of real environments

Pust¹,

Shaw²

2012

Opt. Express

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The visible and NIR maximum degree of polarization (DoP) of cloud-free skylight depends on many factors, including wavelength, sun zenith angle, surface reflectance, and aerosol properties. For clear-sky environments, radiative transfer models accurately estimate the sky DoP when each of these properties is well constrained. (The model used here was recently compared with full-sky polarization measurements with excellent agreement.) Using coincident Hyperion satellite observations and AERONET retrievals to provide model inputs, we simulate the maximum sky DoP for a variety of locations. Results show large variations in the wavelength dependence of sky polarization across different Earth environments. Therefore, accurate modeling of the sky DoP depends largely upon proper representation of the surface and aerosols in the model. Simple models which do not incorporate accurate aerosol and surface information have limited utility for simulating cloud-free sky DoP.

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Section: Surface Reflectionmentioning

confidence: 99%

Wavelength dependence of the degree of polarization in cloud-free skies: simulations of real environments

Pust¹,

Shaw²

2012

Opt. Express

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“…Their low cost makes them ideal for projects involving large scale deployment, autonomous monitoring, or citizen science. Successful scientific applications include environmental monitoring [1][2][3][4][5][6][7][8][9][10][11][12][13], cosmic ray detection [14], vegetation mapping [15][16][17][18][19], color science [9,[20][21][22][23][24][25], and biomedical applications [26][27][28][29][30][31][32][33]. However, the use of consumer cameras is made difficult by limited software controls and camera specifications.…”

Section: Introductionmentioning

confidence: 99%

Standardized spectral and radiometric calibration of consumer cameras

Burggraaff¹,

Schmidt²,

Zamorano³

et al. 2019

Opt. Express

108

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Consumer cameras, particularly onboard smartphones and UAVs, are now commonly used as scientific instruments. However, their data processing pipelines are not optimized for quantitative radiometry and their calibration is more complex than that of scientific cameras. The lack of a standardized calibration methodology limits the interoperability between devices and, in the ever-changing market, ultimately the lifespan of projects using them. We present a standardized methodology and database (SPECTACLE) for spectral and radiometric calibrations of consumer cameras, including linearity, bias variations, read-out noise, dark current, ISO speed and gain, flat-field, and RGB spectral response. This includes golden standard ground-truth methods and do-it-yourself methods suitable for non-experts. Applying this methodology to seven popular cameras, we found high linearity in RAW but not JPEG data, inter-pixel gain variations >400% correlated with large-scale bias and read-out noise patterns, non-trivial ISO speed normalization functions, flat-field correction factors varying by up to 2.79 over the field of view, and both similarities and differences in spectral response. Moreover, these results differed wildly between camera models, highlighting the importance of standardization and a centralized database. accuracy of color measurements and their conversion to standard measures, such as the CIE 1931 XYZ and CIELAB color spaces, is limited by distortions in the observed colors [20] and differences in spectral response functions [21,[23][24][25].Extensive (spectro-)radiometric calibrations of consumer cameras are laborious and require specialized equipment and are thus not commonly performed [23,54,60]. A notable exception is the spectral and absolute radiometric calibration of a Raspberry Pi 3 V2 webcam by Pagnutti et al. [57], including calibrations of linearity, exposure stability, thermal and electronic noise, flat-field, and spectral response. Using this absolute radiometric calibration, digital values can be converted into SI units of radiance. However, the authors noted the need to characterize a large number of these cameras before the results could be applied in general. Moreover, certain calibrations are device-dependent and would need to be done separately on each device. Spectral and radiometric calibrations of seven cameras, including the Raspberry Pi, are given in [51]. These calibrations include dark current, flat-fielding, linearity, and spectral characterization. However, for the five digicams included in this work, JPEG data were used, severely limiting the quality and usefulness of these calibrations, as described above.Spectral characterizations are more commonly published since these are vital for quantitative color analysis. Using various methods, the spectral responses of several Canon [1,19,23,24,46,51,54,61], Nikon [1,13,23,24,54,[59][60][61][62], Olympus [23, 24, 51], Panasonic [46], SeaLife [13], Sigma [60], and Sony [1, 23, 46, 51, 61] digital cameras (digicams), as well as a number of smartpho...

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“…Although Rayleigh scattering theory predicts 100% polarization at a scattering angle of 90 deg, the actual maximum DoLP value is smaller because of multiple scattering from aerosols and the ground. [1][2][3][4][5][6][7][8][9][10] This pattern moves through the sky with the sun, with a magnitude that changes with environmental conditions and wavelength. 11,12 The DoLP pattern is, however, independent of the orientation of the polarizing elements used in the measurements.…”

Section: All-sky Imaging Polarimetermentioning

confidence: 99%

“…In the atmosphere, the primary source of polarization is scattering by gas molecules; however, aerosols, [1][2][3][4][5] clouds, [6][7][8] and underlying surface reflectance 9,10 alter the observed sky polarization. To interpret polarization measurements or simulations correctly, it is important to understand how partially polarized skylight varies with environmental factors, [1][2][3][4][5][6][7][8][9][10] as well as with wavelength and solar position. [10][11][12] This is critical for military, environmental, and navigational applications.…”

Section: Introductionmentioning

confidence: 99%

Visualization of all-sky polarization images referenced in the instrument, scattering, and solar principal planes

Eshelman

Shaw

2019

Opt. Eng.

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Sunlight becomes partially linearly polarized when scattered from atmospheric gas molecules and can be quantified using the linear Stokes parameters S 0 ; S 1 ; S 2 and the derived degree of linear polarization and angle of polarization (AoP). The angle-dependent Stokes parameters S 1 and S 2 and the AoP require a reference plane. Commonly used reference planes for polarimetric applications include the instrument, scattering, and solar principal planes, each of which provides unique insights when analyzing sky polarization data. Methods to transform the parameters between each frame of reference are known; however, previous publications have not shown the results of transforming a time series of all-sky polarization images into these different reference planes clearly showing how this alters the image visualization. We review two methods used to rotate all-sky polarization images from the instrument to the scattering plane and the solar principal plane, and for the first time shows all-sky polarization image sequences recorded from sunrise to sunset of Stokes S 1 and S 2 and AoP for each reference frame.

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Measuring the influence of aerosols and albedo on sky polarization

Cited by 22 publications

References 28 publications

Wavelength dependence of the degree of polarization in cloud-free skies: simulations of real environments

Wavelength dependence of the degree of polarization in cloud-free skies: simulations of real environments

Standardized spectral and radiometric calibration of consumer cameras

Visualization of all-sky polarization images referenced in the instrument, scattering, and solar principal planes

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