Observation of sea surface roughness at a pixel scale using multi-angle sun glitter images acquired by the ASTER sensor

Zhang, Huaguo; Yang, Ki Yeon; Lou, Xiulin; Li, Yan; Zheng, Gang; Wang, Juan; Wang, Xiaozhen; Ren, Lin; Li, Dongling; Shi, Aiqin

doi:10.1016/j.rse.2018.02.004

Cited by 30 publications

(20 citation statements)

References 41 publications

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“…A spatially resolved map of relative wind speed was derived from processing sun glitter imagery of the ASTER VNIR (band 3) imagery. The methodology we use stems directly from the original work by Cox and Munk () and follows the formulation detailed in Matthews () and Zhang et al (). The general formula for the observed subresolution glitter intensity is

L = \frac{H_{S} ρ false(ω false)}{4 normalπ σ^{2} \cos^{4} β \cos θ} \exp false(- \tan^{2} β false/ σ^{2} false) false),

where L is the observed radiance at the sensor, H s is the solar irradiance, ρ is the Fresnel reflection coefficient at angle ω , Θ is viewing or incidence angle, β is the tilt of the local (facet) surface, and σ is the half‐width of the slope distribution.…”

Section: Satellite Observationsmentioning

confidence: 99%

“…A spatially resolved map of relative wind speed was derived from processing sun glitter imagery of the ASTER VNIR (band 3) imagery. The methodology we use stems directly from the original work by Cox and Munk (1952) and follows the formulation detailed in Matthews (2005) and Zhang et al (2018). The general formula for the observed subresolution glitter intensity is…”

Section: Estimation Of Relative Wind Speed From Aster Sun Glitter Obsmentioning

confidence: 99%

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Satellite Observations of SST‐Induced Wind Speed Perturbation at the Oceanic Submesoscale

Gaube

Chickadel

Branch

et al. 2019

Geophysical Research Letters

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Sea Surface Temperature (SST) modifies the turbulent mixing, drag, and pressure gradients within the marine atmospheric boundary layer that accelerate near‐surface flow from cool to warm SST and decelerate the flow from warm to cool SST. This phenomenon is well documented on scales of 100–1,000 km (the oceanic mesoscale); however, the nature of this air‐sea coupling at scales on the order of 1–10 km (the submesoscale) remains unknown. The Advanced Spaceborne Thermal Emission and Reflection Radiometer can be used to study submesoscale phenomena because the high‐resolution infrared and near‐infrared images can used to estimate both SST and wind speed. Observations of dramatic temperature and wind gradients along the Gulf Stream landward edge are used to examine the surface wind response to submesoscale fronts in SST. Our analysis indicates that SST‐induced wind speed perturbations are observed at the scales of order 1–10 km, significantly smaller than previously suggested.

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L = \frac{H_{S} ρ false(ω false)}{4 normalπ σ^{2} \cos^{4} β \cos θ} \exp false(- \tan^{2} β false/ σ^{2} false) false),

Section: Satellite Observationsmentioning

confidence: 99%

“…A spatially resolved map of relative wind speed was derived from processing sun glitter imagery of the ASTER VNIR (band 3) imagery. The methodology we use stems directly from the original work by Cox and Munk (1952) and follows the formulation detailed in Matthews (2005) and Zhang et al (2018). The general formula for the observed subresolution glitter intensity is…”

Section: Estimation Of Relative Wind Speed From Aster Sun Glitter Obsmentioning

confidence: 99%

Satellite Observations of SST‐Induced Wind Speed Perturbation at the Oceanic Submesoscale

Gaube

Chickadel

Branch

et al. 2019

Geophysical Research Letters

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“…For ASTER two-angle images, Zhang et al [20] studied and established an SSR estimation model based on multi-angle SG using ASTER channel 3N and channel 3B images. The model was determined as follows:σ02=tan2βB−tan2βNLn(LgNLgB R(ωB)cosθNcos4βNR(ωN)cosθBcos4βB)where σ02 is SSR, θ is sensor viewing angle (SVA) (θN in 3N image and θB in 3B image), ϕ is sensor azimuth angle (ϕN in 3N image and ϕB in 3B image), θ0 is sun zenith angle, and ϕ0 is sun azimuth angle.…”

Section: Methodsmentioning

confidence: 99%

“…The SVA of the ASTER 3N and 3B images selected by Zhang et al [20] were fixed values (θN = 0° and θB = 27.6°), and the purpose of this study was to verify whether an optimal SVA combination that has the best effect on the SSR estimation model of multi-angle SG remote sensing could be determined. The SSR estimation model was modified to the following general situation:σ02=tan2βII−tan2βILn(LgILgII R(ωII)cosθIcos4βIR(ωI)cosθIIcos4βII)…”

Section: Methodsmentioning

confidence: 99%

“…Kudryavtsev et al [18,19] developed a practical method for retrieving directional spectra of ocean surface waves quantitatively from near-multidirectional SG imagery from the staggered detectors of the Copernicus Sentinel-2 Multi-Spectral Instrument. Zhang et al [20] constructed a multi-angle SG remote-sensing model using ASTER stereo multi-angle SG images, and established an SSR estimation model.…”

Section: Introductionmentioning

confidence: 99%

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On Optimal Imaging Angles in Multi-Angle Ocean Sun Glitter Remote-Sensing Platforms to Observe Sea Surface Roughness

Wang

Zhao

Zhang

et al. 2019

Sensors

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Sea surface roughness (SSR) is a key physical parameter in studies of air–sea interactions and the ocean dynamics process. The SSR quantitative inversion model based on multi-angle sun glitter (SG) images has been proposed recently, which will significantly promote SSR observations through multi-angle remote-sensing platforms. However, due to the sensitivity of the sensor view angle (SVA) to SG, it is necessary to determine the optimal imaging angle and their combinations. In this study, considering the design optimization of imaging geometry for multi-angle remote-sensing platforms, we have developed an error transfer simulation model based on the multi-angle SG remote-sensing radiation transmission and SSR estimation models. We simulate SSR estimation errors at different imaging geometry combinations to evaluate the optimal observation geometry combination. The results show that increased SSR inversion accuracy can be obtained with SVA combinations of 0° and 20° for nadir- and backward-looking SVA compared with current combinations of 0° and 27.6°. We found that SSR inversion prediction error using the proposed model and actual SSR inversion error from field buoy data are correlated. These results can provide support for the design optimization of imaging geometry for multi-angle ocean remote-sensing platforms.

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Reconstruction of large complex sand-wave bathymetry with adaptive partitioning combining satellite imagery and sparse multi-beam data

Zhang

Wang

et al. 2022

J. Ocean. Limnol.

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Observation of sea surface roughness at a pixel scale using multi-angle sun glitter images acquired by the ASTER sensor

Cited by 30 publications

References 41 publications

Satellite Observations of SST‐Induced Wind Speed Perturbation at the Oceanic Submesoscale

Satellite Observations of SST‐Induced Wind Speed Perturbation at the Oceanic Submesoscale

On Optimal Imaging Angles in Multi-Angle Ocean Sun Glitter Remote-Sensing Platforms to Observe Sea Surface Roughness

Reconstruction of large complex sand-wave bathymetry with adaptive partitioning combining satellite imagery and sparse multi-beam data

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