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26 2. Remote Sensing with MODIS Figure 2.8 – Sun glint, a major obstacle for the generation of ocean color products appears on MODIS images as a wide and bright vertical stripe. It results from the reflection of sun light off the ocean surface. sun glint radiance L Glint (often close to the sensor saturation) compromises the estimation of water-leaving radiance and all the derived oceanic geophysical variables. In addition, atmospheric correction over ocean targets often fails to distinguish sun glint from high concentrations of white aerosols. Many techniques have been devised to remove the sun glint contribution from the TOA signal. The most common approach for medium resolution instruments relie on the well-known Cox and Munk statistical model of sea surface roughness [Cox and W.Munk, 1954]. For a given sensor viewing geometry, the amount of sun glint radiance can be expressed as a function of the probability density function of sea surface slopes which in turn, depend on the wind speed and direction. The reflectance due to sun glint can be predicted from the sensor viewing geometry and the wind speed with : ρ Glint (λ, θ s ,θ v ,φ s ,φ v ,W)= P (θ s,θ v ,φ s ,φ v ,W)f(w, λ) 4cos 4 βcosθ v cosθ s (2.8) where : - θ s and φ s are the zenith and azimuth angles of the sun - θ v and φ v are the zenith and azimuth angles of the sensor - f(w, λ) is the Fresnel reflectance at the ocean surface for an angle of incidence of w - W is the wind speed - P (θ s ,θ v ,φ s ,φ v ,W) is the probability distribution function of Cox/Munk model corresponding to a 4 th order Gramm-Charlier expansion and often approximated with a Gaussian distribution. Although implemented in numerous ocean colour sensors, Cox and Munk based correcting schemes [Montagner et al., 2003], [Wang and Bailey, 2001], [Fukushima et al., 2007], [Ottaviani et al., 2008] can only process moderate sun glint. Also, the results are limited by the accuracy and resolution of available wind data. Motivated by these limitations, Steinmetz, Deschamps and Ramon, [Steinmetz et al., 2008] recenly introduced a new approach,
27 POLYMER based on neural networks. The TOA reflectance is modelled as : ρ T OA (λ) =c 0 + c 1 λ −1 + c 2 λ −4 + tρ W ater (λ) (2.9) where c 0 includes spectrally flat components such as sun glint, c 1 λ −1 accounts for aerosol effects and c 2 λ −4 represents coupling processes between sun glint and aerosols. The water reflectance ρ W ater is assumed to be a function of chlorophyll concentration, derived from bio-optical models. Rather different techniques are employed to process sun glint effects on high resolution imagery (1-20m)[Hochberg et al., 2003], [Hedley et al., 2005], [Lyzenga et al., 2006], [Kutser et al., 2009]. They are mostly based on the assumption of neglectable water-leaving radiance at Near-Infrared (NIR) bands and the linear relationship of reflectances at visible and NIR bands. An aternative option for sun glint correction is sun glint avoidance. Several instruments are designed with the ability to tilt the sensors viewing direction (SeaWiFS) or to include a multi-angle viewing mode (POLDER). The presence of such mechanisms can reduce considerably the loss of data related to strong sun glint contamination and increase the spatial coverage of ocean products. 2.4.4 Cloud coverage Persisting cloud coverage in satelite imagery disturbs the study of many geological and oceanographic processes because it reduces the availability of exploitable data. More specifically, it disables the detection of changes occuring at temporal frequencies higher than the persistence of clouds and, therefore poses a serious challenge for applications related to disaster management (see section 2.2.4.2). As a reponse to the limited set of measurements available in high level geophysical variables, many studies have focused on possible techniques to enhance the daily spatial coverage. A common approach in ocean color remote sensing is to exploit data measured by many sensors at slightly different times. Under the assumption of stationnay oceanic processes, measurements acquired within a reasonable time frame can be merged to fill in the gaps related to clouds. In the context of ocean color remote sensing, combination of data from multiple instruments was explored for SST in [Reynolds and Smith, 1994] and have since been deeply investigated by the SIMBIOS project of NASA [M. E. and Gregg, 2003], [Fargion and McClain], [Kwiatkowska and Fargion, 2002]. Common techniques for the merging of chlorophyll concentration products include : - Weighted averaging : estimation of missing values is determined from different sensors using a weighted average where the weights are dependent on the accuracy of observations. - Statistical objective analysis, also known as optimal interpolation, consists in filling gaps through spatial and temporal interpolation. More recently, the Short-term Prediction and Research Transition (SPoRT) program of NASA has developped a composite product for MODIS SST [Haines et al., 2007] to improve
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- Alaska Labrador Siberia Restorati
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128 Conclusion
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130 BIBLIOGRAPHIE A. Chambolle. An
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132 BIBLIOGRAPHIE E. Hochberg, S. A
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134 BIBLIOGRAPHIE P. Perona and J.
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