Sun glitter as a "tool" for monitoring the Ocean from Space

Alexander Myasoedov Satellite Oceanography Laboratory

Russian State Hydrometeorological University St. Petersburg, Russia

mag@rshu.ru

Vladimir Kudryavtsev Satellite Oceanography Laboratory

Russian State Hydrometeorological University St. Petersburg, Russia

kudr@rshu.ru

Bertrand Chapron Department of Oceanography and Ecosystem Dynamics

IFREMER Brest, France

Bertrand.Chapron@ifremer.fr

Johnny A. Johannessen

Nansen Environmental and Remote Sensing Center Bergen, Norway

johnny.johannessen@nersc.no

Fabrice Collard Direction of Radar Applications

CLS Plouzané, France

dr.fab@cls.fr

Abstract- A method for retrieval of the spatial variations of the sea surface mean square slope (MSS) in sun glitter imagery is proposed. Observed sun glitter brightness anomalies are converted to the MSS anomalies with use of a transfer function determined from the smoothed shape of the sun glitter brightness. The method is applied to MODIS and MERIS sun glitter imagery of natural oil seeps and the catastrophic Deep-water Horizon oil spill in the Gulf of Mexico. The results clearly demonstrate a highly feasible approach for investigation of surface signatures of the oil slicks, as well as other ocean phenomena.

Keywords- Sun-glitter; Mean square slope;Surface slicks; Oil spills; optical imagery

I. INTRODUCTION In general, the main oceanographic applications of satellite

optical data (e.g. from MODIS and MERIS instruments) are associated with ocean color studies. In such cases the sunlight reflected from the sea surface is a major part of upward radiation and possess significant difficulties for ocean color retrieval algorithms. On the other hand, sun glitter contain valuable information on statistical properties of the sea surface roughness, its mean square slope (MSS), skewness and kurtosis, as demonstrated by Cox and Munk [1] and more recently by Bréon and Henriot [2].

Most ocean surface phenomena, e.g. biogenic and oil slicks, internal waves, ship wakes, spiral eddies, are locally affecting sea surface roughness to become visible in optical data.

Numerous satellite observations of surface slicks in sun glitter were reported, e.g. by Adamo et al. [3], Chust and Sagarminaga [4], and Hu et al. [5]. Hennings et al. [6] presented observations of the surface manifestation of shallow water bottom topography in sun glitter brightness. Other authors also observed and studied non-linear internal waves in sun glitter imagery.

Evidently, sun glitter signatures are caused by spatial variation of short scale sea surface roughness tracing surface manifestation of an ocean phenomenon. The magnitude of the contrasts is connected to the type of surface slick, e.g. biogenic, oil, and possibly thickness of the oil spill producing the slick. Retrieval and quantitative interpretation of these sun glitter brightness contrasts can thus help to better understand damping mechanisms.

Unlike determination of the background statistical properties of the sea surface slopes, to the best of our knowledge, satellite sun glitter imagery has never been specifically used for quantitative estimates of MSS anomalies of the surface roughness. Complexity arises from the fact that the sun glitter brightness and the MSS contrasts depend on the viewing and sun illumination geometries. Contrasts of MSS can then either be visible as dark/bright or bright/dark brightness signatures (see e.g. Hu et al. [5] for the oil slicks). This property is quite straightforward, and as shown recently by Jackson and Alpers [7], is a simple consequence of different viewing distance

Funding for this research was provided by the Mega-grant of the Russian Federation Government to support scientific research under the supervision of leading scientist at RSHU, No. 11.G34.31.0078

978-1-4673-0875-5/12/$31.00 ©2012 IEEE

angles from the specular point.

The goal of this study is to develop a method to quantitatively and consistently retrieve MSS anomalies from brightness signatures under various viewing geometries. The study is mainly focused on MODIS/MERIS observations, though application of the suggested method for interpretation of sun glitter images received from other optical sensors is straightforward. Examples of the application of the suggested method are given for surface roughness variations in presence of oil slicks.

II. ALGORITHM BASIS The algorithm for the sea surface MSS retrieval was first

suggested by Myasoedov et al. in [8] and further developed in [9] and [10] by Kudryavtsev et al.. This approach is based on the idea that sun glitter brightness depends on statistical properties of the sea surface slopes, - its MSS, skewness, peakedness etc. Cox and Munk [1] derived these parameters from analysis of the sun glitter shape, i.e. the derived parameters are averaged over the sun glitter width. The algorithm suggested in [8-10] focused on retrieval of “small-scale” (on scales which are much smaller than the sunglint width) MSS variations 2s from the sunglint brightness variations B . The advantage of the algorithm is that it does not require an a prior speculations about the PDF model, and the transfer function (relating brightness variations to MSS one) founds from the mean brightness field where the “real” PDF is explicitly built in.

III. ALGORITHM APPLICATION

A. Data This study is focused to the MODIS and MERIS imagery.

Over the ocean, satellite optical images collected during the daylight period contain distinct silvery-gray ellipses of reflected sunlight over the oceans within approximately 30 degree off the Sun’s specular reflection point. These sun glitter regions, where standard ocean color products cannot usually be retrieved, can be more favorable for detecting ocean phenomena. To sense the roughness changes, the red channel is the most preferable one, as the light in this channel is absorbed within a “thin” surface layer and thus not too sensitive to the optical properties of the upper water column. Moreover, it does not depend on the sea surface temperature. Considering MODIS and MERIS imagery, we use the Level 1B 250m resolution data in 645nm channel for MODIS and 681 nm channel for MERIS which are supplemented with geolocation, “view and sun geometry” data.

Due to the scanning mirror construction, the MODIS image represents a composition of the stripes. Each strip is formed by 40 detectors with the along track field of view about 0.8 deg and cross track field of view about 110 deg. This instrument view geometry forms a strip with the ground dimensions of 2330 km length and about 10 km width at nadir. Thus each of the strips provides 2D field of the surface brightness. Inside the sunglint these peculiarities of the MODIS imagery may produce step-like changes of the sea surface brightness. The

algorithm of the MODIS data processing in the sunglint area is designed as a strip-processing one, i.e. the whole images is divided on the strips which are then processing strip by strip. The brightness field B of each of the strips is decomposed on two parts, - large B and small B scale parts, i.e. B B B= + . Field B corresponds to variability of the brightness on scale of the sunglint width, and field B contains brightness variations B on the inner (much smaller) scales, which can be treated as the brightness signatures of sub- and meso-scale oceanic phenomena as well as wind filed variability.

In case of MERIS imagery, only cross-track gradients of the surface brightness are available. Therefore, we are inevitably forced to use an a priori PDF model.

B. Oil slicks case studies. The Deepwater Horizon oil spill on April 20, 2010 is

chosen for further demonstration of the algorithm. The MODIS (MODIS/Terra, May, 24, 2010, 16:45 GMT) and MERIS (MERIS/Envisat, May 24, 2010, 16:17 GMT) images from the red channels (645nm and 681nm correspondingly) are shown in Fig. 1. Note the oil spill is not entirely covered by the MODIS/Terra image. The image shown in Fig. 1 (lower) is a composition of two MODIS/Terra images acquired on 16:45 and 16:50 GMT. The time difference between these MERIS and MODIS acquisitions is about half an hour, therefore “geometry” of the oil spill on the ocean surface should not been changed during this period. As evidenced, due to different viewing and sun angles, the spill signatures on the MODIS and MERIS images are quite different.

Figure 1. (upper) Fragments of the original MERIS/Envisat image in red channel (681nm) acquired on May 24, 2010, 16:17 GMT. (lower) Composition of two MODIS/Terra images in red channel (645nm)

acquired on May 24, 2010, 16:45 GMT and 16:50 GMT correspondingly. Color bars indicate radiance of the images in conventional units. Cloud

mask is shown with white and land mask with brown colors. Coordinates of the Deepwater Horizon Platform are 28.73ºN, 88.38ºW

The images were processed using the methodology described in [8-10]. Fields of the mean sun glitter brightness

0B (averaging scale is 30x30 km2) for MERIS and MODIS data are shown in Fig. 2 (upper) and Fig. 3 (upper). The transfer function for the MODIS data is directly calculated from the mean brightness field (following eqs. (5b) with (13) in [9]), and is shown in Fig. 2 (lower). Notice that an inclined linear discontinuity well visible in this figure results from the patching of two MODIS/Terra images. To assess the transfer function for the MERIS data, the wind direction was prescribed from NCEP data, and the mean MSS was then calculated following eq. (10) in [9]. The transfer function for MERIS data is show in Fig. 3 (lower).

The sun glitter brightness contrasts 0/B B for the MERIS and MODIS data are shown in Fig. 4. The brightness contrasts fields are in close agreement. Some apparent differences are still evident. The contrast feature which is the “oil jet” around 87°W is viewed as a bright jet in the MERIS image (Fig. 4 (upper)), but in the MODIS field (Fig. 4 (lower)), the jet varies from bright to dark. Referring to Fig. 2 (lower) and Fig. 3 (lower), one can see that the transfer function T changes sign in this area, which corresponds to the zone of the inversion of brightness contrasts. The oil jet crosses the area of the contrast inversion zone, and thus its sun glitter brightness signature in the MODIS images changes sign.

Figure 2. (upper) The averaged brightness 0B of the MERIS image.

(lower) The transfer function T defined by eq. (10) for MERIS and by eq. (5b) with (13) for MODIS in [9].

Fig. 5 shows the MSS contrasts 2 20s s , derived from the

MERIS and MODIS sun glitter brightness contrasts (Fig. 4) with use of the transfer functions presented in Fig. 2. It was found that the MSS anomalies derived from the MERIS and MODIS images are in good agreement, with magnitudes of the MSS contrasts of the same order. The MSS anomalies derived from two independent images with the use of two different

methods show very similar results. This proves the robustness of the proposed methodology.

Figure 3. (upper) The averaged brightness 0B of the MODIS image.

(lower) The transfer function T defined by eq. (10) for MERIS and by eq. (5b) with (13) for MODIS in [9]. An inclined linear discontinuity in the

field of T in plot (lower right) around 28.50 N results from the patching of two MODIS/Terra images acquired on 16:45 and 16:50 GMT.

Figure 4. Sun glitter brightness contrasts, 0/B B , in the MERIS (upper) and MODIS (lower) images

Few remarkable differences are also found. First, linear features (indicated by red arrows) with singular values trace the zones of the inversion of contrasts. Other differences, confined within the yellow contour, in the vicinity of the mouth of the Mississippi River, present both negative and positive values.

Moreover, these positive/negative values in both images do not overlap. Considering that an oil film suppresses the short waves and the MSS, the “bright” MSS features in Fig. 5 must be considered as artifacts caused by other factors. We can anticipate that the oil film’s thickness in this area may be thick relative to the wavelength of red light (640nm-680nm), i.e. with thickness of order 5-50µm or more. In this case, the radiance of the surface is dominated by the optical properties of the oil itself. The suggested algorithm does not take this effect into account, and the reconstructed contrasts are not valid in either magnitude or sign change.

Figure 5. The MSS anomalies 2 2s s derived from the MERIS (upper plot) and MODIS (lower plot) images. Red arrows indicate zones of the

inversion of contrasts where reconstructed MSS have singular values (without physical meaning). MSS anomalies confined to the yellow

contours are presumably not true, because the oil film thickness in this area is too large relative to the red light wavelength. Since the considered

method does not take this effect into account, bright/dark features inside the yellow contours should be considered artifacts.

The method clearly provides new opportunities for quantitative investigations of surface signatures of different ocean phenomena, including internal waves and mesoscale ocean currents. The roughness changes can indeed help in tracking and quantifying the surface signatures of upper ocean motions. Interestingly, MSS changes can be quantified, and sun glitter imagery can lead to a better understanding of the manifestations of surface ocean phenomena.

ACKNOWLEDGMENT AM, VK, BC and FC acknowledge the support of the

Mega-grant of the Russian Federation Government to support scientific research under the supervision of leading scientist at RSHU, No. 11.G34.31.0078

.

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