Globcurrent

Practicalities of Implementing Maximum Cross-Correlation

Five hourly GOCI images, showing the evolving pattern of chlorophyll. (Image size is 160km x 130 km, and covers the region between South Korea and Japan highlighted by the red box in right-hand image.)

INTRODUCTION

The idea of inferring the movement of water from successive satellite images seems both simple and intuitive, but a routine implementation of such a technique is challenging. Conventional techniques consider a small region of one image showing a characteristic feature and aim to locate the same feature in a later image, typically using a metric such as maximum cross-correlation (MCC). MCC computes the correlation between values of data in the sub-frame of the first image ('the target') with the values in all likely sub-frames in the second image, and it assumes that the position of the sub-frame with the highest correlation represents the true movement of the water between the two images. The advent of GOCI data from a geostationary satellite viewing the Korean peninsula offers a great opportunity to evaluate this technique. This is because it provides high-resolution (500m) multi-spectral imagery 8 times a day. Here we recap on some of the challenges in implementing such a technique, and of how we validate the recovered velocity estimates.

PROBLEMS

1. Poor geolocation of data - early attempts at the MCC technique suffered from limited orbit predictions such that the co-ordinates of ocean features were poorly known. We have compared the supplied data with a coastline database and have determined that this is not a problem for GOCI.

2. Visibility of features - If a region is cloud-covered in either of the images, no useful information can be returned. A second problem occurs if the region is homogeneous with no distinctive features to track.

Median cloud cover per day in Tsushima Strait region.

3. Evolution of features - The standard technique assumes the feature is simply translated with the current. To resolve such movement to an acceptable accuracy requires the feature to have moved several pixels between scenes, thus requiring a long time interval. However too long an interval may allow rotation of feature or deformation, especially shear when near to the coast. Additionally neither ocean colour nor thermal infra-red is recording a conservative property, as sediment may sink, phytoplankton grow and die, and the skin temperature be affected by mixing or heat loss to the atmosphere.

4. Presence of instrumental aretfacts - The existence of spurious features associated with the optical assembly of the instrument can affect the implementation of the MCC technique. The GOCI sensor synthesises its full 2500 km X 2500 km view from an array of 16 slots that it observes sequentially. There can be some movement of features between viewing of neighbouring slots, but a greater problem is the change in stray light pathways between these viewing geometries, leading to sharp linear features in the images.

Three images of the same region during a single day showing that the border between neighbouring camera 'slots' is marked by both discontinuities in features and changes in cloud masking. These borders are not straight across the whole image (see especially 3rd image) and move (presumably corresponding to changes in pointing of satellite sensor).

PROGRESS

i) Simulations indicated that MCC performance would be optimised if the size of the template was chosen to match that expected for the features in that region. Consequently we adopted a target image of 44x44 pixels i.e. ~22 km in width. This is commensurate with the size of features within the narrow strait where our validation data lie.

ii) If the images utilised are only one hour apart, then the minimum speed that can be resolved is 500m/hr (which is ~14 cm/s). We thus found that time separations of around 5 hours were much better, providing greater precision in the speed, and permitting velocity directions other than the 4 cardinal points.

Histogram of eastward velocity component from MCC (red) & HF radar (green) for a) 1 hour separation, and b) 5 hours.

iii) Patchy cloud cover is a significant problem in the Tsushima Straits region where we have our validation data from the HF radar system. If we only consider images that are less than 20% cloud, then the r.m.s. errors in the velocity components are ~20 cm/s. However there are few days with so little cloud.

Scatterplot of eastward velocity component from MCC and from HF radar network, with colour indicating no. of observations. a) 1 hour separation of images. b) 5-hour separation

What is your definition of an ocean surface current?

Can we shed some light on the ocean processes and marine applications that need more attention in GlobCurrent? Satellite altimetry, arguably a mature technique for mapping ocean currents, provides one of the most important views of the large-scale oceanic circulation. Still, we know the ground track spacing of conventional altimeters limits the resolution of ocean currents to scales no better than about 100 km and 10 days. This so-called high resolution "altimetry gap" is what prompts ideas to combine altimeter data with sequences of higher resolution satellite and in situ observations. Horizontal extent and time scale are just two possible ways to improve ocean current analysis. There are of course "deeper" issues when it comes to what we need to know. What are your issues? How would you define an ocean current? GlobCurrent would like to know.  Please post a comment below or simply email globcurrent-at-listes.ifremer.fr (replacing -at- with the @ sign).

The Potential of Delay Doppler Altimetry

For more than two decades, many parameters, such as the range between the satellite and the observed surface, the wave height and the wind speed, have been retrieved from the data provided by conventional altimeters like those on ERS-1/2, Topex/Poseidon, Jason-1/2, etc.

These data are used to monitor and understand global ocean circulation. Understanding water currents at the ocean surface is very important for multiple applications, such as marine search and rescue, emergency response, ship routing... However, altimetric estimates of currents have principally been used far from land; now with new advances in technology, altimetric measurements and their applications can move closer to the coast.

In conventional altimetry, when the satellite is approaching the coast, the ocean echoes get highly contaminated by land reflections (see Picture 1). This is due to the large footprint these kind of altimeters have. Instead, when delay-Doppler altimetry (DDA) is used, the footprint is considerably diminished along-track. In Picture 2, the conventional altimetry range rings are shown together with the division of along-track Doppler cells, which make the footprint for each waveform significantly smaller. Consequently, land contamination is avoided or, at least, reduced (see Picture 3). CryoSat-2, for example, has a footprint with a diameter of around 20 km. When using DDA, the along-track resolution is narrowed down to 300m.

Apart from surface-induced issues, delay-Doppler altimetry improves data quality in other aspects as well. Firstly, since the received echoes are not averaged on-board, waveforms can be better aligned on-ground prior to averaging. This solves the issue of having a blurry leading edge of the waveform. Moreover, with the delay-Doppler technique, the resulting waveform is much less corrupted by speckle noise, improving the signal to noise ratio. This is because the waveforms are uncorrelated (since they were captured from different places in the satellite orbit; see Picture 4) and they are incoherently averaged. The outcome of these two features allows a better retracking of the echo waveforms and hence a better estimation of the altimetric parameters.

In summary, delay-Doppler altimetry not only produces measurements closer to the coast but significantly improves the quality of the data compared with conventional altimeters. For GlobCurrent, this is a great advantage that will help us to better understand and monitor water circulation globally and, with this, be able to provide the user community with very precise, high-value information.

So the future looks great. With a DDA instrument on CryoSat-2 and a SAR (Synthetic Aperture Radar) on Sentinel-1 (2014) in orbit, and other missions to be launched in the upcoming years such as Sentinel-3 (planned for 2015), GlobCurrent will be able to provide global coverage of the Earth and thus a more complete high-resolution picture of the ocean’s surface circulation will be available than ever before.

Surface currents from new GOCI dataset with maximum cross-correlation

Estimates of ocean currents can be obtained by tracking the movement of natural surface features from satellites. The displacements of ocean features (e.g.. chlorophyll pattern) over the time interval between successive images can indicate the surface flow field. The Maximum Cross Correlation (MCC) method is an automated procedure that calculates the displacement of small regions of patterns from one image to another to derive surface currents.

Under the ESA project GlobCurrent, scientists from Plymouth Marine Laboratory have successfully applied the MCC method to derive the surface currents from the Geostationary Ocean Colour Imager.

As the world’s first ocean colour observation imager operated on a geostationary platform, it has six visible bands and two near-infra-red bands. The ground sampling distance of GOCI is about 500 m, and the observational coverage is a 2,500 × 2,500 km. GOCI was designed to observe its coverage area every hour and to transmit eight images each day. Therefore, GOCI offer an optimal platform to test the performance of MCC in the coastal area.

Figure 1 shows the chlorophyll concentration from GOCI at 00:16 GMT on 5th May 2012 for a region just south of Japan. There is a rotating eddy feature centred at 31.5N, 137E to the south of the path of the Kuroshio in this region. Figure 2 shows the chlorophyll map of the same area two hours later, 02:16 on the same day, note the eddy is rotating clockwise. Figure 3 shows the currents derived with the MCC code developed in PML from the pair of GOCI images. The currents are overlaid on the second GOCI image with the arrows showing the velocity of the current and the color scale giving the  chlorophyll concentration. The mean speed of the eddy is around 60 cm/s, a velocity very close to the value from altimetry at similar time in the region, and the eddy location is also identical to altimetry.

Figure 1 : GOCI chlorophyll concentration at 00:16 on 5th May 2012.

Figure 2 : GOCI chlorophyll concentration at 02:16 on 5th May 2012.

Figure 3 : Surface current derived from the GOCI chlorophyll concentration pair at 00:16 and 02:16 on 5th May 2012. The arrow shows the velocity and the colour scale the chlorophyll concentration at 02:16 on 5th May 2012.