Ocean Satellite Data Fusion for High-Resolution Surface Current Maps
<p>Now-cast of surface velocity field estimated by (<b>a</b>) the Mercator numerical model, (<b>b</b>) the AVISO/DUACS interpolated satellite altimetry product, and (<b>c</b>) our CNN model HIRES-CURRENTS-Net. In order to qualitatively compare the accuracy of these different data, the surface velocity vectors (white arrows) are superimposed on the high-resolution SST observation.</p> "> Figure 2
<p>We use an OSSE derived by (<b>a</b>) generating a high-resolution SSH field from a numerical model, (<b>b</b>) sampling the field via synthetic satellite tracks which simulate observation by altimeters and adding realistic noise, (<b>c</b>) inhomogeneous spatio-temporal interpolation between the sampled points to generate the OSSE field. In our experiment, the high-resolution horizontal grid size is 2 km and the low-resolution horizontal grid size is 15 km.</p> "> Figure 3
<p>Map representing drifter observations during 2 years of collection in 2021–2022.</p> "> Figure 4
<p>Example of a set of input/output images during training and validation. The model takes as an input a set of four images: (<b>a</b>) SST (high resolution), (<b>b</b>) SSH (low resolution), and (<b>c</b>) velocity field (low resolution), and outputs a set of three images: (<b>d</b>) SSH (high resolution), and (<b>e</b>) velocity field (high resolution). The velocity field represents a vector of the U and V field components. The high-resolution images (<b>a</b>,<b>d</b>,<b>e</b>) are retrieved from the numerical model reference run, while the low-resolution images (<b>b</b>,<b>c</b>) are retrieved from the OSSE altimetry reproduction. Inhomogeneous downsampling from high resolution to low resolution leads to local circulation often being misrepresented in the input velocities.</p> "> Figure 5
<p>Schematic representation of the HIRES architecture. Our model follows a U-Net architecture, presented in detail in [<a href="#B28-remotesensing-16-01182" class="html-bibr">28</a>]. The encoder–decoder architecture learns the mapping of a multi-modal 4-image input (<b>a</b>) via a downsampling branch (<b>b</b>) and three upsampling branches (<b>c</b>) to provide a 3-image output (<b>d</b>). Skip connections are employed between the downsampling branch and each upsampling branch. The convolution operation uses a <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math> kernel with a ReLu activation function and extracts features from input data. Downsampling reduces spatial dimensions to capture larger context, while upsampling increases them to recover finer details. Skip connections connect corresponding layers between the encoder and the decoder, aiding in the retrieval of high-resolution features and mitigating vanishing gradients.</p> "> Figure 6
<p>Examples of applying synthetic clouds on the SST input images during training. In this study, we divide cloud coverage into four categories based on the percentage of the clouds in SST crop: (<b>a</b>) low (0–40%), (<b>b</b>) medium (40–60%), (<b>c</b>) high (60–80%), and (<b>d</b>) very high (80–100%).</p> "> Figure 7
<p>The (<b>left panel</b>) provides an example of our evaluation metric for the Mercator numerical model along the trajectory of a drifter with ID 6102787 in the eastern Mediterranean basin from March to May 2022 (<b>right panel</b>). The metric computes the angle error between the velocity vector estimated the model and the smoothed drifter trajectory. We divide the errors between the model prediction and the ground truth current direction obtained from drifters into 4 categories: excellent (deep green, error <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo><</mo> </mrow> </semantics></math> 15°), correct (light green, 15°<math display="inline"><semantics> <mrow> <mo><</mo> <mi>θ</mi> <mo><</mo> </mrow> </semantics></math> 45°), inaccurate (orange, 45°<math display="inline"><semantics> <mrow> <mo><</mo> <mi>θ</mi> <mo><</mo> </mrow> </semantics></math> 90°), and wrong (red, <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>></mo> </mrow> </semantics></math> 90°).</p> "> Figure 8
<p>Qualitative examples of the standard AVISO/DUACS method and our HIRES-CURRENTS-Net model on the real satellite data of one <math display="inline"><semantics> <mrow> <mn>256</mn> <mo>×</mo> <mn>256</mn> </mrow> </semantics></math> crop. Background: SST, arrows represent the velocity field, and grey color represents land areas. This qualitative example shows that HIRES-CURRENTS-Net is able to predict small-scale structures visible on the SST signature which are missed due to interpolation of sparse altimetric tracks.</p> "> Figure 9
<p>Qualitative examples of the standard AVISO/DUACS method and our HIRES-CURRENTS-Net model on crops around drifter observations, superimposed on SST images. The black dots track the drifter and the black arrow indicates the drifter direction at one moment of observation. Drifters are shown for two consecutive days for better visualization. (<b>a</b>) Date of SST image: 7 April 2022. Drifter ID: 6102707. Drifter date: 6 April 2022–7 April 2022. Average drifter magnitude: 0.48 m/s. Located at (38.59, 40.89) °N, (0.31, 2.61) °E. (<b>b</b>) Date of SST image: 4 August 2022. Drifter ID: 6102674. Drifter date: 3 August 2022–4 August /2022. Average drifter magnitude: 0.29 m/s. Located at (39.01, 41.31) °N, (5.98, 8.27) °E. (<b>c</b>) Date of SST image: 11 March 2022. Drifter ID: 6102796. Drifter date: 11 March 2022–12 March 2022. Average drifter magnitude: 0.39 m/s. Located at (36.20, 38.50) °N, (4.62, 6.92) °E.</p> "> Figure 9 Cont.
<p>Qualitative examples of the standard AVISO/DUACS method and our HIRES-CURRENTS-Net model on crops around drifter observations, superimposed on SST images. The black dots track the drifter and the black arrow indicates the drifter direction at one moment of observation. Drifters are shown for two consecutive days for better visualization. (<b>a</b>) Date of SST image: 7 April 2022. Drifter ID: 6102707. Drifter date: 6 April 2022–7 April 2022. Average drifter magnitude: 0.48 m/s. Located at (38.59, 40.89) °N, (0.31, 2.61) °E. (<b>b</b>) Date of SST image: 4 August 2022. Drifter ID: 6102674. Drifter date: 3 August 2022–4 August /2022. Average drifter magnitude: 0.29 m/s. Located at (39.01, 41.31) °N, (5.98, 8.27) °E. (<b>c</b>) Date of SST image: 11 March 2022. Drifter ID: 6102796. Drifter date: 11 March 2022–12 March 2022. Average drifter magnitude: 0.39 m/s. Located at (36.20, 38.50) °N, (4.62, 6.92) °E.</p> "> Figure 10
<p>Qualitative examples of current magnitudes (<b>upper row</b>) and vorticity (<b>lower row</b>) of the standard AVISO/DUACS method and our HIRES-CURRENTS-Net model on the CROCO validation data.</p> "> Figure 11
<p><b>Left</b>: Results on drifters for our HIRES-CURRENTS-Net model and two standard methods, Mercator and AVISO/DUACS, on angle error at different thresholds (see <a href="#remotesensing-16-01182-f007" class="html-fig">Figure 7</a>). <b>Right</b>: results on drifters for HIRES-CURRENTS-Net on areas with <40% clouds.</p> "> Figure 12
<p>According to the standard voyage plan, from Tangier to Tunis, the roll−on/roll−off cargo ship would have faced several counter-currents induced by coastal eddies, as predicted in real time by our model and as illustrated in panel (<b>a</b>). By applying an isochrone method to our predicted ocean currents, we identified a route which better optimises fuel and time. Panel (<b>b</b>) shows our short-term optimal routing which provides a handful of waypoints (black dots) that lead to an increase in the mean Speed Over Ground (SOG) of 0.6 knots measured by the ship’s automatic identification system (AIS). The impact of the surface currents can be computed by subtracting measurements of Speed Through Water (STW) from onboard instruments. The green (red) dots indicate an increase (decrease) in the SOG of more than 0.5 knots. Panel (<b>c</b>) shows the optimized route superimposed on the Sea Surface Temperature measured by satellite on 27 November 2023.</p> ">
Abstract
:1. Introduction
2. Data and Methods
2.1. Real Satellite Data
2.2. Simulated Satellite Data
2.3. Drifter Data
2.4. Model Architecture
2.5. Training with Artificial Clouds
2.6. Fine-Tuning on Real Observations
2.7. Evaluation Metrics
2.8. Comparison to Baselines
2.9. Implementation Details
3. Results
3.1. Training on Simulated Data
Model | Correct Angles, % | Correct Magnitudes, % |
---|---|---|
Mercator | 50.26 | 32.56 |
AVISO/DUACS | 67.86 | 38.57 |
HIRES-CUR | 71.73 | 45.73 |
3.2. Cloud Robustness
3.3. Training on Real Data
3.4. Qualitative Results
3.5. Further Ablations and Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | Correct Angles, % | Correct Magnitudes, % |
---|---|---|
AVISO/DUACS | 67.86 | 38.57 |
HIRES-CUR w/o SST | 66.56 | 43.50 |
HIRES-CUR | 71.73 | 45.73 |
Input | Model | Correct Angles, % | Correct Magnitudes, % |
---|---|---|---|
L4 SST | HIRES-CUR | 71.73 | 45.73 |
HIRES-CUR-cloud | 73.20 | 48.13 | |
L3 SST | HIRES-CUR | 61.23 | 40.23 |
HIRES-CUR-cloud | 72.47 | 48.17 |
% Clouds | Model | Correct Ang., % | Correct Mag., % | # Drifter-Days |
---|---|---|---|---|
≥80% (very high) | AVISO/DUACS | 62.28 | 32.70 | 896 |
HIRES-CUR | 62.31 | 35.27 | ||
HIRES-CUR-cloud | 65.18 | 41.18 | ||
60–80% (high) | AVISO/DUACS | 66.43 | 38.52 | 283 |
HIRES-CUR | 68.94 | 44.52 | ||
HIRES-CUR-cloud | 71.02 | 48.76 | ||
40–60% (medium) | AVISO/DUACS | 67.66 | 40.26 | 303 |
HIRES-CUR | 69.33 | 46.86 | ||
HIRES-CUR-cloud | 75.25 | 53.14 | ||
<40% (low) | AVISO/DUACS | 72.57 | 42.73 | 1163 |
HIRES-CUR | 80.49 | 53.83 | ||
HIRES-CUR-cloud | 79.19 | 52.02 |
% Clouds | Model | Input | Correct Ang., % | Correct Mag., % |
---|---|---|---|---|
<40% (low) | HIRES-CUR | Real-time | 80.49 | 53.83 |
Delayed-time | 81.33 | 56.00 |
Input | Model | Fine-Tuning | Correct Ang., % | Correct Mag., % |
---|---|---|---|---|
- | AVISO/DUACS | - | 65.22 | 33.10 |
L4 SST | HIRES-CUR-cloud | None | 72.23 | 43.07 |
HIRES-CUR-ftune | Real | 73.32 | 41.32 | |
L3 SST | HIRES-CUR-cloud | None | 72.29 | 44.04 |
HIRES-CUR-ftune | Real | 73.30 | 41.58 |
Input | Model | Pre-Train | Correct Ang., % | Correct Mag., % |
---|---|---|---|---|
- | AVISO/DUACS | - | 65.22 | 33.10 |
L3 SST | HIRES-CUR-real | None | 70.34 | 43.95 |
L3 CHL | HIRES-CUR-real | None | 69.90 | 42.00 |
Model | Correct Angles, % | Correct Magnitudes, % |
---|---|---|
HIRES-CUR w/o SSH | 71.18 | 39.65 |
HIRES-CUR | 71.73 | 45.73 |
# Decoders | Model | Correct Angles, % | Correct Magnitudes, % |
---|---|---|---|
1 | HIRES-CUR | 69.13 | 45.30 |
3 | HIRES-CUR | 71.73 | 45.73 |
Model | Average Angle Error, Degrees | Average Magnitude Error, m/s |
---|---|---|
AVISO/DUACS | 38.61 | 0.16 |
HIRES-CUR | 26.87 | 0.12 |
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Kugusheva, A.; Bull, H.; Moschos, E.; Ioannou, A.; Le Vu, B.; Stegner, A. Ocean Satellite Data Fusion for High-Resolution Surface Current Maps. Remote Sens. 2024, 16, 1182. https://doi.org/10.3390/rs16071182
Kugusheva A, Bull H, Moschos E, Ioannou A, Le Vu B, Stegner A. Ocean Satellite Data Fusion for High-Resolution Surface Current Maps. Remote Sensing. 2024; 16(7):1182. https://doi.org/10.3390/rs16071182
Chicago/Turabian StyleKugusheva, Alisa, Hannah Bull, Evangelos Moschos, Artemis Ioannou, Briac Le Vu, and Alexandre Stegner. 2024. "Ocean Satellite Data Fusion for High-Resolution Surface Current Maps" Remote Sensing 16, no. 7: 1182. https://doi.org/10.3390/rs16071182