Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa
<p>Schematic visualisation of different features (parameters) of a radar altimeter waveform.</p> "> Figure 2
<p>Map of the study areas by area of interest (AOI) chosen by visual inspection of Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea ice types. AOI first-year ice (FYI) is shown in yellow and AOI multi-year (MYI) in cyan. Averaged (∼30 d) OSI SAF sea ice type map from February 2016 is applied as the background representing MYI in green, FYI in brown and open ocean in dark blue. Dark lines denote FYI/MYI border and ice/ocean edge, respectively.</p> "> Figure 3
<p>Example of AltiKa waveforms. (<b>a</b>) A specular return echo. (<b>b</b>) A diffuse return echo. We would like to emphasise the large difference in power return when you compare the specular and diffuse return echo.</p> "> Figure 4
<p>Box and whiskers plots of computed waveform parameters (MAX in dB, PP in dB, <math display="inline"><semantics> <msub> <mi>σ</mi> <mn>0</mn> </msub> </semantics></math> in dB, LEW in metres and TES in s<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>) in February 2014–2018 from the chosen study areas (<a href="#remotesensing-13-03183-f002" class="html-fig">Figure 2</a>). PP, LEW and TES have user-defined limits applied to maintain the focus of the main distribution of the measurements rather than the outliers. Outliers have been removed to keep the plot simple.</p> "> Figure 5
<p>The cumulative probability distributions (CDFs) for the five different AltiKa waveform parameters for all years (2014–2018) combined are shown by MYI and FYI in green and brown, respectively. LEW, PP and TES use the same limits as in <a href="#remotesensing-13-03183-f004" class="html-fig">Figure 4</a>.</p> "> Figure 6
<p>Spatial distribution of MAX (limits 44–50 dB), <math display="inline"><semantics> <msub> <mi>σ</mi> <mn>0</mn> </msub> </semantics></math>/backscatter (limits 0–10 dB) and LEW (limits 0–1 m) over the Arctic from 70 to 81.6<math display="inline"><semantics> <msup> <mrow/> <mo>°</mo> </msup> </semantics></math> N for a 35 day cycle (∼February) in 2014–2018 interpolated to the OSI SAF 10 km grid with a polar stereographic projection. The black line denotes MYI/FYI ice edge and the magenta line denotes ice/ocean edge.</p> "> Figure 7
<p>Classification of sea ice types by AltiKa radar echo waveforms in February (35 day cycle) 2014–2018, with KNN, RF and Bayesian classifiers (supervised classifiers), using a combination of waveform parameters LEW + <math display="inline"><semantics> <msub> <mi>σ</mi> <mn>0</mn> </msub> </semantics></math> and a threshold-based classifier using waveform parameters MAX + <math display="inline"><semantics> <msub> <mi>σ</mi> <mn>0</mn> </msub> </semantics></math>. For the supervised classifiers, FYI is denoted by bright red and MYI by bright green. For the threshold-based classifier, FYI is denoted by brown and MYI by green. For reference, OSI SAF sea ice type is presented with FYI denoted by brown, MYI by green and open ocean by blue. Black contour lines denote the MYI/FYI edge provided by OSI SAF.</p> "> Figure 8
<p>Weekly drift patterns of the Arctic acquired from 15 to 21 January (in the years 2014–2018). Available from <a href="https://daacdata.apps.nsidc.org/pub/DATASETS/nsidc0116_icemotion_vectors_v4/" target="_blank">https://daacdata.apps.nsidc.org/pub/DATASETS/nsidc0116_icemotion_vectors_v4/</a>, last access 9 August 2021.</p> "> Figure A1
<p>Ice-type charts from February 2014 to 2018. (<b>Top</b>) OSI SAF data without a polar gap of AltiKa applied, (bottom three) AltiKa data classified by intervals from <a href="#remotesensing-13-03183-t003" class="html-table">Table 3</a> in the order of MAX, <math display="inline"><semantics> <msub> <mi>σ</mi> <mn>0</mn> </msub> </semantics></math> (<b>backscatter</b>) and a combination of both MAX and <math display="inline"><semantics> <msub> <mi>σ</mi> <mn>0</mn> </msub> </semantics></math>. The charts are classified as follows: open water (blue), FYI (brown) and MYI (green). Ambiguous values and no class values are not depicted. The black line denotes MYI/FYI ice edge from OSI SAF (in row 2–4).</p> ">
Abstract
:1. Introduction
2. Data
2.1. SARAL/AltiKa
2.2. OSI SAF Sea Ice Type
3. Method
3.1. Parametrisation of Waveforms
- Maximum power (MAX) is the highest recorded power in the calibrated waveform. It has been used in several studies to identify sea ice leads (e.g., [1,13,14]) and characterises, in general, the reflective properties of the surface [14]. Zakharova et al. [14] formulated the expression to calculate MAX for AltiKa:
- Pulse peakiness (PP) describes the specularity of the radar echo return (high PP near surfaces with high reflectances, such as FYI or leads [14,15,26]) and was first defined by Laxon [24] for ERS-1. For AltiKa GDR data, PP must, as described in Zakharova et al. [14], be calculated as follows:
- The leading-edge-width (LEW) is defined as the width between 1 and 99% of the amplitude of a Gaussian fit to the leading edge of a waveform [13]. For specular returns (leads), LEW will be smaller than for scattered returns (e.g., MYI) [15]. LEW is available in the AltiKa data product from the ICE-2 retracker and is directly used in this study.
- Research has shown that the trailing edge slope (TES) is a parameter useful for distinguishing between MYI and FYI with Ku-band radar altimeters while being highly correlated with the trailing edge width (TEW) [15]. TES is defined as the decay factor for the exponential fit [36]. There is a clear difference in the decay after the peak of the waveform when comparing MYI and FYI, as MYI generally has a lower decay rate (with a wider TEW) than FYI (and leads) [15]. TES is available in the AltiKa data product from the ICE-2 retracker.
- The backscatter coefficient () is computed from the power of the returned pulse and provides information about the surface from which the pulse has been reflected. MYI has lower backscatter values due to the rougher surface caused by, e.g., deformation, allowing MYI to scatter the pulse away from the radar at nadir, whereas FYI experiences more specular returns due to less deformation; thus, the return signal from nadir will be stronger [33]. The backscatter coefficient from AltiKa can be obtained from the retracking algorithms ICE-1, ICE-2 and SEA ICE [37]. Rajkumar et al. [33] argues that retracking algorithms ICE-1 and SEA ICE have similar backscatter observations over sea ice, indicating great suitability for sea ice observations, whereas the bias with ICE-2 retracker was apparent for the backscatter observations. Therefore, in this study, instead of using the backscatter coefficient from the ICE-2 retracker as is used when retrieving LEW and TES, the backscatter coefficient obtained from the SEA ICE retracker is used.
3.2. Pre-Processing of Altika Data
Discrimination between Specular and Diffuse Waveforms
3.3. Classification
3.3.1. Threshold-Based Classification
3.3.2. Bayesian Classification
3.3.3. k-Nearest Neighbour (KNN) Classification
3.3.4. Random Forest (RF) Classification
3.3.5. Classification Performance
4. Results
4.1. Waveform Parameters and Discrimination Suitability
4.1.1. Waveform Parameters Acquired over the Study Areas
4.1.2. Pan-Arctic Distribution of the Waveform Parameters
4.2. Classification of Sea Ice Types in the Arctic by Four Classifiers
5. Discussion
5.1. Comparison with Prior Studies
5.2. Snow Cover and Drift Speed Influence
5.3. Uncertainty in Validation Data
5.4. Study Area and Training Data Selection
5.5. Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) Mission and Future Work
6. Conclusions and Reflections
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
MAX | ||||||||
MYI | FYI | |||||||
Year | MYI | FYI | No class | Ambiguous | MYI | FYI | No class | Ambiguous |
2014 | 31.26 | 62.79 | 1.46 | 4.49 | 19.14 | 73.98 | 1.36 | 5.52 |
2015 | 31.91 | 56.28 | 3.99 | 7.82 | 17.05 | 79.66 | 0.67 | 2.62 |
2016 | 39.10 | 42.19 | 5.98 | 12.73 | 20.35 | 74.60 | 1.53 | 3.52 |
2017 | 40.62 | 43.11 | 4.77 | 11.50 | 28.23 | 67.73 | 10.57 | 3.57 |
2018 | 42.08 | 49.05 | 1.94 | 6.93 | 18.24 | 61.47 | 15.16 | 5.13 |
Mean | 37.00 | 50.68 | 3.63 | 8.69 | 18.58 | 71.49 | 5.86 | 4.07 |
MYI | FYI | |||||||
Year | MYI | FYI | No class | Ambiguous | MYI | FYI | No class | Ambiguous |
2014 | 38.55 | 54.13 | 1.57 | 5.74 | 35.28 | 58.18 | 1.08 | 5.46 |
2015 | 35.30 | 15.12 | 4.14 | 8.44 | 18.61 | 66.16 | 11.93 | 3.31 |
2016 | 42.51 | 39.56 | 5.45 | 12.48 | 28.68 | 66.28 | 1.26 | 3.75 |
2017 | 43.56 | 41.76 | 4.06 | 10.62 | 19.73 | 66.19 | 10.40 | 3.68 |
2018 | 43.71 | 47.75 | 1.87 | 6.67 | 20.18 | 59.60 | 15.26 | 5.56 |
Mean | 40.73 | 39.66 | 3.42 | 8.79 | 24.49 | 63.16 | 7.99 | 4.36 |
MAX + | ||||||||
MYI | FYI | |||||||
Year | MYI | FYI | No class | Ambiguous | MYI | FYI | No class | Ambiguous |
2014 | 37.59 | 56.81 | 1.46 | 4.14 | 34.23 | 60.31 | 0.93 | 4.53 |
2015 | 33.74 | 54.40 | 4.02 | 7.83 | 17.28 | 68.41 | 4.70 | 9.61 |
2016 | 41.74 | 40.80 | 5.82 | 11.64 | 26.77 | 68.67 | 1.39 | 3.17 |
2017 | 40.93 | 43.64 | 4.71 | 10.72 | 18.20 | 67.90 | 10.55 | 3.35 |
2018 | 50.70 | 39.94 | 1.52 | 7.84 | 23.79 | 56.56 | 14.84 | 4.81 |
Mean | 39.30 | 49.02 | 3.58 | 8.10 | 23.02 | 65.23 | 6.58 | 5.17 |
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Mission | Organisation | Mission Period | Altimeter | Type | Frequency Band | Coverage |
---|---|---|---|---|---|---|
GEOS–3 | NASA | 1974–1979 | RA | LRM | Ku | 72.0 |
SeaSat | NASA | 1978 | RA | LRM | Ku | 72.0 |
Geosat | US Navy | 1985–1989 | RA | LRM | Ku | 72.0 |
ERS–1 | ESA | 1991–2000 | RA | LRM | Ku | 81.5 |
ERS–2 | ESA | 1995–2003 | RA | LRM | Ku | 81.5 |
Envisat | ESA | 2002–2012 | RA–2 | LRM | Ku/S | 81.5 |
ICESat | NASA | 2003–2009 | GLAS | LiDAR | Optical (1064/532 nm) | 86.0 |
CryoSat–2 | ESA | 2010– | SIRAL | SAR | Ku | 88.0 |
SARAL | CNES/ISRO | 2013– | AltiKa | LRM | Ka | 81.5 |
Sentinel–3A | ESA | 2016– | SRAL | SAR | Ku/C | 81.5 |
Sentinel–3B | ESA | 2018– | SRAL | SAR | Ku/C | 81.5 |
ICESat–2 | NASA | 2018– | ATLAS | LiDAR photons | Optical (532 nm) | 88.0 |
Sentinel–3C | ESA | ∼2021– | SRAL | SAR | Ku/C | 81.5 |
Sentinel–3D | ESA | ∼2023– | SRAL | SAR | Ku/C | 81.5 |
CRISTAL | ESA | ∼2027– | IRIS | SAR | Ku/Ka | 88.0 |
LEW + MAX + | LEW + | LEW + MAX | MAX + | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Year | KNN | RF | Bayesian | KNN | RF | Bayesian | KNN | RF | Bayesian | KNN | RF | Bayesian |
2014 | 85.88 | 86.35 | 85.51 | 86.32 | 86.86 | 86.35 | 85.95 | 87.20 | 86.25 | 78.65 | 80.11 | 83.44 |
2015 | 94.44 | 94.50 | 94.06 | 94.57 | 95.42 | 94.54 | 94.50 | 94.57 | 94.50 | 92.09 | 92.47 | 94.27 |
2016 | 80.58 | 81.36 | 79.97 | 78.88 | 82.57 | 79.93 | 79.86 | 80.75 | 79.97 | 68.75 | 73.93 | 76.88 |
2017 | 77.53 | 80.37 | 80.10 | 77.56 | 84.42 | 79.59 | 76.95 | 80.34 | 79.66 | 72.34 | 75.93 | 79.69 |
2018 | 78.24 | 80.00 | 80.68 | 78.34 | 80.25 | 81.69 | 77.53 | 80.51 | 81.63 | 75.19 | 77.36 | 80.41 |
Mean | 83.33 | 84.52 | 84.06 | 83.13 | 85.90 | 84.42 | 82.96 | 84.67 | 84.42 | 77.40 | 79.96 | 82.94 |
Std | 7.02 | 6.14 | 6.04 | 7.30 | 5.85 | 6.27 | 7.37 | 6.24 | 6.23 | 8.98 | 7.35 | 6.75 |
Cases | Year | MAX (dB) | PP (dB) | (dB) | LEW * (m) | TES (s) |
---|---|---|---|---|---|---|
FYI | 2014 | 48.91 ± 1.26 | 1.47 ± 0.33 | 7.70 ± 1.26 | 0.09 ± 0.33 | −1.98 × 10 ± 3.39 × 10 |
FYI | 2015 | 49.50 ± 1.16 | 1.55 ± 0.28 | 7.76 ± 1.16 | 0.09 ± 0.22 | −1.92 × 10 ± 2.99 × 10 |
FYI | 2016 | 47.55 ± 1.33 | 1.27 ± 0.28 | 6.18 ± 1.33 | 0.09 ± 0.22 | −1.92 × 10 ± 2.44 × 10 |
FYI | 2017 | 48.91 ± 1.32 | 1.56 ± 0.51 | 7.43 ± 1.32 | 0.09 ± 0.33 | −1.93 × 10 ± 3.39 × 10 |
FYI | 2018 | 48.91 ± 1.18 | 1.35 ± 0.40 | 7.93 ± 1.18 | 0.09 ± 0.30 | −1.85 × 10 ± 3.27 × 10 |
MYI | 2014 | 47.00 ± 1.15 | 1.16 ± 0.21 | 5.01 ± 1.16 | 0.28 ± 0.18 | −1.77 × 10 ± 1.75 × 10 |
MYI | 2015 | 45.78 ± 1.24 | 1.12 ± 0.26 | 3.85 ± 1.24 | 0.33 ± 0.21 | −1.79 × 10 ± 1.57 × 10 |
MYI | 2016 | 46.00 ± 1.39 | 1.16 ± 0.30 | 4.31 ± 1.39 | 0.33 ± 0.23 | −1.84 × 10 ± 2.39 × 10 |
MYI | 2017 | 47.01 ± 1.49 | 1.16 ± 0.45 | 4.87 ± 1.49 | 0.28 ± 0.27 | −1.79 × 10 ± 2.22 × 10 |
MYI | 2018 | 47.01 ± 1.49 | 1.16 ± 0.48 | 5.20 ± 1.50 | 0.28 ± 0.27 | −1.81 × 10 ± 2.23 × 10 |
Year | MAX | PP | LEW | TES | |
---|---|---|---|---|---|
2014 | 0.510 | 0.493 | 0.508 | 0.610 | 0.452 |
2015 | 0.578 | 0.501 | 0.579 | 0.788 | 0.420 |
2016 | 0.346 | 0.294 | 0.344 | 0.595 | 0.192 |
2017 | 0.372 | 0.320 | 0.372 | 0.540 | 0.302 |
2018 | 0.444 | 0.250 | 0.456 | 0.479 | 0.172 |
Mean | 0.450 | 0.372 | 0.452 | 0.602 | 0.308 |
Std | 0.096 | 0.117 | 0.096 | 0.116 | 0.128 |
KNN | RF | Bayesian | Threshold * | |||||
---|---|---|---|---|---|---|---|---|
Year | FYI | MYI | FYI | MYI | FYI | MYI | FYI | MYI |
2014 | 93.38 | 26.76 | 93.91 | 24.95 | 95.61 | 17.47 | 60.31 | 37.59 |
2015 | 93.85 | 30.13 | 93.87 | 31.46 | 89.79 | 34.14 | 68.41 | 33.74 |
2016 | 91.40 | 36.90 | 91.17 | 33.73 | 91.49 | 24.10 | 68.67 | 41.74 |
2017 | 87.86 | 42.99 | 90.02 | 39.42 | 90.65 | 33.83 | 67.90 | 40.93 |
2018 | 89.84 | 38.08 | 90.27 | 36.64 | 92.81 | 25.62 | 56.56 | 50.70 |
Mean | 91.27 | 34.07 | 91.85 | 33.24 | 92.82 | 27.03 | 65.23 | 39.30 |
Std | 2.49 | 6.49 | 1.91 | 5.52 | 2.27 | 7.05 | 5.58 | 6.60 |
Shu (ORF) | Shen (RF) | Zygmuntowska (Bayesian) | Rinne (KNN) | |
---|---|---|---|---|
FYI | 92.7 ± 3.3 | 82.9 ± 9.48 | 73.6 ± 13.3 | 90.0 ± 10.9 |
MYI | 83.8 ± 3.59 | 72.5 ± 2.95 | 87.5 ± 1.5 | 64.4 ± 10.5 |
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Fredensborg Hansen, R.M.; Rinne, E.; Skourup, H. Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa. Remote Sens. 2021, 13, 3183. https://doi.org/10.3390/rs13163183
Fredensborg Hansen RM, Rinne E, Skourup H. Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa. Remote Sensing. 2021; 13(16):3183. https://doi.org/10.3390/rs13163183
Chicago/Turabian StyleFredensborg Hansen, Renée Mie, Eero Rinne, and Henriette Skourup. 2021. "Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa" Remote Sensing 13, no. 16: 3183. https://doi.org/10.3390/rs13163183