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24 pages, 22139 KiB  
Article
Improving the Estimation of Lake Ice Thickness with High-Resolution Radar Altimetry Data
by Anna Mangilli, Claude R. Duguay, Justin Murfitt, Thomas Moreau, Samira Amraoui, Jaya Sree Mugunthan, Pierre Thibaut and Craig Donlon
Remote Sens. 2024, 16(14), 2510; https://doi.org/10.3390/rs16142510 - 9 Jul 2024
Viewed by 929
Abstract
Lake ice thickness (LIT) is a sensitive indicator of climate change, identified as a thematic variable of Lakes as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Here, we present a novel and efficient analytically based retracking approach for [...] Read more.
Lake ice thickness (LIT) is a sensitive indicator of climate change, identified as a thematic variable of Lakes as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Here, we present a novel and efficient analytically based retracking approach for estimating LIT from high-resolution Ku-band (13.6 GHz) synthetic-aperture radar (SAR) altimetry data. The retracker method is based on the analytical modeling of the SAR radar echoes over ice-covered lakes that show a characteristic double-peak feature attributed to the reflection of the Ku-band radar waves at the snow–ice and ice–water interfaces. The method is applied to Sentinel-6 Unfocused SAR (UFSAR) and Fully Focused SAR (FFSAR) data, with their corresponding tailored waveform model, referred to as the SAR_LIT and FFSAR_LIT retracker, respectively. We found that LIT retrievals from Sentinel-6 high-resolution SAR data at different posting rates are fully consistent with the LIT estimations obtained from thermodynamic lake ice model simulations and from low-resolution mode (LRM) Sentinel-6 and Jason-3 data over two ice seasons during the tandem phase of the two satellites, demonstrating the continuity between LRM and SAR LIT retrievals. By comparing the Sentinel-6 SAR LIT estimates to optical/radar images, we found that the Sentinel-6 LIT measurements are fully consistent with the evolution of the lake surface conditions, accurately capturing the seasonal transitions of ice formation and melt. The uncertainty in the LIT estimates obtained with Sentinel-6 UFSAR data at 20 Hz is in the order of 5 cm, meeting the GCOS requirements for LIT measurements. This uncertainty is significantly smaller, by a factor of 2 to 3 times, than the uncertainty obtained with LRM data. The FFSAR processing at 140 Hz provides even better LIT estimates, with 20% smaller uncertainties. The LIT retracker analysis performed on data at the higher posting rate (140 Hz) shows increased performance in comparison to the 20 Hz data, especially during the melt transition period, due to the increased statistics. The LIT analysis has been performed over two representative lakes, Great Slave Lake and Baker Lake (Canada), demonstrating that the results are robust and hold for lake targets that differ in terms of size, bathymetry, snow/ice properties, and seasonal evolution of LIT. The SAR LIT retrackers presented are promising tools for monitoring the inter-annual variability and trends in LIT from current and future SAR altimetry missions. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere (Second Edition))
Show Figures

Figure 1

Figure 1
<p>Illustration of the evolution of the bimodal lake ice thickness signature in Sentinel-6 UFSAR radargrams (<b>left column</b>) and the normalized waveforms (<b>right column</b>) at 20 Hz resolution at Great Slave Lake in December 2021 (<b>top</b>), February 2021 (<b>second row</b>), end of April 2021 (<b>third row</b>), and May 2021 (<b>bottom</b>). The black line in the plots of the right column corresponds to the mean waveform in the selected region of the lake.</p>
Full article ">Figure 2
<p>Illustration of the evolution of the bimodal lake ice thickness signature in Sentinel-6 FFSAR radargrams (<b>left column</b>) and the normalized waveforms (<b>right column</b>) at 140 Hz posting rate at Great Slave Lake in December 2021 (<b>top</b>), February 2021 (<b>second row</b>), end of April 2021 (<b>third row</b>), and May 2021 (<b>bottom</b>). The black line in the plots of the right column corresponds to the mean waveform in the selected region of the lake.</p>
Full article ">Figure 3
<p>The target lakes of the LIT analysis, Great Slave Lake and Baker Lake, Canada, are shown on the map (<b>bottom</b>) and with the satellite ground tracks superimposed on the lakes (<b>upper left</b> and <b>right</b>, respectively).</p>
Full article ">Figure 4
<p>Examples of Sentinel-6 UFSAR waveform with, in blue, the <tt>SAR_LIT</tt> fit (<b>left column</b>) and LIT histograms, with the corresponding Gaussian fits (<b>right column</b>), in the RoI of Great Slave Lake at the end of December 2020 (<b>top row</b>), in February 2021 (<b>second row</b>), in April 2021 (<b>third row</b>), and mid-May 2021 (<b>bottom row</b>).</p>
Full article ">Figure 5
<p>Examples of Sentinel-6 FFSAR waveforms with, in blue, the <tt>FFSAR_LIT</tt> fit (<b>left column</b>) and LIT histograms, with the corresponding Gaussian fits (<b>right column</b>), in the RoI of Great Slave Lake at the end of December 2020 (<b>top row</b>), in February 2021 (<b>second row</b>), in April 2021 (<b>third row</b>), and mid-May 2021 (<b>bottom row</b>).</p>
Full article ">Figure 6
<p>Example of the spatial evolution of the LIT estimates at Great Slave Lake (<b>left column</b>) and Baker Lake (<b>right column</b>) in February 2021. The top row plots show the results for the Sentinel-6 UFSAR data at 20 Hz, while the bottom row plots for the Sentinel-6 FFSAR data at 140 Hz. The gray lines in the bottom panels of the figures show the evolution of the reduced <math display="inline"><semantics> <msup> <mi>χ</mi> <mn>2</mn> </msup> </semantics></math> goodness of fit metric.</p>
Full article ">Figure 7
<p>Comparison of the LIT estimates obtained with Sentinel-6 high-resolution SAR data for one ice season at Great Slave Lake (<b>left</b>) and Baker Lake (<b>right</b>). The curves refer to UFSAR at 20 Hz (red), UFSAR at 140 Hz (purple), and FFSAR at 140 Hz (cyan).</p>
Full article ">Figure 8
<p>Evolution of LIT estimates at Great Slave Lake obtained with Sentinel-6 UFSAR at 20 Hz data (red), Sentinel-6 LRM data (green) and Jason-3 data (blue) for the 2020–2021 and 2021–2022 ice seasons (upper panel). The shaded regions of the corresponding colors refer to the LIT error envelopes at 1<math display="inline"><semantics> <mi>σ</mi> </semantics></math> for each case. The orange shaded area shows the evolution of LIT obtained from CLIMo thermodynamic simulations with different on-ice snow scenarios (see text in <a href="#sec4dot3-remotesensing-16-02510" class="html-sec">Section 4.3</a> for details). The middle panel shows the evolution of the mean 2 m air temperature (black) with the minimum and maximum values (gray shading) extracted from ERA5 data. The bottom panel shows the evolution of the 1<math display="inline"><semantics> <mi>σ</mi> </semantics></math> LIT uncertainties for the three datasets.</p>
Full article ">Figure 9
<p>Evolution of the LIT estimates at Baker Lake obtained with different datasets (see the caption of <a href="#remotesensing-16-02510-f008" class="html-fig">Figure 8</a> for details).</p>
Full article ">Figure 10
<p>Sentinel–6 20 Hz UFSAR LIT estimates superimposed on radar/optical images taken on the same dates for Great Slave Lake (<b>left</b>) and Baker Lake (<b>right</b>) on the lake area shown in the red boxes in the top row panels.</p>
Full article ">
22 pages, 4890 KiB  
Article
A Dual-Threshold Algorithm for Ice-Covered Lake Water Level Retrieval Using Sentinel-3 SAR Altimetry Waveforms
by Fucai Tang, Peng Chen, Zhiyuan An, Mingzhu Xiong, Hao Chen and Liangcai Qiu
Sensors 2023, 23(24), 9724; https://doi.org/10.3390/s23249724 - 9 Dec 2023
Viewed by 1130
Abstract
Satellite altimetry has been proven to measure water levels in lakes and rivers effectively. The Sentinel-3A satellite is equipped with a dual-frequency synthetic aperture radar altimeter (SRAL), which allows for inland water levels to be measured with higher precision and improved spatial resolution. [...] Read more.
Satellite altimetry has been proven to measure water levels in lakes and rivers effectively. The Sentinel-3A satellite is equipped with a dual-frequency synthetic aperture radar altimeter (SRAL), which allows for inland water levels to be measured with higher precision and improved spatial resolution. However, in regions at middle and high latitudes, where many lakes are covered by ice during the winter, the non-uniformity of the altimeter footprint can substantially impact the accuracy of water level estimates, resulting in abnormal readings when applying standard SRAL synthetic aperture radar (SAR) waveform retracking algorithms (retrackers). In this study, a modified method is proposed to determine the current surface type of lakes, analyzing changes in backscattering coefficients and brightness temperature. This method aligns with ground station observations and ensures consistent surface type classification. Additionally, a dual-threshold algorithm that addresses the limitations of the original bimodal algorithm by identifying multiple peaks without needing elevation correction is introduced. This innovative approach significantly enhances the precision of equivalent water level measurements for ice-covered lakes. The study retrieves and compares the water level data of nine North American lakes covered by ice from 2016–2019 using the dual-threshold and the SAMOSA-3 algorithm with in situ data. For Lake Athabasca, Cedar Lake, Great Slave Lake, Lake Winnipeg, and Lake Erie, the root mean square error (RMSE) of SAMOSA-3 is 39.58 cm, 46.18 cm, 45.75 cm, 42.64 cm, and 6.89 cm, respectively. However, the dual-threshold algorithm achieves an RMSE of 6.75 cm, 9.47 cm, 5.90 cm, 7.67 cm, and 5.01 cm, respectively, representing a decrease of 75%, 79%, 87%, 82%, and 27%, respectively, compared to SAMOSA-3. The dual-threshold algorithm can accurately estimate water levels in ice-covered lakes during winter. It offers a promising prospect for achieving long-term, continuous, and high-precision water level measurements for middle- and high-latitude lakes. Full article
(This article belongs to the Section Radar Sensors)
Show Figures

Figure 1

Figure 1
<p>Geographical distribution of lakes and hydrological stations in the study area.</p>
Full article ">Figure 2
<p>Judgment of Lake Ice using Backscattering Coefficient and Brightness Temperature Detection.</p>
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<p>Setinel-3 SAR waveform data from different dates on Great Slave Lake. The fringe associated with the single backscattering of the radar echoes due to the open water is visible (<b>a</b>,<b>d</b>). The fringe associated with the double backscattering of the radar echoes due to the ice is visible (<b>b</b>,<b>c</b>).</p>
Full article ">Figure 4
<p>Several waveforms from Sentinel-3 SAR mode: (<b>a</b>) waveforms with peaks appearing too late, (<b>b</b>) waveforms with peaks appearing too early, (<b>c</b>) waveforms generated by open water, (<b>d</b>) double-peaked waveforms generated by lake ice, (<b>e</b>,<b>f</b>) multi-peaked waveforms caused by lake ice, (<b>g</b>) Typical bimodal waveform (To highlight the leading edge power variation, only bin values between 20 and 80 power are shown).</p>
Full article ">Figure 5
<p>The flowchart of the double threshold algorithm and the red part is the improvement compared to the original algorithm (The 3σ guideline refers to the elimination of roughness using three times the median error).</p>
Full article ">Figure 6
<p>Comparison of ground tracks and water levels across Great Slave Lake on 27 September 2017 and 13 March 2018. (<b>a</b>,<b>c</b>) Sentinel-3 ground track; (<b>b</b>,<b>d</b>) Water level comparison. The red triangle is the water level station, and the strip is the ground track.</p>
Full article ">Figure 7
<p>Comparison of the water level measured using the retracker in Great Slave Lake from 2016 to 2019 with the hydrological station and the correlation between the water level obtained using the retracker and the in situ gauge water level. (<b>a</b>) Comparison of estimated water levels for each retrackers, (<b>b</b>) OCOG, (<b>c</b>) ice sheet, (<b>d</b>) SAMOSA-3.</p>
Full article ">Figure 8
<p>Time series variation of backscatter coefficient and brightness temperature during 2016–2019. (<b>a</b>) Great Slave Lake, (<b>b</b>) Cedar Lake, (<b>c</b>) Lake Huron, (<b>d</b>) Lake Erie. The gray background shading represents the presence of lake ice.</p>
Full article ">Figure 9
<p>Consistency of backscattering coefficient and changes in brightness temperature and water level deviation in Great Slave Lake from 2016 to 2017. (<b>a</b>) Time series of brightness temperature and backscatter coefficient, The four line segments (I, II, III and IV) in the picture correspond to the four stages of icing. (<b>b</b>) water level estimated using the corresponding time retrospective performance analysis of the dual-threshold algorithm.</p>
Full article ">Figure 10
<p>Comparing the water level obtained using the dual-threshold algorithm and the measured water level obtained using the SAMOSA-3 retracker. (<b>a</b>) Great Slave Lak, (<b>b</b>) Cedar Lake, (<b>c</b>) Lake Erie, (<b>d</b>) Lake Huron.</p>
Full article ">Figure A1
<p>Comparison of water level time series obtained using dual-threshold algorithm and SAMOSA-3 retracker with in situ water level measurements.</p>
Full article ">
21 pages, 4444 KiB  
Article
Signal Processing and Waveform Re-Tracking for SAR Altimeters on High Mobility Platforms with Vertical Movement and Antenna Mis-Pointing
by Qiankai Wang, Wen Jing, Xiang Liu, Bo Huang and Ge Jiang
Sensors 2023, 23(22), 9266; https://doi.org/10.3390/s23229266 - 18 Nov 2023
Viewed by 1191
Abstract
Synthetic aperture radar (SAR) altimeters can achieve higher spatial resolution and signal-to-noise ratio (SNR) than conventional altimeters by Doppler beam sharpening or focused SAR imaging methods. To improve the estimation accuracy of waveform re-tracking, several average echo power models for SAR altimetry have [...] Read more.
Synthetic aperture radar (SAR) altimeters can achieve higher spatial resolution and signal-to-noise ratio (SNR) than conventional altimeters by Doppler beam sharpening or focused SAR imaging methods. To improve the estimation accuracy of waveform re-tracking, several average echo power models for SAR altimetry have been proposed in previous works. However, these models were mainly proposed for satellite altimeters and are not applicable to high-mobility platforms such as aircraft, unmanned aerial vehicles (UAVs), and missiles, which may have a large antenna mis-pointing angle and significant vertical movement. In this paper, we propose a novel semi-analytical waveform model and signal processing method for SAR altimeters with vertical movement and large antenna mis-pointing angles. A new semi-analytical expression that can be numerically computed for the flat pulse response (FSIR) is proposed. The 2D delay–Doppler map is then obtained by numerical computation of the convolution between the proposed analytical function, the probability density function, and the time/frequency point target response of the radar. A novel delay compensation method based on sinc interpolation for SAR altimeters with vertical movement is proposed to obtain the multilook echo, which can optimally handle non-integer delays and maintain signal frequency characteristics. In addition, a height estimation method based on least squares (LS) estimation is proposed. The LS estimator does not have an analytical solution, and requires iterative solving through gradient descent. We evaluate the performance of the proposed estimation strategy using simulated data for typical airborne scenarios. When the mis-pointing angles are within 10 degrees, the normalized quadratic error (NQE) of the proposed model is less than 10−10 and the RMSE of τ obtained by the re-tracking method fitted by the proposed model is less than 0.2 m, which indicates the high applicability of the model and accuracy of the re-tracking method. Full article
(This article belongs to the Section Remote Sensors)
Show Figures

Figure 1

Figure 1
<p>Delay–Doppler mapping. Each delay–Doppler bin is associated with two delay–Doppler cells on the surface.</p>
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<p>Circles of propagation and Doppler beams in SAR altimetry.</p>
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<p>Effect of the flight path angle on the DDM: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <msup> <mn>0</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <msup> <mn>6</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <msup> <mn>12</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <msup> <mn>18</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math> (Pu = 1, <math display="inline"><semantics> <mi>τ</mi> </semantics></math> = 30 gates, SWH = 2 m, and <math display="inline"><semantics> <mrow> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <msup> <mn>0</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math>).</p>
Full article ">Figure 4
<p>Effect of flight path angle on (<b>a</b>) the multilook echoes and (<b>b</b>) the normalized multilook echoes (Pu = 1, <math display="inline"><semantics> <mi>τ</mi> </semantics></math> = 30 gates, SWH = 2 m, and <math display="inline"><semantics> <mrow> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <msup> <mn>0</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math>).</p>
Full article ">Figure 5
<p>Antenna gain with different mis-pointing angles: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>ξ</mi> <mo>=</mo> <msup> <mn>0</mn> <mi mathvariant="normal">o</mi> </msup> <mo>,</mo> <mover accent="true"> <mi>ϕ</mi> <mo stretchy="false">^</mo> </mover> <mo>=</mo> <msup> <mn>0</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>ξ</mi> <mo>=</mo> <msup> <mn>0</mn> <mi mathvariant="normal">o</mi> </msup> <mo>,</mo> <mover accent="true"> <mi>ϕ</mi> <mo stretchy="false">^</mo> </mover> <mo>=</mo> <msup> <mn>0</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>ξ</mi> <mo>=</mo> <msup> <mn>20</mn> <mi mathvariant="normal">o</mi> </msup> <mo>,</mo> <mover accent="true"> <mi>ϕ</mi> <mo stretchy="false">^</mo> </mover> <mo>=</mo> <msup> <mn>0</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>ξ</mi> <mo>=</mo> <msup> <mn>20</mn> <mi mathvariant="normal">o</mi> </msup> <mo>,</mo> <mover accent="true"> <mi>ϕ</mi> <mo stretchy="false">^</mo> </mover> <mo>=</mo> <msup> <mn>90</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math>.</p>
Full article ">Figure 6
<p>Effect of <math display="inline"><semantics> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>l</mi> </mrow> </msub> </semantics></math> on (<b>a</b>) the multilook echoes and (<b>b</b>) the normalized multilook echoes (Pu = 1, <math display="inline"><semantics> <mi>τ</mi> </semantics></math> = 30 gates, SWH = 2 m, and <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <msup> <mn>0</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math>).</p>
Full article ">Figure 7
<p>Effect of <math display="inline"><semantics> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> </semantics></math> on (<b>a</b>) the multilook echoes and (<b>b</b>) the normalized multilook echoes (Pu = 1, <math display="inline"><semantics> <mi>τ</mi> </semantics></math> = 30 gates, SWH = 2 m, and <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <msup> <mn>0</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math>).</p>
Full article ">Figure 8
<p>Flowchart of re-tracking algorithm implementation step.</p>
Full article ">Figure 9
<p>Overallerror versus <span class="html-italic">m</span> for different <math display="inline"><semantics> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> </semantics></math>, showing the global NQE (continuous line) and NQE of echo maximum (crossed line) for <math display="inline"><semantics> <mrow> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <msup> <mn>6</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math> (in red), <math display="inline"><semantics> <mrow> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <msup> <mn>12</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math> (in green), and <math display="inline"><semantics> <mrow> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <msup> <mn>18</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math> (in blue).</p>
Full article ">Figure 10
<p>Overall error versus <math display="inline"><semantics> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>l</mi> </mrow> </msub> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> (Pu = 1, <math display="inline"><semantics> <mi>τ</mi> </semantics></math> = 30 gates, SWH = 2 m, and <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <msup> <mn>0</mn> <mi mathvariant="normal">o</mi> </msup> </mrow> </semantics></math>).</p>
Full article ">Figure 11
<p>Comparison of multilooked power waveforms in typical airborne scenarios: (<b>a</b>) <math display="inline"><semantics> <mrow> <mfenced separators="" open="(" close=")"> <mrow> <mi>μ</mi> <mo>,</mo> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>l</mi> </mrow> </msub> </mrow> </mfenced> <mo>=</mo> <mfenced separators="" open="(" close=")"> <mrow> <mn>0</mn> <mo>,</mo> <mn>0</mn> <mo>,</mo> <mn>0</mn> </mrow> </mfenced> <mo form="prefix">deg</mo> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <mfenced separators="" open="(" close=")"> <mrow> <mi>μ</mi> <mo>,</mo> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>l</mi> </mrow> </msub> </mrow> </mfenced> <mo>=</mo> <mfenced separators="" open="(" close=")"> <mrow> <mn>18</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>18</mn> </mrow> </mfenced> <mo form="prefix">deg</mo> </mrow> </semantics></math>.</p>
Full article ">Figure 12
<p>Comparison of multilooked power waveforms in typical airborne scenarios: (<b>a</b>) <math display="inline"><semantics> <mrow> <mfenced separators="" open="(" close=")"> <mrow> <mi>μ</mi> <mo>,</mo> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>l</mi> </mrow> </msub> </mrow> </mfenced> <mo>=</mo> <mfenced separators="" open="(" close=")"> <mrow> <mn>10</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>10</mn> </mrow> </mfenced> <mo form="prefix">deg</mo> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <mfenced separators="" open="(" close=")"> <mrow> <mi>μ</mi> <mo>,</mo> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>l</mi> </mrow> </msub> </mrow> </mfenced> <mo>=</mo> <mfenced separators="" open="(" close=")"> <mrow> <mn>0</mn> <mo>,</mo> <mn>10</mn> <mo>,</mo> <mn>0</mn> </mrow> </mfenced> <mo form="prefix">deg</mo> </mrow> </semantics></math>.</p>
Full article ">Figure 13
<p>RMSE of (<b>a</b>) <math display="inline"><semantics> <mi>τ</mi> </semantics></math> and (<b>b</b>) SWH versus SWH in the absence of mis-pointing for the G-PRA and PRA algorithms (Pu = 1, <math display="inline"><semantics> <mi>τ</mi> </semantics></math> = 30 gates, <math display="inline"><semantics> <mrow> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <msup> <mn>0</mn> <mn>0</mn> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <msup> <mn>0</mn> <mn>0</mn> </msup> </mrow> </semantics></math>).</p>
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<p>RMSE of (<b>a</b>) <math display="inline"><semantics> <mi>τ</mi> </semantics></math> and (<b>b</b>) SWH versus <math display="inline"><semantics> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>l</mi> </mrow> </msub> </semantics></math> for the G-PRA and PRA algorithms (Pu = 1, <math display="inline"><semantics> <mi>τ</mi> </semantics></math> = 30 gates, SWH = 2 m, <math display="inline"><semantics> <mrow> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <msup> <mn>0</mn> <mn>0</mn> </msup> </mrow> </semantics></math>).</p>
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<p>RMSE of (<b>a</b>) <math display="inline"><semantics> <mi>τ</mi> </semantics></math> and (<b>b</b>) SWH versus <math display="inline"><semantics> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>c</mi> </mrow> </msub> </semantics></math> for G-PRA and PRA algorithms (Pu = 1, <math display="inline"><semantics> <mi>τ</mi> </semantics></math> = 30 gates, SWH = 2 m, <math display="inline"><semantics> <mrow> <msub> <mi>ψ</mi> <mrow> <mi>a</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <msup> <mn>0</mn> <mn>0</mn> </msup> </mrow> </semantics></math>).</p>
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22 pages, 12061 KiB  
Article
Coastal Waveform Retracking for Synthetic Aperture Altimeters Using a Multiple Optimization Parabolic Cylinder Algorithm
by Jincheng Zheng, Xi-Yu Xu, Ying Xu and Chang Guo
Remote Sens. 2023, 15(19), 4665; https://doi.org/10.3390/rs15194665 - 23 Sep 2023
Cited by 1 | Viewed by 1303
Abstract
The importance of monitoring sea level in coastal zones becomes more and more obvious in the era of global climate change, because, in coastal zones, although satellite altimetry is an ideal tool in measuring sea level over open ocean, but its accuracy often [...] Read more.
The importance of monitoring sea level in coastal zones becomes more and more obvious in the era of global climate change, because, in coastal zones, although satellite altimetry is an ideal tool in measuring sea level over open ocean, but its accuracy often decreases significantly at coast due to land contamination. Although the accuracy of waveform processing algorithms for synthetic aperture altimeters has been improved in the last decade, the computational speed is still not fast enough to meet the requirements of real-time processing, and the accuracy cannot meet the needs of nearshore areas within 1 km from the coast. To improve the efficiency and accuracy in the coastal zone, this study proposed an innovative waveform retracking scheme for the coastal zone based on a multiple optimization parabolic cylinder algorithm (MOPCA) integrated with machine learning algorithms such as recurrent neural network and Bayesian estimation. The algorithm was validated using 153-pass repeat cycle data from Sentinel-6 over Qianliyan Island and Hong Kong–Wanshan Archipelago. The computational speed of the proposed algorithm was four to five times faster than the current operational synthetic aperture radar (SAR) retracking algorithm, and its accuracy within 0–20 km from the island was comparable to the most popular SAMOSA+ algorithm, better than the official data product provided by Sentinel-6. Especially, the proposed algorithm demonstrates remarkable stability in the sense of proceeding speed. It maintains consistent performance, even when dealing with intricate wave patterns within a proximity of 1 km from the coast. The results showed that the proposed scheme greatly improved the quality of coastal altimetry waveform retracking. Full article
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<p>Pulse signal transmission methods for the limited pulse radar altimeter (LRM) and synthetic aperture radar altimeter (SAR).</p>
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<p>Two-dimensional cue diagram of echo waveform 12 km offshore of Sentinel-6: (<b>a</b>) SAR and (<b>b</b>) LRM.</p>
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<p>Three-dimensional schematic diagram of the echo waveform 0~12 km offshore of Sentinel-6: (<b>a</b>) SAR and (<b>b</b>) LRM.</p>
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<p>Schematic diagram of pulse-limited radar altimeter and synthetic aperture radar altimeter footprints.</p>
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<p>Parabolic cylinder model and derivatives (normalized).</p>
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<p><math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">D</mi> </mrow> <mrow> <mi mathvariant="normal">a</mi> </mrow> </msub> <mo>(</mo> <mi mathvariant="normal">z</mi> <mo>)</mo> </mrow> </semantics></math> lookup table visualization graph with a resolution of <math display="inline"><semantics> <mrow> <mn>1</mn> <mo> </mo> <mo>×</mo> <mo> </mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math> power. The blue curve is the value of a = 0.5, and the red curve is the value of a = −0.5.</p>
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<p>Effect diagram of SAR echo wave shape simulated by parabolic cylinder (the blue line represents the original SAR waveform data, and the red line depicts the results after retracking fitting using the parabolic cylinder model).</p>
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<p>The impact of SWH and off-nadir angle on the shape of the backscattering waveform: (<b>a</b>) shows the effect of SWH on the backscattering waveform and (<b>b</b>) shows the effect of off-nadir angle on the backscattering waveform.</p>
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<p>Results of grouped tests (groups one to five, each had two thousand separate test waveforms): (<b>a</b>) time required by the two parabolic cylinder algorithms for every 2000 waveforms and (<b>b</b>) proportional time required by each algorithm for processing 2000 waveforms out of the total.</p>
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<p>Flowchart of the nearshore parabolic cylinder algorithm.</p>
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<p>A schematic diagram of a simple RNN algorithm.</p>
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<p>Figure depicting RNN classification algorithms: (<b>a</b>) conceptual diagram of the n-to-n RNN model structure and (<b>b</b>) schematic diagram of an RNN algorithm designed for echo classification.</p>
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<p>Schematic diagram of RNN training results: (<b>a</b>) graph showing the change in loss function with iteration and (<b>b</b>) graph showing the change in accuracy of the training and test sets with iteration.</p>
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<p>The fitting results of waveform data: (<b>a</b>) shows the fitting using single retracking method for unpolluted echo waveform and (<b>b</b>) shows the fitting using two-step retracking method for severely polluted cone-shaped waveform.</p>
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<p>Schematic diagram of Sentinel-6 153 pass crossing the HK–Wanshan Archipelago (area (<b>A</b>)) and Qianliyan Island (area (<b>B</b>)).</p>
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<p>Schematic diagrams of sample echoes in the study area: (<b>a</b>) waveform schematic of 153 pass crossing the HK–Wanshan Archipelago and (<b>b</b>) waveform schematic of 153 pass crossing Qianliyan Island.</p>
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<p>A processing time graph depicting the variation in six different algorithms with respect to the offshore distance.</p>
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<p>A comparison chart of echo processing times for six algorithms with an interval of five kilometers.</p>
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<p>Time series (days) of the difference between various algorithms and the results from the nearest tidal gauge station are shown for distances from 10–20 km (<b>top</b>), 5–10 km (<b>middle</b>), and 0–5 km (<b>bottom</b>) to the coast.</p>
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<p>Time series (days) of the difference between various algorithms and the results from the nearest tidal gauge station are shown for distances from 10–20 km (<b>top</b>), 5–10 km (<b>middle</b>), and 0–5 km (<b>bottom</b>) to the coast.</p>
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<p>Plots illustrating the average correlation and RMSE time series between the different algorithms and the tidal gauge station sea level at various distances, as well as the proportion of correlation and RMSE within each interval. (<b>a</b>) Average correlation. (<b>b</b>) Average RMSE. (<b>c</b>) Proportion of correlation distribution within each interval. (<b>d</b>) Proportion of RMSE distribution within each interval.</p>
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16 pages, 1180 KiB  
Article
Coastal Assessment of Sentinel-6 Altimetry Data during the Tandem Phase with Jason-3
by Marcello Passaro, Florian Schlembach, Julius Oelsmann, Denise Dettmering and Florian Seitz
Remote Sens. 2023, 15(17), 4161; https://doi.org/10.3390/rs15174161 - 24 Aug 2023
Cited by 2 | Viewed by 1547
Abstract
This study presents a comparative analysis of the coastal performances of Sentinel-6 and Jason-3 altimeters during their tandem phase, considering their different processing modes. We examine the measurements available in the standard geophysical data records (GDR) and also perform dedicated reprocessing using coastal [...] Read more.
This study presents a comparative analysis of the coastal performances of Sentinel-6 and Jason-3 altimeters during their tandem phase, considering their different processing modes. We examine the measurements available in the standard geophysical data records (GDR) and also perform dedicated reprocessing using coastal retracking algorithms applied to the original waveforms. The performances are evaluated, taking into account the quality of retrievals (outlier analysis), their precision (along-track noise analysis), potential systematic biases, and accuracy (comparison against tide gauges). The official SAR altimetry product of Sentinel-6 demonstrates improved coastal monitoring capabilities compared to Jason-3, except for the remaining issues related to significant wave height, which have already been identified. These findings highlight the significance of dedicated coastal retracking algorithms for enhancing the capabilities of both traditional, pulse-limited altimeters and more recent developments utilizing SAR altimetry. Full article
(This article belongs to the Special Issue Validation and Evaluation of Global Ocean Satellite Products)
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<p>Comparison of outlier types in the SLA datasets as a function of the distance to the coast.</p>
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<p>Comparison of outlier types in the SWH datasets as a function of the distance to the coast.</p>
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<p>Noise level of SWH from the official S6 products and probability density function (PDF) of the records as a function of wave height for HR (<b>a</b>) and LR (<b>b</b>).</p>
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<p>SLA noise level of the individual retrackers as a function of SWH for open ocean (<b>a</b>) and coastal (<b>b</b>) data with the sea state noted at the bottom.</p>
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<p>SLA median noise for data grouped by distance to coast. The different panels refer to all sea state conditions (<b>a</b>), low (<b>b</b>), average (<b>c</b>), and high sea state (<b>d</b>). No additional SWH thresholds have been applied.</p>
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<p>SWH noise level of the individual retrackers as a function of SWH for open ocean (<b>a</b>) and coastal (<b>b</b>) data, with the sea state noted at the bottom.</p>
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<p>SWH median noise for data grouped by distance to coast. The different panels refer to all sea state conditions (<b>a</b>), low (<b>b</b>), average (<b>c</b>), and high sea state (<b>d</b>). No additional SWH thresholds have been applied.</p>
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<p>Mean coastal biases relative to Jason-3 LR for (<b>a</b>) SLA (including SSB) and (<b>b</b>) SWH.</p>
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<p>Coastal dependency of SWH biases for S6 with respect to J3-MLE LR. Top row shows the results for the PDAP products (LR left, HR right); the lower plots are based on two retrackers (ALES LR left, CORAL HR right). Black lines indicate median biases over all cycles for all sea states; orange lines for low sea states only (1–2 m SWH); and gray lines indicate the cycle spread for all sea states.</p>
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<p>Temporal evolution of SLA bias in coastal zones (10–20 km) for all sea states; top: S6 PDAP products with respect to J3-MLE LR; bottom: relative differences between both S6 PDAP products (LR–HR).</p>
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<p>Temporal evolution of SWH bias in coastal zones (10–20 km) for all sea states; top: S6 PDAP products with respect to J3-MLE LR; bottom: relative differences between both S6 PDAP products (LR–HR).</p>
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<p>(<b>a</b>) Mean correlations (with 90% confidence intervals) per dataset and distance to coast; (<b>b</b>,<b>c</b>) show number of tide gauges and total number of available tracks for which correlations are computed. These numbers are also an indication of how many data are available in the different datasets; (<b>d</b>,<b>e</b>) show the best correlation per tide gauge for different distances to the coast and for the EUM PDAP HR dataset. One station located west of Hawaii is not shown in (<b>d</b>,<b>e</b>).</p>
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23 pages, 13085 KiB  
Article
A New Method to Combine Coastal Sea Surface Height Estimates from Multiple Retrackers by Using the Dijkstra Algorithm
by Fukai Peng, Xiaoli Deng, Maofei Jiang, Salvatore Dinardo and Yunzhong Shen
Remote Sens. 2023, 15(9), 2329; https://doi.org/10.3390/rs15092329 - 28 Apr 2023
Cited by 4 | Viewed by 1749
Abstract
To increase data availability and accuracy in the coastal zone, especially in the last 5 km to the coast, we present a SCMR (Seamless Combination of Multiple Retrackers) processing strategy to combine sea surface height (SSH) estimates from waveform retrackers of SGDR MLE4, [...] Read more.
To increase data availability and accuracy in the coastal zone, especially in the last 5 km to the coast, we present a SCMR (Seamless Combination of Multiple Retrackers) processing strategy to combine sea surface height (SSH) estimates from waveform retrackers of SGDR MLE4, ALES, WLS3 and MB4 for Jason-3 and Saral missions, and of SAMOSA and SAMOSA+ for Sentinel-3A mission in the Australian coastal zone. The SCMR does not require the waveform classification result. It includes two steps: (1) estimating and removing the SSH bias due mainly to the significant wave height (SWH) difference-dependent height differences, and (2) determining the optimal along-track SSH profile by using the Dijkstra algorithm. In the study region, the results show that the SCMR increases the data availability by up to 15% in the last 5 km to the coast and reduces the noise level by 28–34% at the spatial scales < 2.5 km. The validation results against tide gauges show that SCMR-derived SSH estimates achieve a better accuracy than that from any single retracker, with the improvement percentage of 6.26% and 4.94% over 0–10 km and 20–100 km distance bands, respectively. Full article
(This article belongs to the Special Issue Advances in Satellite Altimetry)
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Graphical abstract

Graphical abstract
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<p>Altimeter ground tracks and tide gauges in the coastal oceans of Australia (<b>a</b>). The solid green circle represents the location of tide gauges. The ground tracks nearby the corresponding tide gauges are highlighted in blue, black and red for Jason-3, Saral and Sentinel-3A missions, respectively. The subplots from (<b>b</b>–<b>m</b>) show the zoom out of track segments near the corresponding tide gauges used for the validation in <a href="#sec4dot3-remotesensing-15-02329" class="html-sec">Section 4.3</a>.</p>
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<p>Flow diagram of the SCMR processing strategy for (<b>a</b>) Jason-3 and Saral missions; (<b>b</b>) Sentinel-3A mission, respectively. SSH, MSS, respectively stand for the sea surface height, mean sea surface. SGDR MLE4, ALES, WLS3, MB4, SAMOSA and SAMOSA+ are the retrackers used in this study.</p>
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<p>The SLA variance and data availability as a function of distance to the coast for different combinations of wet tropospheric and geocentric ocean tide corrections provided by the official products. The subplots from (<b>a</b>–<b>c</b>) are the results of SLA variance for Jason-3, Saral and Sentinel-3A, respectively, while the subplots from (<b>d</b>–<b>f</b>) are the corresponding results of data availability.</p>
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<p>The SLA variance as a function of distance to the coast for CLS15 MSS (blue), DTU21 MSS (red) and along-track MSS (green), respectively. The subplots from (<b>a</b>–<b>c</b>) show the results for Jason-3, Saral and Sentinel-3A, respectively.</p>
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<p>Schematic illustration of finding the shortest path (in blue) between the start node and the end node by using the Dijkstra algorithm.</p>
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<p>An example of SCMR using the Dijkstra algorithm to retrieve the most appropriate SSH profile along track 64 of Jason-3 (<b>a</b>) and track 705 of Sentinel-3A (<b>b</b>) in the Australian coastal zone. The along-track SSH profiles from single retrackers and the SCMR processing strategy are shown as a function of offshore distance. The location of ground tracks 64 and 705 are shown in <a href="#remotesensing-15-02329-f001" class="html-fig">Figure 1</a>.</p>
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<p>Data availability of 20 Hz SLA estimates for (<b>a</b>) Jason-3, (<b>b</b>) Saral and (<b>c</b>) Sentinel-3A missions over coastal oceans of Australia. The results from the SCMR are highlighted in red and compared with those from individual retrackers (i.e., SGDR MLE4, MB4, ALES, WLS3, SAMOSA and SAMOSA+).</p>
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<p>Precision of 20 Hz SLA estimates for all altimetry missions over coastal oceans of Australia. The subplots from (<b>a</b>) to (<b>c</b>) show the standard deviation of 20 Hz SLA estimates within 1 s for Jason-3, Saral and Sentinel-3A within 0–20 km distance band, respectively. The subplots from (<b>d</b>) to (<b>f</b>) show the same results but for the 20–100 km distance band.</p>
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<p>The spectrum of 20 Hz SLA estimates beyond 5 km off the coast from (<b>a</b>) Jason-3, (<b>b</b>) Saral and (<b>c</b>) Sentinel-3A missions. The unit of cpkm is the abbreviation of cycle per kilometer and the wavenumber is the inverse of wavelength.</p>
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<p>Validation of Jason-3 20-Hz SLA estimates from different retrackers against tide gauge measurements as a function of distance to the coast. The subplots from (<b>a</b>,<b>b</b>) show the percentage of available cycles, while the subplots from (<b>c</b>,<b>d</b>) show the along-track RMSE of differences between SLA time series from altimeters and tide gauges. The black arrow describes the moving direction of the satellite. The name of the tide gauge station and ground track number are also shown in the graph. The location of the tracks is shown in <a href="#remotesensing-15-02329-f001" class="html-fig">Figure 1</a>.</p>
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<p>Same as <a href="#remotesensing-15-02329-f010" class="html-fig">Figure 10</a> but for the Saral mission.</p>
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<p>Same as <a href="#remotesensing-15-02329-f010" class="html-fig">Figure 10</a> but for the Sentinel-3A mission.</p>
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<p>The mean value of improvement percentages (%) in terms of the along-track RMSE by comparing SCMR with MLE4, WLS3 and MB4 for Jason-3 mission. The subplots from (<b>a</b>–<b>d</b>) show the results within 10 km to the coast, while the subplots from (<b>e</b>–<b>h</b>) present the results beyond 20 km off the coast. The black solid circle indicates the improvement percentage is negative, while the white solid circle represents the improvement percentage is higher than 10%.</p>
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<p>Same as <a href="#remotesensing-15-02329-f013" class="html-fig">Figure 13</a> but for Saral mission.</p>
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<p>Same as <a href="#remotesensing-15-02329-f013" class="html-fig">Figure 13</a> but for Sentinel-3A mission. The gray solid circle indicates that there are no data available.</p>
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27 pages, 8550 KiB  
Article
Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data
by Jianbo Wang, Jinyang Wang, Shunde Chen, Jianbo Luo, Mingzhi Sun, Jialong Sun, Jiajia Yuan and Jinyun Guo
Remote Sens. 2023, 15(7), 1746; https://doi.org/10.3390/rs15071746 - 24 Mar 2023
Cited by 2 | Viewed by 1665
Abstract
Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 [...] Read more.
Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 109 m3 from July 2002 to December 2022, with a growth rate of 5.3766 × 108 m3/a. The growth rate from January 2005 to January 2015 is 4.4850 × 108 m3/a, and from January 2015 to December 2022, the growth rate is 8.9206 × 108 m3/a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years. Full article
(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods II)
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Graphical abstract

Graphical abstract
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<p>An overview map of the research region (the image shows a schematic depiction of the satellite routes of Sentinel-3A/3B, Saral, Jason-1/2, and Envisat RA-2 during the exact repeat mission periods).</p>
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<p>Data processing of LQ water storage variation.</p>
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<p>A comparison of the reprocessed DTC and products values. (<bold>a</bold>–<bold>f</bold>) The Envisat RA-2, Jason-1, Jason-2, SARAL, Sentinel-3A SRAL, and Sentinel-3B SRAL satellites, respectively, until September 2022. Red represents the reprocessed data, and blue represents the values of the products.</p>
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<p>The comparison of the reprocessed WTC and products. (<bold>a</bold>–<bold>f</bold>) The Envisat RA-2, Jason-1, Jason-2, SARAL, Sentinel-3A SRAL, and Sentinel-3B SRAL satellites, respectively, until September 2022. Red represents the reprocessed data, and blue represents the product values.</p>
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<p>Example of outlier detection in the case of SARAL observations over LQ.</p>
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<p>(<bold>a</bold>) The averaged Lake water level per cycle measured by satellites; (<bold>b</bold>–<bold>g</bold>) histogram distribution of standard deviation of lake water level for the SARAL, Envisat RA-2, Jason-1, Jason-2, Sentinel 3A SRAL, and Sentinel 3B SRAL satellites, respectively.</p>
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<p>(<bold>a</bold>) Lake height difference model constructed using data from ERM missions; (<bold>b</bold>) lake height difference in Pass No. 479 area.</p>
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<p>Accuracy verification of SARAL. (<bold>a</bold>) Trajectory selection; (<bold>b</bold>) comparison of height difference between SARAL and Envisat RA2.</p>
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<p>Image after unsupervised classification.</p>
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<p>Comparison between the results calculated in this paper and the data from hydrological stations. Under station (<bold>a</bold>) is all the data from 2002 to 2023; (<bold>b</bold>) depicts the part in the box in (<bold>a</bold>).</p>
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<p>Comparison between the reduction results of this paper for each satellite in the middle position and the hydrological gauge data from the Xiashe station.</p>
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<p>Comparison of the final results of this paper with the hydrological gauge data from the Xiashe station and DAHITI.</p>
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<p>Comparison of residual errors of the results of this paper with the observations at the lower stations and the results of ice thickness products.</p>
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<p>Comparison between the LQ area extracted from the Landsat image and that of the model data.</p>
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<p>Comparison between Landsat extracted area and fitted area using water level–area formula.</p>
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<p>Time series of water storage growth in LQ.</p>
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<p>Precipitation on LQ from 2002 to 2019. (<bold>a</bold>) the variation of monthly cumulative precipitation. (<bold>b</bold>) the variation of annual average precipitation.</p>
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22 pages, 22379 KiB  
Article
A Partial Reconstruction Method for SAR Altimeter Coastal Waveforms Based on Adaptive Threshold Judgment
by Xiaonan Liu, Weiya Kong, Hanwei Sun and Yaobing Lu
Remote Sens. 2023, 15(6), 1717; https://doi.org/10.3390/rs15061717 - 22 Mar 2023
Viewed by 1532
Abstract
Due to land contamination and human activities, the sea surface height (SSH) data retrieved from altimeter coastal waveforms have poor precision and cannot provide effective information for various tasks. The along-track high-resolution characteristic of the new synthetic aperture radar (SAR) altimeter makes the [...] Read more.
Due to land contamination and human activities, the sea surface height (SSH) data retrieved from altimeter coastal waveforms have poor precision and cannot provide effective information for various tasks. The along-track high-resolution characteristic of the new synthetic aperture radar (SAR) altimeter makes the retracking methods of traditional coastal waveforms difficult to apply. This study proposes a partial reconstruction method for SAR altimeter coastal waveforms. By making adaptive threshold judgments of model matching errors and repairing the contaminated waveforms based on the nearest linear prediction, the success rate of retracking and retrieval precision of SSH are significantly improved. The data from the coastal experimental areas of the Sentinel-3B satellite altimeter are processed. The results indicate that the mean proportion of waveform quality improvement brought by partial reconstruction is 80.30%, the mean retracking success rate of reconstructed waveforms is 85.60%, and the mean increasing percentage is 30.98%. The noise levels of SSH data retrieved by different methods are calculated to evaluate the processing precision. It is shown that the 20 Hz SSH precisions of the original and reconstructed coastal waveforms are 12.75 cm and 6.32 cm, respectively, and the corresponding 1 Hz SSH precisions are 2.85 cm and 1.41 cm, respectively. The results validate that the proposed partial reconstruction method has improved the SSH precision by a factor of two, and the comparison results with mean sea surface (MSS) model data further verify this conclusion. Full article
(This article belongs to the Special Issue Radar Signal Processing and Imaging for Ocean Remote Sensing)
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Figure 1

Figure 1
<p>(<b>a</b>) The locations of the two experimental areas used for coastal waveform processing and the corresponding subsatellite tracks. (<b>b</b>) The offshore distances of the subsatellite points in the two experimental areas. (<b>c</b>,<b>d</b>) The coastal details of experimental areas 1 and 2.</p>
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<p>Normalized coastal waveforms of experimental area 1 in (<b>a</b>) June and (<b>b</b>) September. Normalized coastal waveforms of experimental area 2 in (<b>c</b>) May and (<b>d</b>) September.</p>
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<p>Some typical coastal waveforms in the experimental area, with (<b>a</b>) single-peak (<b>b</b>) double-peak (<b>c</b>) multi-peak (<b>d</b>) slight contamination and (<b>e</b>,<b>f</b>) heavy contamination.</p>
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<p>(<b>a</b>) The epoch sequences obtained by processing the coastal waveforms of experiment area 1 in April after steps 3 (blue curve), 4 (red curve), and 5 (green curve). (<b>b</b>) The coastal waveforms before and after step 5 (dark and light blue curves), and the corresponding fitting models (red and green curves).</p>
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<p>(<b>a</b>) The matching error matrix corresponding to the coastal waveforms of experimental area 1 in March. (<b>b</b>) The median errors at all gates in different months for experimental area 1. (<b>c</b>,<b>d</b>) Two typical error histograms fit by the Rayleigh distribution. (<b>e</b>,<b>f</b>) Two typical error histograms fit by exponential distribution.</p>
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<p>(<b>a</b>) The matching error matrix corresponding to the coastal waveforms of experimental area 1 in March. (<b>b</b>) The median errors at all gates in different months for experimental area 1. (<b>c</b>,<b>d</b>) Two typical error histograms fit by the Rayleigh distribution. (<b>e</b>,<b>f</b>) Two typical error histograms fit by exponential distribution.</p>
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<p>Adaptive thresholds of experimental area 1 for different months and different gates.</p>
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<p>(<b>a</b>) The original contaminated coastal waveform (blue curve) and the corresponding reconstructive result (red dotted line). (<b>b</b>) The reference values (blue dots) used for linear fitting (red line) and the corresponding reconstructive value (green dot).</p>
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<p>The reconstruction results corresponding to the waveforms in <a href="#remotesensing-15-01717-f002" class="html-fig">Figure 2</a>b (<b>a</b>) and <a href="#remotesensing-15-01717-f002" class="html-fig">Figure 2</a>d (<b>b</b>).</p>
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<p>Processing flow of the SAR altimeter coastal waveforms.</p>
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<p>Two typical results of the NPPR algorithm in sub-waveform extraction (<b>a</b>,<b>b</b>). The blue curves represent the total coastal waveforms, the red curves represent the sub-waveforms determined to be false, and the green curves represent the sub-waveforms determined to be correct.</p>
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<p>Two typical retracking results of the reconstructed coastal waveforms (<b>a</b>,<b>b</b>). The blue curves represent the reconstructed coastal waveforms, and the red curves represent the final iteration results of the echo model.</p>
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<p>(<b>a</b>) Epoch-retracking results for experimental area 1 in September. (<b>b</b>) Mean SSH data for experimental area 1. The green and red dotted lines represent the OCOG retracking results and the NPPTR results, and the blue and yellow solid lines represent the retracking results of the reconstructed and original waveforms.</p>
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<p>Epoch-retracking results for experimental area 2 in (<b>a</b>) March and (<b>b</b>) August. The green and red dotted lines represent the OCOG retracking results and the NPPTR results, and the blue and yellow solid lines represent the retracking results of the reconstructed and original waveforms.</p>
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<p>Retrieved SSH data for experimental area 2 of year 2022.</p>
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<p>Validation of retrieved SSH data using the MSS model. The yellow and blue lines represent the SSH retrieval results of the original and reconstructed waveforms, and the purple line represents the MSS model.</p>
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21 pages, 4759 KiB  
Article
Sea Surface Height Estimation from Improved Modified, and Decontaminated Sub-Waveform Retracking Methods over Coastal Areas
by Parisa Agar, Shirzad Roohi, Behzad Voosoghi, Arash Amini and Davod Poreh
Remote Sens. 2023, 15(3), 804; https://doi.org/10.3390/rs15030804 - 31 Jan 2023
Cited by 3 | Viewed by 1908
Abstract
Coastal zones are challenging areas for sensing by satellite altimeters because reflected signals from non-water surfaces and from calm sea surfaces in small bays and ports inside the radar footprint lead to erroneous powers in return waveforms. Accordingly, these contaminated waveforms do not [...] Read more.
Coastal zones are challenging areas for sensing by satellite altimeters because reflected signals from non-water surfaces and from calm sea surfaces in small bays and ports inside the radar footprint lead to erroneous powers in return waveforms. Accordingly, these contaminated waveforms do not follow the so-called Brown model in conventional retracking algorithms and fail to derive qualified ranges. Consequently, the estimated water level is erroneous as well. Therefore, selecting an optimized retracker for post-processing waveforms is significantly important to achieve a qualified water level estimation. To find the optimized retracker, we employed a methodology to minimize the effect of erroneous powers on retracked range corrections. To this end, two new approaches were presented, one based on a waveform decontamination method and the other based on a waveform modification method. We considered the first meaningful sub-waveforms in the decontaminated waveforms and in the modified waveforms to be processed with a threshold retracker. To assess their performance, we also retracked the decontaminated and modified full-waveforms. The first meaningful sub-waveform and full-waveform in the original waveforms were retracked to compare the performance of the modified and decontaminated waveform retracking with the original waveform retracking. To compare the results of our sub-waveform retracking algorithms with those of external sub-waveform retracking algorithms, the (Adaptive Leading Edge Sub-waveform) ALES database was also used. In our retracking scenarios, we used the Sentinel-3A SRAL Altimeter to estimate the water levels over the study area within 10 km from the coastlines in both the Persian Gulf and the Bay of Biscay from June 2016 to October 2020. The water levels from processing L2 products were estimated as well. We evaluated our retracking scenarios and L2, as well as the ALES processing results, against the tide gauges. Our analysis showed that within 0–10 km from the coast, the first meaningful sub-waveform of the decontaminated waveforms had the best performance. We reached maximum RMS improvements in this scenario of 53% and 86% over the Persian Gulf and the Bay of Biscay, respectively, in comparison with L2 processing. Over these distances from the coast, the first sub-waveform from the original waveforms and the modified waveforms stayed in the second and third order of performance. The ALES database with an RMS ranging from 13 to 51 cm had a worse performance than all of our sub-waveform retracking scenarios. Full article
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Figure 1

Figure 1
<p>The location of the Sentinel-3A ground passes and tide gauge stations A (Bushehr) and B (Kangan) in the Persian Gulf.</p>
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<p>The location of the Sentinel-3A ground passes and tide gauge stations A (La-Rochelle) and B (ILE-D-AIX) in the Bay of Biscay.</p>
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<p>Example of waveforms from pass 25 over the Persian Gulf at 10 km from the coast.</p>
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<p>Retracking the full original waveform using threshold retracker with 30% threshold for pass 139 over Persian Gulf; (<b>a</b>) is an ocean-like and (<b>b</b>) is a corrupted waveform.</p>
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<p>Meaningful detected sub-waveforms in (<b>a</b>) pass 485 cycle 3 and (<b>b</b>) pass 216 cycle 14 over the Bay of Biscay.</p>
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<p>Retracked gates obtained from the full-waveform and the first sub-waveform of pass 25 cycle 31 over the Persian Gulf.</p>
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<p>Waveform modification for a waveform in pass 25 of cycle 34 in the Persian Gulf (<b>a</b>) is the original waveform, (<b>b</b>) is the detected outlier powers, and (<b>c</b>) is the modified waveform.</p>
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<p>Retracked the original and modified waveform of a given waveform in pass 25 cycle 44 in the Persian Gulf.</p>
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<p>Fitting the reference waveforms (<b>a</b>) and detection of outliers (<b>b</b>) in a particular waveform of pass 139 and cycle 37 over the Persian Gulf using the modification and decontamination strategies.</p>
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<p>(<b>a</b>) Reference and original waveforms with detected outliers for a waveform of pass 139 in cycle 47 over the Persian Gulf, (<b>b</b>) retracked gates/corrections of the decontaminated and original waveform derived from the threshold retracker.</p>
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<p>Retracking of the first sub-waveform of the original and decontaminated waveform of pass 216 from cycle 56 over the Bay of Biscay.</p>
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<p>Retracking of the first sub-waveform of the original and modified waveform of pass 25 and cycle 34 over the Persian Gulf.</p>
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<p>(<b>a</b>) The instantaneous (blue) and mean (magenta) water level of the Bay of Biscay from track 485 and cycle 39 as well as (<b>b</b>) outliers’ rejection.</p>
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<p>Example of waveforms from pass 139 over the Persian Gulf at 0 to 10 km from the coast.</p>
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<p>Water level time series of the Persian Gulf pass 25 based on our waveform retracking scenarios and ALES dataset in front of tide gauges, (<b>a</b>): full-waveform and (<b>b</b>): sub-waveform.</p>
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<p>Water level time series of the Persian Gulf pass 139 based on our waveform retracking scenarios and ALES dataset in front of tide gauges, (<b>a</b>): full-waveform and (<b>b</b>): sub-waveform.</p>
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<p>Water level time series of the Bay of Biscay pass 216 based on our waveform retracking scenarios and ALES dataset in front of tide gauges, (<b>a</b>): full-waveform and (<b>b</b>): sub-waveform.</p>
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<p>Water level time series of the Bay of Biscay pass 485 based on our waveform retracking scenarios and ALES dataset in front of tide gauges, (<b>a</b>): full-waveform and (<b>b</b>): sub-waveform.</p>
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18 pages, 7085 KiB  
Article
Study of Sea Surface Geophysical Parameter Changes Due to Internal Solitary Waves Using a Sentinel-3 Synthetic Aperture Radar Altimeter
by Changtian Yu, Junmin Meng, Lina Sun, Hao Zhang and Haiqi Wang
Remote Sens. 2022, 14(21), 5375; https://doi.org/10.3390/rs14215375 - 27 Oct 2022
Cited by 4 | Viewed by 1499
Abstract
In this paper, a high-resolution Sentinel-3 synthetic aperture radar altimeter is used to observe ISWs in the Sulu Sea. Based on the advantages of the simultaneous observation of Sentinel-3 OLCI and SRAL, the changes in σ0, SWH, and SSHA caused by [...] Read more.
In this paper, a high-resolution Sentinel-3 synthetic aperture radar altimeter is used to observe ISWs in the Sulu Sea. Based on the advantages of the simultaneous observation of Sentinel-3 OLCI and SRAL, the changes in σ0, SWH, and SSHA caused by the ISWs are quantitatively analyzed. The results show that σ0 decreases and then increases after being modulated by the ISWs in the altimeter operation direction; SWH shows a large change; and the change trend of SSHA is the same as that of σ0. Because of the angle between the propagation direction of the ISWs and the SRAL trajectory, the actual position corresponding to the peak power in the waveform detects the ISWs before the nadir, at which time σ0 is already modulated by ISWs, resulting in the deviation of σ0. In addition, the sea surface roughness within the SRAL footprint in this case is no longer uniform, which violates the assumption of retracking and leads to the incorrect estimation of geophysical parameters such as SWH and SSHA. With a view to correcting these errors, the effect of ISWs on the retracker must therefore be considered and the model for waveform modified accordingly. Full article
(This article belongs to the Special Issue Remote Sensing of the Sea Surface and the Upper Ocean)
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Figure 1

Figure 1
<p>An overview map of the study area. The ISWs within the Sulu Sea intersect multiple Sentinel-3 tracks, where the black line is the ISW crest line extracted using MODIS remote sensing images and the red and green lines are the tracks of Sentinel-3A and B, respectively, with the track numbers given as subscripts. The color bar indicates the depth of the seawater.</p>
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<p>The image on the left shows the result after matching the OLCI and SRAL data from simultaneous observations, and the schematic method of obtaining the changes in geophysical parameters due to ISWs measured based on SRAL data is shown on the right, where the yellow shaded part is the ISW occurrence region.</p>
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<p>Evolution of the geometry of the conventional altimeter (red) and SAR altimeter (green) footprints over time [<a href="#B29-remotesensing-14-05375" class="html-bibr">29</a>]; the bottom figure shows a schematic of the echoes received by the SAR altimeter at different times.</p>
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<p>Schematic diagram of geometric relationships, where the blue dashed line is the SRAL trajectory and the black arrow line is the ISW propagation direction; the circular dashed lines form a schematic diagram of the footprint; the blue part of the thick line corresponds to the footprint range at the front of the echo; and the red parts of the thick line correspond to the footprint range at the back edge of the echo.</p>
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<p>In situ observation of the ISWs profile by Zhang et al. (<b>a</b>) Incident ISW obtained on 20 May; (<b>b</b>) reflected ISW obtained on 21 May. The figures indicate the depth from high to low and from shallow to deep; the different colors indicate the temperature.</p>
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<p>Simultaneous Sentinel-3 SRAL and OLCI results on 11 December 2018, at 01:40 (UTC) and observations of 20 Hz <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> </mrow> </semantics></math> in the Ku-band in the orbital direction; the regions where <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> </mrow> </semantics></math> is modulated by ISWs are marked with red and green shading, corresponding to the labels in the OLCI image.</p>
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<p>The specific value changes in the 27 cases in which <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> </mrow> </semantics></math> is clearly modulated by ISWs, with dashed lines separating the cases.</p>
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<p>Simultaneous Sentinel-3 SRAL and OLCI observations on 4 April 2019, at 01:46 (UTC): the rough and smooth regions of isolines are marked with red and green shading and observations of 20 Hz SWH in the Ku-band in the along-track direction; and the four locations where SWH is significantly modulated by ISWs have been marked with red and green shading in the along-track direction at the peaks of ISWs 1~4 in the OLCI image.</p>
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<p>The maximum SWH of 71 isolated subwaves in 20 cases.</p>
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<p>(<b>a</b>) Simultaneous Sentinel-3 SRAL and OLCI observations on January 16, 2020, at 01:44 (UTC): only the rough region of isolines is visible in the figure. (<b>b</b>) The upper panel shows the black line for the 20 Hz SSHA observation in the Ku-band along the track direction, and the red line indicates the gradient of SSHA variation; the lower panel is the result obtained by summing SSHA and mean sea level (MSL). All the ISW regions are marked with red shading, corresponding to the peaks of 1~3 ISW waves in (<b>a</b>).</p>
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<p>The maximum SSHAs for 53 subwaves in 20 cases.</p>
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<p>(<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> </mrow> </semantics></math> variations based on simultaneous observations: the lower right corner shows a plot of the variation in <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> </mrow> </semantics></math> in the 20 Hz Ku band in the track direction, where the red line is the result of smoothing the original data (black line). The position where <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> </mrow> </semantics></math> is modulated is 8.15° N; the actual position where an ISW occurs is 8.121° N, with a linear distance of approximately 3 km between the two points. (<b>b</b>) Scatter plot of the angle and the deviation X, where the blue line is the fitting result.</p>
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<p>Schematic diagram of <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>0</mn> </msub> </mrow> </semantics></math> starting to be modulated by ISW: the blue dashed line indicates the direction of the altimeter operation; the blue part of the footprint corresponds to the leading edge of the waveform; and the red part corresponds to the trailing edge of the waveform.</p>
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<p>(<b>a</b>) ISW taken by UAV with large incidence angle: a large number of waves can be seen breaking and moving in the direction of ISW propagation (the swell was larger on that day, and the lower left corner was not caused by ISW). (<b>b</b>) ISW taken by UAV with small incidence angle: the rough area and smooth area can be clearly distinguished.</p>
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<p>(<b>a</b>) The full waveforms of the SRAL shaded in blue are selected and shown in (<b>b</b>), where the green and red shaded areas denote the rough and smooth regions of a subwave, respectively; and the average value of the highest echo power in the corresponding region is calculated.</p>
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<p>The effective footprint of ISW surface measured by the altimeter is split between the rough and smooth areas, making the return power at the leading and trailing edges affected in different directions: the blue part of the footprint corresponds to the leading edge of the echo waveform, and the red part corresponds to the trailing edge.</p>
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<p>Image acquired at 01:44 (UTC) on March 10, 2020: (<b>a</b>) the match between the footprint at 7.569° N and the OLCI image, and (<b>b</b>) the echo waveform at this point, where blue and red indicate the echo waveforms at the leading and trailing edges, respectively.</p>
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22 pages, 6937 KiB  
Article
Coastal Waveform Retracking for HY-2B Altimeter Data by Determining the Effective Trailing Edge and the Low Noise Leading Edge
by Zhiheng Hong, Jungang Yang, Shanwei Liu, Yongjun Jia, Chenqing Fan and Wei Cui
Remote Sens. 2022, 14(19), 5026; https://doi.org/10.3390/rs14195026 - 9 Oct 2022
Viewed by 1966
Abstract
As an important remote sensing technology, satellite altimetry provides a large amount of observations of sea surface height over the global ocean. In coastal areas, the accuracy of satellite altimetry data decreases greatly due to issues arise in the vicinity of land, related [...] Read more.
As an important remote sensing technology, satellite altimetry provides a large amount of observations of sea surface height over the global ocean. In coastal areas, the accuracy of satellite altimetry data decreases greatly due to issues arise in the vicinity of land, related to poorer geophysical corrections and artifacts in the altimeter reflected signals linked to the presence of land within the instrument footprint. To improve the application of HY-2B altimetry data in coastal areas, this study proposes a coastal waveform retracking strategy for HY-2B altimetry mission, which depends on the effective trailing edge and the leading edge, which are less affected by coastal ‘contamination’, to retrieve accurate waveform information. The HY-2B pass 323 and pass 196 data are reprocessed, and the accuracy of the reprocessing results in the range of 0–40 km offshore is validated against the tide gauge data and compared with the HY-2B standard SGDR data. According to the analysis conclusion, the accuracy of the reprocessed data is higher than that of the SGDR data and has good performance within 15 km offshore. For the pass 323, the mean value of correlation coefficient and RMS of the reprocessed data against the corresponding tide gauge data are 0.893 and 45.1 cm, respectively, in the range within 0–15 km offshore, and are 0.86 and 33.6 cm, respectively, in the range beyond 15 km offshore. For the pass 196, the mean value of correlation coefficient and RMS of the reprocessed data against the corresponding tide gauge data in the range within 0–12 km offshore are 0.84 and 33.0 cm, respectively, and in the range within 0–5 km offshore to the island are 0.90 and 29.3 cm, respectively, and in the range beyond 5 km offshore to the island are 0.92 and 36.2 cm, respectively, which are all better than the corresponding values of the SGDR data, especially in the range closed to the land. The results indicate that the proposed coastal waveform retracking strategy for HY-2B altimetry greatly improves the quality of HY-2B altimetry data in coastal areas. Full article
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Graphical abstract

Graphical abstract
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<p>The diagrammatic sketch of an ideal waveform based on Brown model.</p>
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<p>The study areas and the ground track of HY-2B altimeter (red lines respectively indicate the trajectory of pass 323 (<b>A</b>) and the trajectory of pass 196 (<b>B</b>), the yellow blocks are the tide gauge stations and the shadow represents coastal land).</p>
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<p>The examples of real waveform with small mispointing angle (The <b>left</b> is the sharpening waveform and the <b>right</b> is the specular waveform).</p>
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<p>Flow diagram of coastal waveform retracking procedure of HY-2B altimetry.</p>
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<p>Examples of the first kind of “possible ending gate of leading edge”, the <span class="html-italic">x</span>-axis is the number of gate and the <span class="html-italic">y</span>-axis is the normalized power of echo. The blue line is whole waveform, the red line is the leading edge depending on the “normal ending gate of leading edge”, the marked yellow gate is the first kind of “possible ending gate of leading edge” with increasing power.</p>
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<p>Examples of the second kind of “possible ending gate of leading edge”, the <span class="html-italic">x</span>-axis is the number of gate and the <span class="html-italic">y</span>-axis is the normalized power of echo. The blue line is whole waveform, the red line is the leading edge depending on the “normal ending gate of leading edge”, the yellow gate marked is the second kind of “possible ending gate of leading edge” with decreasing power.</p>
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<p>Examples of the invalid “possible ending gate of leading edge”.</p>
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<p>Flow diagram of effective trailing edge search.</p>
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<p>The distortion of trailing edge judged by the fitting-straight line, the <span class="html-italic">x</span>-axis is the number of gate and the <span class="html-italic">y</span>-axis is the normalized power of echo. The blue line is real waveform and green line is the fitting-straight line, the red line circles the distortion. (<b>a</b>) the “convex” caused by one distortion of trailing edge against straight line; (<b>b</b>) the “concave” caused by two distortions of trailing edge against straight line.</p>
Full article ">Figure 9 Cont.
<p>The distortion of trailing edge judged by the fitting-straight line, the <span class="html-italic">x</span>-axis is the number of gate and the <span class="html-italic">y</span>-axis is the normalized power of echo. The blue line is real waveform and green line is the fitting-straight line, the red line circles the distortion. (<b>a</b>) the “convex” caused by one distortion of trailing edge against straight line; (<b>b</b>) the “concave” caused by two distortions of trailing edge against straight line.</p>
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<p>The effective trailing edge search. the <span class="html-italic">x</span>-axis is the number of gate and the <span class="html-italic">y</span>-axis is the normalized power of echo, the blue line is real waveform and red line is the effective trailing edge.</p>
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<p>The fitting results of real waveforms.</p>
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<p>The RSL comparisons between the reprocessing results and SGDR data of pass 323 (<span class="html-italic">x</span>-axis is the along-track latitude, the left <span class="html-italic">y</span>-axis is the RSL and the right y-axis is the distance from the nominal points of the track to the shoreline illustrated by the black line, the blue line is the SGDR data and red line is the reprocessing results. The light shadow area represents the land; the direction of flight is from 76.5° to 75.9°).</p>
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<p>The RSL comparisons between the reprocessing results and SGDR data of pass 323 (<span class="html-italic">x</span>-axis is the along-track latitude, the left <span class="html-italic">y</span>-axis is the RSL and the right y-axis is the distance from the nominal points of the track to the shoreline illustrated by the black line, the blue line is the SGDR data and red line is the reprocessing results. The light shadow area represents the land; the direction of flight is from 76.5° to 75.9°).</p>
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<p>The RSL comparisons between the reprocessing results and SGDR data of pass 196 (<span class="html-italic">x</span>-axis is the along-track latitude, the left <span class="html-italic">y</span>-axis is the RSL and the right <span class="html-italic">y</span>-axis is the distance from the nominal points of the track to the shoreline illustrated by the black line, the blue line is the SGDR data and red line is the reprocessing results. The light shadow area represents the land and deep shadow area represents the coastal island, the direction of flight is from 120.35° to 120°).</p>
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<p>The RSL comparisons between the reprocessing results and SGDR data of pass 196 (<span class="html-italic">x</span>-axis is the along-track latitude, the left <span class="html-italic">y</span>-axis is the RSL and the right <span class="html-italic">y</span>-axis is the distance from the nominal points of the track to the shoreline illustrated by the black line, the blue line is the SGDR data and red line is the reprocessing results. The light shadow area represents the land and deep shadow area represents the coastal island, the direction of flight is from 120.35° to 120°).</p>
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<p>Height comparison of the ASSH between HY-2B pass 323 and pass 196 with tide gauge data. (<span class="html-italic">x</span>-axis is the number of cycle. The <span class="html-italic">y</span>-axis is the unified value of sea surface height. The left is the comparison within the range 10 km offshore for pass 323, the right is the comparison within the range from 10 km to the island for pass 196).</p>
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<p>Correlation coefficient (<b>top</b>) and RMS (<b>bottom</b>) of the ASSH data comparing with tide gauge data of Subic Bay Station for HY-2B pass 323; (<span class="html-italic">x</span>-axis is the along-track latitude of the nominal tracks. The shaded light grey indicates the land).</p>
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<p>Correlation coefficient (<b>top</b>) and RMS (<b>bottom</b>) of the ASSH data comparing with tide gauge data of Subic Bay Station for HY-2B pass 196; (<span class="html-italic">x</span>-axis is the along-track latitude of the nominal tracks. The shaded light grey indicates the land and the deep shadow represents coastal island).</p>
Full article ">Figure 16 Cont.
<p>Correlation coefficient (<b>top</b>) and RMS (<b>bottom</b>) of the ASSH data comparing with tide gauge data of Subic Bay Station for HY-2B pass 196; (<span class="html-italic">x</span>-axis is the along-track latitude of the nominal tracks. The shaded light grey indicates the land and the deep shadow represents coastal island).</p>
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18 pages, 3835 KiB  
Article
Validation of an Empirical Subwaveform Retracking Strategy for SAR Altimetry
by Marcello Passaro, Laura Rautiainen, Denise Dettmering, Marco Restano, Michael G. Hart-Davis, Florian Schlembach, Jani Särkkä, Felix L. Müller, Christian Schwatke and Jérôme Benveniste
Remote Sens. 2022, 14(16), 4122; https://doi.org/10.3390/rs14164122 - 22 Aug 2022
Cited by 9 | Viewed by 3010
Abstract
The sea level retrievals from the latest generation of radar altimeters (the SAR altimeters) are still challenging in the coastal zone and areas covered by sea ice and require a dedicated fitting (retracking) strategy for the waveforms. In the framework of the European [...] Read more.
The sea level retrievals from the latest generation of radar altimeters (the SAR altimeters) are still challenging in the coastal zone and areas covered by sea ice and require a dedicated fitting (retracking) strategy for the waveforms. In the framework of the European Space Agency’s Baltic + Sea Level (ESA Baltic SEAL) project, an empirical retracking strategy (ALES + SAR), including a dedicated sea state bias correction, has been designed to improve the sea level observations in the Baltic Sea, characterised by a jagged coastline and seasonal sea ice coverage, without compromising the quality of open ocean data. In this work, the performances of ALES + SAR are validated against in-situ data in the Baltic Sea. Moreover, variance, crossover differences and power spectral density of the open ocean data are evaluated on a global scale. The results show that ALES + SAR performances are of comparable quality to the ones obtained using physical-based retrackers, with relevant advantages in coastal and sea ice areas in terms of quality and quantity of the sea level data. Full article
(This article belongs to the Special Issue Advances in Satellite Altimetry)
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Figure 1
<p>Three examples of ALES + SAR fitting applied to a SAR altimetry waveform from (<b>a</b>) typical open ocean conditions, (<b>b</b>) coastal-like interference along the trailing edge and (<b>c</b>) lead-like peaky leading edge.</p>
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<p>Map of root mean squared error (RMSE) and correlation (<span class="html-italic">r</span>) computed against the TGs for ALES + SAR, SAMOSA2 and SAMOSA+ (considering altimetry points from 0 to 3 km from the coast). The results are displayed for ALES + SAR (left panel) and then for SAMOSA2 and SAMOSA+ as the difference from the ALES + SAR results (middle and right panels respectively). The number of pairs denotes the amount of comparable altimeter and TG sea level measurements and is reported for SAMOSA2 and SAMOSA+ as a difference from ALES + SAR results in the left panel. Grey dots denote not enough good data to form time series or statistically insignificant correlation (<span class="html-italic">p</span>-value &gt; 0.05).</p>
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<p>Zoom of <a href="#remotesensing-14-04122-f002" class="html-fig">Figure 2</a> in the Danish Straits.</p>
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<p>Map of root mean squared error (RMSE) and correlation (<span class="html-italic">r</span>) computed against the TGs for ALES + SAR, SAMOSA2 and SAMOSA+ (considering altimetry points from 3 to 10 km from the coast). The results are displayed for ALES + SAR (left panel) and then for SAMOSA2 and SAMOSA+ as the difference from the ALES + SAR results (middle and right panels respectively). The number of pairs denotes the amount of comparable altimeter and TG sea level measurements and is reported for SAMOSA2 and SAMOSA+ as a difference from ALES + SAR results in the left panel. Grey dots denote not enough good data to form time series or statistically insignificant correlation (<span class="html-italic">p</span>-value &gt; 0.05).</p>
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<p>Zoom of <a href="#remotesensing-14-04122-f004" class="html-fig">Figure 4</a> in the Danish Straits.</p>
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<p>Range bias of Sentinel-3A (ALES+SAR retracker) with respect to Jason-3 (per 10-day Jason-3 cycle) as estimated by a multi-mission crossover analysis.</p>
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<p>The gridded SLAVA from the (<b>a</b>) SAMOSA2 and (<b>b</b>) ALES+SAR retrackers respectively as well as (<b>c</b>) the scaled differences between the respective variances. The coastal grid cells, i.e., grid cells within two degrees of the coast, have been removed due to the two-degree grid size not giving an accurate representation of the coastal variances.</p>
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<p>The 3-km gridded standard deviation of sea level observations obtained using the SAMOSA2 and the ALES+SAR retrackers. The greyed out area is designated to show the first 10 km of the comparison.</p>
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<p>PSD spectrum of sea surface heights computed using ALES + SAR and SAMOSA2. The spectrum is based on 8 full cycles (cycles 30 to 37) over the global ocean. To avoid outliers, points located closer than 20 km to the coast and sea level anomalies exceeding 2 m in absolute value are excluded.</p>
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13 pages, 4434 KiB  
Technical Note
Arctic Sea-Ice Surface Elevation Distribution from NASA’s Operation IceBridge ATM Data
by Donghui Yi, Alejandro Egido, Walter H. F. Smith, Laurence Connor, Christopher Buchhaupt and Dexin Zhang
Remote Sens. 2022, 14(13), 3011; https://doi.org/10.3390/rs14133011 - 23 Jun 2022
Cited by 2 | Viewed by 1662
Abstract
In this paper, we characterize the sea-ice elevation distribution by using NASA’s Operation IceBridge (OIB) Airborne Topographic Mapper (ATM) L1B data over the Arctic Ocean during 94 Spring campaigns between 2009 and 2019. The ultimate objective of this analysis is to better understand [...] Read more.
In this paper, we characterize the sea-ice elevation distribution by using NASA’s Operation IceBridge (OIB) Airborne Topographic Mapper (ATM) L1B data over the Arctic Ocean during 94 Spring campaigns between 2009 and 2019. The ultimate objective of this analysis is to better understand sea-ice topography to improve the estimation of the sea-ice freeboard for nadir-looking altimeters. We first introduce the use of an exponentially modified Gaussian (EMG) distribution to fit the surface elevation probability density function (PDF). The characteristic function of the EMG distribution can be integrated in the modeling of radar altimeter waveforms. Our results indicate that the Arctic sea-ice elevation PDF is dominantly positively skewed and the EMG distribution is better suited to fit the PDFs than the classical Gaussian or lognormal PDFs. We characterize the elevation correlation characteristics by computing the autocorrelation function (ACF) and correlation length (CL) of the ATM measurements. To support the radar altimeter waveform retracking over sea ice, we perform this study typically on 1.5 km ATM along-track segments that reflect the footprint diameter size of radar altimeters. During the studied period, the mean CL values range from 20 to 30 m, which is about 2% of the radar altimeter footprint diameter (1.5 km). Full article
(This article belongs to the Topic Advances in Environmental Remote Sensing)
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Graphical abstract

Graphical abstract
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<p>OIB ground tracks from 2009 to 2019. There are 94 spring flights over the Arctic Ocean. Land cover is in gray. Blue tracks are for first-year ice (FYI) and red tracks are for multi-year ice (MYI). FYI and MYI are based on OSISAF sea surface types [<a href="#B27-remotesensing-14-03011" class="html-bibr">27</a>]. The majority of the campaigns are between March and April. There are three campaigns from early May in 2015 and 2016. For overlapping tracks, later tracks will cover early tracks.</p>
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<p>An example from 21 April 2016 shows the cluster analysis method results. (<b>a</b>) DMS images. (<b>b</b>–<b>d</b>) ATM elevation, RTratio, and floe (green) and lead (red) footprints on top of the DMS images. (<b>e</b>) Color-coded Elevation-RTratio scatter plot. (<b>f</b>) Elevation-RTratio scatterplot, and floe (black) and lead (red) from the cluster analysis. (<b>g</b>) The pdf of floe (black) and lead (red) normalized by floe pdf amplitude.</p>
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<p>ATM measurement noise for the 94 campaigns from 2009 to 2019. The upper curve marks the measurement years.</p>
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<p>(<b>a</b>) Typical first-year flat thin ice and the histogram of a 1.5 km section, 21 April 2016. (<b>b</b>) Typical multi-year rough thick ice and the histogram of a 1.5 km section in central Arctic, 9 March 2017. The cross-track swath width is related to the ATM laser scan angle and airplane flight height. Latitude and longitude are the center locations of the two 1.5 km sections.</p>
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<p>CL values calculated from method 1 (CL1) and method 2 (CL2) for 9 March 2017. The mean of CL1 is 27.4 <math display="inline"><semantics> <mo>±</mo> </semantics></math> 11.3 m. The mean of CL2 is 27.2 <math display="inline"><semantics> <mo>±</mo> </semantics></math> 12.4 m. The mean of CL2 − CL1 is −0.2 <math display="inline"><semantics> <mo>±</mo> </semantics></math> 9.5 m. The correlation coefficient (CC) of CL1 and CL2 is 0.69.</p>
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<p>Examples of lognormal (blue) and EMG (red) pdf fitting.</p>
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<p>Mean elevation and standard deviation of elevation for each 1.5 km section along-track compared with values calculated from EMG and lognormal fitting parameters. (<b>a</b>) 1.5 km mean elevation histogram. (<b>b</b>) 1.5 km STD of the elevation histogram. (<b>c</b>) Mean elevation (Elev) vs. mean elevation from lognormal model (lnElev). (<b>d</b>) STD vs. STD from lognormal model (lnSTD). (<b>e</b>) Mean elevation vs. mean elevation from EMG model (emgElev). (<b>f</b>) STD vs. STD from EMG model (emgSTD). (<b>g</b>) Histograms of emgElev–Elev (black) and lnElev–Elev (red). (<b>h</b>) Histograms of emgSTD–STD (black) and lnSTD–STD (red). Measurement noise is removed from STD, lnSTD, and emgSTD.</p>
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<p>The mean elevation, standard deviation of elevation, skewness, kurtosis, <math display="inline"><semantics> <mrow> <msub> <mi>μ</mi> <mi>e</mi> </msub> </mrow> </semantics></math> (emgMu), <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>e</mi> </msub> </mrow> </semantics></math> (emgSigma), 1/<span class="html-italic">λ</span> (emgLambda), and CL of the 1.5 km sections of 2014. Measurement noise is removed from the standard deviation of elevation.</p>
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<p>An example of autocorrelation and correlation length of 8 April 2018. All data here are over FYI. (<b>a</b>) Examples of autocorrelation vs. lag along the profiles (one for every 20 1.5 km sections is plotted). (<b>b</b>) Correlation length for each 1.5 km section. (<b>c</b>) Histogram of correlation length. The mean correlation length for the day is 29.93 ± 18.63 m.</p>
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<p>The box-whisker plot of correlation length (CL) of FYI (red), MYI (green), and all ice (black) from 2009 to 2019. The medians of CL show a trend of increase for all FYI, MYI, and all ice.</p>
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17 pages, 5395 KiB  
Article
Performance Analysis of Ku/Ka Dual-Band SAR Altimeter from an Airborne Experiment over South China Sea
by Xiaonan Liu, Weiya Kong, Hanwei Sun and Yaobing Lu
Remote Sens. 2022, 14(10), 2362; https://doi.org/10.3390/rs14102362 - 13 May 2022
Cited by 4 | Viewed by 2675
Abstract
Satellite radar altimeters have been successfully used for sea surface height (SSH) measurement for decades, gaining great insight in oceanography, meteorology, marine geology, etc. To further improve the observation precision and spatial resolution, radar altimeters have evolved from real aperture to synthetic aperture, [...] Read more.
Satellite radar altimeters have been successfully used for sea surface height (SSH) measurement for decades, gaining great insight in oceanography, meteorology, marine geology, etc. To further improve the observation precision and spatial resolution, radar altimeters have evolved from real aperture to synthetic aperture, from the Ku-band to Ka-band. Future synthetic aperture radar (SAR) altimeter of the Ka-band is expected to achieve better performance than its predecessors. To verify the SAR altimeter data processing method and explore the system advantage of the Ka-band, a Ku/Ka dual-band SAR altimeter airborne experiment was carried out over South China Sea on 6 November 2021. Through dedicated hardware design, this campaign has acquired the Ku and Ka dual-band echo data simultaneously. The airborne data are processed to estimate the SSH retrieval precision after a series of procedures (including height compensation, range migration correction, multi-look processing, waveform re-tracking). To accustom to the airborne experiment design, a SAR echo model that fully considers both the attitude variation of the aircraft and the elliptical footprint of radar beam is established. The retrieved SSH data are compared with the public SSH data along the flight path at the experiment day, showing good consistence for both bands. By calculating the theoretical precision of waveform re-tracking and re-processing the dual-band airborne data into different bandwidths, it is demonstrated that the Ku/Ka precision ratio is possible to achieve 1.4 within the 27 km offshore area, which indicates that Ka-band has better performance. Full article
(This article belongs to the Special Issue Advanced RF Sensors and Remote Sensing Instruments)
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<p>The antennas of the airborne SAR altimeter system.</p>
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<p>Map of the airborne experiment area. The red line represents the flight path, and the other colored curves represent the MSS contours.</p>
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<p>Geometric schematic diagram of height measurement by radar altimeter.</p>
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<p>The variation of altitude along the flight path.</p>
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<p>Flowchart of the airborne data processing procedure.</p>
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<p>The variation of (<b>a</b>) roll angle and (<b>b</b>) pitch angle along the flight path.</p>
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<p>The SAR echo model with different (<b>a</b>) echo epoch (SWH = 2 m) and (<b>b</b>) SWH (Epoch = 40).</p>
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<p>Flowchart of re-tracking algorithm implementation step.</p>
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<p>Airborne waveforms before and after SAR processing of (<b>a</b>) the Ku-band and (<b>b</b>) the Ka-band.</p>
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<p>Airborne multi-look SAR echoes of (<b>a</b>) the Ku-band and (<b>b</b>) the Ka-band.</p>
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<p>The waveform re-tracking iteration and result of Ku-band (<b>a</b>,<b>b</b>) and Ka-band (<b>c</b>,<b>d</b>).</p>
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<p>The SSH retrieval results of Ku-band (blue curve) and Ka-band (red curve). (<b>a</b>) The complete SSH retrieval results. (<b>b</b>) The enlarged section that is indicated in (<b>a</b>).</p>
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<p>Verification of dual-band SSH retrieval results. The blue curve is the SSH data of Ku-band retrieval, the red curve is the SSH data of Ka-band retrieval, and the green curve is the public SSH data along the flight path at the experiment day.</p>
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<p>Comparison of re-processed dual-band SSH retrieval results. The blue curve is the SSH retrieval result of Ku-band under 320 MHz bandwidth, the red curve is the SSH retrieval result of Ka-band under 480 MHz bandwidth, and the green curve is the public SSH data of the flight path.</p>
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17 pages, 4294 KiB  
Article
Ice Sheet Topography from a New CryoSat-2 SARIn Processing Chain, and Assessment by Comparison to ICESat-2 over Antarctica
by Jérémie Aublanc, Pierre Thibaut, Amandine Guillot, François Boy and Nicolas Picot
Remote Sens. 2021, 13(22), 4508; https://doi.org/10.3390/rs13224508 - 9 Nov 2021
Cited by 2 | Viewed by 2959
Abstract
In this study, we present a new level-2 processing chain dedicated to the CryoSat-2 Synthetic Aperture Radar Interferometric (SARIn) measurements acquired over ice sheets. Compared to the ESA ground segment processor, it includes revised methods to detect waveform leading edges and perform retracking [...] Read more.
In this study, we present a new level-2 processing chain dedicated to the CryoSat-2 Synthetic Aperture Radar Interferometric (SARIn) measurements acquired over ice sheets. Compared to the ESA ground segment processor, it includes revised methods to detect waveform leading edges and perform retracking at the Point of Closest Approach (POCA). CryoSat-2 SARIn mode surface height measurements retrieved from the newly developed processing chain are compared to ICESat-2 surface height measurements extracted from the ATL06 product. About 250,000 space–time nearly coincident observations are identified and examined over the Antarctic ice sheet, and over a one-year period. On average, the median elevation bias between both missions is about −18 cm, with CryoSat-2 underestimating the surface topography compared to ICESat-2. The Median Absolute Deviation (MAD) between CryoSat-2 and ICESat-2 elevation estimates is 46.5 cm. These performances were compared to those obtained with CryoSat-2 SARIn mode elevations from the ESA PDGS level-2 products (ICE Baseline-D processor). The MAD between CryoSat-2 and ICESat-2 elevation estimates is significantly reduced with the new processing developed, by about 42%. The improvement is more substantial over areas closer to the coast, where the topography is more complex and surface slope increases. In terms of perspectives, the impacts of surface roughness and volume scattering on the SARIn mode waveforms have to be further investigated. This is crucial to understand geographical variations of the elevation bias between CryoSat-2 and ICESat-2 and continue enhancing the SARIn mode level-2 processing. Full article
(This article belongs to the Special Issue Remote Sensing of Ice Sheets)
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<p>Flow chart showing the methodology developed to retrieve ice sheet elevations from the PDGS L1b SARIn products.</p>
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<p>Illustration of the leading edge detection operation for two random SARInM measurements over the Antarctic ice sheet, showing the normalized waveform (black), normalized smoothed waveform (blue), and waveform gradient (yellow) as computed by the LED algorithm. Red dotted and solid lines respectively display the first and last samples of the detected leading edge(s).</p>
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<p>(<b>left</b>) The CryoVEx (Cryosat Validation Experiment) airborne laser scanner (ALS) height data collected over Austfonna in April 2016. (<b>right</b>) Differences between the CryoSat-2 SARIn mode elevation derived by the CLS processing chain at level-2 and elevations mapped with ALS. Outside the area of interest, the CS2 data geolocations are marked with small black dots (black polygon).</p>
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<p>Median difference (<b>top</b>), median absolute deviation (<b>middle</b>) between SARIn CryoSat-2 and ICESat-2 ATL06 space–time co-located elevations over the Antarctic ice sheet, as a function of the surface slope. The <b>bottom</b> panel displays the percentage of elevation differences greater than 5 m. SARIn mode elevations were derived from the PDGS level-2 product (red) and from the level-2 CLS processor (blue). Elevations were computed as CryoSat-2 − ICESat-2. Gray bars display the number of co-located measurements for each slope interval.</p>
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<p>Gridded statistics of the elevation differences between CryoSat-2 SARIn mode and ICESat-2 ATL06 co-located elevations: median difference (<b>left</b>), median absolute deviation (<b>middle</b>), and box count (<b>right</b>). SARIn mode elevations are derived from the level-2 CLS processing (<b>top</b>) and from the PDGS level-2 products (<b>bottom</b>). Elevations are computed as CryoSat-2 − ICESat-2. Grid resolution is 80 km.</p>
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<p>Gridded statistics of the elevation differences between CryoSat-2 SARIn mode and ICESat-2 ATL06 co-located elevations: median difference (<b>left</b>), median absolute deviation (<b>middle</b>), and box count (<b>right</b>). SARIn mode elevations are derived from the level-2 CLS processing (<b>top</b>) and from the PDGS level-2 products (<b>bottom</b>). Elevations are computed as CryoSat-2 − ICESat-2. Grid resolution is 80 km.</p>
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<p>Median difference (<b>left</b>) and median absolute deviation (<b>right</b>) between SARIn CryoSat-2 and ICESat-2 ATL06 space–time co-located elevations over the Antarctic ice sheet, as a function of the surface slope. SARIn mode elevations are derived from the CLS processing chain with different retrackers: LMC (blue), gradient (orange), and 50% threshold (green). The LMC retracker is the default one within the CLS processor. Elevations are computed as CryoSat-2 − ICESat-2.</p>
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<p>(<b>left</b>) Median absolute deviation between SARIn CryoSat-2 and ICESat-2 ATL06 space–time co-located elevations over the Antarctic ice sheet, as a function of the gradient of the CryoSat-2 first detected waveform leading edge. SARIn mode elevations were derived from the PDGS level-2 products (red) and the CLS processing (blue). Gray bars display the number of co-located measurements for each gradient interval. (<b>right</b>) Gridded value of the CryoSat-2 first detected waveform leading edge over the Antarctic ice sheet, only for CryoSat-2 measurements co-located with ICESat-2. Grid resolution is 80 km.</p>
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