Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods
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Abstract: Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed (SSWS) from HH-polarized Sentinel-1 (S1) SAR images. The Polarization Ratio (PR) models combined with the CMOD5.N Geophysical Model Function (GMF) is used for SSWS retrieval from the HH-polarized SAR data. We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data. The recently proposed CMODH, i.e., retrieving SSWS directly from the HH-polarized S1 data is also validated. The results indicate that the CMODH model performs better than results achieved using the PR models. We proposed a neural network method based on the backward propagation (BP) neural network to retrieve SSWS from the S1 HH-polarized data. The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods. Compared to the buoy measurements, the bias, root mean square error (RMSE) and scatter index (SI) of wind speed retrieved by the BP neural network model are 0.10 m/s, 1.38 m/s and 19.85%, respectively, while compared to the ASCAT dataset the three parameters of training set are –0.01 m/s, 1.33 m/s and 15.10%, respectively. It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.
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Key words:
- Sentinel-1 /
- HH-polarization /
- sea surface wind speed /
- retrieval methods
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Figure 2. The calculated radiometric resolution as a function of cell sizes based on a dataset of 2 277 scenes of Sentinel-1 HH polarization data.
Figure 4. Comparisons of the retrieved SSWS using the PR models of Mouche-PR1 (a), Mouche-PR2 (b), Zhang-PR (c) and Liu-PR (d) and CMODH (e) model with the collocated buoy wind speeds.
Figure 5. Comparisons of the retrieved SSWS using the PR models of Mouche-PR1 (a), Mouche-PR2 (b), Zhang-PR (c) and Liu-PR (d) and CMODH model (e) with the collocated ASCAT wind speeds.
Figure 6. Comparisons of the retrieved SSWS using the training set of BP neural network (a), the test set of BP neural network (b) and the trained optimal BP neural network (c) models with the collocated ASCAT and buoy wind speeds.
Figure 7. An example of SSWS retrieval for Sentinel-1 HH polarization data using Mouche-PR1 model (a), Mouche-PR2 model (b), Zhang-PR model (c), Liu-PR model (d), CMODH model (e), and BP neural network model (f). The collocated ERA5 wind vectors within the coverage of the Sentinel-1 image are overlaid. The red pentagram marks the location of the R/V Xuelong.
Table 1. Technical specifications of Sentinel-1 IW and EW modes
Mode Incidence angle/(°) Nominal resolution Swath width/km Polarization IW 29–46 5 m×20 m 250 HH+HV, HH, VH+VV, VV EW 19–47 25 m×40 m 400 HH+HV, HH, VH+VV, VV 
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Table 2. Information table of buoy data used in this study
Station Latitude Longitude Height/m Station Latitude Longitude Height/m 46022 40.720°N 124.531°W 4 46047 32.398°N 119.498°W 5 46014 39.233°N 123.967°W 4 46029 46.143°N 124.485°W 5 46013 38.238°N 123.307°W 4 46041 47.353°N 124.742°W 5 46015 42.779°N 124.874°W 4 51003 19.289°N 160.569°W 5 46050 44.677°N 124.515°W 4 51000 23.535°N 153.781°W 5 46092 36.751°N 122.029°W 4 44258 44.500°N 63.400°W 5 46025 33.749°N 119.053°W 4 45138 49.540°N 65.710°W 5 51004 17.602°N 152.395°W 4 SMKF1 24.628°N 81.109°W 6.1 32303 5.000°N 95.000°W 4 VCAF1 24.711°N 81.107°W 6.5 43301 8.000°N 95.000°W 4 NPSF1 26.132°N 81.807°W 6.5 51002 17.037°N 157.696°W 4 MTBF1 27.661°N 82.594°W 6.7 46035 57.026°N 177.738°W 5 LONF1 24.844°N 80.864°W 7 46027 41.852°N 124.382°W 5 VCVA2 57.125°N 170.285°W 8.5 46089 45.925°N 125.771°W 5 CDRF1 29.136°N 83.029°W 10 46011 34.956°N 121.019°W 5 VENF1 27.072°N 82.453°W 11.6 46028 35.712°N 121.858°W 5 SANF1 24.456°N 81.877°W 14.6 46042 36.785°N 122.398°W 5 MLRF1 25.012°N 80.376°W 15.8 46069 33.674°N 120.212°W 5 PLSF1 24.693°N 82.773°W 17.7 46086 32.491°N 118.035°W 5
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Table 3. Coefficients of Mouche-PR1 model
Coefficient Value A0 0.006 507 04 B0 0.128 983 00 C0 0.992 839 00 Aπ/2 0.007 821 94 Bπ/2 0.121 405 00 Cπ/2 0.992 839 00 Aπ 0.005 984 16 Bπ 0.140 952 00 Cπ 0.992 885 00
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Table 4. Coefficients of PR model with incidence angle dependence
PR model A B C Mouche-PR2 0.00799793 0.125465 0.997379 Zhang-PR 0.282 8 0.045 1 0.289 1 Liu-PR 0.453 041 0 0.0324573 0.5243030
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Table 5. Statistical parameters of SSWS retrieval using different methods
Models Bias/(m·s–1) RMSE/(m·s–1) SI/% Compared with buoy Mouche-PR1 0.11 1.60 22.89 Mouche-PR2 0.13 1.63 23.42 Zhang-PR –0.33 1.50 21.09 Liu-PR 0.25 1.78 25.36 CMODH –0.31 1.48 20.89 BPNN 0.10 1.38 19.85 Compared with ASCAT Mouche-PR1 0.27 1.57 17.73 Mouche-PR2 0.41 1.70 18.85 Zhang-PR –0.09 1.40 16.26 Liu-PR 0.91 2.52 26.86 CMODH –0.35 1.39 15.82 BPNN (training set) 0.03 1.33 15.39 BPNN (test set) –0.01 1.33 15.10
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