Sea surface temperature retrieval based on simulated microwave polarimetric measurements of a one-dimensionalsynthetic aperture microwave radiometer
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Abstract: Compared with traditional real aperture microwave radiometers, one-dimensional synthetic aperture microwave radiometers have higher spatial resolution. In this paper, we proposed to retrieve sea surface temperature using a one-dimensional synthetic aperture microwave radiometer that operates at frequencies of 6.9 GHz, 10.65 GHz, 18.7 GHz and 23.8 GHz at multiple incidence angles. We used the ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts and a radiation transmission forward model to calculate the model brightness temperature. The brightness temperature measured by the spaceborne one-dimensional synthetic aperture microwave radiometer was simulated by adding Gaussian noise to the model brightness temperature. Then, a backpropagation (BP) neural network algorithm, a random forest (RF) algorithm and two multiple linear regression algorithms (RE1 and RE2) were developed to retrieve sea surface temperature from the measured brightness temperature within the incidence angle range of 0°–65°. The results show that the retrieval errors of the four algorithms increase with the increasing Gaussian noise. The BP achieves the lowest retrieval errors at all incidence angles. The retrieval error of the RE1 and RE2 decrease first and then increase with the incidence angle and the retrieval error of the RF is contrary to that of RE1 and RE2.
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Figure 2. The schematic diagram of the incidence angle of a one-dimensional synthetic aperture radiometer.
Figure 3. Distribution histogram of environment parameters. a. Sea surface temperature, b. wind direction, c. wind speed, d. total column cloud water vapor content, and e. total column cloud liquid water content.
Figure 6. The relationship between sensitivity of brightness temperature to SST and incidence angle.
Figure 7. The relationship between sensitivity of brightness temperature to SST and SST at
$\theta \!=\! {30^ \circ }$ .
Figure 9. Relationships between the retrieval errors and sea surface temperature when the added noise is 0.50 K and
$\theta \!=\! 30^\circ$ .
Figure 10. Scatterplots of retrieval sea surface temperature and true sea surface temperature for Gaussian error of 0.5 K and
$\theta \!= \!30^\circ$ .Table 1. Parameters of the one-dimensional synthetic aperture radiometer
Parameter Values Frequency/GHz 6.9 Bandwidth/MHz 200 Polarization modes vertical and horizontal polarization Integral time/s 0.5 Number of antenna elements 55 Minimum spacing between antenna elements 0.73λ Angular range of the field of view –43° to 43° Angular resolution 0.43° Antenna element positions [0 1 2 3 7 11 12 14 21 24 26 28 38 43 58 59 60 65 68 70 73 75 76 78 83 86 88 93 101 103 105 112 118
121 122 123 132 133 134 137 147 148 153 156 164 166 172 173 174 175 178 180 181 182 183]Parabolic size 12 m×10 m Spatial resolution at nadir/km 5 Range of incidence angle θ with vertical observation –51° to 51° 
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Table 2. Geophysical parameter values for the background field used in the sensitivity analysis
Scene S TS/K W/(m·s–1 ) φ/(°) V/mm L/mm Scene2 35 273.15–313.15 15 30 30 0.1
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Table 3. Mean RMS errors, where the top values are the training set and the bottom values are the test set
Noise/K Mean RMS error/K BP RF RE1 RE2 0.25 0.30 0.76 0.60 0.61 0.30 0.78 0.60 0.61 0.50 0.42 0.90 0.68 0.69 0.42 0.92 0.68 0.68 0.75 0.50 1.04 0.74 0.75 0.51 1.07 0.74 0.75
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Table 4. The average mean biases, where the top values are the training set and the bottom values are the test set
Noise/K Mean bias/K BP RF RE1 RE2 0.25 −1.0×10−4 −6.0×10−4 2.1×10−4 −1.5×10−4 −1.1×10–4 −0.001 −1.2×10−3 −1.3×10−3 0.50 −3.7×10−4 −6.0×10−4 −3.2×10−5 7.1×10−5 −5.7×10−4 −4.7×10−4 −7.7×10−4 −8.9×10−4 0.75 −0.005 −5.4×10−4 −1.2×10−5 −2.6×10−5 −0.005 −6.4×10−4 6.6×10−4 −6.6×10−4
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Table 5. SST interval and sample size for each interval in the training set and the corresponding RMS error and mean bias
SST/K N RMS error and mean bias/K BP RF RE1 RE2 271.15–274.84 58 459 0.29/−0.03 0.67/−0.32 0.77/0.47 0.72/0.50 274.85–278.53 29 087 0.28/0.02 0.74/−0.09 0.49/−0.05 0.48/−0.08 278.54–282.21 28 518 0.28/0.01 0.81/−0.05 0.70/−0.50 0.72/−0.53 282.22–285.90 24 194 0.29/−0.04 0.89/−0.06 0.84/−0.71 0.86/−0.71 285.91–289.59 25 850 0.32/−0.09 0.99/−0.13 0.83/−0.67 0.87/−0.69 289.60–293.27 31 802 0.32/−0.03 1.06/−0.15 0.65/−0.40 0.70/−0.43 293.28–296.96 44 625 0.33/0.05 1.08/−0.09 0.49/−0.05 0.55/−0.06 296.97–300.65 70 249 0.36/0.10 0.97/−0.01 0.59/0.32 0.64/0.33 300.66–304.33 52 658 0.37/0.001 1.05/0.74 0.66/0.29 0.71/0.32 304.34–308.02 154 0.36/0.20 4.09/3.86 1.01/0.79 1.13/0.91 All 365 624 0.38/−0.01 0.93/0.004 0.67/0.00 0.69/0.00 Note: The RMS error and the mean bias are separated by “/”.
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Table 6. SST interval and sample size for each interval in the test set and the corresponding RMS error and mean bias
SST/K N RMS error and mean bias/K BP RF RE1 RE2 271.15–274.84 58 001 0.29/−0.04 0.69/−0.34 0.78/0.47 0.72/0.50 274.85–278.53 29 091 0.28/0.03 0.76/−0.09 0.49/−0.06 0.49/−0.08 278.54–282.21 28 662 0.28/0.01 0.85/−0.07 0.70/−0.50 0.72/−0.53 282.22–285.90 24 139 0.29/−0.04 0.92/−0.06 0.84/−0.70 0.85/−0.71 285.91–289.59 25 993 0.32/−0.09 1.03/−0.15 0.82/−0.67 0.86/−0.69 289.60–293.27 31 739 0.33/−0.03 1.11/−0.16 0.66/−0.41 0.71/−0.44 293.28–296.96 44 534 0.34/0.05 1.11/−0.09 0.50/−0.06 0.56/−0.07 296.97–300.65 70 595 0.36/0.13 1.01/0.00 0.59/0.33 0.65/0.34 300.66–304.33 52 703 0.37/0.00 1.07/0.76 0.70/0.30 0.72/0.33 304.34–308.02 167 0.43/0.16 4.03/3.76 0.98/0.69 1.07/0.78 All 365 624 0.39/−0.01 0.97/0.004 0.67/0.00 0.69/−0.001 Note: The RMS error and the mean bias are separated by “/”.
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