# Evaluation of Multi-Incidence Angle Polarimetric Gaofen-3 SAR Wave Mode Data for Significant Wave Height Retrieval

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

_{c}) to retrieve SWH, with consideration of the effect of incidence angle, were developed for the C-band satellite systems, as well [16,17,18]. Then, the linear algorithms were improved for Gaofen-3 SAR by using the basic formulation of the quadratic CWAVE model with additional introduction of variables of NRCS, cvar, etc. [19]. Currently, machine learning and deep learning have become the mainstream methods for the SAR SWH estimation, owing to their ability to consider a variety of SAR features and approximate nonlinear behavior, without prior knowledge of the interrelationships among the features [20,21,22].

_{c}-based algorithm using the RADARSAT-2 fine quad-polarization SAR data. Pramudya et al. [23] proposed a polarization-enhanced λ

_{c}-based algorithm for Sentinel-1 SAR, which uses the combination of the spectra of VV and vertical-horizontal (VH) polarization SAR images to optimize the estimate of λ

_{c}, and thus, the estimate of SWH. Wang et al. [24] developed a new λ

_{c}-involved quadratic model, based on the quad-polarization Gaofen-3 SAR wave mode data, which additionally introduces VH NRCS, besides the VV features, and found the dual-polarized model performs better in the high sea state. Collins et al. [25] investigated the effect of polarization on the CWAVE-type models using the quad-polarization RADARSAT-2 images. Wang et al. [4] proposed a novel deep convolutional neural network for SWH retrieval from Gaofen-3 SAR wave mode data and found that quad-polarimetry information can improve SAR SWH retrieval under high sea conditions. Besides, most of the empirical models introduced the effect of incidence angle, either by being implemented within incidence angle bins or by including the incidence angle as an independent variable.

## 2. Materials and Methods

#### 2.1. Gaofen-3 Wave Mode

_{c}) were the three SAR features assumed to be strongly correlated with SWH, and they were most commonly used for SAR SWH inversion. In addition, the radar incidence angle (θ) was assumed to be an important parameter and has been considered for SAR SWH retrieval in recent studies. Therefore, in this paper, these four features were selected. The ways to extract these features are provided below.

^{0}is the NRCS in dB, <DN

_{pq}> denotes the mean value, DN = I

_{s}× (qv/32767)

^{2}denotes the image intensity, I

_{s}= I

^{2}+ Q

^{2}with I (Q) being the value of real (imaginary) channel for the single look complex SAR image, qv is the maximum qualified value stored in the product annotation file according to the polarizations, and K is the calibration constant also stored in the product annotation file according to the polarizations. However, only a small portion of the official Gaofen-3 wave mode products provide the quad-polarization K values. Moreover, there are still some problems with the official radiometric calibration, though great efforts have been made. The comparisons of the Gaofen-3 NRCS values calibrated using the calibration constant of officially released values with those predicted by the empirical geophysical model functions (GMFs) at HH, HV, VH, and VV polarizations are shown in Figure 3a. The GMF CMOD5.n was used for VV; the combination of CMOD5.n and the VV-HH polarization ratio (PR) model proposed in Zhang et al. [27] was used for HH; and the C-3PO developed in Zhang et al. [28] was used for HV and VH. As seen, the calibrated NRCSs by the calibration constant of official released values significantly deviated from the GMF predictions with an RMSE up to ~4 dB, even in the best performing case of VV polarization. That is to say, extra calibration consideration and activity are needed to improve the accuracy of the Gaofen-3 SAR wave mode products.

_{pq}> is the mean intensity of the pq polarization Gaofen-3 image in linear unit. In this study, the normalized variances for HH-, HV-, VH-, and VV-polarized wave mode images were considered.

_{c}), in meters, can be written as:

#### 2.2. Buoy, Altimeter, and ERA5 SWH Data

#### 2.3. PolR and GPR Models

_{s}is the SWH, s

_{i}represents the SAR-based parameters, and a

_{i}

_{, j}(i ≤ j ≤ n) represents the tuned coefficients. The PolR model states that the SWH is expressed as linear combinations of the SAR-derived parameters (s

_{1}, …, s

_{n}) with the extended coefficient vector (a

_{0}, …, a

_{n}, a

_{11}, …, a

_{nn}) in a dimension of 0.5 (n

^{2}+ 3n + 2). The second-order terms in the model function reflect the nonlinear combinations among the SAR image parameters. The derivation of the PolR model was based on the collocated Gaofen-3 SAR wave mode imagettes and ERA5 SWH data, using a least squares minimization procedure.

**y**is the model output,

**X**is the model input, ε is the independent identically distributed Gaussian noise with zero mean and constant variance, and f (

**X**) is a Gaussian process that can be specified by its mean (which is taken to be zero) and covariance matrix K. The elements of K can be computed by using a kernel function. Several kernel functions were evaluated here, and it was found that the anisotropic exponential kernel was the most suitable. This exponential kernel function can be expressed as:

_{i}, x

_{j}) is the (i, j) element of covariance matrix K, x

_{i}and x

_{j}are the ith and jth input parameters, and θ

_{1}and θ

_{2}represent hyper-parameters that should be optimized. In this work, the hyper-parameters of kernel function were estimated based on minimization of the negative log marginalized likelihood (NLML) [34]. To optimize the NLML, the quasi-newton optimization method was employed. The extracted features from the polarimetric Gaofen-3 SAR images were used as the input, and the ERA5 SWH was used as the training output. The inputs were transformed into the standardized values, so that the mean was 0 and the standard deviation was 1. Of particular note is that the GPR model does not need to include squared terms and cross-terms as input because it can model the nonlinear interactions between the input independent variables.

## 3. Results

#### 3.1. Effects of Polarization

_{c}/β were used as inputs for the models. The θ was not considered. The training dataset and the validation dataset were kept unchanged in this experiment.

#### 3.2. Effects of Incidence Angle

#### 3.3. Final Model Performance

## 4. Discussion

#### 4.1. Importance Study of the Polarization Features

#### 4.2. Impact of Radiometric Calibration

_{s}in dB ($10{\mathrm{log}}_{10}\langle {I}_{s}\rangle $). The polarization modes of HH, HH+HV, and HH+HV+VH+VV were taken as examples for this discussion. The cvar and ${\lambda}_{c}/\beta $ were included in the models, while θ was not.

_{recal}) than to the official calibration value (K

_{official}), indicating that ocean recalibration is necessary for the accurate estimation of SWH from Gaofen-3 wave mode data.

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Table A1.**The values of the quad-polarization recalibration constants of the 24 Gaofen-3 radar beams.

Radar Beam ID | HH | HV | VH | VV |
---|---|---|---|---|

189 | 15.01408 | 12.2541 | 25.80991 | 20.05543 |

190 | 18.25585 | 14.88368 | 29.15318 | 20.59579 |

191 | 18.83953 | 16.64376 | 24.84276 | 19.13042 |

193 | 21.26735 | 17.98476 | 25.49417 | 20.49475 |

195 | 26.72555 | 22.70524 | 26.20774 | 20.39201 |

197 | 28.2592 | 23.84215 | 28.21244 | 22.28621 |

198 | 23.32183 | 19.98011 | 26.98858 | 22.41353 |

199 | 25.65079 | 21.67706 | 26.67518 | 21.47405 |

200 | 30.30135 | 25.63828 | 28.95669 | 22.93642 |

201 | 25.4069 | 21.24098 | 26.03701 | 20.61089 |

202 | 29.10564 | 23.96265 | 28.57081 | 22.89158 |

203 | 29.10564 | 23.96265 | 28.57081 | 22.89158 |

205 | 30.62352 | 25.31018 | 29.40096 | 23.89272 |

206 | 29.19057 | 23.55115 | 27.69605 | 22.62887 |

207 | 29.52643 | 23.61636 | 27.19983 | 21.79812 |

208 | 29.22087 | 23.15519 | 27.52491 | 22.39281 |

209 | 29.19051 | 23.29532 | 28.81778 | 23.13645 |

210 | 28.88105 | 22.1761 | 27.64764 | 22.71903 |

211 | 28.18358 | 22.06203 | 26.72792 | 21.29466 |

212 | 30.90046 | 25.10704 | 29.39566 | 24.53799 |

213 | 30.05091 | 24.13004 | 28.63036 | 24.10765 |

214 | 30.05091 | 24.13004 | 28.63036 | 24.10765 |

215 | 30.05091 | 24.13004 | 28.63036 | 24.10765 |

216 | 30.05091 | 24.13004 | 28.63036 | 24.10765 |

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**Figure 1.**Map of the selected Gaofen-3 wave mode acquisitions during the years 2016–2020 in data density for 2° × 2° bins.

**Figure 2.**Case of Gaofen-3 SAR wave mode imagette, acquired on February 7, 2017, at UTC 18:17:16. Image of normalized backscattering for (

**a**) HH, (

**b**) HV, (

**c**) VH, and (

**d**) VV polarizations.

**Figure 3.**Comparison of the calibrated quad-polarization Gaofen-3 SAR NRCSs with the GMF predictions. (

**a**) The SAR NRCSs were calibrated using the official calibration constants. (

**b**) The SAR NRCSs were calibrated using the ocean recalibration procedure. Different colors represent different polarizations, with blue for HH, orange for HV, green for VH, and red for VV.

**Figure 4.**Estimation of azimuth cutoffs for (

**a**) HH, (

**b**) HV, (

**c**) VH, and (

**d**) VV polarizations from the Gaofen-3 wave mode imagette shown in Figure 1. Dashed blue line represents the inter-correlation along the azimuth direction, and the solid red line represents the Gaussian fit.

**Figure 6.**(

**a**) Scatter point density plot of Jason-3 SWH versus buoy SWH. (

**b**) Scatter point density plot of ERA5 SWH versus buoy SWH. The black solid lines indicate the one-to-one diagonal. The red solid lines join the mean values from SAR estimates in each 0.1 m bin of buoy SWH. Colors denote the data point numbers within 0.1 m × 0.1 m bins. Jason-3 collocation was limited to 1 h and 100 km. ERA5 collocation was performed based on the time/space interpolation over the year of 2017.

**Figure 7.**Plots of Gaofen-3 SWH retrievals from the GPR model versus ERA5 SWH for the nine polarization modes of (

**a**) HH, (

**b**) HV, (

**c**) HH+HV, (

**d**) VV, (

**e**) VH, (

**f**) VV+VH, (

**g**) HH+VV, (

**h**) HV+VH, and (

**i**) HH+HV+VH+VV. The red solid lines join the mean values from SAR estimates in each 0.1 m bin of ERA5 SWH. Colors denote the data numbers within 0.1 m × 0.1 m bins.

**Figure 8.**Comparison of SWH residuals against ERA5 SWH, with error bars presenting the standard deviation. The Gaofen-3 SAR SWH estimates were obtained from the GPR model under the nine polarization modes of (

**a**) HH (green), HV (red), HH+HV (blue); (

**b**) VV (green), VH (red), VV+VH (blue); and (

**c**) HH+VV (green), HV+VH (red), HH+HV+VH+VV (blue). (

**d**) Histogram of ERA5 SWH in bin size of 1 m, where the data count is labeled in black text.

**Figure 9.**Plots of Gaofen-3 SWH retrievals from (

**a**) the piecewise quad-polarization PolR model, (

**b**) the piecewise quad-polarization GPR model, (

**c**) the quad-polarization PolR model that included θ as an independent variable, (

**d**) the quad-polarization GPR model that included θ as an independent variable against ERA5 SWH. Red lines join the mean values from SAR estimates in each 0.1 m bin of ERA5 SWH. Colors denote the data numbers within 0.1 m × 0.1 m bins.

**Figure 10.**Plots of Gaofen-3 SWH retrievals from the final PolR and GPR models versus SWH measurements from Jason-3 altimeter and NDBC buoys. (

**a**) Estimates via PolR versus Jason-3 SWH. (

**b**) Estimates via GPR versus Jason-3 SWH. (

**c**) Histogram of Jason-3 SWH. (

**d**) Estimates via PolR versus buoy SWH. (

**e**) Estimates via GPR versus buoy SWH. (

**f**) Histogram of buoy SWH. Red lines join the mean values of SAR estimates in each 0.1 m bin of the measured SWH.

Data | Incidence Angle | Number of Gaofen-3 Wave Mode Data | ||||
---|---|---|---|---|---|---|

Range | Mean | Standard Deviation | Total | Training | Validation | |

All | 20–50° | 39.13° | 5.40° | 11164 | 7813 | 3351 |

WV01 | 20–33° | 26.72° | 3.79° | 845 | 591 | 254 |

WV02 | 33–37° | 35.92° | 0.68° | 3731 | 2612 | 1119 |

WV03 | 37–42° | 40.12° | 0.79° | 3775 | 2642 | 1133 |

WV04 | 42–46° | 44.10° | 1.08° | 1358 | 950 | 408 |

WV05 | 46–50° | 47.38° | 1.19° | 1455 | 1018 | 437 |

Input | PolR | GPR | ||||||
---|---|---|---|---|---|---|---|---|

Corr | RMSE (m) | Bias (m) | SI (%) | Corr | RMSE (m) | Bias (m) | SI (%) | |

HH | 0.77 | 0.586 | −0.007 | 4.68 | 0.80 | 0.551 | 0.002 | 5.76 |

HV | 0.78 | 0.573 | 0.007 | 14.04 | 0.81 | 0.535 | 0.011 | 12.14 |

VH | 0.79 | 0.566 | 0.007 | 8.56 | 0.82 | 0.533 | 0.008 | 0.69 |

VV | 0.79 | 0.567 | −0.004 | 1.26 | 0.81 | 0.535 | 0.006 | 2.43 |

HH+VV | 0.79 | 0.563 | 0.001 | 1.65 | 0.86 | 0.477 | 0.007 | 4.19 |

HV+VH | 0.80 | 0.549 | 0.009 | 7.77 | 0.84 | 0.499 | 0.015 | 11.55 |

HH+HV | 0.85 | 0.487 | 0.002 | 5.54 | 0.90 | 0.406 | 0.012 | 5.01 |

VV+VH | 0.86 | 0.474 | 0.007 | 1.93 | 0.90 | 0.403 | 0.017 | 5.52 |

Quad | 0.87 | 0.449 | 0.015 | 9.77 | 0.92 | 0.365 | 0.018 | 5.64 |

**Table 3.**Performance of the quad-polarized PolR and GPR models under different incidence angle bins.

Data | PolR | GPR | ||||||
---|---|---|---|---|---|---|---|---|

Corr | RMSE (m) | Bias (m) | SI (%) | Corr | RMSE (m) | Bias (m) | SI (%) | |

WV01 | 0.81 | 0.401 | 0.007 | 6.68 | 0.91 | 0.281 | 0.011 | 5.50 |

WV02 | 0.90 | 0.419 | 0.024 | 8.71 | 0.93 | 0.361 | 0.026 | 7.84 |

WV03 | 0.84 | 0.384 | 0.014 | 5.27 | 0.89 | 0.318 | 0.002 | 1.29 |

WV04 | 0.90 | 0.419 | 0.003 | 22.77 | 0.92 | 0.375 | 0.016 | 18.15 |

WV05 | 0.88 | 0.511 | 0.029 | 13.07 | 0.92 | 0.410 | 0.042 | 0.91 |

Input | PolR | GPR | ||||||
---|---|---|---|---|---|---|---|---|

Corr | RMSE (m) | Bias (m) | SI(%) | Corr | RMSE (m) | Bias (m) | SI (%) | |

HH+HV | 0.85 | 0.487 | 0.002 | 5.54 | 0.90 | 0.406 | 0.012 | 5.01 |

$\mathrm{No}{\sigma}_{HH}^{0}$ | 0.83 | 0.520 | 0.001 | 7.43 | 0.87 | 0.455 | 0.013 | 11.62 |

$\mathrm{No}{\sigma}_{HV}^{0}$ | 0.81 | 0.534 | −0.001 | 12.21 | 0.87 | 0.460 | 0.011 | 12.60 |

$\mathrm{No}cva{r}_{HH}$ | 0.82 | 0.521 | 0.003 | 3.06 | 0.88 | 0.433 | 0.016 | 8.26 |

$\mathrm{No}cva{r}_{HV}$ | 0.83 | 0.520 | −0.001 | 5.03 | 0.88 | 0.438 | 0.008 | 4.47 |

$\mathrm{No}{\lambda}_{cHH}/\beta $ | 0.84 | 0.495 | 0.003 | 6.45 | 0.89 | 0.420 | 0.014 | 4.08 |

$\mathrm{No}{\lambda}_{cHV}/\beta $ | 0.85 | 0.490 | 0.001 | 6.90 | 0.90 | 0.409 | 0.010 | 9.93 |

Input | PolR | GPR | ||||||
---|---|---|---|---|---|---|---|---|

Corr | RMSE (m) | Bias (m) | SI(%) | Corr | RMSE (m) | Bias (m) | SI (%) | |

VV+VH | 0.86 | 0.474 | 0.007 | 1.93 | 0.90 | 0.403 | 0.017 | 5.52 |

$\mathrm{No}{\sigma}_{VV}^{0}$ | 0.83 | 0.511 | 0.005 | 3.65 | 0.87 | 0.451 | 0.012 | 2.07 |

$\mathrm{No}{\sigma}_{VH}^{0}$ | 0.82 | 0.530 | −0.001 | 9.92 | 0.87 | 0.459 | 0.010 | 9.18 |

$\mathrm{No}cva{r}_{VV}$ | 0.83 | 0.512 | 0.002 | 2.83 | 0.88 | 0.430 | 0.015 | 5.59 |

$\mathrm{No}cva{r}_{VH}$ | 0.84 | 0.493 | 0.006 | 4.00 | 0.88 | 0.440 | 0.015 | 6.05 |

$\mathrm{No}{\lambda}_{cVV}/\beta $ | 0.85 | 0.480 | 0.010 | 3.79 | 0.89 | 0.415 | 0.015 | 4.59 |

$\mathrm{No}{\lambda}_{cVH}/\beta $ | 0.86 | 0.476 | 0.007 | 0.37 | 0.90 | 0.409 | 0.016 | 9.53 |

Input | PolR | GPR | ||||||
---|---|---|---|---|---|---|---|---|

Corr | RMSE (m) | Bias (m) | SI (%) | Corr | RMSE (m) | Bias (m) | SI (%) | |

Quad | 0.87 | 0.449 | 0.015 | 9.77 | 0.92 | 0.365 | 0.018 | 5.64 |

$\mathrm{No}{\sigma}_{HH}^{0}$ | 0.87 | 0.454 | 0.014 | 11.51 | 0.92 | 0.370 | 0.019 | 9.36 |

$\mathrm{No}{\sigma}_{HV}^{0}$ | 0.87 | 0.457 | 0.012 | 9.54 | 0.92 | 0.371 | 0.002 | 6.82 |

$\mathrm{No}{\sigma}_{VH}^{0}$ | 0.86 | 0.472 | 0.011 | 11.92 | 0.91 | 0.374 | 0.016 | 4.49 |

$\mathrm{No}{\sigma}_{VV}^{0}$ | 0.87 | 0.452 | 0.013 | 7.98 | 0.92 | 0.371 | 0.018 | 4.16 |

$\mathrm{No}cva{r}_{HH}$ | 0.87 | 0.453 | 0.013 | 10.14 | 0.92 | 0.371 | 0.020 | 8.08 |

$\mathrm{No}cva{r}_{HV}$ | 0.87 | 0.449 | 0.015 | 5.73 | 0.92 | 0.372 | 0.019 | 3.52 |

$\mathrm{No}cva{r}_{VH}$ | 0.87 | 0.448 | 0.014 | 10.73 | 0.92 | 0.370 | 0.016 | 7.53 |

$\mathrm{No}cva{r}_{VV}$ | 0.87 | 0.461 | 0.010 | 8.62 | 0.92 | 0.370 | 0.016 | 4.36 |

$\mathrm{No}{\lambda}_{cHH}/\beta $ | 0.87 | 0.453 | 0.015 | 2.48 | 0.92 | 0.369 | 0.017 | 2.87 |

$\mathrm{No}{\lambda}_{cHV}/\beta $ | 0.87 | 0.450 | 0.014 | 10.67 | 0.92 | 0.363 | 0.017 | 3.66 |

$\mathrm{No}{\lambda}_{cVH}/\beta $ | 0.87 | 0.451 | 0.014 | 10.01 | 0.92 | 0.365 | 0.017 | 9.38 |

$\mathrm{No}{\lambda}_{cVV}/\beta $ | 0.87 | 0.450 | 0.013 | 4.97 | 0.92 | 0.369 | 0.020 | 5.36 |

**Table 7.**Impact of radiometric calibration on Gaofen-3 SAR SWH estimation for the single polarization mode of HH.

NRCS-Related Input (dB) | PolR | GPR | ||||||
---|---|---|---|---|---|---|---|---|

Corr | RMSE (m) | Bias (m) | SI (%) | Corr | RMSE (m) | Bias (m) | SI (%) | |

${\sigma}_{recal}^{0}$ | 0.76 | 0.469 | −0.002 | 16.05 | 0.89 | 0.330 | 0.014 | 0.65 |

${\sigma}_{official}^{0}$ | 0.73 | 0.492 | −0.008 | 15.01 | 0.87 | 0.354 | 0.015 | 0.69 |

$10{\mathrm{log}}_{10}\langle DN\rangle $ | 0.72 | 0.499 | −0.008 | 14.29 | 0.87 | 0.353 | 0.015 | 0.72 |

$10{\mathrm{log}}_{10}\langle {I}_{s}\rangle $ | 0.67 | 0.532 | −0.013 | 19.67 | 0.79 | 0.443 | −0.005 | 0.24 |

**Table 8.**Impact of radiometric calibration on Gaofen-3 SAR SWH estimation for the dual-polarization mode of HH+HV.

NRCS-Related Input (dB) | PolR | GPR | ||||||
---|---|---|---|---|---|---|---|---|

Corr | RMSE (m) | Bias (m) | SI (%) | Corr | RMSE (m) | Bias (m) | SI (%) | |

${\sigma}_{recal}^{0}$ | 0.83 | 0.386 | 0.006 | 14.78 | 0.91 | 0.283 | 0.004 | 0.18 |

${\sigma}_{official}^{0}$ | 0.79 | 0.428 | 0.017 | 13.53 | 0.90 | 0.297 | 0.010 | 0.49 |

$10{\mathrm{log}}_{10}\langle DN\rangle $ | 0.80 | 0.417 | 0.018 | 13.56 | 0.90 | 0.297 | 0.010 | 0.48 |

$10{\mathrm{log}}_{10}\langle {I}_{s}\rangle $ | 0.72 | 0.486 | 0.008 | 14.83 | 0.86 | 0.358 | 0.011 | 0.53 |

**Table 9.**Impact of radiometric calibration on Gaofen-3 SAR SWH estimation for the quad-polarization mode of HH+HV+VH+VV.

NRCS-Related Input (dB) | PolR | GPR | ||||||
---|---|---|---|---|---|---|---|---|

Corr | RMSE (m) | Bias (m) | SI (%) | Corr | RMSE (m) | Bias (m) | SI (%) | |

${\sigma}_{recal}^{0}$ | 0.82 | 0.399 | −0.013 | 15.21 | 0.92 | 0.265 | −0.012 | 0.53 |

${\sigma}_{official}^{0}$ | 0.77 | 0.466 | −0.015 | 14.26 | 0.92 | 0.268 | −0.011 | 0.49 |

$10{\mathrm{log}}_{10}\langle DN\rangle $ | 0.75 | 0.483 | −0.018 | 15.47 | 0.92 | 0.269 | −0.010 | 0.47 |

$10{\mathrm{log}}_{10}\langle {I}_{s}\rangle $ | 0.72 | 0.496 | −0.026 | 21.04 | 0.85 | 0.367 | −0.010 | 0.46 |

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## Share and Cite

**MDPI and ACS Style**

Fan, C.; Song, T.; Yan, Q.; Meng, J.; Wu, Y.; Zhang, J.
Evaluation of Multi-Incidence Angle Polarimetric Gaofen-3 SAR Wave Mode Data for Significant Wave Height Retrieval. *Remote Sens.* **2022**, *14*, 5480.
https://doi.org/10.3390/rs14215480

**AMA Style**

Fan C, Song T, Yan Q, Meng J, Wu Y, Zhang J.
Evaluation of Multi-Incidence Angle Polarimetric Gaofen-3 SAR Wave Mode Data for Significant Wave Height Retrieval. *Remote Sensing*. 2022; 14(21):5480.
https://doi.org/10.3390/rs14215480

**Chicago/Turabian Style**

Fan, Chenqing, Tianran Song, Qiushuang Yan, Junmin Meng, Yuqi Wu, and Jie Zhang.
2022. "Evaluation of Multi-Incidence Angle Polarimetric Gaofen-3 SAR Wave Mode Data for Significant Wave Height Retrieval" *Remote Sensing* 14, no. 21: 5480.
https://doi.org/10.3390/rs14215480