Retrieval of Rain Rates for Tropical Cyclones From Sentinel-1 Synthetic Aperture Radar Images

Zhao, Xianbin; Shao, Weizeng; Lai, Zhengzhong; Jiang, Xingwei

doi:10.1109/jstars.2023.3255922

Cited by 10 publications

(4 citation statements)

References 60 publications

(72 reference statements)

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“…Since 2018, tropical cyclones have been captured by S-1 during the Satellite Hurricane Observation Campaign [16]. In the literature, the wind speed and SWH are explicitly related following the fetch-and duration-limited features inside a tropical cyclone [46].…”

Section: Discussionmentioning

confidence: 99%

“…The co-polarized (VV and horizontal-horizontal (HH)) GMFs have been further improved through SAR measurements, i.e., C-SARMOD for Sentinel-1 (S-1) [12], C-SARMOD2 [13] for RADARSAT-2 (R-2), and CSARMOD-GF for Gaofen-3 (GF-3) [14]. Other studies have been conducted for wind retrieval using the SAR-derived azimuthal cut-off wavelength [15,16] and theoretical backscattering model [17]. However, due to the saturation of the co-polarized SAR backscattering signal at the regular sea state [18] and at a strong wind speed of >25 m/s (i.e., cyclonic wind profile), GMF usually inverts cross-polarized vertical-horizontal and horizontal-vertical images [19,20] using a machine learning method [21].…”

Section: Introductionmentioning

confidence: 99%

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A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning

Leng,

Hao,

Shao

et al. 2024

Remote Sensing

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This study introduces a new machine learning-based algorithm for the retrieving significant wave height (SWH) using synthetic aperture radar (SAR) images. This algorithm is based on the azimuthal cut-off wavelength and was developed in quad-polarized stripmap (QPS) mode in coastal waters. The collected images are collocated with a wave simulation from the numeric model, called WAVEWATCH-III (WW3), and the current speed from the HYbrid Coordinate Ocean Model (HYCOM). The sea surface wind is retrieved from the image at the vertical–vertical polarization channel, using the geophysical model function (GMF) CSARMOD-GF. The results of the algorithm were validated against the measurements obtained from the Haiyang-2B (HY-2B) scatterometer, yielding a root mean squared error (RMSE) of 1.99 m/s with a 0.82 correlation (COR) and 0.27 scatter index of wind speed. It was found that the SWH depends on the wind speed and azimuthal cut-off wavelength. However, the current speed has less of an influence on azimuthal cut-off wavelength. Following this rationale, four widely known machine learning methods were employed that take the SAR-derived azimuthal cut-off wavelength, wind speed, and radar incidence angle as inputs and then output the SWH. The validation result shows that the SAR-derived SWH by eXtreme Gradient Boosting (XGBoost) against the HY-2B altimeter products has a 0.34 m RMSE with a 0.97 COR and a 0.07 bias, which is better than the results obtained using an existing algorithm (i.e., a 1.10 m RMSE with a 0.77 COR and a 0.44 bias) and the other three machine learning methods (i.e., a >0.58 m RMSE with a <0.95 COR), i.e., convolutional neural networks (CNNs), Support Vector Regression (SVR) and the ridge regression model (RR). As a result, XGBoost is a highly efficient approach for GF-3 wave retrieval at the regular sea state.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning

Leng,

Hao,

Shao

et al. 2024

Remote Sensing

Self Cite

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“…Although this method works well for homogeneous images, other marine phenomena [35], e.g., eddies and internal waves, also produce the same feature on the spatial scale of several kilometers, and these non-wind streaks contaminate the wind direction retrieval. In addition, rain cells [36], up/down-welling, and fronts [37] also distort the homogeneity of the image. The third method is to utilize the linear features in SAR images by using image processing techniques, e.g., local gradient [38] and wavelet analysis [39][40][41][42][43].…”

Section: Machine Learning-based Wind Direction Retrieval From Quad-po...mentioning

confidence: 99%

Machine Learning-Based Wind Direction Retrieval From Quad-Polarized Gaofen-3 SAR Images

Shao,

Zhou,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In our study, an intelligent method for inverting wind direction from quad-polarized Gaofen-3 (GF-3) synthetic aperture radar (SAR) images is proposed. Specifically, 11300 acquired in wave (WAV) mode are used to retrieve the wind directions using a spectrum-transformation approach and prior information from European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis at version 5 (ERA-5) data at 1-hour intervals with 0.25° grids. The dependence of the wind direction on the polarimetric correlation coefficient (PCC) between the co-(vertical-vertical (VV) and horizontal-horizontal (HH)) and cross-polarization (vertical-horizontal (VH) and horizontal-vertical (HV)) channels is studied. It is found that the PCCs in four combination polarizations have asymmetric characteristics with respect to the wind direction with correlation coefficients (CORs) of greater than 0.4 or less than -0.4. Following this rationale, the scheme for inverting wind direction from quad-polarized SAR is trained according to machine learning, in which the matrix PCCs, wind directions, azimuthal angles, and slopes from SAR intensity spectra at the peaks are used as inputs. Subsequently, this intelligent approach is applied to 1300 images in quad-polarization stripmap (QPS) mode, and the retrieval results are validated against advanced scatterometer (ASCAT) measurements. The statistical analysis shows that the root mean squared error (RMSE) of the wind direction is 17.7°, the COR is 0.98, and the scatter index (SI) is 0.11. In addition, the wind speeds inverted using a geophysical model function (GMF) CSARMOD-GF are compared with well-calibrated ASCAT products, resulting in an RMSE of 1.85 m/s, a COR of 0.78, and an SI of 0.28 for the wind speed. Thus, this work provides an automatic scheme for inverting wind from quad-polarized GF-3 SAR images without any external information.

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“…Shao et al [13] simulated VV-polarized NRCS using the geophysical model function (GMF) CMOD5.N based on dual-polarized channel SAR images, and retrieved TC wind speed using VH-polarized images, and an empirical algorithm for SAR rain rate retrieval that considers the influence of the maximum wind speed radius has been proposed. Using the dual-polarized channel SAR images as a foundation, Zhao et al [14] established a method for extracting the tropical cyclone rain rate from C-band SAR image by using GMF S1IW.NR and the difference between the observed NRCS and the simulated NRCS in the rainfall unit. However, despite these advancements, it still remains challenges in observing hurricanes and rain rate effectively.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Hurricane Rain Rate from SAR Images Based on Artificial Neural Network

Liu,

Ai,

Zhao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Spaceborne synthetic aperture radar (SAR) is gradually being applied to hurricane observation because of its allweather, high-resolution observation capability. In particular, the retrieval of rain rate using SAR images holds significant scientific and practical importance. However, accurately retrieving rain rate over the sea surface, particularly for high rain rate events under hurricane conditions, remains a significant challenge. The study proposes a new method for rain rate retrieval from hurricane SAR images. We've developed a cascaded feedforward neural network (CFNN) model that based on Sentinel-1's doublepolarized C-band SAR images of 46 hurricanes to retrieve rain rate under hurricane conditions. In order to overcome the problem of local optimal solution of neural network, genetic algorithm is employed for optimized model parameter selection. Preliminary results indicate that this approach not only enhances the neural network's iteration speed but also improves its prediction accuracy. Compared with the rain rate of the Stepped-Frequency Microwave Radiometers (SFMR), the RMSE of retrieved rain rate is 3.05 mm/hr and the correlation coefficient is 0.88. Furthermore, we independently verify rain rate during hurricane Douglas and compared with Global Precipitation Mission (GPM) 2ADPR rain rate product, the results demonstrate that our model can effectively retrieve rain rate in the range of 0-60 mm/hr under hurricane conditions. The encouraging results prove the feasibility of the method in SAR rain rate retrieval.

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Retrieval of Rain Rates for Tropical Cyclones From Sentinel-1 Synthetic Aperture Radar Images

Cited by 10 publications

References 60 publications

A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning

A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning

Machine Learning-Based Wind Direction Retrieval From Quad-Polarized Gaofen-3 SAR Images

Retrieval of Hurricane Rain Rate from SAR Images Based on Artificial Neural Network

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