Wind Speed Retrieval Research Articles

The Fusion of Physical, Textural, and Morphological Information in SAR Imagery for Hurricane Wind Speed Retrieval Based on Deep Learning

This study developed a deep-learning-based model to retrieve sea surface hurricane winds from synthetic aperture radar (SAR) imagery. We introduce the essential idea, residual learning, of the Residual Net into the artificial neural network and design a deep cross-layer concatenation network. The model inputs include SAR measured physical parameters in backscattering energy, the texture feature represented by the grey level co-occurrence matrix, and the morphological hurricane feature. We collected 45 satellite SAR images from Sentinel-1 over hurricane conditions. These images were divided into 39 and 6 for model development and independent testing. A total of 16,127 wind samples acquired from 39 SAR images and simultaneously measured by the Stepped Frequency Microwave Radiometer were collected as model tuning datasets, among which 80% and 20% were used for training and validation. Our validation results show that the deep-learning-based model achieved a correlation coefficient (CORR) and root-mean-square error (RMSE) of 0.98 and 1.72 m/s for wind speeds up to 75 m/s. We further applied the model to six independent SAR images. The model significantly outperformed two existing geophysical algorithms and one backpropagation neural network algorithm with the RMSE is 2.61 m/s and a CORR of 0.95. Moreover, statistical analysis in different wind speed regimes indicates that our model shows a stable performance improvement than comparable algorithms. The RMSE decreases 10%~ 70%, especially the reduction of RMSE is more than 45% at high wind speed (> 42 m/s). Furthermore, adding an independent rainfall estimate to the deep-learning model further enhanced the wind retrieval algorithm.

A New Cyclic Iterative Correction Algorithm for Retrieving Sea Surface Wind Speed Based on a Multilayer-Medium Model

At high sea conditions, e.g., typhoons and hurricanes, the sea surface waves break and produce foams, spray droplets, and bubbles, which are collectively known as whitecaps. Whitecaps covering the rough sea surface greatly absorb the electromagnetic pulses emitted by satellite remote sensors, such as altimeters, scatter-meters, and the Surface Waves Investigation and Monitoring (SWIM). Thus, the whitecaps reduce the sea surface backscattering, which results in the sea surface wind speed being overestimated, an error that is particularly prevalent at high wind speeds. Many algorithms of retrieving wind speeds can retrieve effectively sea surface wind speed when the wind speed is less than 15m/s, but the bias of retrieving wind speed is become bigger and bigger when wind speed is more than 20m/s. In order to eliminate the effect of whitecaps on retrieving the sea surface wind speed at high wind speeds(>15m/s), we propose a Cyclic Iterative Correction Algorithm(CICA). Firstly, a four-layer medium with including whitecaps is established. Secondly, the effect of whitecaps is calculated. Finally, the retrieving and correcting wind speed is continuously repeated. The ultimate goal is to remove the whitecaps’ influence on the backscattering coefficient and re-correct the overestimated wind speed until it reaches a constant value. The wind speeds measured by SWIM were corrected by using CICA, and then were compared with buoy measurements. Those comparisons show that using CICA can improve the wind speed inversion accuracy to 1.5 m/s, an improvement that is particularly evident in high sea conditions.

A Nonparametric Tropical Cyclone Wind Speed Estimation Model Based on Dual-Polarization SAR Observations

The C-band synthetic aperture radar (SAR) observation is one of the most popular sources for high-resolution tropical cyclone (TC) wind speed estimation. The scarcity of high wind speed data with good quality restricts the inversion accuracy of high wind speed. It is a challenge to obtain a high-precision wind speed inversion model with sparse data points. In this paper, the TC wind speed is successfully estimated from the dual-polarization SAR signal. Firstly, the training dataset with a total of 327 data points is formed using the Sentinel-1A EW/IW mode images and their temporal-spatial matched Stepped Frequency Microwave Radiometer (SFMR) measurements. Then, a novel nonparametric TC wind speed estimation model (hereafter NWSE model) is proposed with this dataset by using the Bayesian nonparametric general regression method. The wind speed is interpreted as a function of the cross-polarized normalized radar cross-sections (VH-NRCS) and incident angle for the NWSE model. Moreover, the wind speed obtained from the co-polarized signal is used to improve the accuracy of NWSE model under low wind speed. Finally, the validation results show the excellent overall consistency between the model retrieved wind speed and the collocated SFMR and SMAP measurements. Specifically, considering all the wind speeds, the overall root-mean-square error and absolute bias of NWSE model are 2.85 m/s and 2.26 m/s compared with the SMAP wind speed. When considering the wind speeds larger than 30 m/s, the RMSE and bias of NWSE model are 3.75 m/s and 2.78 m/s, respectively.

Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones

The NASA CYGNSS satellite constellation measures ocean surface winds using the existing network of the Global Navigation Satellite System (GNSS) and was designed for measurements in tropical cyclones (TCs). Here, we focus on using a consistent methodology to validate multiple CYGNSS wind data records currently available to the public, some focusing on low to moderate wind speeds, others for high winds, a storm-centric product for TC analyses, and a wind dataset from NOAA that applies a track-wise bias correction. Our goal is to document their differences and provide guidance to users. The assessment of CYGNSS winds (2017–2020) is performed here at global scales and for all wind regimes, with particular focus on TCs, using measurements from radiometers that are specifically developed for high winds: SMAP, WindSat, and AMSR2 TC-winds. The CYGNSS high-wind products display significant biases in TCs and very large uncertainties. Similar biases and large uncertainties were found with the storm-centric wind product. On the other hand, the NOAA winds show promising skill in TCs, approaching a level suitable for tropical meteorology studies. At the global level, the NOAA winds are overall unbiased at wind regimes from 0–30 m/s and were selected for a test assimilation into a global wind analysis, CCMP, also presented here.

Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry

Abstract. Wind retrieval parameters, i.e. quality indicators and the two-dimensional variational ambiguity removal (2DVAR) analysis speeds, are explored with the aim to improve wind speed retrieval during rain for tropical regions. We apply the well-researched support vector machine (SVM) method in machine learning (ML) to solve this complex problem in a data-oriented regression. To guarantee the effectiveness of SVM, the inputs are extensively analysed to evaluate their appropriateness for this problem, before the results are produced. The comparisons between distributions and differences between data of rain-contaminated winds, corrected winds and good quality C-band winds illustrate that the rain-distorted wind distributions become more nominal with SVM, hence much reducing the rain-induced biases and error variance. Further confirmation is obtained from a case with synchronous Himawari-8 observation indicating rain (clouds) in the scene. Furthermore, the estimation of simultaneous rain rate is attempted with some success to retrieve both wind and rain. Although additional observations or higher resolution may be required to better assess the accuracy of the wind and rain retrievals, the ML results demonstrate benefits of such methodology in geophysical retrieval and nowcasting applications.

FA-RDN: A Hybrid Neural Network on GNSS-R Sea Surface Wind Speed Retrieval

Based on deep learning, this paper proposes a new hybrid neural network model, a recurrent deep neural network using a feature attention mechanism (FA-RDN) for GNSS-R global sea surface wind speed retrieval. FA-RDN can process data from the Cyclone Global Navigation Satellite System (CYGNSS) satellite mission, including characteristics of the signal, spatio-temporal, geometry, and instrument. FA-RDN can receive data extended in temporal dimension and mine the temporal correlation information of features through the long-short term memory (LSTM) neural network layer. A feature attention mechanism is also added to improve the model’s computational efficiency. To evaluate the model performance, we designed comparison and validation experiments for the retrieval accuracy, enhancement effect, and stability of FA-RDN by comparing the evaluation criteria results. The results show that the wind speed retrieval root mean square error (RMSE) of the FA-RDN model can reach 1.45 m/s, 10.38%, 6.58%, 13.28%, 17.89%, 20.26%, and 23.14% higher than that of Backpropagation Neural Network (BPNN), Recurrent Neural Network (RNN), Artificial Neural Network (ANN), Random Forests (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR), respectively, confirming the feasibility and effectiveness of the designed method. At the same time, the designed model has better stability and applicability, serving as a new research idea of data mining and feature selection, as well as a reference model for GNSS-R-based sea surface wind speed retrieval.

Wind Speed Retrieval Algorithm Using Ku-Band Radar Onboard GPM Satellite

The algorithm to retrieve wind speed in a wide swath from the normalized radar cross section (NRCS) was developed for the data of Dual Frequency Precipitation Radar (DPR) operating in scanning mode installed onboard a Global Precipitation Measurement (GPM) satellite. The data for Ku-band radar were used. Equivalent NRCS values at nadir were estimated in a wide swath under the geometrical optics approximation from off-nadir observations. Using these equivalent NRCS nadir values and the sea buoys data, the new parameterization of dependence between NRCS at nadir and the wind speed was obtained. The algorithm was validated using ASCAT (Advanced Scatterometer) data and revealed good accuracy. DPR data are promising for determining wind speed in coastal areas.

Model functions for retrieving sea surface wind speed with wide-swath imaging altimeter

Using satellite remote sensing technology to observe sea surface wind fields has become an important instrument. The successful launch of Tiangong-2, the future Surface Water and Ocean Topography (SWOT) mission, and demonstrating GaoFen Project provides the future understanding for the generation of new wide-swath imaging altimeter. The constructed data sets and validated data sets of the model function were obtained by combining the observable conditions of imaging altimeter with the theoretical model of sea surface backscattering coefficient. A model function for wind speed retrieval at incidence angles from 1 deg to 7 deg and from 7 deg to 11 deg was proposed, which is suitable for wide-swath imaging altimeter, model coefficients were calculated using the constructed data sets, model functions were validated using the validated data sets. The validated results of model function show that the root-mean-squared error (RMSE) ranged from 0.005 to 0.72 m / s. Wind speed was retrieved with China France Oceanography Satellite (CFOSAT) Surface Waves Investigation and Monitoring instrument (SWIM) L2 data for incidence angles from 3 deg to 10 deg with 0.5 interval, and wind speed was validated with European Centre for Medium-Range Weather Forecasts (ECMWF) data. The results of wind speed validation show that the average of RMSE is 1.6 m / s. Therefore, the wind speed retrieval was validated with the wind speed from ECMWF, which showed good coincidence.

A Study of Sea Surface Rain Identification Based on HY-2A Scatterometer

Rain affects the wind measurement accuracy of the Ku-band spaceborne scatterometer. In order to improve the quality of the retrieved wind field, it is necessary to identify and flag rain-contaminated data. In this study, an HY-2A scatterometer is used to study rain identification. In addition to the conventional parameters, such as the retrieved wind speed, the wind direction relative to the along-track direction, and the normalized beam difference, the experiment expands the mean deviation of the backscattering coefficient, the beam difference between fore and aft, and the node number of the wind vector cell (WVC) as the sensitive parameters according to the microwave scattering characteristics of rain and the actual measurement situation of the HY-2A. Furthermore, a rain identification model for HY2 (HY2RRM) with the K-Nearest Neighborhood (KNN) algorithm was built. After several tests, the accuracy of the selected HY2RRM approach is found to about 88%, and about 70% of rain-contaminated data can be accurately identified. The research results are helpful for better understanding the characteristics of microwave backscattering and provide a possible way to further improve the wind field retrieval accuracy of the HY-2A scatterometer and other Ku-band scatterometers.

Sea Surface Salinity and Wind Speed Retrievals Using GNSS-R and L-Band Microwave Radiometry Data from FMPL-2 Onboard the FSSCat Mission

The Federated Satellite System mission (FSSCat), winner of the 2017 Copernicus Masters Competition and the first ESA third-party mission based on CubeSats, aimed to provide coarse-resolution soil moisture estimations and sea ice concentration maps by means of the passive microwave measurements collected by the Flexible Microwave Payload-2 (FMPL-2). The mission was successfully launched on 3 September 2020. In addition to the primary scientific objectives, FMPL-2 data are used in this study to estimate sea surface salinity (SSS), correcting for the sea surface roughness using a wind speed estimate from the L-band microwave radiometer and GNSS-R data themselves. FMPL-2 was executed over the Arctic and Antarctic oceans on a weekly schedule. Different artificial neural network algorithms have been implemented, combining FMPL-2 data with the sea surface temperature, showing a root-mean-square error (RMSE) down to 1.68 m/s in the case of the wind speed (WS) retrieval algorithms, and RMSE down to 0.43 psu for the sea surface salinity algorithm in one single pass.

Cyclonic wind speed retrieval based on Bayesian regularized neural network using CYGNSS data

The destruction created by cyclones depends upon their intensity. Ocean wind is the crucial parameter to understand and forecast the intensity of cyclones. During cyclones, due to dynamic ocean conditions and the limited data availability, a neural network with the regularization approach is used to retrieve cyclonic wind (CW) speed. Several optimization algorithms are compared to get a robust neural network. The Bayesian regularization with the Levenberg–Marquardt optimized network (BNN) is found suitable to develop the geophysical model to retrieve CW speed using Cyclone Global Navigation Satellite System (CYGNSS) measurements. To select the suitable observables for BNN, sensitivity analysis of CYGNSS observables is carried out. The root-mean-square difference between retrieved CW speed and the airborne radiometer data is found to be 4.35 ms − 1, which is smaller than the value quoted in the literature for CYGNSS CW speed. Further, a rigorous analysis is also done to find out the effect of the rain on the retrieved wind speed. Independent validation of our approach is carried out using Soil Moisture Active Passive (SMAP) radiometer high-wind data with a special case of category-5 cyclone Lorenzo. The results support that the proposed algorithm agrees well with the SMAP CW data.

Tropical Cyclone Wind Speeds from WindSat, AMSR and SMAP: Algorithm Development and Testing

The measurement of ocean surface wind speeds in precipitation from satellite microwave radiometers is a challenging task. Rain attenuates the signal that is emitted from the ocean surface. Moreover, the rain and wind signals are very similar, which makes it difficult to distinguish wind from rain. The rain contamination can be mitigated for radiometers that operate simultaneously at C-band and X-band channels, such as WindSat, AMSR-E and AMSR2. The basic principle is to use combinations between C-band and X-band channels that are sensitive to wind speed but relatively insensitive to rain. Based on this principle, we have developed algorithms for retrieving wind speeds in rain from the WindSat and AMSR sensors. These algorithms are statistical regressions and are trained specifically under tropical cyclone conditions. We lay out the steps of the algorithm development, training, and testing. The major source for training the algorithm is provided by wind speeds from the SMAP L-band radiometer, which have been proven to provide reliable wind speeds in strong storms and are not affected by rain. We show that the WindSat and AMSR tropical cyclone wind algorithms perform well under precipitation where standard passive wind speed retrievals fail. We examine the possibility of extending the C/X-band tropical cyclone wind algorithm to X/K-band channels and discuss how it can be broadened from tropical cyclone conditions to global winds in rain retrievals.

Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network

This paper demonstrates the capability and performance of sea surface wind speed retrieval in coastal regions (within 200 km away from the coastline) using spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data from NASA's Cyclone GNSS (CYGNSS) mission. The wind speed retrieval is based on the Artificial Neural Network (ANN). A feedforward neural network is trained with the collocated CYGNSS Level 1B (version 2.1) observables and the wind speed from European Centre for Medium-range Weather Forecast Reanalysis 5th Generation (ECMWF ERA5) data in coastal regions. An ANN model with five hidden layers and 200 neurons in each layer has been constructed and applied to the validation set for wind speed retrieval. The proposed ANN model achieves good wind speed retrieval performance in coastal regions with a bias of −0.03 m/s and a RMSE of 1.58 m/s, corresponding to an improvement of 24.4% compared to the CYGNSS Level 2 (version 2.1) wind speed product. The ANN based retrievals are also compared to the ground truth measurements from the National Data Buoy Center (NDBC) buoys, which shows a bias of −0.44 m/s and a RMSE of 1.86 m/s. Moreover, the sensitivities of the wind speed retrieval performance to different input parameters have been analyzed. Among others, the geolocation of the specular point and the swell height can provide significant contribution to the wind speed retrieval, which can provide useful reference for more generic GNSS-R wind speed retrieval algorithms in coastal regions.

Compact Polarimetry Synthetic Aperture Radar Ocean Wind Retrieval: Model Development and Validation

AbstractWe have developed C-band compact polarimetry geophysical model functions for RADARSAT Constellation Mission ocean surface wind speed retrieval. A total of 1594 RADARSAT-2 images acquired in quad-polarization SAR imaging mode were collocated with in situ buoy observations. This dataset is first used to simulate compact polarimetric data and to examine their dependencies on radar incidence angle and wind vectors. We find that right circular transmit, right circular receive (RR-pol) radar backscatters are less sensitive to incidence angles and wind directions but are more dependent on wind speeds, compared to right circular transmit, horizontal receive (RH-pol), right circular transmit, vertical receive (RV-pol), and right circular transmit, left circular receive (RL-pol). Subsequently, the matchup data pairs are used to derive the coefficients of the transfer functions for the proposed compact polarimetric geophysical model (CMOD) functions, and to validate the associated wind speed retrieval accuracy. Statistical comparisons show that the retrieved wind speeds from CMODRH, CMODRV, CMODRL, and CMODRR are in good agreement with buoy measurements, with root-mean-square errors of 1.38, 1.51, 1.47, and 1.25 m s−1, respectively. The results suggest that compact polarimetry is a good alternative to linear polarization for wind speed retrieval. CMODRR is more appropriate to retrieve high wind speeds than CMODRH, CMODRV or CMODRL.

Retrieving Aerosol Optical Depth and High Spatial Resolution Ocean Surface Wind Speed From CALIPSO: A Neural Network Approach

A neural network nonlinear regression algorithm is developed for retrieving ocean surface wind speed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements. The neural network is trained with CALIPSO ocean surface and atmospheric backscatter measurements together with collocated Advanced Microwave Scanning Radiometer for EOS (AMSR-E) ocean surface wind speed. Ocean surface wind speeds are derived by applying the neural network algorithm to CALIPSO measurements between 2008 and 2020. CALIPSO wind speed measurements of 2015 are also compared with Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements on the Global Change Observation Mission–Water “Shizuku” (GCOM-W) satellite. Aerosol optical depths are then derived from CALIPSO’s ocean surface backscatter signal and theoretical ocean surface reflectance calculated from CALIPSO wind speed and Cox-Munk wind–surface slope variance relation. This CALIPSO wind speed retrieval technique is an improvement from our previous studies, as it can be applied to most clear skies with optical depths up to 1.5 without making assumptions about aerosol lidar ratio.

Impact of rain on wave retrieval from Sentinel-1 synthetic aperture radar images in tropical cyclones

The main objective of our work was to investigate the impact of rain on wave observations from C-band (~5.3 GHz) synthetic aperture radar (SAR) in tropical cyclones. In this study, 10 Sentinel-1 SAR images were available from the Satellite Hurricane Observation Campaign, which were taken under cyclonic conditions during the 2016 hurricane season. The third-generation wave model, known as Simulating WAves Nearshore (SWAN) (version 41.31), was used to simulate the wave fields corresponding to these Sentinel-1 SAR images. In addition, rainfall data from the Tropical Rainfall Measuring Mission satellite passing over the spatial coverage of the Sentinel-1 SAR images were collected. The simulated results were validated against significant wave heights (SWHs) from the Jason-2 altimeter and European Centre for Medium-Range Weather Forecasts data, revealing a root mean square error (RMSE) of ~0.5 m with a 0.25 scatter index. Winds retrieved from the VH-polarized Sentinel-1 SAR images using the Sentinel-1 Extra Wide-swath Mode Wind Speed Retrieval Model after Noise Removal were taken as prior information for wave retrieval. It was discovered that rain did indeed affect the SAR wave retrieval, as evidenced by the 3.21-m RMSE of SWHs between the SAR images and the SWAN model, which was obtained for the ~1000 match-ups with raindrops. The raindrops dampened the wave retrieval when the rain rate was < ~5 mm/hr; however, they enhanced wave retrieval for higher rain rates. It was also found that the portion of the rain-induced ring wave with a wave number > 0.05 rad/m (~125 m wavelength) was clearly observed in the SAR-derived wave spectra.

Sea surface wind speed retrieval and validation with future SWOT data

The paper focuses on the sea surface wind speed retrieval and validation using the future SWOT data. Ka band backscatter coefficient and wind speed were simulated by using theoretical model of Quasi-specular scatter and the simulated position of observation point. The Ka band model function of incident angles from 1° to 4° was proposed based the form of KuLMOD2. Wind speed was retrieved by using Ka band model function, and the results of validation show that the RMSE (Root Mean Squared Error) is 0.35. Therefore, the wind speed retrieval was validated with the ECMWF, which showed good coincidence. Ka band model function was proposed for applicable to the future SWOT data.

Multi-Parameter Neural Network for Altimeter Tropical Cyclone Wind Speed Estimation

The ability of satellite altimeter to estimate wind speed in tropical cyclone condition has been investigated. In the extreme condition with higher spatio-temporal variation, the ocean-atmosphere interaction is very complex and makes the existing algorithm become an ill-posed solution. In such condition, the developed algorithm from single frequency backscatter and significant waves height were insufficient. Besides, wind speed estimates become saturated at high regimes and the reflected backscatter was contaminated by rain. Therefore, other simultaneously observed parameters are needed to comprehensively account for this condition and is expected to improve the accuracy of wind speed retrieval. Aside from altimeter instrument, the microwave radiometer onboard Jason-2 concurrently records the brightness temperature and the rain information. To accommodate related multiple parameters for wind speed derivation, the neural network approach is proposed. Its unique advantage is relationship among multi-parameters can be easily established without prior knowledge on their physical attributes. Therefore, this study intended to determine the multi-parameter neural network (MPNN) model in estimating altimeter wind speed during the tropical cyclone condition. The results proved that the MPNN technique has potential in reducing the root mean square error by 30% in comparison between tropical cyclone wind speed estimate by the existing algorithm.

Influence of Swell Wave on Wind Speed Retrieval Using ENVISAT ASAR Wave Mode Imagery

The main work of this paper is to explore the influence of swell wave on retrieval of wind speed using ENVISAT ASAR wave mode imagery. The normalized radar cross section (NRCS) scene under different sea states is simulated to investigate the relationship between NRCS variation with swell height, together with swell direction. Moreover, the key parameter of imagery variance (Cvar) is selected to describe the swell wave on SAR imagery. In addition, the imagery parameters of skewness and kurtosis are together analyzed as a function of collocated significant swell wave height and wind speed. Based on the analyzed results, a new method for wind speed retrieval is proposed using ENVISAT ASAR, namely, F(n). Besides the CMOD parameters of NRCS, incidence angle, and relative wind direction, the imagery parameters of Cvar, skewness, and kurtosis are used to compensate for the influence of swell wave on wind speed retrieval in F(n). Finally, the collocated European Centre for Medium‐Range Weather Forecast (ECMWF) wind speed dataset and ENVISAT ASAR wave mode imagery are used to verify the retrieval precision and compare with CMOD functions. It is concluded that the F(n) model performs much better than other CMOD functions, with a correlation of 0.89, a bias of 0.08, a RMSE of 1.2 m/s, and an SI of 0.1.

Wind speed retrieval from the Gaofen-3 synthetic aperture radar for VV- and HH-polarization using a re-tuned algorithm

ABSTRACT In this study, a re-tuned algorithm based on the geophysical model function (GMF) C-SARMOD2 is proposed to retrieve wind speed from Synthetic Aperture Radar (SAR) imagery collected by the Chinese C-band Gaofen-3 (GF-3) SAR. More than 10,000 Vertical-Vertical (VV) and Horizontal-Horizontal (HH) polarization GF-3 images acquired in quad-polarization stripmap (QPS) and wave (WV) modes have been collected during the last three years, in which wind patterns are observed over open seas with incidence angles ranging from 18° to 52°. These images, collocated with wind vectors from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis at 0.125° resolution, are used to re-tune the C-SARMOD2 algorithm to specialize it for the GF-3 SAR (CSARMOD-GF). In particular, the CSARMOD-GF performs differently from the C-SARMOD2 at low-to-moderate incidence angles smaller than about 34°. Comparisons with wind speed data from the Advanced Scatterometer (ASCAT), Chinese Haiyang-2B (HY-2B) and buoys from the National Data Buoy Center (NDBC) show that the root-mean-square error (RMSE) of the retrieved wind speed is approximately 1.8 m/s. Additionally, the CSARMOD-GF algorithm outperforms three state-of-the-art methods – C-SARMOD, C-SARMOD2, and CMOD7 – that, when applied to GF-3 SAR imagery, generating a RMSE of approximately 2.0–2.4 m/s.