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Wave parameters retrieval for dual-polarization C-band synthetic aperture radar using a theoretical-based algorithm under cyclonic conditions
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Yingying DING1, Juncheng ZUO1, Weizeng SHAO1, *, Jian SHI2, Xinzhe YUAN3, Jian SUN4, Jiachen HU1, Xiaofeng LI5
Acta Oceanologica Sinica | 2019, 38(5) : 21 - 31
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Acta Oceanologica Sinica | 2019, 38(5): 21-31
• Physical Oceanography,Marine Meteorology and Marine Physics •
Wave parameters retrieval for dual-polarization C-band synthetic aperture radar using a theoretical-based algorithm under cyclonic conditions
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Yingying DING1, Juncheng ZUO1, Weizeng SHAO1, *, Jian SHI2, Xinzhe YUAN3, Jian SUN4, Jiachen HU1, Xiaofeng LI5
Affiliations
  • 1 Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316000, China
  • 2 College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 210007, China
  • 3 National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing 100081, China
  • 4 Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China
  • 5 Global Science and Technology, National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS), College Park, MD 20740, USA
Published: 2019-05-25 doi: 10.1007/s13131-019-1438-y
Outline
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Theoretical-based ocean wave retrieval algorithms are applied by inverting a synthetic aperture radar (SAR) intensity spectrum into a wave spectrum, that has been developed based on a SAR wave mapping mechanism. In our previous studies, it was shown that the wave retrieval algorithm, named the parameterized first-guess spectrum method (PFSM), works for C-band and X-band SAR at low to moderate sea states. In this work, we investigate the performance of the PFSM algorithm when it is applied for dual-polarization c-band sentinel-1 (S-1) SAR acquired in extra wide-swath (EW) and interferometric wide-swath (IW) mode under cyclonic conditions. Strong winds are retrieved from six vertical-horizontal (VH) polarization S-1 SAR images using the c-band cross-polarization coupled-parameters ocean (C-3PO) model and then wave parameters are obtained from the image at the vertical-vertical (VV) polarization channel. significant wave height (SWH) and mean wave period (MWP) are compared with simulations from the WAVEWATCH-III (WW3) model. The validation shows a 0.69 m root mean square error (RMSE) of SWH with a –0.01 m bias and a 0.62 s RMSE of MWP with a –0.17 s bias. Although the PFSM algorithm relies on a good quality SAR spectrum, this study confirms the applicability for wave retrieval from an S-1 SAR image. Moreover, it is found that the retrieved results have less accuracy on the right sector of cyclone eyes where swell directly affects strong wind-sea, while the PFSM algorithm works well on the left and rear sectors of cyclone eyes where the interaction of wind-sea and swell is relatively poor.

wave parameters  /  synthetic aperture radar  /  cyclonic condition
Yingying DING, Juncheng ZUO, Weizeng SHAO, Jian SHI, Xinzhe YUAN, Jian SUN, Jiachen HU, Xiaofeng LI. Wave parameters retrieval for dual-polarization C-band synthetic aperture radar using a theoretical-based algorithm under cyclonic conditions[J]. Acta Oceanologica Sinica, 2019 , 38 (5) : 21 -31 . DOI: 10.1007/s13131-019-1438-y
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1 Introduction
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Ocean waves are the main feature of the upper ocean dynamic processes and play an important role in atmosphere-ocean interactions. Moreover, an extreme wave is also a natural hazard in coastal waters. Therefore, waves are a crucial factor in oceanography and offshore engineering. At present, waves are derived from operational wave models and are usually forced by forecast winds. However, forecast waves have less accuracy when using less accurate values for winds. Satellites carrying an altimeter sensor are a remote-sensing technique for wave monitoring over global seas. For instance, open-access wave data derived from such satellites as Topex/Poseidon, Jason-2 and HY-2, have been popularly used by investigators world-wide. The spatial coverage of an altimeter wave is relatively small (~10 km) with a revisit frequency of 10–15 d and the available wave data from an altimeter only covers the track footprints of a satellite orbit. This kind of limitation does not satisfy the requirements of marine research, especially when researching cyclonic conditions. Synthetic aperture radar (SAR) has the capability of monitoring the sea surface with a large swath coverage (up to 600 km) and fine spatial resolution (up to 1 m). SAR-derived waves can improve wave forecasts and make up the gaps in remotely sensed measurements at coastal waters.
Traditionally, the methodology of a wave retrieval algorithm was exploited for deriving a wave spectrum from a SAR intensity spectrum based on the SAR wave mapping mechanism, e.g., tilt modulation, hydrodynamic modulation (Alpers et al., 1981), and non-linear velocity bunching (Alpers and Bruening, 1986). To date, these algorithms have included the “max-planck institute” (MPI) algorithm (Hasselmann and Hasselmann, 1991), the semi parametric retrieval algorithm (SPRA) (Mastenbroek and de Valk, 2000), the partition rescaling and shift algorithm (PARSA) (Schulz-Stellenfleth et al., 2005), and the parameterized first-guess spectrum method (PFSM) (He, 1999; Sun and Guan, 2006). In particular, it has been shown that the PFSM algorithm works for C-band (Lin et al., 2017) and X-band (Shao et al., 2015) SAR at low to moderate sea states. It is necessary to understand that prior information on wind is needed in order to produce a first-guess wave spectrum when applying the theoretical-based algorithms that are used to deal with the modulation transfer function (MTF) of non-linear velocity bunching. Moreover, the MTF of velocity bunching was theoretically derived at low to moderate sea state (Hasselmann and Hasselmann, 1991), therefore, the applicability MTF of velocity bunching at high sea seta needs to be studied. In practice, wind can be directly retrieved from a SAR image.
The geophysical model function (GMF) is commonly applied for wind retrieval from a SAR image. It describes the empirical relationship between a SAR-measured backscattering signal in co-polarization (vertical-vertical (VV) and horizontal-horizontal (HH)), called the normalized radar cross section (NRCS), and a wind vector at 10 m height above the sea surface (Masuko et al., 1986). C-band GMFs include CMOD4 (Stoffelen and Anderson, 1997), CMOD-IFR2 developed at the Institut Francais de Recherche pour Exploitation de la MER (IFREMER) (Quilfen et al., 1998), CMOD5N for neutral wind (Hersbach, 2010), C-SARMOD (Mouche and Chapron, 2015), CMOD7 (Stoffelen et al., 2017) and C-SARMOD2 (Lu et al., 2018). The CMOD family has been well studied over the last few decades and has been implemented for various C-band SAR data, e.g., ENVISAT-ASAR (Yang et al., 2011a), RADARSAT-1/2 (R-1/2) (Yang et al., 2011b; Shao et al., 2014) and Sentinel-1A/1B (S-1) (Monaldo et al., 2016) and Gaofen-3 (GF-3) (Shao et al., 2017a, 2019). However, the backscattering signal encounters a saturation problem at strong winds (probably at wind speeds greater than 25 m/s) (Hwang et al., 2010; Voronovich and Zavorotny, 2014; Shao et al., 2017b), indicating these GMFs do not work under these conditions. Under these circumstances, the PFSM algorithm is not applicable for typhoon and hurricane wave retrieval from SAR images.
Several empirical models, e.g., CWAVE_ERS for ERS-1/2 (Schulz-Stellenfleth et al., 2007), CWAVE_ENVI for ENVISAT-ASAR (Li et al., 2011), CWAVE_S1 for S-1 (Stopa and Mouche, 2007) and CSAR_WAVE for GF-3 (Sheng et al., 2018), have also been developed, and are aimed at directly retrieving the wave parameters from SAR images without having to calculate the complex MTF of each modulation. These models work at low to moderate sea states, because data collection taken at high sea states is unavailable in the tuning process. Although two empirical models are preliminarily exploited for wave retrieval in hurricanes and typhoons (Romeiser et al., 2015; Shao et al., 2018a), these have only been tuned through a few images and simulations from a numerical wave model and they need to be refitted for different SAR bands and imaging modes.
Several recent works are devoted to study the characteristic of typhoon/hurricane generated waves on SAR (Mouche et al., 2017; Hwang and Walsh, 2016; Zhang et al., 2018), however, typhoon and hurricane wave monitoring is still a challenge for the SAR research community. Interestingly, strong winds (up to 55 m/s) can be retrieved from cross-polarization (basically vertical-horizontal, VH) NRCS, because cross-polarization NRCS does not saturate as easily as the co-polarization backscattering signal (Hwang and Fois, 2015; Shao et al., 2017a). Recently, a methodology for strong wind retrieval using C-band cross-polarization NRCS was developed for R-2 SAR (Zhang and Perrie, 2012; Shen et al., 2014; Zhang et al., 2017) and GF-3 SAR (Shao et al., 2018c). This issue presents an interesting question and that is whether the MTF of velocity bunching and PFSM algorithm is suitable for wave retrieval at the VV-polarization channel as using a SAR-derived strong wind from an image at the VH-polarization channel under cyclonic conditions.
We organize this paper as follows: S-1 SAR images and other auxiliary data are introduced in Section 2. Section 3 shows the methodologies of cross-polarization strong wind retrieval algorithms, scheme of co-polarization wave retrieval algorithm PFSM and the setup of WW3 model. The validation is presented in Section 4 when comparing the retrieval results with measurements from the simulations of the numeric wave model WAVEWATCH-III (WW3). The discussion is included in Section 5 and the conclusion and summary are given in Section 6.
2 Description of dataset
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In total, six S-1 images with visible cyclone eyes acquired in dual-polarization (VV and VH) during the period of August 27 to September 23, 2016 were made available for this study. These images were acquired in extra wide-swath (EW) and interferometric wide-swath (IW) mode with a pixel size of 40 m and 10 m respectively at both azimuth and range directions. The quick-look images of Typhoon Lionrock, Hurricane Lester, Hurricane Gaston, Hurricane Hermine, and Hurricane Karl overlaying the tracks of cyclones are shown in Fig. 1, in which the maximum wind speeds are up to 60 m/s at the several SAR imaging moments.
Although measurements from a satellite altimeter, e.g., Jason-2, are useful for wave analysis (Liu et al., 2016), wave measurements from altimeter Jason-2 passing the six S-1 SAR images were unavailable. Therefore, the WW3 model (the latest version 5.16) in the spirit of the previous WAM model, which was developed by the National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA), was employed to simulate the wave fields during the period of the five cyclones. Since 1979, the European Centre for Medium-Range Weather Forecasts (ECMWF) has released a daily global reanalysis dataset at intervals of six hours, e.g., sea surface wind and wave parameters, which has a fine spatial resolution. We employed ECMWF winds at 0.125° grids as the forcing fields and water depth information is derived from 30 arc-second topography data consisting of the General Bathymetric Chart of the Oceans (GEBCO) from the British Oceanographic Data Centre (BODC). The WW3 model performs simulations of wave fields well (Bi et al., 2015; Zheng et al., 2016; Liu et al., 2017) and the validation of WW3-simulated hurricane waves against National Data Buoy Center (NDBC) buoys of the NOAA was used in our recent study (see Fig. 8 in Shao et al., 2018c). WW3-simulated wave parameters, e.g., significant wave height (SWH) and mean wave period (MWP), are used to study the accuracy of SAR-derived waves under cyclonic conditions.
In this study, we also use independent sources, e.g., polarimetric radiometer WindSAT, in order to validate the SAR-derived wind speed from VH-polarization S-1 SAR images by using a cross-polarization wind retrieval algorithm. WindSAT is a space borne satellite for measuring sea surface winds using a polarimetric radiometer with a swath coverage of more than 350 km following the orbit, and the standard deviation (STD) of wind speed is about 1.4 m/s as validated against the measurements of aircraft (Meissner and Wentz, 2012). Therefore, WindSAT wind products satisfy the requirements of global wind monitoring, especially in typhoon and hurricane conditions. It is proved that overall root mean square error (RMSE) difference of the retrieved wind speeds from WindSAT with respect to the H* wind analysis data developed by the Hurricane Research Division (HRD) is 2.75 m/s in hurricanes (Zhang et al., 2016). The WindSAT products with wind speed smaller than 30 m/s are used in this study, due to we think the WindSAT winds are most reliable at such wind condition.
3 Methodology
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When applying the PFSM wave retrieval algorithm to retrieve waves, prior information on wind speed is necessary. Therefore, we first present the methodology of the cross-polarization coupled-parameters ocean (C-3PO) model, which was developed for retrieving hurricane winds using cross-polarized SAR NRCS at C-band. Then the theoretically based PFSM wave retrieval algorithm scheme is briefly introduced.
3.1 C-3PO model
It was initially revealed in a study by Vachon and Wolfe (2011) that cross-polarization NRCS has a strong linear relationship with wind speed and a quadratic linear model was developed to retrieved wind speed at low to moderate winds (up to 25 m/s). Later, a C-band cross-polarized ocean surface strong wind retrieval model for dual-polarization SAR, named C-2POD, was developed based on a collocated dataset, including R-2 VH-polarized NRCS, measurements from a stepped-frequency microwave radiometer (SFMR) and wind speeds from the NOAA H* wind model (Zhang and Perrie, 2012). In fact, VH-polarized NRCS has also been related to radar incidence angle based on a statistical analysis (Hwang et al., 2015). However, C-2POD models only include the term of wind speed, taking the following function,
${\sigma _0}={{{p}}_{{1}}}{{ + }}{{{p}}_{{2}}} \times {{{U}}_{{{10}}}},$
where ${\sigma _0}$ is the cross-polarized NRCS united in dB and coefficients p1 and p2 are the tuned constants, U10 is the wind speed at 10 m above sea surface united in m/s.
As proposed in Zhang et al. (2017), an advanced C-3PO model considers the terms of wind speed and radar incidence angle through theoretical analysis by using a hybrid backscattering model. The C-3PO model is stated as follows:
${\sigma _0}{{ = A}}\left( {{{{U}}_{{{10}}}}} \right) \times \left[ {{{1 + B}}\left( \theta \right)} \right],$
${{A(}}{{{U}}_{{{10}}}}{{) = }}{{{a}}_{{1}}} \times {{U}}_{{{10}}}^{{2}}{{ + }}{{{a}}_{{2}}} \times {{{U}}_{{{10}}}}{{ + }}{{{a}}_{{3}}},$
${{B(}}\theta {{) = }}{{{b}}_{{\,1}}} \times \frac{{\theta {{ - 34}}{{.5}}}}{{{{34}}{{.5}}}},$
where ${\sigma _0}$ is the cross-polarized NRCS united in dB, $\theta$ is the radar incidence angle united in degree and matrix a and coefficient b1 are the tuned constants. The C-3PO model has a better performance than the C-2POD model, because a 2.81 m/s RMSE of wind speed is achieved when using the C-3PO model at wind speeds ranging from 9 to 40 m/s, which is less than a 2.90 m/s RMSE using the C-2POD model (Zhang et al., 2017).
3.2 The PFSM algorithm
The advantage of the PFSM algorithm is that a SAR-derived wave spectrum is composed of two portions, including wind-sea and swell spectrum, which are retrieved from two corresponding portions separated from a prior SAR spectrum. The separation wave number ks is calculated using the following equation,
${{{k}}_{\rm{s}}}={\left( {\frac{{{{2}}{{.87g}}{{{V}}^{{2}}}}}{{{{{R}}^{{2}}}{{U}}_{{{10}}}^{{4}}{{\cos}}{ ^{{2}}}\varphi \left( {{{\sin}}{ ^{{2}}}\varphi {{\sin}}{ ^{{2}}}\theta {{ + \cos}}{ ^{{2}}}\varphi } \right)}}} \right)^{0{{.33}}}},$
where g is the gravity acceleration, V is the satellite flight velocity, R is the satellite slant range, U10 is the SAR-derived wind speed, θ is the radar incidence angle and $\varphi$ is the angle of wave propagation direction relative to radar look direction. The portion of the SAR spectrum at wave numbers greater than the separation wave number ks is non-linearly mapped by the wind-sea and the left portion is linearly mapped by the swell.
For wind-sea retrieval, a “first-guess” spectrum is produced using the widely-used parametric JONSWAP model (see Appendix) after searching for several best-fit parameters of the model, including the sea surface wind speed at 10 m height U10, wave propagation velocity at peak cp and wave propagation direction at peak $\varphi $, which are prior obtained from a SAR image, similar to the SPRA scheme. Following this, the MPI scheme is employed to retrieve the wind-sea spectrum by minimizing a cost function (Hasselmann and Hasselmann, 1991). In the meantime, the swell spectrum is directly obtained by inverting the linear-mapping SAR spectrum, which considers the tilt modulation and hydrodynamic modulation (Alpers et al., 1981) without the velocity bunching (Hasselmann and Hasselmann, 1991). The detailed flowchart of retrieval process has been exhibited as Fig. 2 in our previous study (Shao et al., 2015).
According to traditional wave theory, SWH Hs and MWP T0 is calculated using Eqs (4) and (5) from a retrieved one-dimensional wave number spectrum Wk,
${{{H}}_{\rm{s}}}{{ = 4}} \times \sqrt { \int {{{W}}_{{k}}}{{{\rm d}k}}} ,$
${{{T}}_0}=\displaystyle\frac{{ \displaystyle\int {{{W}}_{{k}}}{{{\rm d}k}}}}{{ \displaystyle\int {{{k}}^{{2}}}{{{W}}_{{k}}}{{{\rm d}k}}}}.$
3.3 WW3 model setup
As mentioned in Section 2, ECMWF winds at 0.125° grids are the forcing field for the wave simulation. It should be noted that ECMWF winds have a coarser spatial resolution than water depth data from 30 arc-second GEBCO. Therefore, ECMWF winds and GEBCO bathymetric data are both bi-linearly interpolated to be 0.1° in order to obtain reasonable simulations. The simulated two-dimensional wave spectrum is default resolved into 24 regular azimuthal directions at an interval of 15° and the frequency bins f are logarithmically ranged from 0.041 18 to 0.718 6 at an interval of Δf/f=0.1. The time step of spatial propagation is set to 300 s in both the longitude and latitude directions. In particular, the package of the non-linear term for four wave-wave interactions (quadruplets), named Generalized Multiple Discrete Interaction Approximation (DIA), was implemented for the WW3 model, because it showed a good performance for simulation of typhoon waves according to our recent study (Shao et al., 2018b).
The global simulations from the WW3 model with 0.5° grids are treated as the open boundary. The ultimate WW3-simulated wave fields at 0.2° grids, including the spatial coverages of typhoon and hurricanes, are stored at an interval of 30 min, indicating that the time difference between SAR acquisition time and WW3-simulated results is within 15 min.
4 Validation
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In this section, we first present the validation of SAR-derived wind speed against measurements from WindSAT. Then a comparison between the inverted wave parameters and WW3-simulated results is shown.
4.1 Wind speed
Figure 2 shows the SAR-derived wind maps corresponding to the six S-1 SAR images acquired in VH-polarization, in which the cyclone eyes are clearly observed. Although the retrieved wind speed is up to 40 m/s, the saturation problem is not found which encounters as applying the C-band co-polarization GMFs. However, discontinuities exist in the retrieved wind maps, indicated by more obvious changes of wind speed at the edge of each radar beam than in other regions. This is caused by the instrumental noise of the radar beam, because the S-1 SAR image acquired in EW and IW mode is comprised of several radar beams. In fact, R-2 (Shen et al., 2014) and GF-3 (Shao et al., 2018c) SAR acquired in dual-polarization also suffer from this problem. For this reason, the European Space Agency (ESA) is privately working on reducing the influence of instrumental noise by means of re-calibrating the S-1 SAR image.
The sub-scenes of S-1 SAR images covering the WindSAT grids are used here. Moreover, the time difference between collected sub-scenes and WindSAT winds is less than 1 h. In total, in this study, around 1 000 matchups are available. Figure 3 gives a visual comparison, showing the SAR-derived wind speeds when using the empirical C-3PO model and WindSAT winds for 2 m/s of wind speed bin, in which the error bars represent the standard deviation of each bin. It is found that the RMSE of wind speed is 2.9 m/s with a 1.1 m/s bias and there is a trend of underestimation at wind speeds greater than 10 m/s. It is not surprising that this performance is worse than the standard error 2 m/s of SAR-derived wind speed for co-polarization, because the instrumental noise of the radar beam from S-1 is different from that of the R-2, and the C-3PO model should be adapted for S-1 SAR. However, winds retrieved from VH-polarization SAR images do not encounter the saturation problem. In this situation, we think the retrieved wind is useful for wave retrieval from VV-polarization S-1 SAR images.
4.2 Wave parameters
In the wave retrieval process, the whole S-1 SAR image is divided into a number of sub-scenes with 128×128 pixels, which have a spatial coverage of 1×1 km2 and 4×4 km2 for EW and IW mode, respectively. It is well known that the non-linear effect due to velocity bunching is more pronounced in extreme weather conditions, causing the short waves to be undetectable. Additionally, precipitation also contaminates the SAR backscattering signature of cyclones. In this circumstance, the inhomogeneous sub-scene with poor-quality SAR intensity spectra, where the ratio of image variance and squared image mean is greater than 1.05 (Li et al., 2011), are excluded here. Out of those sub-scenes, $\varphi $ is extracted from the SAR intensity spectrum, which is an indispensable variable when employing the PFSM algorithm.
As an example, we present the sub-scene extracted from the S-1 SAR image taken on September 1, 2016 at 23:44 UTC in Hurricane Hermine, as shown in Fig. 4a. Figure 4b shows the two-dimensional SAR spectrum of the corresponding sub-scene in polar coordinates. The retrieved one-dimensional wave spectrum is shown in Fig. 4c. The SAR-derived SWH is 1.8 m and MWP is 5.4 s, while SWH is 2.4 m and MWP is 5.2 s from the WW3 model.
In order to systematically evaluate the retrieval accuracy of SWH and MWP, we compare the retrieval results with simulations from the WW3 model at strong winds. Figure 5 shows that the RMSE of SWH is 0.69 m with a –0.01 m bias and the RMSE of MWP is 0.62 s with a –0.17 s bias when using the PFSM algorithm. It is found that there is larger deviation at low sea state. We think this is caused by inaccurate wind speeds retrieved from VH-polarization S-1 SAR images at low winds smaller than 5 m/s. The accuracy of wave parameters retrieval is anticipated to be improved using more accurate wind speeds from co-polarization S-1 SAR images at low winds or re-calibrating images with low instrumental noise at the edge of radar beams. The pixel size of collected S-1 SAR images are 40 m and 10 m for azimuth and range directions, respectively, indicating that ocean wave with wave length less than 50 m (approximately a 5 s MWP) cannot been measured due to the coarse spatial resolution of collected SAR images. This is the probable explanation that the minimum MWP of retrieved waves is about 5 s in Fig. 5.
We also compare the SAR-derived SWHs of wind-sea and swell with simulations from WW3 model, as shown in Fig. 6. It is not surprising that a 0.52 RMSE of linear-inverted swell SWH is achieved, which is better than a 0.66 RMSE of nonlinear-inverted wind-sea SWH. In general, the above validation indicates, that the PFSM algorithm can be applied for wave retrieval from a S-1 SAR image under cyclonic conditions, which is similar to wave retrieval from C-band SAR at low to moderate sea states.
5 Discussion
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As pointed in our previous study (Shao et al., 2017a), wave characteristics under extreme weather conditions are complicated (Hwang, 2016; Hwang and Fan, 2017; Hwang and Walsh, 2016; Young, 2017), e.g., wind-sea dominates at the right side of a cyclone movement, wind-sea and cross swell mix at the left side of a cyclone movement and opposing swell dominates at the rear side. The wave system in Hurricane Gaston moving north-eastern in the Northern Hemisphere is illustrated in Fig. 7 (reproduce the Fig. 8 in Shao et al., 2017a). Therefore, we have undertaken further study around the accuracy of SWH and MWP at different parts of cyclone eyes.
Figure 8 shows cases at the right side of a cyclone eye, where wind-sea dominates in typical conditions of strong winds on the right-hand sector. Wind-sea and swell propagate in the same direction, causing the interaction of swell and strong wind-sea energy. This leads to a situation whereby the inverted waves using the PFSM algorithm show a large deviation from the WW3-simulated results (a 0.74 m RMSE of SWH and a 0.71 s RMSE of MWP) especially at SWH greater than 3 m. In contrast, wind-sea and swell orthogonally propagate at the left side of the cyclone eye, where the wind-sea energy is independent of swell energy. Retrieval results show a 0.59 m RMSE of SWH and a 0.52 s RMSE of MWP, as shown in Fig. 9, indicating a good agreement with simulations from the WW3 mode. As for wave retrieval at the rear of the cyclone where wind-sea and swell propagate in opposite directions, Fig. 10 shows that the RMSE of SWH is 0.60 m and RMSE of MWP is 0.42 s.
6 Conclusion and summary
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In our previous studies, it was shown that the theoretically-based PFSM wave retrieval algorithm worked for C- and X-band SAR at low to moderate conditions. From August to September 2016, several cyclones, e.g., Typhoon Lionrock, Hurricane Lester, Hurricane Gaston, Hurricane Hermine, and Hurricane Karl, were captured by S-1. Therefore, it poses an interesting question as to whether wave can be retrieved from an S-1 SAR image acquired in dual-polarization under cyclonic conditions.
Because the co-polarization backscattering signal encounters saturation problems at strong winds, the winds were retrieved from a VH-polarization S-1 SAR image using the C-3PO model. The RMSE of wind speed is 2.9 m/s with a 1.1 m/s bias as compared with a retrieved wind speed of up to 40 m/s using measurements from WindSAT, although the instrumental noise of S-1 SAR results in obvious changes of wind speed around the edge of radar beams. Together with SAR-derived winds, we demonstrate the applicability of the PFSM algorithm at high sea state. Validation against the simulations from the WW3 model shows a 0.69 m RMSE of SWH with a –0.01 m bias and a 0.62 s RMSE of MWP with a –0.17 s bias. We further investigate the performance at different parts of cyclone eyes. The interaction between swell energy and strong wind-sea energy to the right of a cyclone eye leads to less accurate retrieval results than that at left and rear of a cyclone eye, where this type of interaction is relatively poor.
It is concluded that the PFSM algorithm is suitable for estimating wave parameters from a C-band S-1 SAR image under cyclonic conditions, although the PFSM algorithm does rely on a good quality of SAR spectrum. The adaptability of the PFSM algorithm in details will be further studied through more images the improvement of MTF at high winds, e.g., the change of sea water dielectric coefficient and feature of azimuthal cut-off of velocity bunching.
Acknowledgements
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We appreciate the provision by the National Centers for Environmental Prediction (NCEP) of National Oceanic and Atmospheric Administration (NOAA) of the source code for the WAVEWATCH-III (WW3) model supplied free of charge. We also thank the following: European Space Agency (ESA) for providing Sentinel-1 (S-1) synthetic aperture radar (SAR) images via https://scihub.copernicus.eu. The European Centre for Medium-Range Weather Forecasts (ECMWF) for providing reanalysis wind data at a 0.125° grid that can be openly downloaded via http://www.ecmwf.int. The General Bathymetry Chart of the Oceans (GEBCO) for data downloaded via: ftp.edcftp.cr.usgs.gov. The information on cyclones provided by NOAA was downloaded via https://coast.noaa.gov/hurricanes. WindSAT winds at a 0.25° grid we kindly provided by the Remote Sensing System (RSS) team, which authorizes an account issued for downloading the data via the sever: ftp.remss.com. The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official NOAA or U.S. Government position, policy or decision.
Funding
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  • The National Key Research and Development Program of China under contract No. 2017YFA0604901; the National Natural Science Foundation of China under contract Nos 41806005 and 41776183; the Public Welfare Technical Applied Research Project of Zhejiang Province of China under contract No. LGF19D060003.
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doi: 10.1007/s13131-019-1438-y
  • Receive Date：2019-02-20
  • Online Date：2026-03-31
  • Published：2019-05-25
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  • Received：2019-02-20
  • Accepted：2019-03-11
Funding
The National Key Research and Development Program of China under contract No. 2017YFA0604901; the National Natural Science Foundation of China under contract Nos 41806005 and 41776183; the Public Welfare Technical Applied Research Project of Zhejiang Province of China under contract No. LGF19D060003.
Affiliations
    1 Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316000, China
    2 College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 210007, China
    3 National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing 100081, China
    4 Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China
    5 Global Science and Technology, National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS), College Park, MD 20740, USA

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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