Ocean Wind and Current Retrievals Based on Satellite SAR Measurements in Conjunction with Buoy and HF Radar Data

Fang, He; Xie, Tianwu; Perrie, William; Zhao, Lilong; Yang, Jingsong; He, Yijun

doi:10.3390/rs9121321

Cited by 13 publications

(12 citation statements)

References 28 publications

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“…Therefore, the optimal PR function for wind speed retrieval from RL-polarized data is Z2011. For simulated RH-polarized SAR data, we select CMOD5 function with E1996, T1998, H2000, V2000, M2005, and Z2011 PR models to estimate wind speed based on Equation (2). Figure 6 shows the relation between SAR-retrieved wind speeds and buoy-measured wind speeds from 256 simulated RH-polarized SAR images.…”

Section: Wind Speed Retrievalmentioning

confidence: 99%

“…Among these techniques, SAR has become a popular technical means of monitoring sea surface parameters because of its capacity to operate in almost all-weather conditions, day or night, with high spatial resolution below a scale of 1 km. And more importantly, given a sufficient quantity of images, the SAR technique has the potential to precisely assess coastal wind fields [2].…”

Section: Introductionmentioning

confidence: 99%

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Ocean Surface Wind Speed Retrieval Using Simulated RADARSAT Constellation Mission Compact Polarimetry SAR Data

et al. 2019

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We investigated the use of C-band RADARSAT Constellation Mission (RCM) synthetic aperture radar (SAR) for retrieval of ocean surface wind speeds by using four new channels (right circular transmit, vertical receive (RV); right circular transmit, horizontal receive (RH); right circular transmit, left circular transmit (RL); and right circular transmit, right circular receive (RR)) in compact polarimetry (CP) mode. Using 256 buoy measurements collocated with RADARSAT-2 fine beam quad-polarized scenes, RCM CP data was simulated using a "CP simulator". Provided that the relative wind direction is known, our results demonstrate that wind speed can be retrieved from RV, RH and RL polarization channels using existing C-band model (CMOD) geophysical model function (GMF) and polarization ratio (PR) models. Simulated RR-polarized radar returns have a strong linear relationship with speed and are less sensitive to relative wind direction and incidence angle. Therefore, a model is proposed for the RR-polarized synthetic aperture radar (SAR) data. Our results show that the proposed model can provide an efficient methodology for wind speed retrieval.Ocean surface wind speed has been routinely retrieved by various SARs since the first SAR scenes became available in 1978. Many wind speed retrieval methods have been developed. The most commonly used wind speed retrieval model from SAR uses C-band normalized radar cross section (NRCS) in the form of a geophysical model function (GMF), called the C-band model (CMOD), which is related to the NRCS as a function of parameters such as relative wind direction, wind-relative radar azimuth, local incidence angle, and of course, wind speed, at 10-height above ocean level. In order to improve the accuracy of wind speed from vertical transmit, vertical receive (VV)-polarized images, a series of CMOD functions were developed, such as CMOD4, CMOD-IFR2, CMOD5, CMOD5.N, CMOD5.H, C-SARMOD2 and CMOD6 [3][4][5][6][7][8][9]. Recently, the latest CMOD function, named CMOD7, was proposed for application to inter-calibrate ERA(ASCAT)and ESCAT scatterometers [10].However, no similar wind field retrieval models exist for horizontal transmit, horizontal receive (HH)-polarized SAR data. To remedy this difficulty, the polarization ratio (PR) was proposed to map the expected NRCS at VV-polarized mode to HH-polarized values for the same wind direction and speed. Thus, when these CMOD GMFs are applied to HH-polarized SAR images, various PR models are used to convert HH-NRCS to VV-NRCS before application for wind retrieval [11]. The modelled VV-polarized NRCS corresponding to the measured HH-polarized NRCS can be computed and then used in the CMOD GMFs in the normal manner.In severe weather, SAR-measured wind speed increases with increasing co-polarized (VV-and HH-polarized) NRCS values, eventually reaching a maximum and then beginning to decrease for higher values of wind speed (probably for winds greater than 20 m/s). Saturation of the co-polarized NRCS can be a disadvantage for high wind speed retrieval from...

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Section: Wind Speed Retrievalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ocean Surface Wind Speed Retrieval Using Simulated RADARSAT Constellation Mission Compact Polarimetry SAR Data

et al. 2019

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“…After the measurement, the buoys need to be recovered, which is inconvenient and expensive to use. In recent decades, in-situ remote-sensing instruments such as synthetic aperture radar (SAR) [3,4], high-frequency (HF) radar [5,6], and X-band marine radar have been broadly used to retrieve sea surface wind [7]. X-band marine radar has high spatial and temporal resolution, as well as a low extra cost relative to other remotesensing instruments [8].…”

Section: Introductionmentioning

confidence: 99%

An Energy Spectrum Algorithm for Wind Direction Retrieval From X-Band Marine Radar Image Sequences

Wang

Qiu

et al. 2021

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

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In this study, an energy spectrum (ES) algorithm is proposed to retrieve wind direction from Xband marine radar image sequences. This algorithm is based on utilizing the occlusion area zero-pixel percentage (OZPP) to distinguish rain-free and rain-contaminated radar data. And then the rain-contaminated images are detected and discarded. The effect of radar radial attenuation in radar image sequences is modified by the piecewise fitting technique. Wind direction is determined from rain-free and radial correction data, based on the energy spectrum of small-scale wind streaks. The energy spectrum of small-scale wind streaks is obtained by establishing an energy spectrum scale separation filter. Based on the wind streak characteristics, a two-dimensional fast Fourier transform (FFT) is used to obtain the energy spectrum of radar images. The wind streak characteristics are derived from the distribution of the azimuth normalization radar cross-section (NRCS). The proposed algorithm is tested using data collected from X-band radar images and in-situ anemometer data from the coast of the East China Sea. Compared with the anemometer data, after using the proposed algorithm, the root-mean-square difference for wind direction is 12.13°, which is an acceptable result for engineering application.

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“…At this time, the retrieval of OSWS at high (<1 km) resolution from quad-polarized spaceborne SAR images is a mature geophysical application. Many efforts have been devoted to developing optimal reliable methodologies to elucidate the geophysical relationship between the normalized radar cross section (NRCS) and OSWS and to apply this relationship to accurately compute wind speeds [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Comparison of C-Band Quad-Polarization Synthetic Aperture Radar Wind Retrieval Models

et al. 2018

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This work discusses the accuracy of C-2PO (C-band cross-polarized ocean backscatter) and CMOD4 (C-band model) geophysical model functions (GMF) for sea surface wind speed retrieval from satellite-born Synthetic Aperture Radar (SAR) images over in the Northwest Pacific off the coast of China. In situ observations are used for comparison of the retrieved wind speed using two established wind retrieval models: C-2PO model and CMOD4 GMF. Using 439 samples from 92 RADARSAT-2 fine quad-polarization SAR images and corresponding reference winds, we created two subset wind speed databases: the training and testing subsets. From the training data subset, we retrieve ocean surface wind speeds (OSWSs) from different models at each polarization and compare with reference wind speeds. The RMSEs of SAR-retrieved wind speeds are: 2.5 m/s: 2.11 m/s (VH-polarized), 2.13 m/s (HV-polarized), 1.86 m/s (VV-polarized) and 2.26 m/s (HH-polarized) and the correlation coefficients are 0.86 (VH-polarized), 0.85(HV-polarized), 0.87(VV-polarized) and 0.83 (HH-polarized), which are statistically significant at the 99.9% significance level. Moreover, we found that OSWSs retrieved using C-2PO model at VH-polarized are most suitable for moderate-to-high winds while CMOD4 GMF at VV-polarized tend to be best for low-to-moderate winds. A hybrid wind retrieval model is put forward composed of the two models, C-2PO and CMOD4 and sets of SAR test data are used in order to establish an appropriate wind speed threshold, to differentiate the wind speed range appropriate for one model from that of the other. The results show that the OSWSs retrieved using our hybrid method has RMSE of 1.66 m/s and the correlation coefficient are 0.9, thereby significantly outperforming both the C-2PO and CMOD4 models.

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Ocean Wind and Current Retrievals Based on Satellite SAR Measurements in Conjunction with Buoy and HF Radar Data

Cited by 13 publications

References 28 publications

Ocean Surface Wind Speed Retrieval Using Simulated RADARSAT Constellation Mission Compact Polarimetry SAR Data

Ocean Surface Wind Speed Retrieval Using Simulated RADARSAT Constellation Mission Compact Polarimetry SAR Data

An Energy Spectrum Algorithm for Wind Direction Retrieval From X-Band Marine Radar Image Sequences

Comparison of C-Band Quad-Polarization Synthetic Aperture Radar Wind Retrieval Models

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