Wind speed retrieval from RADARSAT-2 quad-polarization images using a new polarization ratio model

Zhang, Biao; Perrie, Will; He, Yijun

doi:10.1029/2010jc006522

Cited by 132 publications

(128 citation statements)

References 36 publications

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“…Figure 15b shows the GF-3 PR dependence on wind speed. Overall, GF-3 PR is significantly lower than the model in Zhang et al [11] for all wind speeds. There is a difference of approximately 1.0 dB between the two when the wind speed is 6 m·s −1 .…”

Section: Wind Sensitivity For the Polarization Combinationcontrasting

confidence: 56%

“…There is a difference of approximately 1.0 dB between the two when the wind speed is 6 m·s −1 . The model in Zhang et al [11] decreases with wind speed, whereas GF-3 PR remains basically unchanged. Figure 15c shows the GF-3 PR dependence on azimuth angle.…”

Section: Wind Sensitivity For the Polarization Combinationmentioning

confidence: 91%

“…Some recent models are also wind speed (Zhang et al [11]) or azimuth angle (Mouche et al [10]) dependent. We analyze the GF-3 PR dependence on incidence angle, wind speed, and azimuth angle, and compare these to the proposed PR models (Figure 15).…”

Section: Wind Sensitivity For the Polarization Combinationmentioning

confidence: 99%

“…In general, their correlation is described using a polarization ratio (PR) model. Currently, most proposed PR models are incidence angle-dependent only [6][7][8][9], while the other models are also a function of wind speed or azimuth angle [10,11]. Using a PR model, the HH data can be transformed to match the VV data.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Preliminary Analysis of Chinese GF-3 SAR Quad-Polarization Measurements to Extract Winds in Each Polarization

et al. 2017

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This study analyzed the noise equivalent sigma zero (NESZ) and ocean wind sensitivity for Chinese C-band Gaofen-3 (GF-3) quad-polarization synthetic aperture radar (SAR) measurements to facilitate further operational wind extraction from GF-3 data. Data from the GF-3 quad-polarization SAR and collocated winds from both NOAA/NCEP Global Forecast System (GFS) atmospheric model and National Data Buoy Center (NDBC) buoys were used in the analysis. For NESZ, the co-polarization was slightly higher compared to the cross-polarization. Regarding co-polarization and cross-polarization, NESZ was close to RadarSAT-2 and Sentinel-1 A. Wind sensitivity was analyzed by evaluating the dependence on winds in terms of normalized radar cross-sections (NRCS) and polarization combinations. The closest geophysical model function (GMF) and the polarization ratio (PR) model to GF-3 data were determined by comparing data and the model results. The dependence of co-polarized NRCS on wind speed and azimuth angle was consistent with the proposed GMF models. The combination of CMOD5 and CMOD5.N was considered to be the closest GMF in co-polarization. The cross-polarized NRCS exhibited a strong linear relationship with moderate wind speeds higher than 4 m·s −1 , but a weak correlation with the azimuth angle. The proposed model was considered as the closest GMF in cross-polarization. For polarization combinations, PR and polarization difference (PD) were considered. PR increased only with the incidence angle, whereas PD increased with wind speed and varied with azimuth angle. There were three very close PR models and each can be considered as the closest. Preliminary results indicate that GF-3 quad-polarization data are valid and have the ability to extract winds in each polarization.

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Section: Wind Sensitivity For the Polarization Combinationcontrasting

confidence: 56%

Section: Wind Sensitivity For the Polarization Combinationmentioning

confidence: 91%

Section: Wind Sensitivity For the Polarization Combinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Preliminary Analysis of Chinese GF-3 SAR Quad-Polarization Measurements to Extract Winds in Each Polarization

et al. 2017

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“…Mouche et al [39] showed that the PR model of Elfouhaily [3] produced generally the best agreement with their observations and developed two new PR model using airborne real aperture radar data acquired at the C-band with both vertical and horizontal polarizations for moderate incidence angles. The first attempt to analyze C-band RADARSAT-2 measurements of the normalized radar cross-sections in quad-polarization acquisition mode over the ocean has been presented in Zhang et al [42]. Results showed that the constructed PR model with both wind speed and incidence angle dependence (SAD), in conjunction with CMOD5.N, achieved the smallest bias and standard deviation by comparing retrieved wind speeds from different CMOD algorithms with buoy measurements.…”

Section: Wind Speed Retrievalmentioning

confidence: 99%

Reconstructed Wind Fields from Multi-Satellite Observations

Tang

Liu

et al. 2014

Remote Sensing

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Abstract:We present and validate a method of reconstructing high-resolution sea surface wind fields from multi-sensor satellite data over the Grand Banks of Newfoundland off Atlantic Canada. Six-hourly ocean wind fields from blended products (including multi-satellite measurements) with 0.25° spatial resolution and 226 RADARSAT-2 synthetic aperture radar (SAR) wind fields with 1-km spatial resolution have been used to reconstruct new six-hourly wind fields with a resolution of 10 km for the period from August 2008 to December 2010, except July 2009 to November 2009. The reconstruction process is based on the heapsort bucket method with topdown search and the modified Gauss-Markov theorem. The result shows that the mean difference between the reconstructed wind speed and buoy-estimated wind speed is smaller than 0.6 m/s, and the standard deviation is smaller than 2.5 m/s. The mean difference in wind direction between reconstructed and buoy estimates is 3.7°; the standard deviation is 40.2°. There is fair agreement between the reconstructed wind vectors and buoy-estimated ones.

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Cross‐polarization geophysical model function for C‐band radar backscattering from the ocean surface and wind speed retrieval

Hwang¹,

Stoffelen

Zadelhoff

et al. 2015

JGR Oceans

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The wind speed sensitivity of cross-polarization (cross-pol) radar backscattering cross section (VH) from the ocean surface increases toward high winds. The signal saturation problem of VH, if it exists, occurs at a much higher wind speed compared to the copolarization (copol: VV or HH) sea returns. These properties make VH a better choice over VV or HH for monitoring severe weather. Combined with high spatial resolution of the synthetic aperture radar (SAR), the development of hurricane wind retrieval using VH is advancing rapidly. This paper describes a cross-pol C-band radar backscattering geophysical model function (GMF) with incidence angle dependence for the full wind speed range in the available data sets (up to 56 m/s). The GMF is derived from RADARSAT-2 (R2) dual-polarization (dual-pol) ScanSAR modes with 300 and 500 km swaths. The proposed GMF is compared to other published algorithms. The result shows that the simulated VH cross section and the retrieved wind speed with the proposed GMF is in better agreement with measurements. With careful treatment of noise, the VH-retrieved wind speeds may extend to mild or moderate conditions. The higher fraction of non-Bragg contribution in VH can be exploited for analysis of surface wave breaking.

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Wind speed retrieval from RADARSAT-2 quad-polarization images using a new polarization ratio model

Cited by 132 publications

References 36 publications

Preliminary Analysis of Chinese GF-3 SAR Quad-Polarization Measurements to Extract Winds in Each Polarization

Preliminary Analysis of Chinese GF-3 SAR Quad-Polarization Measurements to Extract Winds in Each Polarization

Reconstructed Wind Fields from Multi-Satellite Observations

Cross‐polarization geophysical model function for C‐band radar backscattering from the ocean surface and wind speed retrieval

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