Tropical Cyclone Wind Speed Retrieval from Dual-Polarization Sentinel-1 EW Mode Products

Gao, Yuan; Guan, Changlong; Sun, Jian; Xie, Lian

doi:10.1175/jtech-d-19-0148.1

Cited by 16 publications

(7 citation statements)

References 35 publications

(42 reference statements)

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“…The red lines represent the SFMR tracks. Recently, aimed to wind retrieval from VH-polarized S-1 SAR images after thermal noise removal, an algorithm termed as S-1 IW Mode Wind Speed Retrieval Model after Noise Removal (S1IW.NR) was developed [43,44] and the specific formulation is expressed as follows, considering the different performance at various incidence angle θ, e.g., IW1: [31.0 • -35.9 • ], IW2: [35.9 • -41. where σ V H 0 is the VH-polarized NRCS united in dB and U 10 is the wind speed at 10-m height. The cross-polarized GMF is conveniently applied for SAR strong wind retrieval without be prior information on wind direction.…”

Section: Methodsmentioning

confidence: 99%

Wind Field Retrieval with Rain Correction from Dual-Polarized Sentinel-1 SAR Imagery Collected during Tropical Cyclones

et al. 2022

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The purpose of this study is to include rain effects in wind field retrieval from C-band synthetic aperture radar (SAR) imagery collected under tropical cyclone conditions. An effective and operationally attractive approach to detect rain cells in SAR imagery is proposed and verified using four Sentinel-1 (S-1) SAR images collected in dual-polarized (vertical-vertical (VV) and vertical-horizontal (VH)) interferometric-wide swath imaging mode during the Satellite Hurricane Observation Campaign. SAR images were collocated with ancillary observations that include sea surface wind and rain rate from the Stepped-Frequency Microwave Radiometer (SFMR) on board of the National Oceanic and Atmospheric Administration aircraft. The winds are inverted from VV- and VH-polarized S-1 image using the CMOD5.N and S1IW.NR geophysical model functions (GMFs), respectively. Location and radius of cyclone’s eye, together with the TC central pressure, are calculated from the VV-polarized SAR-derived wind and a parametric model. A cost function is proposed that consists of the difference between the measured VV-polarized SAR normalized radar cross section (NRCS) and the NRCS predicted using CMOD5.N forced with the wind speed retrieved by the VH-polarized SAR images using S1IW.NR GMF and the wind direction retrieved from the patterns visible in the SAR image. This cost function is related to the SFMR rain rate. Experimental results show that the difference between measured and predicted NRCS values range from 0.5 dB to 5 dB within a distance of 100 km from the cyclone’s eye, while the difference increases spanning from 3 dB to 6 dB for distances larger than 100 km. Following this rationale, first the rain bands are extracted from SAR imagery and, then, the composite wind fields are reconstructed by replacing: (1) dual-polarized SAR-derived winds over the rain-free regions; (2) winds simulated using the radial-vortex model over the rain-affected regions. The validation of the composite wind speed against SFMR winds yields a <2 m s−1 and >0.7 correlation (COR) at all flow directions up to retrieval speeds of 70 m s−1. This result outperforms the winds estimated using the VH-polarized S1IW.NR GMF, which call for high error accuracy, such as about 4 m s−1 with a 0.45 COR ranged from 330° to 360°.

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Section: Methodsmentioning

confidence: 99%

Wind Field Retrieval with Rain Correction from Dual-Polarized Sentinel-1 SAR Imagery Collected during Tropical Cyclones

et al. 2022

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“…The MMS1A model was modified from MS1A through a correction of SFMR data [17]. The S1EW.NR model was fitted from ERA5 wind speeds [29]. For wind retrieval, the two models just required VH NRCS and incident angle inputs.…”

Section: Validation With Smap Windsmentioning

confidence: 99%

“…For co-polarized data, there is the CMOD family, including CMOD4 [20], CMOD5.N [21], CMOD7 [22], etc., originally developed for scatterometers, together with the C_SARMOD [23] and C_SARMOD2 [24] models re-tuned for SAR. For cross-polarized SAR imagery, there are C-2PO [2], C-2POD [25], C-3PO [26], and MS1A [27], and a large number of modified models, such as S1IW.NR [28], S1ER.NR [29] and MMS1A [17]. For most GMFs, the sigma nought (σ 0 ) is on the left side of the equation.…”

Section: Introductionmentioning

confidence: 99%

“…According to the relationships between NRCS and wind speed, the most significant difference between co-and cross-polarization GMFs is the wind retrieval range. Since co-polarized signals saturate under high wind conditions (>25 m/s), the imagery cannot recognize the high wind area; therefore, they are often used to retrieve low-to-moderate wind speeds (<25 m/s) [29]. Meanwhile, cross-polarized acquisitions keep high sensitivity with high winds but are not good at low-wind retrieval due to the strong impact of thermal noise compared with conventional co-polarized acquisitions [27,29,30].…”

Section: Introductionmentioning

confidence: 99%

“…Since co-polarized signals saturate under high wind conditions (>25 m/s), the imagery cannot recognize the high wind area; therefore, they are often used to retrieve low-to-moderate wind speeds (<25 m/s) [29]. Meanwhile, cross-polarized acquisitions keep high sensitivity with high winds but are not good at low-wind retrieval due to the strong impact of thermal noise compared with conventional co-polarized acquisitions [27,29,30]. In addition, the wind direction parameter is another difference between co-and cross-polarization GMFs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Approach for Ocean Surface Wind Speed Retrieval Using Sentinel-1 Dual-Polarized Imagery

Gao,

Wang,

Wang

2023

Remote Sensing

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A synthetic aperture radar (SAR) has the capability to observe ocean surface winds with a high spatial resolution, even under extreme conditions. The purpose of this work was to develop a new method for wind speed retrieval with the combination of SAR dual-polarized signals. In this study, we collected 28 tropical cyclone imageries observed using the Sentinel-1 dual-polarization mode. These imageries were collocated with radiometer wind speed measurements and reanalysis of wind vector products. In the new method, the wind speed was set as the output. VV-polarized (vertical transmitting–vertical receiving polarized) normalized radar cross section (NRCS), incident angle, VH-polarized (vertical transmitting–horizontal receiving polarized) NRCS, and wind direction were set as the inputs. Based on different output combinations, wind retrieval models were developed with multiple linear regression (MLR). According to the validation and comparison, the proposed models performed better than the traditional piecewise VH-polarization geophysical model functions (GMFs). The impact of thermal noise on the retrieval of low wind speeds (<10 m/s) could be partially reduced. The input of wind direction is unnecessary if the combination of VV- and VH-polarized imageries has been utilized. These results suggest that the use of MLR and the dual-polarization combination can improve SAR wind retrieval accuracy. Compared with SMAP measurements, our SAR retrievals can provide fine structures of TC wind fields.

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

Zhao

Shao

Zhao

et al. 2021

Advances in Space Research

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Tropical Cyclone Wind Speed Retrieval from Dual-Polarization Sentinel-1 EW Mode Products

Cited by 16 publications

References 35 publications

Wind Field Retrieval with Rain Correction from Dual-Polarized Sentinel-1 SAR Imagery Collected during Tropical Cyclones

Wind Field Retrieval with Rain Correction from Dual-Polarized Sentinel-1 SAR Imagery Collected during Tropical Cyclones

A New Approach for Ocean Surface Wind Speed Retrieval Using Sentinel-1 Dual-Polarized Imagery

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

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