A Wind Speed Retrieval Model for Sentinel-1A EW Mode Cross-Polarization Images

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

doi:10.3390/rs11020153

Cited by 18 publications

(12 citation statements)

References 27 publications

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“…While a denoising method for S1 EW data in HV polarization has recently been developed [33], both the HH and HV polarization information from the EW data acquired in the Arctic will be included in the BP neural network, which might facilitate better retrieval of high wind cases [32,34]. Along with developing a robust method for discriminating sea ice and open water from S1 EW data, we will combine this method with sea surface wind retrievals to obtain synergistic information on sea ice and ocean dynamic parameters in the MIZ of the Arctic.…”

Section: Discussionmentioning

confidence: 99%

Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network

Qin

2020

Remote Sensing

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In this paper, we presented a method for retrieving sea surface wind speed (SSWS) from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide (EW) swath mode, which have been extensively acquired over the Arctic for polar monitoring. In contrast to the conventional algorithm, i.e., using a geophysical model function (GMF) to retrieve SSWS by spaceborne SAR, we introduced an alternative retrieval method based on a GMF-guided neural network. The SAR normalized radar cross section, incidence angle, and wind direction are used as the inputs of a back propagation (BP) neural network, and the output is the SSWS. The network is developed based on 11,431 HH-polarized EW images acquired in the marginal ice zone (MIZ) of the Arctic from 2015 to 2018 and their collocated scatterometer wind measurements. Verification of the neural network based on the testing dataset yields a bias of 0.23 m/s and a root mean square error (RMSE) of 1.25 m/s compared to the scatterometer wind data for wind speeds less than approximately 30 m/s. Further comparison of the SAR retrieved SSWS with independent buoy measurements shows a bias and RMSE of 0.12 m/s and 1.42 m/s, respectively. We also analyzed the uncertainty of the retrieval when reanalysis model wind direction data are used as inputs to the neural network. By combining the detected sea ice cover information based on SAR data, sea ice and marine-meteorological parameters can be derived simultaneously by spaceborne SAR at a high spatial resolution in the Arctic.

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Section: Discussionmentioning

confidence: 99%

Retrieval of Sea Surface Wind Speed from Spaceborne SAR over the Arctic Marginal Ice Zone with a Neural Network

Qin

2020

Remote Sensing

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“…The extra-wide swath mode of Sentinel-1A (up to 410 km wide) covers incidence angles from about 18.9°to 47.0°. It combines five sub-bands in the range direction [69]. Fig.…”

Section: E Measurement Issuesmentioning

confidence: 99%

Impact on Sea-Surface Electromagnetic Scattering and Emission Modeling of Recent Progress on the Parameterization of Ocean Surface Roughness, Drag Coefficient, and Whitecap Coverage in High Wind Conditions

Hwang

2020

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

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Ocean surface wind stress is the force generating ocean surface roughness and whitecaps. The surface wind stress is related to the surface wind speed by the drag coefficient. Consequently, the magnitude of roughness and whitecaps calculated from wind speed input depends on the applied drag coefficient formula, which remains uncertain especially for high wind conditions. Because roughness and whitecaps are two major components of the ocean surface response in microwave remote sensing, a clear understanding of the drag coefficient, ocean surface roughness, and whitecap coverage is critical to interpreting the microwave signals emitted or scattered from the ocean surface. In reverse, the microwave signals represent precious data sources of these ocean surface properties [surface wind stress (or equivalently the drag coefficient), surface roughness, and whitecaps] that are difficult to measure with traditional oceanographic instruments, especially in severe weather conditions. This article describes the improvement of ocean surface properties achieved by incorporating microwave remote sensing measurements. In essence, the global data of microwave monostatic and bistatic radar cross sections contribute to the revision of ocean surface roughness spectrum model, and microwave radiometer measurements in tropical cyclones provide clarification regarding the dependence on wind speed of the drag coefficient and whitecap coverage. In return, the calculated excess emissivities and scattering radar cross sections are improved from implementing the updated results of drag coefficient, whitecap coverage, and surface roughness in microwave emission and scattering models.

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“…Another wind retrieval model is proposed for the European Space Agency (ESA) Sentinel-1A (S-1A) Extra-Wide swath mode VH-polarized images [5]. The new model is validated by comparing the wind speeds retrieved from S-1A images with the wind speeds measured by Soil Moisture Active Passive (SMAP) radiometer under tropical cyclone conditions.…”

Section: Overview Of Contributionsmentioning

confidence: 99%