CMODH Validation for C-Band Synthetic Aperture Radar HH Polarization Wind Retrieval Over the Ocean

Lu, Yiru; Zhang, Biao; Perrie, Will; Mouche, Alexis; Zhang, Guosheng

doi:10.1109/lgrs.2020.2967811

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…In this study, the input data were exploited to evaluate the GFM efficiency. It was also observed that the GFM-based OSW was consistent with buoy measurements [203].…”

Section: ) Ocean Surface Wind (Osw)supporting

confidence: 76%

“…In [202], the efficacy of Geophysical Model Functions (GMFs) in retrieving OSW through the VV-polarized Sentinel-1A SAR data over the Caspian sea was studied [202]. Additionally, an HH-polarized GMF based on RADARSAT-2 and Sentinel-1A modes for OSW retrieval was developed by Lu et al [203]. In this study, the input data were exploited to evaluate the GFM efficiency.…”

Section: ) Ocean Surface Wind (Osw)mentioning

confidence: 99%

See 1 more Smart Citation

Remote Sensing Systems for Ocean: A Review (Part 2: Active Systems)

Amani¹,

Mohseni

Layegh

et al. 2022

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

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As discussed in the previous part of this review paper, Remote Sensing (RS) creates unprecedented opportunities by providing a variety of systems with different characteristics to study and monitor oceans. Part 1 of this review paper was dedicated to reviewing passive RS systems and their main applications in the ocean. Here, in part 2, seven active RS systems, including scatterometers, altimeters, gravimeters, Synthetic Aperture Radar (SAR), Light Detection and Ranging (LiDAR), Sound Navigation and Ranging (SONAR), High-Frequency (HF) radars are comprehensively reviewed. For consistency, this part is structured similarly to part 1. The aforementioned systems, along with their characteristics and primary applications, are introduced in separate sections. This review paper provides useful information to all students and researchers who are interested in the oceanographic applications of active RS systems.

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“…In this study, the input data were exploited to evaluate the GFM efficiency. It was also observed that the GFM-based OSW was consistent with buoy measurements [203].…”

Section: ) Ocean Surface Wind (Osw)supporting

confidence: 76%

Section: ) Ocean Surface Wind (Osw)mentioning

confidence: 99%

Remote Sensing Systems for Ocean: A Review (Part 2: Active Systems)

Amani¹,

Mohseni

Layegh

et al. 2022

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

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show abstract

“…The model was available for HH-and VV-polarized SAR without the need for the polarized ratio model. According to the verification with the WS observed by the buoy, its RMSE was 1.66 m/s [85].…”

Section: Methodmentioning

confidence: 74%

A Review for Retrieving Wind Fields by Spaceborne Synthetic Aperture Radar

Zhang

Guo

et al. 2022

Journal of Sensors

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A high-resolution sea surface wind field (SSWF) has high application requirements, such as weather forecast, wind energy evaluation, and oil spill monitoring. The models for retrieving SSWFs based on spaceborne synthetic aperture radar (SAR) are important methods for obtaining high-resolution SSWFs. These models are continuously updated and improved from the prototype to the model to achieve high-resolution and high-precision SSWF retrieval. With the development of SAR technology and the gradual maturation of global ocean observations, SSWF quantitative estimation using SAR has developed from scientific research to operational monitoring. This study summarises the principles and methods of SSWF with multipolarized SAR. Finally, research suggestions and future development directions are put forward.

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“…The comparison with buoy measurements has a bias of 0.12 m/s and an RMSE of 1.42 m/s. A recent study [29]shows that the retrieval of by S1 HH-polarized data using the CMODH [12] has a bias of 0.49 m/s and an RMSE of 2.05 m/s compared with buoy measurements. This suggests that the proposed machine learning-type retrieval method can also yield accurate estimates of but avoids the complicatedly tuning of coefficients in the CMOD functions.…”

Section: B Comparison Of the Sar-retrieved With In Situ Measurementsmentioning

confidence: 99%

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

Li¹,

Qin²,

Wu³

2020

Preprint

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In this paper, we presented a method of retrieving sea surface wind speed from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide mode, which have been extensively acquired over the Arctic for sea ice monitoring. In contrast to the conventional algorithm, i.e., using a geophysical model function (GMF) to retrieve sea surface wind by spaceborne SAR, we introduced an alternative method based on physical model guided neural network. Parameters of SAR normalized radar cross section, incidence angle, and wind direction are used as the inputs of the backward propagation (BP) neural network, and the output is the sea surface wind speed. The network is developed based on more than 11,000 HH-polarized EW images acquired in the marginal ice zone (MIZ) of the Arctic and their collocations with scatterometer 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 speed. Further comparison of the SAR retrieved sea surface wind speed 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 retrieval when the wind direction data of a reanalysis model are used as inputs to the neural network. By combining the detected sea ice cover information based on the EW data, one can expect to derive simultaneously sea ice and marine-meteorological parameters by spaceborne SAR in a high spatial resolution in the Arctic.

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CMODH Validation for C-Band Synthetic Aperture Radar HH Polarization Wind Retrieval Over the Ocean

Cited by 14 publications

References 21 publications

Remote Sensing Systems for Ocean: A Review (Part 2: Active Systems)

Remote Sensing Systems for Ocean: A Review (Part 2: Active Systems)

A Review for Retrieving Wind Fields by Spaceborne Synthetic Aperture Radar

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

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