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Remote Sensing for Wind Speed and Ocean Currents

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 7728

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Special Issue Editor

Dr. Sergey Stanichny

E-Mail Website
Guest Editor

Remote Sensing Department, Marine Hydrophysical Institute (MHI), Sevastopol, Russia
Interests: remote sensing and its applications; radiation transfer; satellite data processing; oceanography

Special Issue Information

Dear Colleagues,

The dynamics of the ocean’s upper layer is an important parameter that determines the heat and mass transfer between the ocean and the atmosphere. Wind is one of the main external parameters that form the dynamic characteristics of the surface layer–turbulence (mixing), advection, and vertical movements due to wave breaking, Ekman drift, and pumping, which form geostrophic currents. The main parameter associated with wind speed is the roughness of the sea surface. Scatterometric methods of restoring the surface wind speed are based on the change in roughness by the wind speed. However, in optics, the surface roughness is well manifested in the reflected component of solar radiation and can be used to estimate wind speed.

Surface currents are usually reconstructed from altimetry data or the Doppler component from radar measurements. However, using sequential images and “optical flow” methods, it is possible to reconstruct the current fields. Changes in wave characteristics can also be used to estimate current velocities.

The purpose of this issue is:

- The application of standard products for wind speed estimation, intercomparison, and algorithm improvement;

- Methods of using optical and SAR data to reconstruct wind speed features with high spatial resolution;

- Description of perspective sensors;

- The application and estimation of the accuracy of altimetry data to restore the speed of currents, combined with drift currents;

- The development of new panoramic altimeters;

- The use of the “optical flow” methods for estimation of surface currents and ice drift;

- The investigation of surface currents in mesoscale and submesoscale structures based on the analysis of the reflected component in the optical range.

Dr. Sergey Stanichny
Guest Editor

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Keywords

Wind
Surface currents
Altimetry
Scatterometry
Surface roughness
Reflected component
“Optical flow”

Published Papers (4 papers)

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Research

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11 pages, 2518 KiB

Open AccessCommunication

Data Quality Evaluation of Sentinel-1 and GF-3 SAR for Wind Field Inversion

by Yong Wan, Sheng Guo, Ligang Li, Xiaojun Qu and Yongshou Dai

Remote Sens. 2021, 13(18), 3723; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183723 - 17 Sep 2021

Cited by 5 | Viewed by 1448

Abstract

Synthetic aperture radar (SAR) is an important means to observe the sea surface wind field. Sentinel-1 and GF-3 are located on orbit SAR satellites, but the SAR data quality of these two satellites has not been evaluated and compared at present. This paper mainly studies the data quality of Sentinel-1 and GF-3 SAR satellites used in wind field inversion. In this study, Sentinel-1 SAR data and GF-3 SAR data located in Malacca Strait, Hormuz Strait and the east and west coasts of the United States are selected to invert wind fields using the C-band model 5.N (CMOD5.N). Compared with reanalysis data called ERA5, the root mean squared error (RMSE) of the Sentinel-1 inversion results is 1.66 m/s, 1.37 m/s and 1.49 m/s in three intervals of 0~5 m/s, 5~10 m/s and above 10 m/s, respectively; the RMSE of GF-3 inversion results is 1.63 m/s, 1.45 m/s and 1.87 m/s in three intervals of 0~5 m/s, 5~10 m/s and above 10 m/s, respectively. Based on the data of Sentinel-1 and GF-3 located on the east and west coasts of the United States, CMOD5.N is used to invert the wind field. Compared with the buoy data, the RMSE of the Sentinel-1 inversion results is 1.20 m/s, and the RMSE of the GF-3 inversion results is 1.48 m/s. The results show that both Sentinel-1 SAR data and GF-3 SAR data are suitable for wind field inversion, but the wind field inverted by Sentinel-1 SAR data is slightly better than GF-3 SAR data. When applied to wind field inversion, the data quality of Sentinel-1 SAR is slightly better than the data quality of GF-3 SAR. The SAR data quality of GF-3 has achieved a world-leading level. Full article

(This article belongs to the Special Issue Remote Sensing for Wind Speed and Ocean Currents)

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Other

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18 pages, 25688 KiB

Open AccessTechnical Note

Submesoscale Currents from UAV: An Experiment over Small-Scale Eddies in the Coastal Black Sea

by Yury Yu. Yurovsky, Arseny A. Kubryakov, Evgeny V. Plotnikov and Pavel N. Lishaev

Remote Sens. 2022, 14(14), 3364; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143364 - 13 Jul 2022

Cited by 5 | Viewed by 1590

Abstract

A commercial unmanned aerial vehicle (UAV) is used for coastal submesoscale current estimation. The measurements were conducted in the Black Sea coastal area with a DJI Mavic quadcopter operated in self-stabilized mode at different look geometry (200–500-m altitude, 0–30

^{\circ}

incidence angle). The [...] Read more.

^{\circ}

incidence angle). The results of four flights during 2020–2021 are reported. Some scenes captured a train of or individual eddies, generated by a current flowing around a topographic obstacle (pier). The eddies were optically visible due to the mixing of clear and turbid waters in the experiment area. Wave dispersion analysis (WDA), based on dispersion shell signature recognition, is used to estimate the sea surface current in the upper 0.5-m-thick layer. The WDA-derived current maps are consistent with visible eddy manifestations. The alternative method, based on 4D-variational assimilation (4DVAR), agrees well with WDA and can complement it in calm wind conditions when waves are too short to be resolved by the UAV sensor. The error of reconstructed velocity due to the uncontrolled UAV motions is assessed from referencing to static land control points. At a 500-m altitude and 7–10 m s

^{- 1}

wind speed (reported by a local weather station for 10-m height), the UAV drift velocity, or the bias of the current velocity estimate, is about

0.1

m s

^{- 1}

, but can be reduced to

0.05

m s

^{- 1}

if the first 10 s of the UAV self-stabilization period are excluded from the analysis. The observed anticyclonic eddies (200–400 m in diameter with 0.15–0.30 m s

^{- 1}

orbital velocity) have an unexpectedly high Rossby number,

Ro

∼15, suggesting the importance of nonlinear centrifugal force for such eddies and their significant role in coastal vertical transport. Full article

(This article belongs to the Special Issue Remote Sensing for Wind Speed and Ocean Currents)

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Graphical abstract

12 pages, 2308 KiB

Open AccessTechnical Note

Wind Speed Retrieval Algorithm Using Ku-Band Radar Onboard GPM Satellite

by Maria Panfilova and Vladimir Karaev

Remote Sens. 2021, 13(22), 4565; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224565 - 13 Nov 2021

Cited by 3 | Viewed by 1632

Abstract

The algorithm to retrieve wind speed in a wide swath from the normalized radar cross section (NRCS) was developed for the data of Dual Frequency Precipitation Radar (DPR) operating in scanning mode installed onboard a Global Precipitation Measurement (GPM) satellite. The data for Ku-band radar were used. Equivalent NRCS values at nadir were estimated in a wide swath under the geometrical optics approximation from off-nadir observations. Using these equivalent NRCS nadir values and the sea buoys data, the new parameterization of dependence between NRCS at nadir and the wind speed was obtained. The algorithm was validated using ASCAT (Advanced Scatterometer) data and revealed good accuracy. DPR data are promising for determining wind speed in coastal areas. Full article

(This article belongs to the Special Issue Remote Sensing for Wind Speed and Ocean Currents)

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Graphical abstract

16 pages, 2602 KiB

Open AccessTechnical Note

Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar

by Yuefeng Zhao, Xiaojie Zhang, Yurong Zhang, Jinxin Ding, Kun Wang, Yuhou Gao, Runsong Su and Jing Fang

Remote Sens. 2021, 13(18), 3549; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183549 - 07 Sep 2021

Cited by 5 | Viewed by 2412

Abstract

Real-time measurement of atmospheric wind field parameters plays an important role in weather analysis and forecasting, including improving the efficiency of wind energy, particle tracking, boundary layer measurements, and airport security. In this study, a wind profile coherent wind Light Detection and Ranging (Lidar) measurement with a wavelength of 1.55 µm was developed and demonstrated based on the principle of eight-beam velocimetry. The wind speed information was retrieved, and vertical and horizontal profiles were calculated via power spectrum estimation of sampled echo signals through the measurement of the atmospheric wind field in Hefei for several consecutive days. The experimental results show that the wind profiles produced using different techniques are quite consistent and the standard error is less than 0.42 m/s compared with three-beam and five-beam wind measurements. Full article

(This article belongs to the Special Issue Remote Sensing for Wind Speed and Ocean Currents)

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