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Quasi-synchronous wide swath quad-polarization SAR imagery of Hurricane Epsilon acquired by RCM and RADARSAT-2 at 21:53 and 21:56 UTC on October 24, 2020. (a) VV and (b) VH for RADARSAT-2, (c) HH and (d) HV for RCM. The blue, red, green and purple solid lines are the transects along the range direction. RADARSAT-2 Data and Product MacDonald, Dettwiler, and Associates Ltd., all rights reserved. RADARSAT Constellation
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This is the first presentation of quasi-synchronous spaceborne synthetic aperture 1 radar (SAR) high-resolution images acquired from C-band Radar Constellation Mission 2 (RCM) and RADARSAT-2 consisting of quad-polarization (HH+HV+VH+VV) wide 3 swath observations of Hurricane Epsilon. These measurements clearly show that the 4 denoised HV-and VH-pol...
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... satellites. These IMERG precipitation estimates were calibrated with gauge analysis of the Global Precipitation Climatology Centre (GPCC) [14]. IMERG V06B rain data can be accessed from NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). The spatial and temporal resolutions are 0.1 • × 0.1 • and 30 min, respectively. Fig. 2 shows quad-polarized SAR imagery of Hurricane Epsilon acquired by RCM and RADARSAT-2 at 21:53 and 21:56 UTC on October 24, 2020, respectively. These images are resampled to 3-km resolution for elimination of speckle noise. Since the RADARSAT-2 swath is 150 km wider than that of RCM, only the portions of the images corresponding to the ...
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... than that of RCM, only the portions of the images corresponding to the common "overlap" area observed by the two sensors are shown in the following discussion. To analyze the dependence of NRCS on radar incidence angle, transects are designated from the near range to far range in HH-, VV-, HV-, and VH-polarized SAR images (see colored lines on Fig. 2). Compared with RADARSAT-2 measurements at VV-and VH-polarizations, the noise equivalent sigma zero (NESZ) values of RCM measurements at HH-and HV-polarizations are much lower, ranging between −32 and −36 dB (compared with −23 to −30 dB for RADARSAT-2), as shown in Fig. 3. For each polarization, we subtract the NESZ values from the ...
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... Dependence of quasi-synchronous quad-polarized NRCSs on incidence angle Fig. 2 shows quad-polarized SAR imagery of Hurricane Epsilon acquired by RCM and RADARSAT-2 at 21:53 and 21:56 UTC on October 24, 2020, respectively. These images are re-sampled to 3 km resolution for elimination of speckle noise. Since the RADARSAT-2 swath is 150 km wider than that of RCM, only the portions of the images corresponding to the ...
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... that of RCM, only the portions of the images corresponding to the common 'overlap' area observed by the two sensors are shown in the following discussion. In order to analyze the dependence of NRCS on radar incidence angle, transects are designated from the near range to far range in HH-, VV-, HV-and VHpolarized SAR images (see colored lines on Fig. 2). Compared to RADARSAT-2 measurements at VV-and VH-polarizations, the noise equivalent sigma zero (NESZ) values of RCM measurements at HH-and HV-polarizations are much lower, ranging between -32 and -36 dB (compared to -23 ~ -30 dB for RADARSAT-2), as shown in the Fig. 3. For each polarization, we subtract the NESZ values from the NRCS ...
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... In general, TCs are observed by single-or dual-polarization SARs. Recently, a virtual radar constellation comprising RCM-3 and RADARSAT-2 has offered an unprecedented opportunity to obtain quasi-synchronous wide-swath fully polarimetric (HH + HV + VH + VV) SAR TC observations [54]. This is because it could acquire TC SAR images within a very short time interval (<5 minutes) using different polarization options (HH + HV or VV + VH). ...
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Accurate knowledge of wind directions plays a critical role in ocean surface wind retrieval and Tropical Cyclone (TC) research. Under TC conditions, apparent wind streaks induced by marine atmospheric boundary layer rolls can be detected in VV- and VH-polarized Synthetic Aperture Radar (SAR) images. It suggests that though relatively noisy, VH signals may help enhance wind streak orientation magnitudes contained in VV signals and thus to achieve a more accurate wind direction estimation. The study proposes a new method for wind direction retrieval from TC SAR images. Unlike conventional approaches, which calculate wind directions from single-polarization imagery, the method combines VV and VH signals to obtain continuous wind direction maps across moderate and extreme wind speed regimes. The technique is developed based on the Histogram of Oriented Gradient descriptor and Hann window function, accounting for the contribution of neighbouring wind streak information (weighted by separation distances). As a case study, the wind directions over four TCs (Karl, Maria, Douglas and Larry) are derived and verified by estimates from simultaneous dropsonde, ASCAT and ECMWF winds, showing a promising consistency. Furthermore, a more comprehensive statistical analysis is carried out with fourteen SAR images, revealing that obtained wind directions have a correlation coefficient of 0.98, a bias of -6.07
$^{\circ }$
and a root-mean-square difference of 20.24
$^{\circ }$
, superior to estimates from VV (0.97, -7.84
$^{\circ }$
and 24.23
$^{\circ }$
, resp.) and VH signals (0.96, -10.46
$^{\circ }$
and 29.53
$^{\circ }$
, resp.). The encouraging results prove the feasibility of the technique in SAR wind direction retrieval.
We present an approach for automatic classification of Arctic sea ice and open water from spaceborne C-band RADARSAT-2 fully polarimetric (HH, HV, VH, VV) synthetic aperture radar (SAR) imagery, based on the random forest (RF) model. The HH- and HV-polarized radar backscatter, incidence angle and optimal polarimetric features are inputs of the RF model.
First
, we use a physics-based unsupervised scheme to generate well-annotated sea ice and open water samples. This scheme can alleviate labeling errors arising from visual interpretation or temporal-spatial mismatch between SAR images and operational ice charts.
Subsequently
, we estimate a set of characteristic polarimetric parameters and select the most representative features, using the recursive feature elimination method.
Finally
, the RF model is trained and validated using more than one million labelled samples. Statistical validation results show that the overall sea ice and water classification accuracy is 99.94% and the Kappa coefficient is 0.999. We find that ice–water discrimination accuracy can be improved by about 4%–10%, when optimal polarimetric features are involved in the RF model input. Moreover, the
polarization difference
(PD, VV–HH) is found to be the foremost polarimetric parameter for distinguishing sea ice from open water. The proposed approach has capability of yielding satisfactory ice–water classification over the complicated marginal ice zone. High-resolution ice–water classification maps clearly show that sea ice leads and their surroundings are also well separated.
