FN Archimer Export Format
PT J
TI Automated Rain Detection by Dual-Polarization Sentinel-1 Data
BT 
AF Zhao, Yuan
   Longépé, Nicolas
   Mouche, Alexis
   Husson, Romain
AS 1:1;2:2;3:1;4:3;
FF 1:;2:;3:PDG-ODE-LOPS-SIAM;4:;
C1 Laboratoire d’Océanographie Physique et Spatiale (LOPS), Ifremer, 29280 Plouzané, France
   ESA, 00044 Frascati, Italy
   CLS, 29280 Plouzané, France
C2 IFREMER, FRANCE
   ESA, ITALY
   CLS, FRANCE
SI BREST
SE PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer UMR
   DOAJ
   copubli-france
   copubli-europe
IF 5.349
TC 9
UR https://archimer.ifremer.fr/doc/00718/83013/87904.pdf
LA English
DT Article
DE ;SAR;rain signatures;rain rate;sea surface winds
AB Rain Signatures on C-band Synthetic Aperture Radar (SAR) images acquired over ocean are common and can dominate the backscattered signal from the ocean surface. In many cases, the inability to decipher between ocean and rain signatures can disturb the analysis of SAR scenes for maritime applications. This study relies on Sentinel-1 SAR acquisitions in the Interferometric Wide swath mode and high-resolution measurements from ground-based weather radar to document the rain impact on the radar backscattered signal in both co- and cross-polarization channels. The dark and bright rain signatures are found in connection with the timeliness of the rain cells. In particular, the bright patches are demonstrated by the hydrometeors (graupels, hails) in the melting layer. In general, the radar backscatter under rain increases with rain rate for a given sea state and decreases when the sea state strengthens. The rain also has a stronger impact on the radar signal in both polarizations when the incidence angle increases. The complementary sensitivity of the SAR signal of rain in both channels is then used to derive a filter to locate the areas in SAR scenes where the signal is not dominated by rain. The filter optimized to match the rain observed by the ground-based weather radar is more efficient when both polarization channels are considered. Case studies are presented to discuss the advantages and limitations of such a filtering approach 
PY 2021
PD AUG
SO Remote Sensing
SN 2072-4292
PU MDPI AG
VL 13
IS 16
UT 000690249300001
DI 10.3390/rs13163155
ID 83013
ER

EF