Impact of rain on wave retrieval from Sentinel-1 synthetic aperture radar images in tropical cyclones

Zhao, Xianbin; Shao, Weizeng; Zhao, Liangbo; Gao, Yuan; Hu, Yuan; Yuan, Xiaojun

doi:10.1016/j.asr.2021.01.050

Cited by 17 publications

(8 citation statements)

References 45 publications

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“…Although these phenomena contribute to the SAR roughness, ring waves are assumed to be the dominant feature in the co-polarization channel [38]. In our recent study [39], ring waves with a wavelength of less than 125 m were clearly observed in the SAR-derived wave spectra. Because rain-induced backscattering dominates the sea surface backscattering in moderate to heavy rains, a scatterometer-derived rain GMF [34], [35] for three different incidence angle ranges was developed using HH-polarized RADARSAT-1 (R-1) scanning SAR (ScanSAR) images collocated with NEXRAD data.…”

Section: Introductionmentioning

confidence: 74%

Retrieval of Rain Rates for Tropical Cyclones From Sentinel-1 Synthetic Aperture Radar Images

Zhao

Shao

Lai

et al. 2023

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

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The purpose of this study was to develop a method for retrieving the rain rate from C-band (~5.3 GHz) synthetic aperture radar (SAR) images during tropical cyclones (TCs). Seven dual-polarized (vertical-vertical [VV] and vertical-horizontal [VH]) Sentinel-1 (S-1) SAR images were acquired in the interferometric-wide (IW) swath mode during the Satellite Hurricane Observation Campaign. These images were collocated with rain rates measured by the Stepped-Frequency Microwave Radiometers onboard National Oceanic and Atmospheric Administration aircraft. Wind speeds were retrieved from the VH-polarized SAR images using the geophysical model function (GMF) S1IW.NR. We determined the difference between the measured normalized radar cross section (NRCS) based on VV-polarized SAR and the predicted NRCS derived using the geophysical model function CMOD5.N forced with wind speeds retrieved from VH-polarized SAR images. Rain cells were identified as regions in the images where the NRCS difference was greater than 0.5 dB or smaller than -0.5 dB. We found that the difference in the NRCS decreased and the VH-polarized wind speed increased with increasing rain rate. Based on these findings, we developed an empirical function for S-1 SAR rain retrieval in a TC, naming it CRAIN_S1. The validation of the CRAIN_S1 results with Tropical Rainfall Measuring Mission data resulted in a root mean square error of 0.58 mm/hr and a correlation of 0.89. This study provides an alternate method for rain monitoring utilizing SAR data with a fine spatial resolution.

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Section: Introductionmentioning

confidence: 74%

Retrieval of Rain Rates for Tropical Cyclones From Sentinel-1 Synthetic Aperture Radar Images

Zhao

Shao

Lai

et al. 2023

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

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“…In this paper, the influence of rainfall was not taken into account in the SS-ICM model, and the retrieved high wind speeds would be underestimated. The existence of atmospheric attenuation, body scattering, and ring wave makes the influence mechanism of rainfall on NRCS very complicated [39,40], which needs to be deeply studied in future work.…”

Section: Discussionmentioning

confidence: 99%

“…In 2005, the National Oceanic and Atmospheric Administration (NOAA)/Aircraft Operations Center (AOC) first used a Stepped-Frequency Microwave Radiometer (SFMR) on the WP-3D research aircraft for actual measurements of hurricane surface wind speeds [35][36][37][38] and rain rates [39,40]. The SFMR is a C-band microwave radiometer that works at six frequency bands (4.55, 5.06, 5.64, 6.34, 6.96, 7.22 GHz) and can be used to retrieve the 1-min sustained ground wind speeds, with the measured maximum wind speeds up to 70 m/s.…”

Section: Sfmr Measurementsmentioning

confidence: 99%

Development of a New Tropical Cyclone Strip Segment Retrieval Model for C-Band Cross-Polarized SAR Data

Zhang

Wang

et al. 2022

Remote Sensing

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Compared with co-polarized (HH/VV) normalized radar cross-section (NRCS) backscattered from the sea surface, there is no saturation phenomenon in cross-polarized (HV/VH) NRCS when wind speed is greater than about 20 m/s, so cross-polarized synthetic aperture radar (SAR) images can be used for high wind speed monitoring. In this work, a new geophysical model function (GMF) is proposed to describe the relation of the C-band cross-polarized NRCS with wind speed and radar incidence angle. Here, sixteen ScanSAR wide mode SAR images acquired by RADARSAT-2 (RS-2) under tropical cyclone (TC) conditions and the matching wind speed data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Stepped-Frequency Microwave Radiometer (SFMR) are collected and divided into datasets A and B. Dataset A is used for analyzing the effects of the wind field and radar incidence angle on the reference noise-removed cross-polarized NRCS, and for proposing the new GMF for each sub-swath of the SAR images, while dataset B is used to retrieve wind speed and evaluate the validity of the new GMF. The comparisons between the wind speeds retrieved by the new GMF and the collocated ECMWF and SFMR data demonstrate the excellent performance of the new GMF for wind speed retrieval. To analyze the universality of the new GMF, wind speed retrievals based on 32 Sentinel-1A/B (S-1A/B) extra-wide-swath (EW) mode images acquired under TC conditions are also compared with the collocated wind speeds measured by the Soil Moisture Active Passive (SMAP) radiometer, and the retrieved wind speeds have RMSE of 3.667 m/s and a bias of 2.767 m/s. The successful applications in high wind speed retrieval of different tropical cyclones again supports the availability of the new GMF.

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“…mission, ocean phenomena, i.e., sea surface winds, sea surface waves, internal waves, oil spills, and eddies, were observed, verifying the great potential of spaceborne SAR in marine applications. In particular, SAR is a remote-sensing technology that has the advantage of sea surface wind [1] and wave monitoring [2], especially for tropical cyclones (TCs) [3]. The Chinese domestic SAR, denoted as Gaofen-3 (GF-3), was started from 2016 to conduct oceanography and photogrammetry.…”

Section: Introduction He Seasat-a Satellite Is An Experimental Scient...mentioning

confidence: 99%

Machine Learning-Based Wind Direction Retrieval From Quad-Polarized Gaofen-3 SAR Images

Shao,

Zhou,

Zhang

et al. 2024

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

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In our study, an intelligent method for inverting wind direction from quad-polarized Gaofen-3 (GF-3) synthetic aperture radar (SAR) images is proposed. Specifically, 11300 acquired in wave (WAV) mode are used to retrieve the wind directions using a spectrum-transformation approach and prior information from European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis at version 5 (ERA-5) data at 1-hour intervals with 0.25° grids. The dependence of the wind direction on the polarimetric correlation coefficient (PCC) between the co-(vertical-vertical (VV) and horizontal-horizontal (HH)) and cross-polarization (vertical-horizontal (VH) and horizontal-vertical (HV)) channels is studied. It is found that the PCCs in four combination polarizations have asymmetric characteristics with respect to the wind direction with correlation coefficients (CORs) of greater than 0.4 or less than -0.4. Following this rationale, the scheme for inverting wind direction from quad-polarized SAR is trained according to machine learning, in which the matrix PCCs, wind directions, azimuthal angles, and slopes from SAR intensity spectra at the peaks are used as inputs. Subsequently, this intelligent approach is applied to 1300 images in quad-polarization stripmap (QPS) mode, and the retrieval results are validated against advanced scatterometer (ASCAT) measurements. The statistical analysis shows that the root mean squared error (RMSE) of the wind direction is 17.7°, the COR is 0.98, and the scatter index (SI) is 0.11. In addition, the wind speeds inverted using a geophysical model function (GMF) CSARMOD-GF are compared with well-calibrated ASCAT products, resulting in an RMSE of 1.85 m/s, a COR of 0.78, and an SI of 0.28 for the wind speed. Thus, this work provides an automatic scheme for inverting wind from quad-polarized GF-3 SAR images without any external information.

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Impact of rain on wave retrieval from Sentinel-1 synthetic aperture radar images in tropical cyclones

Cited by 17 publications

References 45 publications

Retrieval of Rain Rates for Tropical Cyclones From Sentinel-1 Synthetic Aperture Radar Images

Retrieval of Rain Rates for Tropical Cyclones From Sentinel-1 Synthetic Aperture Radar Images

Development of a New Tropical Cyclone Strip Segment Retrieval Model for C-Band Cross-Polarized SAR Data

Machine Learning-Based Wind Direction Retrieval From Quad-Polarized Gaofen-3 SAR Images

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