The impact of rain to observed signal from Chinese Gaofen-3 synthetic aperture radar in typhoons

Shi, Junpeng; Hu, Jiachen; Shao, Weizeng; Wang, Xiaoqing; Yuan, Xiaojun; Zhao, Liangbo; Li, Xiaofeng

doi:10.1007/s13131-019-1502-7

Cited by 15 publications

(6 citation statements)

References 34 publications

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“…This behavior initially increased (26 August to 7 September) before decreasing (19 September). The significant increase in backscatter coefficients on 7 September was mainly caused by heavy rain (63 mm/d, Figure 4 c), similar to findings from previous studies [ 29 , 30 ].…”

Section: Resultssupporting

confidence: 90%

Analysis of Combining SAR and Optical Optimal Parameters to Classify Typhoon-Invasion Lodged Rice: A Case Study Using the Random Forest Method

Wang

Shao

et al. 2020

Sensors

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Lodging, a commonly occurring rice crop disaster, seriously reduces rice quality and production. Monitoring rice lodging after a typhoon event is essential for evaluating yield loss and formulating suitable remedial policies. The availability of Sentinel-1 and Sentinel-2 open-access remote sensing data provides large-scale information with a short revisit time to be freely accessed. Data from these sources have been previously shown to identify lodged crops. In this study, therefore, Sentinel-1 and Sentinel-2 data after a typhoon event were combined to enable monitoring of lodging rice to be quickly undertaken. In this context, the sensitivity of synthetic aperture radar (SAR) features (SF) and spectral indices (SI) extracted from Sentinel-1 and Sentinel-2 to lodged rice were analyzed, and a model was constructed for selecting optimal sensitive parameters for lodging rice (OSPL). OSPL has high sensitivity to lodged rice and strong ability to distinguish lodged rice from healthy rice. After screening, Band 11 (SWIR-1) and Band 12 (SWIR-2) were identified as optimal spectral indices (OSI), and VV, VV + VH and Shannon Entropy were optimal SAR features (OSF). Three classification results of lodging rice were acquired using the Random Forest classification (RFC) method based on OSI, OSF and integrated OSI–OSF stack images, respectively. Results indicate that an overall level of accuracy of 91.29% was achieved with the combination of SAR and optical optimal parameters. The result was 2.91% and 6.05% better than solely using optical or SAR processes, respectively.

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Section: Resultssupporting

confidence: 90%

Analysis of Combining SAR and Optical Optimal Parameters to Classify Typhoon-Invasion Lodged Rice: A Case Study Using the Random Forest Method

Wang

Shao

et al. 2020

Sensors

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“…As was mentioned in Shi et al ( 2019) [61], the rain rate is correlated with the difference in the SAR roughness during TCs after removing the influence of the Bragg resonant component. The Bragg resonant component in the NRCS was calculated using the profiles, including the VH-polarized SAR-derived wind, HYCOM current, and WW3-simulated waves.…”

Section: Resultsmentioning

confidence: 77%

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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“…This phenomenon is closely related to the frequency band and polarization mode. Alpers et al (Shi et al, 2019) compared the rain cells in the L-band, X-band, and C-band SAR images and different polarizations and found that there are obvious differences in the manifestations of the rain cell phenomenon.…”

Section: Sar Rain Cells Phenomenon 41 Effect Of Rain Cells On Nrcsmentioning

confidence: 99%

“…By matching Tropical Precipitation Measuring Mission (TRMM) PR, ERS-1/2, and ECMWF reanalysis data, Nie et al (Nie and Long, 2008) established a wind-rain backscattering model of the scatterometer and reevaluated the effect of precipitation on the C-band scatterometer. By analyzing co-located typhoon data observed by SAR and TRMM PR precipitation data, Shi et al (Shi et al, 2019) found that when the incidence angle is in the range of 15 °-30 °, precipitation will suppress NRCS, and when the incidence angle is less than 10 °and the incidence angle is in the range of 30 °-45 °, precipitation will enhance NRCS. Based on the difference between the co-and cross-polarization retrieval of the wind speed affected by precipitation, Hui et al (Hui et al, 2018) proposed the quality index of the retrieval SAR vector wind speed polluted by rain and also proposed a method to rectify the effect of precipitation in the hurricane core area; this method effectively improved the wind speed retrieval in precipitationcontaminated areas.…”

Section: Introductionmentioning

confidence: 99%

Effect of precipitation on synthetic aperture radar hurricane wind field retrieval

Guo

et al. 2022

Front. Environ. Sci.

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C-band synthetic aperture radar (SAR) with all-weather observation capability is gradually being used for the high-resolution observation of hurricane. The extreme wind speed in hurricanes is retrieved by combining the co- and cross-polarized SAR signals. However, the heavy precipitation associated with hurricanes will seriously affect the retrieval of SAR sea surface wind speed. In this study, the effects of precipitation-related parameters and phenomena on wind speed retrieval under strong hurricane conditions were examined by using the data from co-located global precipitation measurement Dual-frequency Precipitation Radar, SAR on Sentinel-1 and Radarsat-2, and stepped frequency microwave radiometer. The results show that precipitation will increase the retrieval error of wind speed, and the effect of convective precipitation is more obvious than that of stratiform precipitation. When the wind speed is less than 30 m/s and the rain rate is less than 20 mm/h, the distribution of the melting layer has no obvious effect on SAR wind speed retrieval. Rain cells are mainly the product of convective precipitation and can also be produced when the storm top height is very low. An increase in the vertical-horizontal (VH) polarized normalized radar cross section (NRCS) in rain cells is usually greater than that of the vertical-vertical (VV) polarized NRCS because the sensitivity of the VH signal in a hurricane area is usually greater than that of the VV signal. When the rain rate is low, the difference between the retrieved wind speed inside and outside the rain cells is within the error range of hurricane wind speed retrieval. When the rain rate is further increased, the difference will exceed 10 m/s.

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The impact of rain to observed signal from Chinese Gaofen-3 synthetic aperture radar in typhoons

Cited by 15 publications

References 34 publications

Analysis of Combining SAR and Optical Optimal Parameters to Classify Typhoon-Invasion Lodged Rice: A Case Study Using the Random Forest Method

Analysis of Combining SAR and Optical Optimal Parameters to Classify Typhoon-Invasion Lodged Rice: A Case Study Using the Random Forest Method

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

Effect of precipitation on synthetic aperture radar hurricane wind field retrieval

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