Modeling COSMO-SkyMed measurements of precipitating clouds over the sea using simultaneous weather radar observations

Roberto, Nicoletta; Baldini, Luca; Facheris, Luca; Chandrasekar, V.

doi:10.1016/j.atmosres.2014.01.016

Cited by 4 publications

(3 citation statements)

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“…In both the training and detection steps, the Sentinel-1 images were preprocessed before being input into the network, including geocoding and speckle filtering [30]. As per some other studies [31,32], weather conditions-such as wind speed and rainfall-can also change the Bragg scattering effect generated by the sea surface, thus hindering the oil spill detection approach from SAR images. Considering the above issue, we also took wind speed and rainfall information as features to train the detection networks.…”

Section: Data Sourcesmentioning

confidence: 99%

“…Considering the above issue, we also took wind speed and rainfall information as features to train the detection networks. The wind speed information was obtained from the corresponding Sentinel-1 imagery As per some other studies [31,32], weather conditions-such as wind speed and rainfall-can also change the Bragg scattering effect generated by the sea surface, thus hindering the oil spill detection approach from SAR images. Considering the above issue, we also took wind speed and rainfall information as features to train the detection networks.…”

Section: Data Sourcesmentioning

confidence: 99%

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Monitoring of Oil Spill Risk in Coastal Areas Based on Polarimetric SAR Satellite Images and Deep Learning Theory

Liao,

Zhao,

Song

2023

Sustainability

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Healthy coasts have a high ecological service value. However, many coastal areas are faced with oil spill risks. The Synthetic Aperture Radar (SAR) remote sensing technique has become an effective tool for monitoring the oil spill risk in coastal areas. In this study, taking Jiaozhou Bay in China as the study area, an innovative oil spill monitoring framework was established based on Polarimetric SAR (PolSAR) images and deep learning theory. Specifically, a DeepLabv3+-based semantic segmentation model was trained using 35 Sentinel-1 satellite images of oil films on the sea surface from maritime sectors in different regions all over the world, which not only considered the information from the PolSAR images but also meteorological conditions; then, the well-trained framework was deployed to identify the oil films in the Sentinel-1 images of Jiaozhou Bay from 2017 to 2019. The experimental results show that the detection accuracies of the proposed oil spill detection model were higher than 0.95. It was found that the oil films in Jiaozhou Bay were mainly concentrated in the vicinity of the waterways and coastal port terminals, that the occurrence frequency of oil spills in Jiaozhou Bay decreased from 2017 to 2019, and that more than 80 percent of the oil spill events occurred at night, mainly coming from the illegal discharge of waste oil from ships. These data indicate that, in the future, the PolSAR technique will play a more important role in oil spill monitoring for Jiaozhou Bay due to its capability to capture images at night.

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Section: Data Sourcesmentioning

confidence: 99%

Section: Data Sourcesmentioning

confidence: 99%

Monitoring of Oil Spill Risk in Coastal Areas Based on Polarimetric SAR Satellite Images and Deep Learning Theory

Liao,

Zhao,

Song

2023

Sustainability

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“…An analysis of the CSK Ping-Pong mode (alternate HH-VV) was also carried out, showing some interesting features. Roberto et al (2014) extend this model approach to marine environments, a fairly challenging goal due to the difficulties in modelling the SAR response of the sea surface background. The detection of the hurricane eye, hurricane tracking, and the estimation of wind fields from SAR data has also been recently attempted (Li 2017).…”

Section: Introductionmentioning

confidence: 99%

X-Band Synthetic Aperture Radar Methods

Mori

Marzano

Pierdicca

2020

Advances in Global Change Research

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Spaceborne Synthetic Aperture Radars (SARs), operating at L-band and above, offer microwave observations of the Earth at very high spatial resolution in almost all-weather conditions. Nevertheless, precipitating clouds can significantly affect the signal backscattered from the ground surface in both amplitude and phase, especially at X band and beyond. This evidence has been assessed by numerous recent efforts analyzing data collected by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) missions at X band. This sensitivity can be exploited to detect and quantify precipitations from SARs at the spatial resolution of a few hundred meters, a very appealing feature considering the current resolution of precipitation products from space. Forward models of SAR response in the presence of precipitation have been developed for analyzing SAR signature sensitivity and developing rainfall retrieval algorithms. Precipitation retrieval algorithms from SARs have also been proposed on a semi-empirical basis. This chapter will review experimental evidences, modelling approaches, retrieval methods and recent applications of X-band SAR data to rainfall estimation.

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An algorithm to retrieve precipitation with synthetic aperture radar

Xie

Liu

2016

J Meteorol Res

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Modeling COSMO-SkyMed measurements of precipitating clouds over the sea using simultaneous weather radar observations

Cited by 4 publications

References 40 publications

Monitoring of Oil Spill Risk in Coastal Areas Based on Polarimetric SAR Satellite Images and Deep Learning Theory

Monitoring of Oil Spill Risk in Coastal Areas Based on Polarimetric SAR Satellite Images and Deep Learning Theory

X-Band Synthetic Aperture Radar Methods

An algorithm to retrieve precipitation with synthetic aperture radar

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