The project aims at exploiting microwave satellite measurements to generate innovative added-value products to observe coastal areas also under extreme weather conditions. The following added-values products are addressed: coastal water pollution, coast erosion, ship and metallic target detection, typhoon monitoring.

To better state the goals, the project is framed into three subtopics: 1) SARCO - SAR-based Coast Observation; 2) Ship and Coastal Water Pollution Observation with Polarimetric SAR Architectures (SCoPeSAR); 3) SHENLONG: Sea-surface High-wind ExperimeNts with Long-range (satellite) Observations using Numerical Geophysical methods.

To reach the above-mentioned goals, single-polarization and polarimetric models will be analyzed and/or developed to generate added-value products that consist of: a) time-evolution of oil seeps in Gulf of Mexico; b) ship detection methods using European and Chinese SAR data; c) data assimilation scheme to assimilate Sentinel-1 SAR winds in the Weather Research and Forecasting (WRF) model for typhoon observation purposes; d) a new SAR azimuth cut-off scheme to estimate wind from SAR imagery.

Typhoons are among the most powerful and destructive natural disasters. Accurate forecasting of Typhoon track and intensity is very important to disaster prevention and reduction. Satellite observations can effectively compensate for the shortcomings of traditional methods of sea surface measurement and provide all-weather observation over the sea surface, which is of great significance to improve the numerical prediction of strong convective weather over ocean. The spaceborne radar observes the backscattering caused by the sea surface roughness, and then, the sea surface wind can be retrieved. Within this context, the Synthetic Aperture Radar (SAR) is an important data source for sea surface monitoring, since a variety of meteorological hydrological elements can be retrieved by SAR observation, and it has been used in data assimilation in recent years. SAR imagery is also used to monitor strength and structure of typhoons. The accuracy of sea surface winds retrieved from SAR has been found to be comparable to that of scatterometer data, and these wind fields can be used with a data assimilation system to provide the initial conditions for the numerical weather prediction (NWP) model.

In this study, a data assimilation scheme is proposed to assimilate the Sentinel-1 SAR retrieved winds in the Weather Research and Forecasting (WRF) model. Numerical simulation experiments of the typhoon Lionrock (2016) are carried out to test and compare different data assimilation methods. A series of Sentinel-1A EW swath mode dual-polarization (VV/VH) images are used to retrieve sea surface wind speed. Their overpass time were around 20:35 UTC on 29 August 2016. We use two different methods to derive the sea surface wind maps. The first is based on the use of the VV+VH dual pol geophysical model functions, while the second is based on the azimuth cut-off method. The Weather Research and Forecasting model data assimilation system (WRFDA) developed by the National Center for Atmospheric Research (NCAR) is adopted in this study. The grid size of the assimilation region is 260×250; the horizontal resolution is 15 km; and the vertical discretization is 30 layers. The time of assimilation is 0900 UTC 29 August 2016. The NCEP FNL Operational Global Analysis data are used as the initial field and boundary conditions. We take the 21-h forecast adjustment from 1200 UTC 28 August 2016 to 0900 UTC 29 August 2016 as the background field of the assimilation system. After the assimilation, a 30-h forecast is made, which is a forecast to 1500 UTC 30 August 2016. In this study, a set of assimilation and comparison experiments is carried out. Preliminary results show that the forecast track from SAR observations agree better with the best track than the control experiment.

Satellite Synthetic Aperture Radar (SAR) has been proved to be a key tool for a broad range of environmental applications in the context of oceans and coastal areas monitoring, including ship detection, coastline extraction, land use/cover classification, oil spill observation and sea surface parameters retrieval. In particular, the capability of satellite SAR measurements to support operational activities in case of natural disasters and environmental hazards as the Deepwater Horizon oil spill accident occurred in the Gulf of Mexico in 2010 or the most recent Sanchi accidental oil spill occurred off the eastern coast of China.

In this study, we focused on one of the richest areas in offshore oil seepages, i. e, the northern part of the Gulf of Mexico near the Mississippi river delta, where the Taylor Energy oil drilling platform is located (28°56’17’’N,88°58’16’’W). The platform was destroyed by the Hurricane Ivan in 2004 and, since then, the underwater wells were continuously leaking oil. It was estimated that more than 100 oil gallons enters into the marine environment from the Taylor Energy platform site. This results in surface oil slicks whose average thickness and life–time are about 1 μm and 4 days, respectively.

The area was continuously observed from satellite SAR platforms since the accidental oil spill occurred. Space-borne SAR imagery witness that this coastal area was almost persistently affected by this anthropogenic oil seep as the slicks were detected in about 80% of the data collected over the site. Even if strictly speaking this leakage cannot be considered as a natural oil seep, the underwater origin of the oil seep together with the involved weathering and aging processes are fairly the same.

Hence, it represents a good opportunity to have a large and consistent time series of SAR imagery that covers a well-known oil seepage. A large time series of dual-polarimetric co-polarized TerraSAR-X high-resolution (1.2 x 6.6 slant range x azimuth nominal spatial resolution) SAR imagery, collected in StripMap mode between July 2011 and April 2016 in a wide range of incidence angles (25° - 45°) and sea state conditions (low-to-moderate wind conditions applied, i. e., 1.5 m/s – 8.5 m/s), is exploited.

In this study, despite of the rather high noise floor that characterizes TerraSAR-X StripMap SAR imagery (an estimated noise equivalent sigma zero, NESZ, in the range -20 dB – -23 dB), the time series is effectively exploited to monitor the Taylor Energy oil spill. A multi-polarization analysis, that includes co-polarized intensity and phase difference information, is undertaken on which the oil spill detection and characterization is grounded.

Wind speed retrieval is a topic of great interest since wind estimation is very useful for a number of meteorological and oceanographic applications: wind is the major responsible of problematic like coastal erosion, climate change, marine life and so on. Most of the remote-sensing satellite radar systems are able to provide sea-surface wind field information, and they can be considered the main sea-surface wind information source. Within this context, active microwave remote sensing, in particular scatterometer and Synthetic Aperture Radar (SAR), is worldwide recognized as one of the best suitable tools to perform a reliable sea-surface wind speed retrieval. Radar backscatter intensities and their statistical properties contain quantitative information about the state of the sea surface roughness and, therefore, can be used to derive sea-surface wind information. When using radar systems such as the scatterometer and the SAR, the backscatter signal from the sea surface is dominated by the so-called Bragg resonant mechanism (mainly for wind speeds lower than 15m/s). In this case, there is a strong relationship between Normalized Radar Cross Section (NRCS) and wind speed linked by a Geophysical Model Function (GMF), while an alternative spectral based approach to retrieve wind speed is represented by the azimuth cut-off procedure.

When dealing with SAR microwave sensors, Doppler misregistration in azimuth are induced by gravity wave orbital motion. This issue is the major responsible of a distortion of the imaged spectrum and of a strong cut-off in the azimuthal direction: this is the azimuth cut-off. In literature, the azimuth cut-off method is used to retrieve wind speed and several studies have been carried out to analyze the dependence of λc on sea surface parameters. In particular, there is a linear relationship between λc values and geophysical parameters, like wind speed and significant wave height. Recently, in [1] the ACF-based λc approach has been improved to deal with high wind speed regimes, e.g.; extreme weather conditions. The key issues that allow to extend the method to high wind regimes concern the tuning of the method with respect to pixel spacing, box size and the homogeneity of the SAR imagery. In particular, the box size is set at 1 km × 1 km and the median filter window is set at 90-120 m.

In this study, this novel SAR azimuth cut-off implementation is applied to an actual SAR dataset collected under high wind regimes, like tropical cyclones. Finally, the soundness of this improved azimuth-cut-off method under extreme weather conditions is discussed.

[1] M. Portabella, V. Corcione, X. Yang, Z. Jelenak, P. Chang, G. Grieco, A. Mouche, F. Nunziata, W. Li, “Analysis of the SAR-derived wind signatures over extra-tropical storm conditions”, Dragon 4 Symposium, Copenhagen, Denmark, 26-30 June

Tropical cyclone is a generic term that designs a rapidly rotating storm system characterized by a
low pressure center that produces strong winds and heavy rainfall. Cyclones are differently named according to the ocean basin where they develop: “Hurricanes” in the Northern Atlantic and Northeast Pacific Ocean, “Typhoons” in the Northwest Pacific Ocean, and “Cyclones” in the Indian Ocean and Southwest Pacific Ocean. Although tropical cyclones are among the most dangerous and destructive natural disasters, current models are still not able to give an accurate forecast of their intensity and track. Hence, within this context, spaceborne active microwave sensors, like the Synthetic Aperture Radar (SAR), are of paramount importance because of their all-weather and all-day capabilities together with a fine spatial resolution. In particular, SAR data can be used to directly monitor position and intensity of the storms, to analyze their structure or the rainfall-rate but also to estimate wind speed and direction. The purpose of this study is to apply the azimuth cut-off approach under extreme weather conditions.
In literature, the azimuth cut-off method is used to retrieve wind speed and several studies have
been carried out to analyze the dependence of λc on sea surface parameters. In particular, there is a linear relationship between λc values and geophysical parameters, like wind speed and significant wave height Recently, in [1] the ACF-based λc approach has been improved to deal with high wind speed regimes, e.g.; extreme weather conditions. The key issues that allow to extend the method to high wind regimes concern the tuning of the method with respect to some parameters: the pixel spacing, the box size and the homogeneity of the SAR imagery.
In this study, an actual SAR dataset collected in occasion of tropical cyclones is used to discuss
the validity of this improved azimuth-cut- off method under extreme weather conditions.

[1] M. Portabella, V. Corcione, X. Yang, Z. Jelenak, P. Chang, G. Grieco, A. Mouche, F. Nunziata, W. Li, “Analysis of the SAR-derived wind signatures over extra-tropical storm conditions”, Dragon 4 Symposium, Copenhagen, Denmark, 26-30 June.

Polarimetric Synthetic Aperture Radar (PolSAR) is a microwave imaging system with the capabilities to image day-and-night and penetrate cloud cover. It is becoming an effective means of monitoring the Earth’s surface. Examples of applications are disaster damage estimation, urban classification, and
object detection. In particular, ship detection with PolSAR data has attracted a great deal of attention in the last several decades. Ship location can help us deal with many troublesome issues, e.g., marine traffic, illegal fishing, illegal immigration. However, ship detection is a complex problem, which is not
only depended on the nature of ship but also depended on the sea state.

Compared with sea surface, a different backscattering behavior can be expected in ships. Due to the complicated structures, ship backscatter is often various, including single-bounce returns, double-bounce returns, multiple-bounce returns and so on [1]. By analyzing the different scattering mechanisms between sea surface and ships, many excellent works have been done on ship detection. The most straightforward approaches, such as the polarimetric whitening filter (PWF) and SPAN detectors [2], directly used the three channels of PolSAR data for ship detection. In [3], Nunziata et al. effectively utilized the reflection symmetry (RS) properties of the sea and man-made targets to detect ships. Marino et al. [4] further constructed a new scheme, called the geometrical perturbation-polarimetric notch filter method (GP-PNF), from the polarimetric target complex space to detect ships at sea.

In essence, the above ship detectors only exploit one single pixel information to extract the polarimetric features, which hardly consider the spatial information and still belong to ’pixel level’ category [5]. As a fact, the background pixels surrounding ship pixels can also provide rich information for ship detection. In this paper, we proposed a new strategy to add the phase information when computing the polarimetric covariance difference matrix (PCDM) [6]. Then a complete polarimetric covariance difference matrix (CPCDM) is developed, and a CPCDM-based algorithm is also proposed to detect ships. Experimental results demonstrate the effectiveness of the proposed algorithm.