Conference Agenda

Spaceborne SAR has been proved to be a valuable tool in measuring sea surface winds and waves in coastal waters. In this study we present evidence to show that 1) ships and ocean front have impact on SAR wind retrieval and 2) uncertainly wave retrieval exists under the hurricane conditions.

We acquired a large number of Sentinel-1 SAR images in the Yangtze River estuary, China, where the ocean environment is very complicated due to the exchanges of coastal and Changjiang Diluted Waters. The root-mean-square error between SAR wind speeds and buoy measurements reaches up to 3.81 m/s. Analysis of quasi-synchronous SAR images and sea surface temperature (SST) observations shows that the main possible causes for such a large bias are the impact of ships or changes in the atmospheric stability induced by SST change across the ocean front on the sea surface backscatter signal received by radar. A change of 1 °C in SST at low wind conditions may lead to an error of 1~2 dB in the satellite observed normalized radar backscatter cross section (NRCS). The existence of ships at sea surface even results in a falsely high NRCS value. Since the accuracy of wind speed estimation from SAR is strongly dependent on the accuracy of the NRCS measurement, great cautions should be taken when generating or using SAR wind products. Consideration of the above-mentioned effects on the NRCS may improve the accuracy of the estimated wind speeds to a certain extent.

We also investigate the performance of the wave retrieval algorithm (PFSM) when it is applied for dual-polarization C-band Sentinel-1 SAR. SAR-derived significant wave height (SWH) and mean wave period (MWP) are compared with simulation results from the WAVEWATCH-III model. The validation shows a 0.69 m root mean square error (RMSE) of SWH with a -0.01 m bias and a 0.62 s RMSE of MWP with a -0.17 s bias. Although the PFSM algorithm relies on a good quality SAR spectrum, this study confirms the applicability for wave retrieval from Sentinel-1 SAR images. Moreover, it is found that the retrieved results have less accuracy on the right sector of cyclone eyes where swell directly affects strong wind-sea, while the PFSM algorithm works well on the left and rear sectors of cyclone eyes where the interaction of wind-sea and swell is relatively weak.

Within the context of coastal area management, that includes promoting sustainable economy, preserving biodiversity and ensuring population safety, the continuous and effective monitoring of the shoreline is primary need. Nonetheless, a rigorous definition of the shoreline is ambiguous to some extent since it is influenced by bathymetry, tide level, etc. and, in addition, shoreline position continuously changes due to urbanization, deforestation, accretion/erosion, etc. It was shown that remote sensing tools, including optical/microwave satellite sensors and aerial UAV surveys, are valuable information sources to provide systematic observations of the shores to be integrated with ground surveys, i.e., GPS measurements.

The exploitation of spaceborne SAR measurements can improve optical-based shoreline extraction, which is affected by solar illumination and weather conditions. In addition, it was shown that polarimetric information provides extra-benefits for shoreline extraction purposes, i. e., the algorithms are more robust and accurate. Nevertheless, reliable pixel-wise land/sea separation is still a challenging task since the latter is hampered by the several SAR imaging and environmental effects that include inherent speckle noise, limited spatial resolution, bathymetry, high sea state conditions and coastal morphology.

In this framework, in this study, the applicability of patch-based filters to reduce speckle noise in polarimetric SAR imagery is investigated for shoreline extraction purposes. In fact, with respect to standard pixel-wise speckle filters, the patch-based approaches for speckle reduction exploit the measurements redundancy to look for similar local patches within the polarimetric SAR image. Then, according to a statistical-based similarity criterion, a speckle-reduced polarimetric SAR observable, i. e., coherency matrix, is obtained from which land/sea separation can be performed according to a given metric. Hence, in this study, the capability of the patch-based paradigm to be applied on polarimetric SAR images for speckle filtering is investigated and evaluated in terms of accuracy in the shoreline position. Selected showcases will be presented at the conference time to quantitatively evaluate the improvements in shoreline extraction accuracy performance.

SAR measurements have proven to be a very useful tool for tropical cyclone monitoring and forecasting applications. The sea surface wind maps derived from the SAR cross-polarized channel can provide fine-scale information about the tropical cyclone (TC) inner core. A hurricane morphology and sea surface wind vector estimation model (SHEW) based on measurements acquired by the C-band SAR onboard RADARSAT-2 has been recently proposed [1]. A limitation of this model is that it only deals with hurricanes of circular or elliptical-shaped eyewalls. In this study, a new parametric model, which uses SAR observations and allows for asymmetric description of the TC wind structure around the eyewall in storm centric coordinates, is developed. SAR observations from
 TCs in the North Atlantic and East Pacific basins are analyzed to determine the azimuthal and radial asymmetry typical in these mesoscale systems. The new asymmetric directional wind model adjusts the widely used Holland (1980) axis-symmetric model to account for the different azimuthal asymmetries of TC winds. The model will be tested against collocated NOAA hurricane hunter observations (i.e., dropsondes and the Step-Frequency Microwave Radiometer or SFMR) and its performance will be compared with other existing models, such as, the Holland [2], SHEW [1], and Olfateh [3] models. Showcases will also be presented to demonstrate the improvements related to the proposed model.

[1] Zhang, G., W. Perrie, X. Li, and J.A. Zhang. (2017), A Hurricane Morphology and Sea Surface Wind Vector Estimation Model Based on C-Band Cross-Polarization SAR Imagery, IEEE TGRS, 55(3), 1743-1751.

[2] Holland, G. J. (1980), An analytic model of the wind and pressure profiles in hurricanes, Mon. Weather Rev., 108(8), 1212–1218.

[3] Olfateh, M., D. P. Callaghan, P. Nielsen, and T. E. Baldock. (2017), Tropical cyclone wind field asymmetry— Development and evaluation of a new parametric model, J. Geophys. Res. Oceans, 122, 458–469, doi:10.1002/ 2016JC012237.

Wind is a sustainable and alternative resource for producing energy and it has a good reputation of being a green form of electricity. Within this context, wind turbines are widely used at onshore and offshore sites to convert the energy of moving air into electrical power. For this reason, wind turbines are a critical infrastructure whose monitoring is an important issue for both economy and environment protection. Within this context, remote sensing can be an important tool to guarantee an effective and relatively cheaper monitoring. Optical images have the great advantage of being simple to interpret and they are easily obtainable. However, optical radiation is severely affected by cloud cover, solar illumination, and other adverse meteorological conditions. These problems can be solved using radar sensors, which guarantee all-day and almost all-weather acquisitions, together with a wide area coverage. In particular, the Synthetic Aperture Radar (SAR) can be very useful for intertidal zone monitoring purposes, because of its fine spatial resolution.

The objective of this study is quantifying the added-value of polarimetric information to detect metallic targets. On this purpose, a very challenging scenario is considered that consists of mud flat area where wind turbines are present. To make the analysis fair, we selected detection algorithm that are able to work with both full- and partial-polarimetric information, i.e.; Polarimetric Notch Filter (PNF) and the change detection approach proposed in [1] and [2], respectively.

Experiments, undertaken on actual SAR data collected over the intertidal zone near Jiangsu, China, by the C-band RadarSAT-2 and Sentinel-1 missions show that the proposed methodologies, well detect the wind turbines inside mud flat areas. Furthermore, a detailed analysis shows that polarimetric information always guarantees performance better than the single–polarization counterpart.

[1] A. Marino, (2013), “A Notch Filter for Ship Detection With Polarimetric SAR Data", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3), 1219-1232.

[2] A. Marino; S.R. Cloude, and J. M. Lopez-Sanchez, (2013), “A New Polarimetric Change Detector in Radar Imagery”, IEEE Transactions on Geoscience and Remote Sensing, 51(5), 2986 -3000.

The study of coastal wetlands is of paramount importance due to both anthropomorphic activities and natural phenomena, which threaten the stability of land and safety of the people.

However, the monitoring of coastal wetlands is not trivial due to the presence of different kind of habitats that include coastal plain, coastal beaches, rocky shorelines, salt marshes, mangrove, seagrass beds, mud flats and sand bars. For this reason, the study of wetlands results very challenging.

Within this context, remote sensing plays an important role for coastal wetlands monitoring. Optical images have the great advantage of being simple to interpret and they are easily obtainable. However, optical radiation is severely affected by cloud cover, solar illumination, and other adverse meteorological conditions. These problems can be solved using radar sensors, which guarantee all-day and almost all-weather acquisitions, together with a wide area coverage. In particular, the Synthetic Aperture Radar (SAR) can be very useful for intertidal zone monitoring purposes, because of its fine spatial resolution.

The main goals of this study are to develop multi-polarimetric and multi-temporal methods to effectively monitor the wetland area of the WWT Caerlaverock in Scotland one of the most important wetland in the United Kingdom. The test site was selected since it is severely affected by coastal erosion that makes the monitoring a very important issue.

For this purpose, two methodologies based on the joint use of co- and cross-polarized channels [1] and on the polarimetric notch filter [2], are used to both extract the profile of the coastal area and to detect the wetlands.

Preliminary results are obtained processing a set of full polarimetric SAR (PolSAR) data collected at C-band from RadarSAT-2 sensor. The results show that PolSAR data can be effectively used to detect both coastline and wetlands.

[1] Nunziata F., Buono A., Migliaccio M., Benassai G. (2016), “Dual-Polarimetric C- and X-Band SAR Data for Coastline Extraction" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-Stars), Volume: 9, Issue: 11, Pages: 4921 - 4928

[2] Marino A. (2013), “A Notch Filter for Ship Detection With Polarimetric SAR Data” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-Stars), Volume: 6, Issue: 3, Pages: 1219 - 1232

Wind speed retrieval is a subject of paramount importance since wind estimation is extremely useful for different meteorological and oceanographic applications: problematics like coastal erosion, climate change and so on are strictly connected with wind parameter. In this context, most of the remote sensing satellite radar can provide sea surface wind field data. In particular, microwave sensors, mainly scatterometer and Synthetic Aperture Radar (SAR), are worldwide recognized as the most suitable sensors for wind field retrieval. Radar backscattered signal contains quantitative information about the condition of the sea surface roughness and, hence, can be used to infer sea surface wind data. One of the most challenging case of wind speed retrieval is represented by the tropical cyclone case. 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. Typically, in literature, scatterometer, and then SAR, data are used to implement Geophysical Model Function (GMF) to extract wind speed information. These functions link the Normalized Radar Cross Section (NRCS) with wind speed and wind direction.

While in this work, a spectral based technique is adopted: the azimuth cut-off approach.

When managing SAR microwave sensors, Doppler misregistration in azimuth occur because of the gravity wave orbital movement. This issue is the major responsible of the imaged spectrum and of a strong cut-off in the azimuthal direction: this is the azimuth cut-off. This technique is used to retrieve wind speed and a few investigations have been carried out improve this approach [1]. More in detail, there is a straight link between λc values and geophysical parameters, similar to wind speed and critical wave tallness. In [1] the ACF-based λc approach has been improved to manage high wind speed routines, e.g.; extreme weather conditions. The key issues that allow to stretch out the technique to high wind regimes concern the tuning of a strategy that takes into account pixel spacing, box size and the homogeneity of the SAR image. In particular, the box size is set to be 1 km × 1 km and the median filter window is set at 90-120 m.

It is revealed in recent study that λc is related with wind speed at strong winds. In this study, we try to retrieve wind speed from Sentinel-1 SAR images in hurricanes and typhoons. The SAR-derived λc is compared with simulated azimuthal cutoff wavelength using the wave spectrum from numeric wave model in the three part of typhoon wave system. The retrieval wind speed is validated against measurements from the Soil Moisture Active Passive (SMAP) radiometer.

[1] V. Corcione, G. Grieco, M. Portabella, F. Nunziata and M. Migliaccio, “A novel azimuth cut-off implementation to retrieve sea surface wind speed from SAR imagery,” IEEE Transaction on Geoscience and Remote Sensing, vol. XXX, no. XXX, pp. XXXX-XXXX, 2018.

Sea oil spill monitoring is of extreme importance for researchers, ecologists, local authorities and a wider set of stakeholders since ocean pollution is a serious threat since, every day, a significant amount of oil is released into the maritime environment due to operational vessel procedures, accidental collisions, land-based discharges and all oil-related human activities. From a scientific perspective, a systematic and reliable support to sea oil pollution monitoring can be found in the exploitation of satellite-based sensors. Among them, it was shown that Synthetic Aperture Radar (SAR) plays a key role due to its almost all-weather capability to provide fine resolution (few meters) imagery with dense revisit time (few days).
The sea oil spill detection by means of SAR imagery is possible due to the damping effects oil slicks have on the wind-driven short sea waves that result, in the intensity images, in more or less homogeneous patches darker than the surrounding sea. Nevertheless, SAR-based sea oil spill detection still remains a challenging task since there exist several natural phenomena that generate the same signature in the intensity SAR imagery. They are termed as oil look-alikes and include internal waves and currents, grease ice, oceanic fronts, algal blooms, phytoplankton, etc. The latter represent false alarms that significantly reduce the performance of oil spill detection and classification algorithms.
In literature, when dealing with single-polarization SAR data, intensity-, texture- and morphologicalbased features, i.e., grey level co-occurrence matrix parameters, backscattering coefficients, shape, size and perimeter of the slick, etc. are usually adopted that often result in non-reliable results or in low detection accuracy. Those issues can be at least partly overcome by exploiting polarimetric information, even though this is paid at the expense of the area coverage thus limiting the operational application of polarimetric SAR architectures.
Hence, in this study, a new spectral method is applied on single-polarization SAR imagery for sea oil spill monitoring: the azimuth cut-off. The latter is a distortive non-linear effect occurring when the observed surface is moving during the SAR acquisition time. This is the case, for instance, of the ocean. In literature, several studies have been carried out to analyze the dependence of azimuth cutoff wavelength on sea surface parameters as wind speed, wave height, etc. In this study, preliminary results obtained on actual C-band SAR measurements collected over certified oil spills and lookalikes are shown that exhibit a different sensitivity with respect to the azimuth cut-off wavelength.