With contributions from: Markku Similä, Xi Zhang, Bernat Martinez, Jungang Yang, Jacqueline Boutin, Jin Wang, and their co-authors.

The project is divided into three major parts: (1) development of an algorithm for the retrieval of sea surface salinity based on combined active/passive microwave imagers; (2) data validation and oceanic application of new satellite altimeters and SWIM (“Surface Waves Investigation and Monitoring“, which is a scanning radar altimeter); and (3) devising techniques for sea ice parameter extraction and sea ice monitoring using data from most advanced satellite sensors.

The recent studies in part (1) deal with the development, evaluation and improvement of the sea surface salinity (SSS) retrieval algorithm under unfavorable conditions. A method for considering the effect of rain, implemented by the Chinese team, is based on L-band CAP (Combined Active-Passive) observations. The L-band GMFs (Geophysical Model Functions) are developed and the radiation characteristic of the rough sea surface is analyzed for rain-free and rainy conditions, respectively. Using this approach, the effect of rain-induced roughness can be corrected when calculating the SSS.

The objective of part (2) is the identification of eddies and their spatial-temporal variation characteristics on the Chinese seas using radar altimeters. The recent work of the European team is structured as a three-step approach: (a) improvement of retracker algorithms for the European altimeter missions, (b) development of geophysical corrections adapted to coastal zones, in particular focussing on the wet troposphere, (c) computations of geostrophic currents and derivation of mesoscale eddies. The Chinese partners recently have been working on inter-comparisons of data from Sentinel-3 SRAL, HY-2A RA, and Jason-2. The accuracy of, e. g., significant wave height retrievals as a function of the distance from the coast is analysed. The new algorithms shall be evaluated and validated in the next phases of the project (2018-2019) using in-situ and other reference data.

The work on sea ice monitoring (part 3) has been focussed on (a) the use of radar altimetry and SAR images for calculating ice thickness and sea ice concentration, and (b) a feasibility study concerning the performance of single-pass interferometric SAR for the retrieval of sea ice surface topography. The Chinese team, with contributions from the European partners, developed a new algorithm for retrieving the sea ice freeboard based on a new approximation of the echo waveform. This method resulted in more accurate freeboard estimates. A European group evaluated the achievable accuracy of ice surface height variations for different satellite InSAR configurations and analysed Tandem-X data acquired over sea ice as an example. Another Sino-European team concentrated on the determination of sea ice concentration and thickness from RS-2 ScanSAR images obtained over the Bohai Sea in winter 2012/2013.

In this introductory talk we will provide a brief overview of the different activities, which then will be described in more detail in subsequent presentations.

ESA’s Sentinel-3 mission was successfully launched in February 2016. It carries a new altimeter-SAR Radar Altimeter (SRAL) which works on two modes of Low-Resolution Mode (LRM) and SAR mode. SRAL improves the along-track resolution (approximately 300m) in SAR mode using a delay/Doppler technique. SRAL is expected to provide even better measurements of sea surface height, significant wave height and wind speed than the conventional altimetry, especially in the coastal zone. In addition, Chinese first satellite altimeter-HY-2A Radar Altimeter (RA) has been in orbit more than 4 years, and Sentinel-3 SRAL and HY-2A RA are Simultaneous in orbit currently. In this study, sea surface height data of Sentinel-3 SRAL are analyzed by the comparison of the distribution and time series variability of SLA data between the different altimeters (Sentinel-3 SRAL, HY-2A RA, Jason-2) data and tide gauge data in the China Sea. Significant wave height data of SRAL are evaluated by the comparison with buoys data in the China Sea. The performance of SRAL observation over the coastal regions is analyzed. The accuracy and reliability of SRAL data are analyzed in the different locations with the different distances to the coast. Finally, the preliminary performance of Sentinel-3 SRAL and the evaluation of HY-2A RA in the China Sea are summarized.

The sea surface salinity (SSS) is one of the key parameter for scientific community to understanding the ocean better. Equipped with an L-band radiometer, SMOS and Aquarius provide an unprecedented SSS data set of the global oceans. Enormous efforts are devoted to the development, evaluation and improvement of the SSS retrieval algorithm especially under some unfavorable conditions, i.e., the rain. Rain drops induce freshening and roughness effect to the sea surface. The fact that both mechanisms causing TB to increase makes it challenging to retrieval sea surface salinity when it rains. This presentation describes a method to retrieval the SSS under the rainy conditions based on the L-band CAP (Combined Active-Passive) observations. The L-band GMFs (Geophysical Model Functions) are developed and the radiation characteristic of the rough sea surface is analyzed for the rain free and rainy conditions respectively. The excess emissivity of H/V polarization is found to rise as the rain rate increases with a slope of 0.0003/mm·h^-1, which means a 1-psu error at the rain rate of 10mm/h. The dependence of the sea surface emissivity (sensitive to both roughness and freshening) on the backscatter (only sensitive to roughness) is obtained and the rain-induced roughness effect is corrected. The bias of retrieved SSS shows no clear dependence on the rain rate and the standard deviation of SSS error is about 0.5psu. The above results confirm the feasibility of this new retrieval algorithm for the SSS remote sensing in the rainy weather with the CAP observations.

Scientists from the Chinese National Satellite Ocean Application Service (NSOAS) and the Finnish Meteorological Institute (FMI) have jointly studied two fundamental sea ice parameters, sea ice concentration (SIC) and sea ice thickness (SIT) in the Bohai Sea. The case study was motivated by the risks the sea ice can cause for off-shore activities. Although the ice season is relatively short, usually from December to March, it can cause damage even in mild winters. On average level-ice thickness varies from 20 to 40 cm depending on the weather conditions.

We targeted the winter of 2012–2013 due to the availability of RADARSAT-2 (RS-2) ScanSAR imagery over the Bohai Sea. Our approach was to utilize several satellite sensors to create SIC and SIT charts.

The data set at our disposal consisted of 11 RS-2 SAR HH/HV images, daily AMSR2 imagery, four MODIS images and 31 in-situ measurements collected from the oil field platforms during the period from 2 January to 19 February 2013.

Basis for the calculations was the segmentation of SAR images using the Iterated Conditional Mode optimization and the extraction of different segment-wise features from them. The following texture features turned out to be the most useful in this connection: entropy, HH-polarization spatial autocorrelation, two quantities describing the shape of the variogram and HH-polarized corner points. These features were utilized in the SAR based SIC estimation. The scaled entropy alone yielded the best results in the SIT estimation.

The selection of the features in the SIC estimates was performed automatically by searching through all feature combinations and minimizing the residual error sum. Two different methods were tested: a linear model and a nonlinear Multi-Layer Perceptron neural network. Both approaches provided good results for very low or very high ice concentrations. For this data set, the linear model performed better. As a reference data we have used the in-situ measurements, the MODIS based SIC charts and the optical HJ-1B image.

The total ice volume in the study area was estimated using the thermodynamic sea ice model HIGHTSI with atmospheric boundary layer forcing (the reanalyzed ECMWF data). The ice drift was taken into account using the AMSR2 based ASI SIC chart with a resolution of 3.125 km. The ASI SIC chart was adjusted for the Bohai Sea conditions by the University of Bremen. The spatial distribution of the HIGHTSI SIT model field was then improved using statistics calculated from the AMSR2 data. The result was interpolated into a 1 km spatial grid and used as the background field h_B for the SAR based SIT estimation.

The background field and the SAR features were combined using a liner model that included a modulation term between h_B and each feature. Also here the minimization of the residual sum was the objective criterion in the feature selection. Somewhat surprisingly only a single feature, the scaled entropy, gave the best estimation results. As a reference data we used the MODIS based ice thickness chart as well as the field measurements.

The approach applied in this study has potential to be implemented operationally for the Bohai Sea ice service. However, more in-situ measurements as well as tuning the estimation model parameters are necessary, because the current study covers only one winter season. The obtained results should be regarded as guidelines for further research

Altimeter waveform retracking correction is essential to get accurate sea ice freeboard. Some empirical fitting methods have been proposed, and these methods simulate the waveform by fitting to echo and empirically positioning the retracking point. In this paper, we develop a new method to retrack lead elevation by simulating the lead echo waveform using a composite cubic Bezier curve which is stable. The lead waveform is first divided into several segments, for each segment, the cubic Bezier curve is used to simulate the waveform. For ice, an improved offset center of gravity (OCOG) method is used. A new sea ice freeboard retrieval algorithm by using Cryosat-2 SAR-mode data is then performed combining the Bezier curve waveform fitting and an improved OCOG method.

The sea ice freeboard is calculated in following steps. The first step is to discriminate sea surface and the ice. The discrimination is based on the fact that the shape variation in echo waveform depending on whether the echo is dominated by specular reflections from leads, or by diffuses reflections from ice. The pulse peakiness (PP) and the stack standard deviation (SSD) parameters are used to discriminate the ice/water type. Returns from leads are identified by PP > 18 and a SSD<4, while echoes from ice floes are identified by PP < 9 and SSD > 4. Secondly, for echoes from leads, we use a more sophisticated Bezier curve to fit the echo to get retracking point, and the retracking point is set where rise reached 70% of the waveform maximum. For echoes from ice floes, a modified OCOG is used, and the retracking point is positioned at the point where the rise reaches 95% of the computed OCOG amplitude. Thirdly, to obtain an accurate freeboard, we obtain local sea level height as shown followed. We use a linear interpolation on the difference between lead elevations and mean sea surface height (MSS) for each Cryosat-2 track, and then yield the sea surface anomaly (SSA, the deviation between the actual sea surface elevation and the MSS). At last, the sea ice freeboard can be retrieved by sea ice surface elevations minus MSS and SSA.

We directly compare the freeboard retrievals by proposed method and L2I products of CryoSat-2 to IceBridge data. A mean bias of 1.3cm was found with the uncertainty of 0.025m which is a latitude-dependent gradient. For two IceBridge campaign periods in March 2015 and April 2016, mean differences of 1.57 and 1.09cm are for the freeboard retrievals by proposed method, while mean differences of 1.75 and 1.84cm are found when using Cryosat-2 L2I products. This suggests the proposed method is capable of reconciling the sea ice freeboard from radar altimetry data sets with a high accuracy.

Ocean wave is a kind of fluctuation caused by the wind in the ocean. The ocean waves are very complex phenomena, the study of the ocean waves is of great significance on the marine engineering, marine development, transportation and shipping, marine fishing and aquaculture and other activities. In-situ observations, satellite remote sensing and numerical models are the important means of obtaining ocean waves information. Especially, satellite remote sensing is the important method of obtaining global ocean waves information synchronously. In this study, the significant wave height (SWH) of ocean waves obtained by T/P, Jason-1/2, ENVISAT, Cryosat-2 and HY-2A satellite altimeter and ENVISAT ASAR wave spectrum are used to generate the global ocean wave remote sensing data of 2000~2015 with the spatial and temporal resolution of 0.25° and one day. Firstly, all satellite data are validated and corrected by the comparison between them and the National Data Buoy Center (NDBC) buoys data. Then global ocean wave remote sensing data are obtained by the inverse distance weighting method. Finally, the resulting data are evaluated by the buoys data.

In this paper, sea-ice radial drift is studied based on the single-look complex (SLC) data of Sentinel-1A/B SAR. The method for sea-ice drift retrieval is developed using Doppler shift. The estimated Doppler frequency and predicted geometric Doppler shift are calculated for each unit of the Doppler grid, then the Doppler centroid anomaly can be obtained. According to the analysis of the uncertainties from the orbit, attitude, antenna pattern, topography, etc., the error in the Doppler centroid anomaly can be eliminated as far as possible to acquire the standard Doppler centroid anomaly. Furthermore, the standard Doppler centroid anomaly is converted to sea-ice radial drift velocity. Finally, the developed method will be validated through the comparison with not only a conventional cross-correlation method, but also measurements from a drifting ice buoy.

HY-2A is the first satellite mission for the dynamic environmental parameters measurements of China which has been launched successfully on August 16th, 2011. The multi-bands scanning microwave radiometer (RM) is one of the key sensors onboard the HY-2A satellite with the primary objective of measuring SST, surface wind speed, water vapor and cloud liquid content from space. On the basis of HY-2A RM observations, the sensitivity of some brightness temperature (TB) channels to the rain rate and the wind speed are analyzed. Two TB combinations which show minor sensitivity to rain are obtained. Meanwhile, the sensitivity of the TB combination to the wind speed is even better to the original TB channel. Based on these TB combinations, a wind speed retrieval algorithm is developed and validated. The wind speed retrieval accuracy is better than 2 m/s for rainy conditions, which is evidently superior to the HY-2A RM standard product.

Sea ice is not only an important part of the global climate system, but also affects the development of oil ,gas and mineral resources in the Arctic. In addition, the sea ice can affect the perforation of the "Arctic Waterway" and cause harm to production activities such as sea-related production and maritime traffic. That monitor the sea ice drift and obtain accurate sea ice drift direction and speed is of great significance to the study of climate analysis, safe navigation of ships and management of offshore oil platform.

At present, all the study of sea ice drift monitoring is based on the intensity information of SAR satellite image. However, due to the influence of speckle noise, the Algorithms of ice motion monitoring could not work well. With the development of synthetic aperture radar (SAR) technology, the texture have been shown to greatly improve the classification ability of sea ice with SAR image. Therefore, the texture of sea ice SAR image is expected to improve the sea ice drift detection capability.

Based on the above, The Sentinel-1 SAR data were utilized to detect and analyze the strengthen ability of sea ice drift monitoring by using the SAR image texture. Eight kinds of texture features such as contrast, correlation, dissimilarity, entropy, mean were extracted from Sentinel-1 SAR data. A total of 66 sea ice samples which were divided into new ice, young ice and first year ice three types were selected for the experiment. First, normalized cross-correlation method is used to evaluate whether the texture is suitable for sea ice drift monitoring by comparing the following four results: match position is correct or not, the ratio of the maximum value and the second value of match results, the ratio of the maximum value and the mean value of match results, and the ratio of the maximum value and the triple variance of match results. In addition to that the effect of different sea ice types and resolution on the correctness of matching is analyzed from the results.

Then, the SURF method is used to evaluate the sea ice by comparing the following three results: the matching accuracy rate of from SAR image and eight texture, the correct matching logarithm of feature point, and the accuracy of correct matching feature pairs. The use of the SURF method for sea ice drift monitoring can produce many misaligned feature pairs, we use the following ways to determine the feature pairs is correct or not: The approximate velocity and direction of the sea ice drift are determined by artificial visual judgment, and then the velocity and direction calculated by the SURF feature point are considered correct which is within the error range of the approximate velocity and direction. The experimental results show that the texture feature is more suitable for the sea ice drift monitoring than the SAR image itself.

Ship surveillance plays an important role in maritime traffic control, shipping safety, fishery supervision, maritime accident rescue, and oceanic rights protection. Synthetic Aperture Radar (SAR) has been widely used in ship surveillance as it has a full day, all-weather imaging capabilities. A great deal of research has been done on vessel detection using SAR; however, it is still difficult to detect the "weak" target under terrible sea conditions. The "weak" target with low target-sea contrast (TSC) is easy to be lost, and the existence of strong sea clutter can cause false alarm target. Therefore, this paper mainly focuses on the detection of "weak" vessels with polarimetric SAR.

1) High-resolution SAR sensors have a wide azimuth beam width, as well as a large range bandwidth. During SAR image formation, multiple squint angles and radar wavelengths are integrated to synthesize the full resolution SAR image. Based on this principle, the original single-look complex image SAR data was decomposed into different sub-aperture images in azimuth direction and sub-band images in range direction through time-frequency decomposition method. The scattering difference between the vessel and the ocean in the sub-aperture images or sub-band images is studied. We found that the vessel targets had coherence attributes between different sub-apertures, but the sea surface is significant differences and exists decoherence effect.

2) The complex coherence product of the sub-images polarimetric SAR is introduced and a plurality of sub images are formed into image pairs similar to the interferogram. Polarization coherent optimal parameter named PSCO (polarmetric SAR sub-aperture /spectral coherence optimal) is constructed by combining multiple sub images pairs and the concept of permanent scatterers’ detection in polarimetric interferometry. The performance of PSCO is tested using RADARSAT-2 quad-pol data. By comparing the enhancement ability of ship-sea contrast, the ability of clutter suppression and the calculation time, we choose 3 as the number of sub-images for PSCO. The CA-CFAR method is used to detect the ship for PSCO, and compared with CA-CFAR method based on HV polarization. The results show that the proposed method can suppress the sea clutter well and improve the detection performance. This method is suitable for terrible sea conditions, strong clutter and other complex sea background for vessel detection.

Internal waves occur almost everywhere on the shelves and slope. The generation and propagation of internal waves are closely related to the ocean bottom topography, tidal current, large-scale circulation and stratification. Therefore, internal waves have an obvious local property which shows different characteristics in different areas. Remote sensing data has been widely used in the temporal-spatial distributions, generation and evolution, parameter inversion of the internal waves and is the best instrumentality for detection of internal waves on a near-global scale. The MODIS imagery owns the advantages of large swath area and near-daily global coverage, but influenced by the weather greatly. SAR has the ability of all-weather and day-and-night observation, although data coverage is limited. This paper combines SAR and MODIS images effectively to observe internal waves on a near-global scale. The ASAR and MODIS imagery on global scale over the period of 2011 was processed. The distribution of internal waves is presented by extracting crests of internal waves. Regions of internal waves are frequently observed of Pacific, Indian Ocean and Atlantic of the internal waves are presented. The further analysis of the scale, propagation law and the temporal-spatial distributions characteristics of the internal waves in the typical occurrence area are also conducted.

In this paper, a support vector machines (SVM) regression model is proposed to extract integral ocean wave parameters such as significant wave height (SWH) and mean wave period (MWP) from the ASAR wave mode images. Calibrated ASAR wave images can be applied directly to retrieve SWH and MWP without prior information. The model was established based on the nonlinear relationship between the sigma0, the variance of the normalized SAR image, SAR image spectrum spectral decomposition parameters, SWH and MWP. The input parameters of the SVM regression model are feature parameters extracted from ASAR images and the SWH provided by European Centre for medium range weather forecasts (ECMWF) is the output. A global data set of 30771 pairs of ASAR wave mode images and collocated ERA-Interim data from ECMWF is used to train SVM model. Based on the matching dataset, the particle swarm optimization (PSO) algorithm is applied to optimize the input kernel parameters of the SVM regression model and establish the SVM model. The SWH extracted by this model was compared with the ERA-Interim data and the buoy data. The RMSE of SWH is 0.34m and 0.48m and the correlation is 0.94 and 0.81, respectively. The MWP was also validated by ERA-Interim data and buoy data, the RMSE is 0.68s and 1.08s, and the Scatter Index is 0.04 and 0.09, respectively. The results show that the SVM regression model is an effective method to extract SWH from SAR data. The advantage of this model is that SAR data may serve as an independent data sources to extract SWH, which can avoid the complicated solution process of wave spectrum.

Abstract：As a kind of marine natural disaster, Green Tide has been appearing every year along the Qingdao Coast, bringing great loss to this region, since the large-scale bloom in 2008. Therefore, it is of great value to obtain the real time dynamic information about green tide distribution. In this paper, methods of optical remote sensing and microwave remote sensing are employed in Green Tide Monitoring Research. A specific remote sensing data processing flow and a green tide information extraction algorithm are designed, according to the optical and microwave data of different characteristics. In the aspect of green tide spatial distribution information extraction, an automatic extraction algorithm of green tide distribution boundaries is designed based on the principle of morphology dilation/erosion. And key issues in information extraction, including the division of green tide regions, the obtaining of basic distributions, the limitation of distribution boundary, and the elimination of islands, have been solved. The automatic generation of green tide distribution boundaries from the results of remote sensing information extraction is realized. Finally, a green tide monitoring system is built based on IDL/GIS secondary development in the integrated environment of RS and GIS, achieving the integration of RS monitoring and information extraction.

A series of oil spill accidents has occurred frequently with the development of petroleum industry and marine oil transportation,which lead to serious oil pollution.For instance,the oil spill in Penglai,China 19-3 oil field and Dalian,Xingang,China has caused great ecological and economic losses,Therefore,it is meaningful for monitoring oil spill information in real time to prevent oil spill disasters.Based on the multi-source Synthetic Aperture Radar(SAR)image data,this paper has carried out research on the remote sensing monitoring of oil spill,and the operational monitoring system of marine oil spill based on Geographic Information System(GIS)is developed,which realize the integration of preprocessing multi-source remote sensing image data,extracting oil spill monitoring information,making oil spill thematic map and publishing oil spill monitoring reports.In order to realize the rapid processing of multi-source image data integration,the original data is analyzed based on Geospatial Data Abstraction Library(GDAL)open source raster spatial data conversion library and realized image reading,geometric correction and so on;the algorithm of multi-scale image segmentation and gray contrast characteristic extraction are encapsulated on the basis of the previous step,which achieve automatic processing of SAR image block,segmentation,oil spill identification,merging,parameter assignment,etc,the one-key processing of oil spill extraction is realized;for the application demand of oil spill monitoring,production of various kinds of standardized products, automatically add the elements of cartographic decoration elements,image parameters,oil spill area,the source of oil spill and other kinds of information,real-time generate thematic reports.

GF-3 (GF stands for GaoFen, which means High Resolution in Chinese) is the China's first C band multi-polarization high resolution microwave remote sensing satellite. It was successfully launched on Aug. 10, 2016 in Taiyuan satellite launch center. The synthetic aperture radar (SAR) on board GF-3 works at incidence angles ranging from 20 to 50 degree with several polarization modes including single-polarization, dual-polarization and quad-polarization. GF-3 SAR is also the world’s most imaging modes SAR satellite, with 12 imaging modes consisting of some traditional ones like stripmap and scanSAR modes and some new ones like spotlight, wave and global modes. GF-3 SAR is thus a multi-functional satellite for both land and ocean observation by switching the different imaging modes.

TG-2 (TG stands for TianGong, which means Heavenly Palace in Chinese) is a Chinese space laboratory which was launched on 15 Sep. 2016 from Jiuquan Satellite Launch Centre aboard a Long March 2F rocket. The onboard Interferometric Imaging Radar Altimeter (InIRA) is a new generation radar altimeter developed by China and also the first on orbit wide swath imaging radar altimeter, which integrates interferometry, synthetic aperture, and height tracking techniques at small incidence angles and a swath of 30 km. The InIRA was switch on to acquire data during this mission on 22 September.

This paper gives some preliminary results for the quantitative remote sensing of ocean winds and waves from the GF-3 SAR and the TG-2 InIRA. Comparisons to the ECMWF ERA-Interim reanalysis data show good agreements but more valuable details.

Sentinel-1 wave mode operates in novel ‘leap frog’ acquisitionmode. A vignette is acquired every 100 km at two alternate incidence angles (23° and 36.5° respectively ), withtwo images at the same incidence 200km apart. This mode enables us not only to conduct global analysis for each incidence angle, but also to investigatethe dependence of geophysical parameters on incidence angles.

The SAR image cross-spectrum between a pair of sublooks separated in time has been widely used to help remove 180° ambiguity of detected ocean swell systems. Ocean swells move along the waves' propagation direction during this offset time of the order of SAR integration time (~0.4s for Sentinel-1), therefore, co- and cross-spectra can help to consistently estimate velocity characteristics of the randomly moving sea surface scatterers related to scales larger than SAR spatial resolution. The short radial waves longer than SAR spatial resolution but shorter than ocean swells are, to first order, driven by surface winds. Thus, a new parameter, averaged complex cross-spectra over short radial waves domain, is proposed to manifest impacts of wind speed and direction on these moving scatterers.

Large data sets have been constituted systematically by co-locating Sentinel-1A/Bwave mode acquisitions and ECMWF forecast winds. Efficiently, distinctive features are revealedfor two incidence angles as well as dual-polarizations.

Specifically, to first order,there exhibits a linear relationship between imaginary cross-spectra and radial winds. More encouragingly, it reaches the maximum at downwind (wind blowing toward to the antenna), drops to zero at crosswind (wind blowing along azimuth direction) and continuously decreases to the minimum at upwind. Its sensitivity to wind direction is potential to wind retrieval applications.

At 23° incidence, the spectral parameter does not exhibit up-to-downwind difference of spectral. At variance, a strong asymmetry with respect to radial wind at 36.5° incidence is found. This asymmetry is much more pronounced than up-to-downwind asymmetry for the Normalized Radar Cross Section (NRCS).

Taking advantage of exceptionally continuous HH acquisitions by Sentinel-1B wave mode, we also present preliminary results for HH polarization as well as dual-polarizations' comparisons. In general, HH shows similar features to VV at each incidence angle. However, both spectral magnitude and imaginary part are larger for HH than those for VV for given incidence and radial wind, which could be explained by larger tilt modulation for HH polarization.

As a signed quantity, the imaginary cross-spectra of short radial waves can be considered to be a parameter strongly correlated with the Doppler Centroid Anomaly (DCA). Indeed, both parameters are directly linked to the time evolution of the detected sea surface scatters. As such, both parameters shall closely trace the wind direction within a single SAR imagette. Yet, Sentinel-1 DCA is, today, unfortunately unreliable, with large latitudinal variations for given surface winds. Free of geometrical configuration, the imaginary cross-spectra can thus serve to replace the DCA estimate to help constrain the retrieval of wind field at moderate to high spatial resolution.
In addition, similarly to the residual DCA, a residual parameter can be derived for the residual imaginary cross-spectral parameter, i.e. the difference between the expected parameter for a given wind speed and direction and the local measurement. As anticipated, this residual spectral parameter shall complement observations of surface wind-wave-current interactions.

Conventional VV-polarized NRCS is saturated as wind speed approaching typhoon intensity. In view of this, high wind speeds are underestimated by CMOD5.N especially in the typhoon eyewall regions. Compared to VV-polarization, NRCS in VH polarization is more appropriate to retrieve high wind speeds with cross-polarized retrieval model such as C-2PO or C-2POD. However, cross-polarization wind speed retrieval models overestimate low and moderate winds. Typhoon has capacity of inducing strong dynamical and thermal impacts on the ocean. During the passage of a typhoon, surface wind stress generates intense turbulence and currents in the upper ocean. In general, CSFR data are used as a wind forcing to simulate ocean surface and subsurface response to the typhoon, while the coarse resolution of CSFR can not accurately depict fine structure characteristc of typhoon. Furthermore, there are obvious difference between typhoon intensity and eye center location from CSFR data and satellite observations.

In order to obtain reasonable wind speeds in typhoon eye and eyewall regions as well as in periphery areas, we build a cost function both involving VV and VH-polarization SAR observation. Co- and cross-polarization wind speed retrieval models, CMOD5.N and C-2POD, can yield simulated NRCS in VV and VH poalrizations. The initial first guess wind field is not dervied from numerical weather prediction model, but VH-polarized SAR image itself. According to wind steak features appearing on the typhoon SAR images, we first extracted wind directions by using local gradient method and two-diemensional interpolation technique.The retrieved wind directions are compared with HWRF simulations and WindSat measurements. Subquently, we use C-2POD model and VH-polarized NRCS to retrieve wind speeds. As a result, the initial first guess u and v components can be estimated by using retrieved wind speeds and wind directions. The optimum wind vector estimate for any given wind cell will therefore correspond to a minimum in the cost function.

We use the Regional Oceanic Modeling System (ROMS) to simulate characteristics of upper ocean response to the typhoon. The SAR-derived wind fields are used to force the ROMS. The model outputs reveal the sea surface temperature (SST) cooling and sea surface salinity (SSS) augment on the righ side of the typhoon track. The decrease of SST and increase of SSS are caused by the fact that the entrainment of cold and high salt water breaks through the thermocline and gets into the mixed layer and surface. We compare SST and SSS simulations with microwave Optimally interpolated SST products and SMAP SSS data. Moreover, we also use ROMS simulate the mixed layer depth and upwelling velocity during the passage of the typhoon. Results show the deepening of the mixed layer and increase of the upwelling velocity due to the typhoon-induced vertical mixing. We analyze NPP VIIRS-derived chlorophyll concentration before and after the passage of typhoon, and catch sight of obvious chlorophyll bloom. The increase of clorophyll concentration provides us with evidence for the cold water uplifting. The typhoon pumped cold water from the lower layer with rich nutrients which enhanced the surface biological production.

Secchi disk depth (SDD, m^-1), which can reflect the clarity and turbidity of coastal seawater, is an important parameter to describe the optical properties of water. In this work, an empirical model for SDD retrieval was established on the basis of the MODIS Rrs data and the in-situ SDD data which were collected in the Yellow Sea and the East China Sea. The SDD model was further applied to retrieve the monthly SDD during the period of 2002-2016 for the Bohai Sea, the Yellow Sea, and the East China Sea; and the variations in SDD was first analyzed with the river discharges and the eutrophication indexed by Chl-a. Over the western Yellow Sea which is characterized by "green tides" of floating Ulva spp blooms in the summer since 2007, the variations in SDD were specially analyzed to investigate the effects of "green tides" on the optical environment.

On August 10, 2016, carrying the first Chinese C-band multi-polarization SAR, Gaofen-3 (GF-3) satellite was successfully launched into a polar sun-synchronous orbit of 755 km altitude with an inclination of 98° and 26 days repeat cycle. Following several months of on-orbit commissioning phase, GF-3 SAR images have been in operation since January, 2017. As one of the primary users, the State Oceanic Administration (SOA) is operationally conducting GF-3 SAR ocean wind retrieval and plan to officially release the near real time SAR wind products soon. In this paper, we present the first results of GF-3 SAR derived winds and preliminary assessment using the buoy measurements.

In principle, the GF-3 SAR wind inversion methodology is to combine SAR observed NRCS at co-polarized channel with a priori wind information from ECMWF, taking into account that both NRCS observations and models may contain errors. In order to extract the wind speed and direction, the cost function is minimized with the help of look-up table computed from geophysical model function (GMF), making the inversion scheme more efficient.

The coastal winds were estimated from the GF-3 SAR at 1 km resolution in four imaging mode, including Standard Strip, Quad‐Polarization Strip I, Quad‐Polarization Strip II and Narrow Scan imaging mode, and the retrievals are presented. One case of the coastal katabatic wind off U.S. west coast captured by GF-3 is discussed.

The preliminary accuracy assessment of GF-3 SAR wind speed retrievals is carried out against in situ measurements from NDBC buoys. Only the buoys located inside the GF-3 SAR wind cell (1 km) were considered as co-located in space, while the time interval between observations of SAR and buoy was limited to less the 30 min. This criteria yield 38 co-locations during the period from January to April 2017, showing the RMSE of 2.59 m/s. Different performances due to GMF and Polarization Ratio (PR) are discussed. The preliminary results present that GF-3 wind retrievals are encouraging for operational implementation.

Arctic region is experiencing faster warming than that in the middle and low latitude region, which triggers extensive change of physical and biological Arctic systems. Sea ice, as one of the most important indicator of climate change in Arctic, is showing an accelerating declination trend. The loss of sea ice in the Arctic is not only a simple outcome of climate change, but it also has strong feedback to regional and global climate. In addition to the climate change issue, Arctic is being paid more attentions than ever before, as increasingly accessible for extensive shipping (e.g., the recently opened Northern passages in summer) and oil and gas exploration. Satellite observation have been exploited for Arctic sea ice monitoring for more than a few decades, as they have advantages of large coverage and flexible acquisitions. In contrast to optical imaging, microwave sensors are not impeded by cloud coverage or lack of daylight. Among the microwave sensors, Synthetic Aperture Radar (SAR) has unique advantages of high resolution, as well as large coverage.

For Arctic sea ice monitoring, in the first step, we focus on deriving static and dynamic information, e.g., classification of ice types, ice-water discrimination, ice concentration and drift of sea ice. We will present our recently collaboration on deriving such information based on C- and X-band SAR, including TerraSAR-X, Radarsat-2 and the Chinese recent launched GaoFen-3 (GF-3). Different algorithms and approaches have been developed for these spaceborne SAR data derive sea ice information. In particular, the SAR data acquired in multiple polarizaiton are exploited. In addition to derive sea ice information from SAR data, we will also present some preliminary studies on interaction of swell and ice in the marginal ice zone (MIZ).

A fully automatic SAR oil spill monitoring system briefly consists of three parts, 1) dark target segmentation on SAR image 2) dark target feature extraction 3) dark target classification. In order to build a fully automatic system with high detection and high classification performance, the first step requires all oil spill targets to be extracted in the first step, the second step requires the category distinction of dark targets in the feature space to be as high as possible to reduce the classification complexity and the third step requires the classifier to deal with imbalance problems.

The classification complexity can be simply expressed as the size of the overlapping regions of the distribution of the two types in the feature space. The larger the overlap, the more difficult it is to train a high performance classifier. The negative effect of imbalance is equivalent to a multiplicative amplification factor attached to the complexity. The imbalance problem would disappear if complexity is reduced zero. However, the complexity is impossible to be zero, therefore the technical effort to build a high performance automatic oil spill monitoring system is to try to reduce classification complexity and to deal with imbalance problem.

In order to ensure that all oil spills are extracted, the existing segmentation algorithm for extracting dark targets from SAR images will lead to a large number of look-alikes appear, which contain not only dark targets caused by common atmospheric or oceanic phenomena, but also lots of unexplained ones. That is the source of the imbalance problem. In the segmentation stage, an adaptive threshold segmentation algorithm based on multi-scale background normalization is adopted at first to ensure that all oil spills are extracted, and then a post-processing filter chain is used to reduce the imbalance of the targets from 1:100 to n1:5 below.

77 features collected from different researchers are used to improve the category distinction of the types in the feature space, that is, to reduce the complexity of the classification problem. The more the features, the more weights the network needs to adjust, therefore the more training samples are required. However, all 77 features are still used in our study since there are sufficient samples available and computation amount is not so high. Some wrong category labels of samples due to human error may increase classification complexity, especially in the case of a large number of samples. By using an iterative sample category designation method combined with manual classification and machine classification, the number of error labels is significantly reduced and thus the classification complexity is reduced either.

Double-hidden-layer neural network has strong capability to fit complex decision surface, but it also easy to fall in over-fitting and suffer high false discovery rate under the circumstance of imbalance. The Adaboost integrating multiple double-hidden-layer neural networks plays a role in smoothing the decision surface, thus weakening the over-fitting effect and enhancing the generalization performance of the classifier. Moreover, Adaboost trends to reduce the false alarms in imbalance condition since its component classifiers pay more attention on the samples misclassified by the preceding ones. Therefore the Adaboost based on neuronal network has capability to deal with the imbalance problem.

By applying the techniques mentioned above, a fully automatic SAR oil spill monitoring system with detection rate of 81%, false discovery rate of 17%, false alarm rate of 5% and correct recognition rate of 92% is obtained for the sample set consisting of 23768 dark targets extracted from 337 scences of Envisat ASAR and Cosmo-SkyMed images.

In 2008, an extensive Ulva Prolifera bloom occurred in the Yellow Sea (YS) just before the Olympic Sailing Regatta and draw much attention of the world. Then Ulva bloom annually occurred in the YS each summer and attracted the interest of the ocean remote sensing and marine biologist community. Wide swath and short re-visit time optical satellite data were first utilized to monitor the Ulva bloom and estimate their coverage area using methods such as NDVI and FAI. However, the estimated Ulva bloom coverage area were very different in the published literature mainly due to adopted different thresholds T₀ and T₁. T₀ was used to differentiate the algae-containing pixel with seawater pixel while T₁ was used to differentiate algae full-covered pixel with partial-covered pixel. In this study, those two thresholds were further studied.

Threshold T₀ was derived as follows: (1) First, generate the FAI and FAI gradient image for each optical satellite imagery; (2) classify all pixels in the satellite image as seawater pixel and non-seawater pixels based on the FAI gradient image;(3) replace non-seawater FAI values with median FAI of the neighboring seawater pixels to construct the seawater FAI background image; (4) generate seawater-removed FAI image by subtracting the seawater FAI background image from the FAI image, where pixels greater then zero will be algae pixels.

In the recent study, the author developed the Ulva bloom biomass estimation model and found that (1) FAI threshold T₁ was significantly underestimated in the former study (2) FAI linearly increased with the increase of algae coverage but increased slowly after algae completely covered, thus the turn-point in the FAI-biomass curve was the FAI threshold T₁. In this study, the lookup table of threshold T₁ for specific satellite sensor were simulated in different sun/sensor viewing geometry and atmospheric conditions based on the filed measured algae spectrum.

The high-resolution satellite data were used to validate the results in this study. (1) The Ulva bloom coverage estimated from MODIS data and concurrent high-spatial resolution Chinese GF-1 WFV data (16m) were compared with the relative difference 10%. (2) The threshold T₁ was validated with high-spatial Worldview-2 satellite data.

This method can be implemented to OLCI/Sentinel-3 data and the subsequent estimation Ulva bloom coverage from OLCI data will be conducted in this summer.

Synthetic aperture radar (SAR) imagery provides an effective source of data for observing, measuring and quantifying oceanographic phenomena. The ability of SAR sensors in retrieving data in almost all weather conditions, independently of sunlight illumination, is an extremely useful aspect, important for oceanographic applications. The quality of SAR imagery is dependent on the mode of acquisition and raw data processing. SAR data acquisition techniques introduce a significant backscattering trend in the range direction of the received signal. In Wide Swath and Extra Wide Swath modes, results in a progressive reduction of brightness over images from near to far range, introducing errors on the detection and classification of dark features to oil spills and lookalikes. The present research, aims to examine normalization techniques previously applied to ENVISAT SAR Wide Swath data. We investigate possible methods for limiting the issue of Normalized Radar Cross-Section (NRCS or σ°) variation, due to the incidence angle variations in Sentinel-1 Wide and Extra Wide Swath and data. NRCS depends on the relative azimuth angle between the radar look direction and wind direction. At low incidence angles over a certain wind speed and direction NRCS values are different from these at high incidence angles. In order to eliminate errors during image processing and analysis for oceanographic feature extraction, a normalization is required to limit the NRCS variation over the various incidence angles is suggested. Based on previous studies, the most widely used incidence angle correction technique is the square cosine correction. However, the square cosine correction is valid for surfaces with Lambertian reflectance properties and is not expected to perform in a satisfactory way over the sea. Here we apply two sensor independent functions aiming to improve the dark object detection in SAR imagery over ocean: a theoretical backscattering shape function which is derived from a minimum wind speed and an empirical range fit of NRCS against incidence angle θ. The former method exploits only the modelled NRCS values, while the latter only the image content. The aim of this paper is to apply a simple but consistent scheme for reducing the dynamic range of SAR images by removing the mean incidence angle dependence. The approach targets to normalize the Wide and Extra Wide Swath SAR image to a fixed reference angle. From the approach a single global threshold is applied to detect dark objects in SAR imagery.

ABSTRACT

The pollution of the marine environment due to deliberate discharge of untreated ballast waters is not only an operational procedure done by ships but also by offshore platforms. Although, the amount of oil released into the ocean by the offshore platforms using this procedure on one-time basis is derisory, if compared with the massive spill event in case of platform accidents like the Montara and Deepwater Horizon, the long term damages on the flora and fauna are severe.

As the North Sea is characterized by fairly shallow water bathymetry, it has been extensively exploited in the past years for oil extraction and production. By now, it hosts a significant number of offshore installations and therefore the probability of minor leaks is quite high. A large number of Synthetic Aperture Radar (SAR) images are being acquired and processed operationally over North Sea platform installations for potential oil pollution using the TerraSAR-X satellite [1]. The ScanSAR and WideScanSAR mode in VV polarization imagery have been preferred due to large coverage and higher oil-water contrast. Among the dataset collected, a constant leakage from the platforms belonging to the Forties oil field has been observed.

Thanks the low TerraSAR-X orbit altitude and relatively high latitude location of the Forties oil field, leaks from the same platforms have been observed with temporal interval of less than 13 h. While most previous studies on tracking oil spills assume that the observed slicks by consecutively acquired SAR images are the same and spatial displaced by the drift effect [2], a completely different situation is outlined by the analysis in [3]. By model simulation it is shown that leaks were not start-stop but continuous with only part of the old oil being drifted.

REFERENCES

[1] S. Singha, D. Velotto, and S. Lehner, “Near real time monitoring of platform sourced pollution using TerraSAR-X over the North Sea,” Mar. Pollut. Bull., vol. 86, no. 1–2, pp. 379–390, Sep. 2014.

[2] Y. Cheng et al., “Monitoring of Oil Spill Trajectories With COSMO-SkyMed X-Band SAR Images and Model Simulation,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 7, no. 7, pp. 2895–2901, Jul. 2014.

[3] X. M. Li, T. Jia, and D. Velotto, “Spatial and Temporal Variations of Oil Spills in the North Sea Observed by the Satellite Constellation of TerraSAR-X and TanDEM-X,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 9, no. 11, pp. 4941–4947, Nov. 2016.

China successfully launched the geostationary orbiting optical satellite GF-4 on December 29^th, 2015. The satellite can image the specific location of interest many times during the daytime with 50m (400m) spatial resolution in the visible (infrared) bands. Based on the GF-4 data, the following researches were performed. (1) The signal-to-noise ratio and georeference accuracy of GF-4 images was evaluated. (2) The vicarious radiometric calibration of GF-4 was performed with the concurrent GOCI data, and the atmospheric correction of GF-4 was further carried out. (3) The diurnal variability of the drifting velocity of the floating macro-algal bloom (green tide) in the Yellow Sea was analyzed with the GF-4 images.

Every satellite has its own revisited period, so it is impossible to monitor a whole process of green tide bloom using only one kind of data source acquired from one satellite. The requirement of monitoring green macroalgae collaboratively by multi satellites instruments has been put on the table. In order to make green macroalgae monitoring by MODIS (250-m resolution) and Landsat TM/ETM+ (30-m resolution) images possible, the detection results in the same region and the same time from the two data sources are compared, and the relationship between them is analyzed. It is found that the detection result from MODIS image is over 5 times of the one from Landsat TM/ETM+ image. According to this statistic relationship, a transformation-detection method of green macroalgae from 250m resolution MODIS image to 30m resolution results is presented. The experimental results show that the proposed approach can provide an effective method to monitor green macroalgae collaboratively by MODIS and Landsat TM/ETM+ images.

On February 16th, 2016, ESA successfully launched the Sentinel-3A satellite. Among the four Earth-observing instruments, the OLCI (Ocean and Land Colour Instrument) is designed to continue the ENVISAT MERIS capability of ocean color monitoring.

Based on the OLCI L1 data, the following researches were performed. (1) The TOA reflectance, signal-to-noise ratio and georeference accuracy of OLCI images were evaluated and compared with those of MODIS and VIIRS. (2) The floating macro-algal bloom (green tide) in the Yellow Sea was detected using the OLCI images.

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 risk/vulnerability maps, targets at sea maps and pollutants maps, cyclone/typhoon monitoring. Typhoon monitoring will be addressed using a multi-sensor approach based on the exploitation of SAR, scatterometer and radiometer measurements. Hence, models to deal with extreme wind conditions will be analyzed/developed.

The monitoring and forecasting of tropical and extra-tropical storm tracks and intensities are strategic for the protection of coastal infrastructures and residents. The Synthetic Aperture Radar (SAR) can potentially be very useful for such purposes. In fact, its high spatial resolution and its capability to observe the sea surface in all weather conditions and at all times (regardless of the day or night-time) makes it one of the best candidate instrument for these goals.

Several empirical forward models or geophysical model functions (GMFs), which relate the normalized radar backscatter cross section to the sea surface wind vector, have been developed and successfully used for a wide variety of scatterometer and SAR systems at different frequencies and polarizations. The GMFs have high accuracy under no-rain and low-to-moderate conditions, although for C-band radar systems good-quality winds are also derived under rainy conditions. For high wind speed measurements, the accuracy rapidly decreases due to saturation of the co-polarized backscattered intensity and the reduction of friction between the sea surface and the wind. An improved high-wind GMF for C-band and SAR spatial scales (most GMF developments are based on scatterometer data) as well as new high-wind GMFs for other frequencies (e.g., X-band) are required for the successful wind retrievals under tropical and extratropical cyclone conditions.

In this study, the radar backscatter sensitivity to high winds (as given by the different GMFs) is revisited for both C-band and X-band systems, as well as for co-polarized and cross-polarized beams, using the NOAA P-3 flight winter campaign data from January-February 2017. During this campaign, the NOAA P-3 plane, equipped with several sea-surface wind sensing systems, i.e., the Step Frequency Microwave Radiometer (SFMR), the Imaging Wind and Rain Profiler (IWRAP), and dropsondes, underflew both Sentinel-1 (S-1) and Cosmo-SkyMed (CSK) satellite passes under storm conditions in the North Atlantic region. These extra-tropical storms are characterized by vast areas of nearly uniform very high-wind conditions, up to 30-35 m/s. The sensitivity analysis will therefore provide a comprehensive view of the main sensitivities of the radar backscatter (e.g., co-polarized, cross-polarized, polarization difference, polarization ratio) to relatively high winds, for a variety of incidence angles.

Moreover, two wind retrieval approaches commonly used by the SAR community, i.e., the azimuth cut-off method and the combined radar backscatter and doppler centroid scheme, will be used to derive the sea surface wind field for the mentioned SAR scenes and validated against the mentioned NOAA wind data sources as well as collocated scatterometer and Soil Moisture Active Passive (SMAP) derived winds.

The amount of offshore platform is increasing significantly due to the improvements in drilling technology [1]. In particular, the advances due to deep water drilling technology allows installation to be mobile (to not have a stable location) and to be in regions far from coastal water.

Offshore platforms pose a risk to environment with the threat of oil and gas spillage, especially due to their exposition to extreme weather conditions. Besides being a risk to the environment, since their location is not mapped on maps, they are also obstacles for yachts, low flying airplanes and merchant ships in low visibility conditions.

Offshore platforms are generally large metallic constructions, which should make them easily detected and mapped by using satellite Synthetic Aperture Radar (SAR) medium resolution imagery [2]. However, we recently obtained analysed measurements [3] showing that some of the platforms in some acquisition geometries may be invisible in single-polarization backscattering images, leading to miss-detection. On the other hand the detection is still feasible if the dual polarimetric information is used.

In this work we exploiting a time series of dual-polarization TerraSAR-X data acquisitions over a cluster of offshore platform in the Gulf of Mexico. Among others, factors affecting the backscattering include polarization, resolution and incidence angle. Finally in this paper we also address how incoherent and coherent polarimetric observables can be exploited to detect platforms when the single polarimetric acquisition may fail detection.

REFERENCES

[1] “International Energy Agency.” [Online]. Available: http://www.iea.org/.

[2] S. Casadio, O. Arino, and A. Minchella, “Use of ATSR and SAR measurements for the monitoring and characterisation of night-time gas flaring from off-shore platforms: The North Sea test case, “ Remote Sens. Environ., vol. 123, pp. 175–186, Aug. 2012.

[3] A. Marino, D. Velotto and F. Nunziate, “Offshore metallic platforms observation using dual-polarimetric TS-X/TD-X satellite imagery: a case study in the Gulf of Mexico,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., under review, 2016.

The C-band synthetic aperture radar on-board ESA’s Sentinel satellites have the capability to provide high resolution wind vector information over the ocean surface. These wind vector data derived from SAR observations are available to the data assimilation system with real-time information of high accuracy. In this study, several comparison experiments are designed to investigate the impact of Sentinel-1 SAR winds data in the three-dimensional variational assimilation system for the Weather Research and Forecast model (WRF 3DVAR). The powerful Typhoon Lionrock is selected for this case study. Typhoon Lionrock struck and caused significant flooding and casualties in Japan and Russia in late August 2016. Its route changed several times and there are also several rapid intensification processes during its lifetime, which made its track very difficult to predict. Totally, 10 SAR images from 3 overpasses from August 27 to 29, 2016 are used in this study. The preliminary results demonstrate that SAR wind data can complement the scarce observations over the sea surface and improve the prediction of wind and pressure fields of Typhoon Lionrock. More detailed experimental results and analyses will be provided in the Dragon-4 symposium.

Coastal regions are rapidly growing with increase in urbanization and population density. This results in increasing threats for terrestrial habitats and marine ecosystems. Hence, ever growing attention must be paid to the pollution of the coastal waters fed from urban watersheds and human-related activities discharges. Pollutants include pesticided, fertilizers, hydrocarbons, trace of heavy metals, organic compounds, pathogens and other anthropogenic debris. They alter the physical and biogeochemical state of coastal waters affecting marine life negatively.

Stormwater and wastewater runoff are the main sources of pollution in coastal areas that result from untreated runoff and pollutants coming from urban watersheds entering the coastal waters after rainstorms, through river discharges or during publicly owned treatment works. They impact coastal water quality severely through an increase in bacterial contamination due to the significant load of particles and dissolved compounds discharged during and immediately after storm events.

The environmental observation of stormwater runoff in near-shore waters is commonly undertaken through sparse sampling onboard coastal research ships. Nonetheless, the information those field measuremets provide is sporadic, expensive and coarse. The space/time gap left by in-situ monitoring can be filled in by satellite measurements.

Satellite synthetic aperture radar (SAR)-based and ocean color imagery from Aqua MODIS have been used to analyze the different compounds of runoff plumes, surfactants and sediment discharge. Nevertheless, optical observations are limited by rainy events, cloud cover and solar illumination. In particular, polarimetric SAR (polSAR) represents the most suitable tool for monitoring the pollution of coastal waters due to the all-day and almost all-weather detailed information it provides on the scattering mechanisms of the observed scene routinely, with wide area coverage, dense revisit time and fine spatial resolution. The capability of polSARs to detect marine oil spills, to identify natural oil seepages and to roughly characterize the damping properties of surfactants was widely proved.

In this study, the sensitivity of polarimetric parameters derived from fully-polarimetric (FP) SAR measurements on the presence of surface pollutants in coastal waters is investigated. Preliminary results are obtained processing a set of FP SAR data collected at C-band from Radarsat-2 over the coastal area of Piana del Sele (Salerno, Italy). The test site was selected since it is one of the most industrialized areas of Southern Italy that is severely affected by coastal water pollution. Preliminary results show that polSAR data can be effectively used to retrive detailed information on the coastal water quality that may be useful for the sustainable development and managament of coastal areas, including marine ecosystems protection, aquacultures and fisheries safety, water resources management.

REFERENCES:

1) Holt, B, Trinh, R. and Gierach, M.M., 2017,

“Stormwater runoff plumes in the Southern California Bight: a comparison study with SAR and MODIS imagery”,

in print on Marine Pollution Bullettin.

2) DiGiacomo, P. M., Washburn, L., Holt, B. and Jones, B. H., 2004,

“Coastal pollution hazards in southern California observed by SAR imagery: stormwater plumes, wastewater plumes, and natural hydrocarbon seeps”,

Marine Pollution Bulletin, vol. 49, pp. 1013-1024.

3) Migliaccio, M., Nunziata, F., Kurata, N., Vella, K., Matt, S., Soloviev, A. and Perrie, W., 2013, “Marine bacteria monitoring via polarimetric SAR”,

ESA PolInSAR Proceedings, ESA-ESRIN, Frascati, Italy.

4) Svejkovsky, J. and Jones, B., 2001,

“Satellite imagery detects coastal stormwater and sewage runoff”,

Eos Transactions, vol. 82, no. 50, pp. 621, 624, 625, 630.

5) Migliaccio, M., Nunziata, F. and Buono, A., 2015,

“SAR polarimetry for sea oil slick observation”,

International Journal of Remote Sensing, vol. 36, no. 12, pp. 3243-3273.

This study proposes a multi-sensor approach to promote an effective coastal area monitoring strategy over areas that include critical infrastructures, e.g. on-shore and off-shore oil/gas extraction platforms and groundwater reservoirs*. The monitoring strategy includes both land-side and sea-side observations using remotely sensed measurements.

With respect to the land-side, multi-temporal differential Interferometric Synthetic Aperture Radar (DInSAR) and Global Navigation Satellite System (GNSS) techniques are exploited to monitor subsidence phenomena along on-shore hydrocarbon and groundwater reservoirs, where surface deformations can be correlated to the extraction of resources from the subsoil.

With respect to the sea-side, effective SAR techniques are exploited to take benefit of multi-polarization SARs to observe oil/gas rigs/platforms and to observe oil discharges close to the oil extraction sites.

The proposed approach aims at testing and improving the standards of security for the exploitation of underground resources, as well as providing ad-hoc procedures to monitor interested areas.

*The present work is supported and funded both by Italian Economic Development Ministry (MISE) under the MISE-DGRME research project (ID 0752.010) and the DRAGON-4 Cooperation Proposal “SARCO - SAR-based Coast Observation” (ID 32235).

ABSTRACT

Many countries are focusing on maritime surveillance due to the interests in maritime shipping and the environment. The fact that more than eighty percent of goods traded worldwide are transported by sea is an indicator of the large amount of maritime traffic [1]. Not all the vessels uses the Automatic Identification System (AIS) [2], especially some smaller ships, and therefore they need to be monitored with independent systems. Therefore, using SAR data to detect ships has become more and more valuable.

Because of the complex structure of ships, their scattering mechanisms, which mainly consist of multiple reflections, are different from those of ocean surface [3]. These differences can be easily detected by using some polarimetric features. Compared with single-polarization backscattering images, dual- or quad-polarization images can provide more information about the scattering mechanisms composing the ships. This will therefore increase the discrimination between ships and ocean also in the case of ships where the overall amplitude is comparable to the one of the sea.

In this paper, we start from the Geometric Perturbation Polarimetric Notch Filter (GP-PNF) and extend the detector feature vector with many more polarimetric features [4-5]. Then we perform a Principal Component Analysis (PCA) to reduce the size of the vector and use this in the represent the full dataset in a new basis and use this basis to perform the PNF.

In our work, we choose some dual-polarization Sentinel-1 data as the experimental datasets. Experimental results show the effectiveness of the new method, especially in extreme weather conditions.

REFERENCES

[1]Gao, “Preliminary observations on efforts to target security inspection of cargo containers,”

USA Office Report, Dec. 2003.

[2]P. A. Lessing, L. J. Bernard, and C. B. J. Tetreault et al., “Use of the Automatic Identification

System (AIS) on autonomous buoys for maritime domain awareness applications,” Oceans, pp.18-

21, Feb. 2007.

[3]D. Velotto, M. Scoccorsi, and S. Lehner, “Azimuth ambiguities removal for ship detection using full polarimetric X-band SAR Data,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 1, pp. 76-88, Jan. 2014.

[4]A. Marino, S. R. Cloude, and I. Woodhouse, “Detecting depolarized targets using a new

geometrical perturbation filter,” IEEE Trans. Geosci. Remote Sens., vol. 50, no. 10, pp. 3787-3799,

Oct. 2012.

[5]A. Marino, “A Notch Filter for Ship Detection With Polarimetric SAR Data,” IEEE J. Sel.

Topics Appl. Earth Observ., vol. 6, no. 3, pp. 1219-1232, 2013.

Satellite Earth Observation is of paramount importance for a wide range of applications. In fact, the synoptic view of the Earth offered by remotely sensed space-borne measurements allows gathering useful information on climate, environment, etc. Among those applications, the observation of oceans from space represents one of the key topics since oceans cover more than 70% of the Earth’s surface and they are responsible for life. In addition, monitoring the oceans makes us able to better understand natural and human-related processes as marine pollution, climate change, sea level rise, etc.

In this context the synthetic aperture radar (SAR), being a microwave active sensor, routinely provides, information on the Earth’s surface during day and nighttime and in almost any weather condition, with fine resolution and dense revisit time if virtual constellation are exploited.

In this study, European Space Agency (ESA) Sentinel-1 scanSAR Extra Wide Swath (EW) Ground Range Multilook Detected Medium Resolution (GRDM) dual-polarimetric (DP) VH-VV SAR data measurements are considered to extract information, at C-band, on hurricanes. The underpinning idea is to retrieve wind speed both with co-polar and with cross-polar channels. We used a Geophysical Model Function (GMF), the C-band Cross-Polarization Ocean (C-2PO) which is developed for cross-polarized channel and it is independent of incidence angle and wind direction, for the VH channel [1]. With respect to the VV channel, the azimuth cut-off method is studied to calculate a coefficient of linearity, that allows linking directly the azimuth cut-off with wind speed. The link between azimuth cut-off and wind speed is here investigated under extreme wind conditions.

The water-area variations of large lakes are affected by both long-term climate change and short-term localized human activities. In recent years, there has been increasing human activities in large inland lakes worldwide [1]. Within this context, remote sensing plays an important role for lake monitoring. Optical images have the great advantage of being simple to interpret and 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, Synthetic Aperture Radar (SAR) can be very useful for lake monitoring purposes, because of its fine spatial resolution. Nevertheless, the monitoring using single-polarization SAR data is not simple due to both speckle noise, that makes image interpretability a challenge.

The main goals of this study are to develop multi polarimetric and multi-temporal methods to effectively monitor the change of surface water area of the dam of Monte Cotugno, Basilicata (Italy), the largest embankment dam in Europe. The test site was selected since it is one of the main civil strategic infrastructures of Southern Italy that is severely affected by seepage and leakage.

For this purpose, a method based on the joint use of co- and cross-polarized channels, is used in order to extract the profile of the artificial lake [2].

Preliminary results are obtained processing a set of dual polarimetric (DP) SAR data collected at C-band from Sentinel-1. The results show that Polarimetric SAR data can be effectively used to detect the changes of the water-level in the artificial lake.

[1] Ding, X., Li, X. (2011), “Monitoring of the water-area variations of Lake Dongting in China with ENVISAT ASAR images”, International Journal of Applied Earth Observation and Geoinformation, Volume: 13, Issue: 6, Pages: 894-901

[2] 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

1) In the first year a journal paper was completed, which was initiated during Dragon 3 within the project OPAC with the title “Oil spill detection by imaging radars: challenges and pitfalls” by Werner Alpers (University of Hamburg, Ben Holt (JPL/NASA), and Kan Zeng (Ocean University of China). It has been submitted to “Remote Sensing of Environment” and is presently undergoing a second review.

2) The Envisat and Sentinel-1 archives have been screened extensively for radar signatures of upwelling in Chinese waters and a large number of synthetic aperture radar (SAR) images showing such signatures have been identified.

3) For several prominent cases SAR images showing radar signatures of upwelling have been compared with SST, Chl-a images and with sea surface wind field maps retrieved from Quikscat and ASCAT.

4) Upwelling events at the east coast of Hainan have been compared with model results obtained by the HAMSON model.

5) Upwelling induced by the typhoon Soudelor (July/August 2015), which was the strongest tropical cyclone of the 2015 Pacific typhoon season, has been studied, which gave rise to plankton bloom and an eddy-like Chl-a concentration pattern.

6) The Chinese Ph. D. student Zheen Zhang, who is supervised by Thomas Pohlmann of the University of Hamburg, will carry out in the next year simulations with the MITgcm model with the aim to show that internal waves can be generated by upwelling. The focus will be on the upwelling area at the east coast of Hainan.

7) No exchange of European and Chinese partners could be arranged in the first year of the project.

Typhoons have very strong “Wind-Pump” effects on marine ecosystem, via inducing upwelling and vertical mixing. This paper introduces our recent related studies using satellite remote sensing data.

1, Typhoon wind-pump effects on air-sea CO2 flux

In-situ oceanographic measurements were made before and after the passage of Typhoon Wutip in September 2013 over the northern South China Sea (SCS). The surface geostrophic circulation over this region estimated from satellite altimetry data features a large-size anti-cyclonic eddy, a small-size cyclonic eddy, and smaller-size eddies during this period. Significant typhoon-induced changes occurred in the partial pressure of CO₂ at the sea surface (pCO_2sea) during Wutip. Before the passage of Wutip, pCO_2sea was about 392.92±1.83, 390.31±0.50, and 393.04±4.31 μatm over the cyclonic eddy water, the anti-cyclonic eddy water, and areas outside two eddies, respectively. The entire study region showed a carbon source (1.31±0.46 mmol CO₂ m^-2 d^-1) before Wutip. In the cyclonic eddy water after Wutip, high sea surface salinity (SSS), low sea surface temperature (SST), and high pCO_2sea (413.05±7.56 μatm) made this area to be a carbon source (3.30±0.75 mmol CO₂ m^-2 d^-1). In the anti-cyclonic eddy water after Wutip, both the SSS and SST were lower, pCO_2sea was also lower (383.03±3.72 μatm), and this area became a carbon sink (-0.11±0.55 mmol CO₂ m^-2 d^-1), in comparison with the pre-typhoon conditions. The typhoon-induced air-sea CO₂ flux reached about 0.03 mmol CO₂ m^-2 d^-1. Noticeable spatial variations in pCO_2sea were affected mainly by the Wind-pimp Effects - typhoon-induced mixing/upwelling and vertical stratifications.

Our study suggests that the impact of the typhoon Wind-pimp on the local air-sea CO₂ flux is highly correlated with the oceanographic conditions during the typhoon.

2. Upwelling effecting Distribution characteristics of phytoplankton size structure in the western SCS in summer

Driven by the southwest monsoon, an offshore jet is usually formed in western South China Sea (SCS) and sandwiched by a cyclonic eddy in the north and an anti-cyclonic eddy in the south, which effects ecosystem of the region. Using in-situ and satellite data in September 2014, the present study analyze the joint impact of this jet with two eddies on phytoplankton size structure in this region. The data showed that picophytoplankton (0.2-2µm) dominated the surface, taking average 76.7% of total chlorophyll. The contribution of nanophytoplankton (2-20µm) and microphytoplankton（20-200µm）in jet area was respectively higher and had a positive relationship with total chlorophyll. Comparatively higher percentage of microphytoplankton appeared in anti-cyclonic eddy in surface (av.10.3%) than in cyclonic eddy (av.3.6%). The results suggest that physical processes significantly influence summertime surface phytoplankton size structure in western South China Sea. Both jet and eddies can effect phytoplankton size structure by increasing the contribution of microphytoplankton. Surface horizontal advection of phytoplankton by northeastward jet form the coastal upwelling area is the main source of microphytoplankton in open sea. The interactions of convergence and divergence in eddies with jet form a chlorophyll front and increase the microphytoplankton component. Upwelling in the center cyclonic eddy bring up nutrients which raises microphytoplankton component.

3．Mixed layer depth responses to tropical cyclones in the northeastern SCS

Utilizing the vertical profiles of temperature and salinity data obtained by Argo floats and multi-source satellite remote sensing data, including sea surface temperature (SST) and sea surface wind fields, combined with the National Centers for Environmental Prediction (NCEP) Ⅱ reanalysis data, we analyzed changes of mixed layer depth (MLD) in the northeastern South China Sea (SCS) in responses to tropical cyclones Kalmaegi (typhoon) and Fung-Wong (tropical storm), which passed the SCS in succession in mid and late September 2014. The results indicate that the maximum net heat flux (upward into the air) increased from 170 to 400 W·m–2 at the air-sea interface, caused the maximum SST cooling of 3℃ by the “wind pump” effect after Kalmaegi and Fung-Wong passed through. The “cold wake” induced by Kalmaegi lasted for more than 10 days thanks to the following tropical storm Fung-Wong, indicating the effect of superposition in SST cooling. MLD was deepened from 23 to 50 m in the “cold wake” one day after Kalmaegi passed by. MLD was deepened from 31 to 91 m eight hours after Fung-Wong passed by, due to the coastal upwelling induced by offshore Ekman transport driven by wind stress at the southwestern of Taiwan Island. After the tropical cyclones passed by, salinity profile in the mixed layer showed uniformity later than temperature profile, and recovered earlier than temperature profile, revealing the time lag in mixed layer responses. For the spatial variation response to the two tropical cyclones, the changes of SST and MLD were larger on the right-hand side of the tropical cyclones (along the moving directions of tropical cyclones) than on the left-hand side. The uneven deepening even shallowing in MLD in the cold wake may reveal that different depths of deep cold water uplifted by the vertical current switch between upwelling and downwelling in the Ekman layer due to the change of Ekman pumping velocity

Sea Surface Temperature (SST) in South China Sea Retrieved from Chinese Satellite FY-3B VIRR Data

Chuqun CHEN^(1)(2)*, Quanjun HE^(3)**, Shilin TANG ^(1)(2)***,Haibin YE⁽¹⁾

(1) State key Lab of Tropical Oceanography，South China Sea Institute of Oceanology, Chinese Academy of Sciences,164 West Xingang Road, Guangzhou, China, 510301.

(2) University of Chinese Academy of Sciences，19A Yuquan Road, Beijing, China,100049.

(3) The Guangdong Ecological meteorological Center, 312 Dongguanzhuang Road, Guangzhou, China, 510080.

*cqchen@scsio.ac.cn，**hequanjunsx@163.com；***sltang@scsio.ac.cn

Abstract

In the surface layer of the ocean, Sea Surface Temperature (SST) is the most important parameter, which is widely applied for studying water masses, air-sea interaction, marine ecosystem and environment, and other subjects. In decades, a great many satellites with thermal infrared sensors have been launched and huge thermal infrared remote sensing data were collected for detection of SST. With the continuous improvement on accuracy, the satellite remote sensing technique has become the dominant approach for SST detection.

In this report, the thermal infrared data collected by FY-3B were employed for retrieval of SST in the South China Sea. FY-3B is one of the second generation of Chinese meteorological satellite on polar orbit, it has VIRR (Visible Infrared Radiometer) sensor with 10 bands, of which, band 4 covers 10.3~11.3um and band 5 covers 11.5~12.5um, similar to NOAA/AVHRR.

The ship-measured SST dataset in 2011 and 2012 were collected and totally 20607 (of which 11419 in daytime and 9188 in nighttime) of the ship-measured SSTs were selected on consideration of the quality, the measurement time and the measurement location matching with cloudy-free Fy-3B data. Based on the well matched ship-measured SST and FY-3B VIRR data, a non-linear SST (NLSST) algorithm was developed and applied for retrieval of SST in the South China Sea. The monthly mean SST distribution image maps of South China Sea were integrated. The monthly mean SST image maps show that the maximum monthly mean SST occurs in June, although in July and August there is a stronger solar heating. It possibly due to the monsoon-induced mixing, which results in lower SST.

Keywords: FY-3B satellite, Visible Infrared Radiometer (VIRR), Sea Surface Temperature (SST), South China Sea.

利用中国风云卫星（FY-3B）数据反演南海海面温度

陈楚群^{（1）（2）（*）}，何全军^{（3）（**）}，唐世林^{（1）（2）（***）}，叶海彬^（1）

（1） 中科院南海所热带海洋环境国家重点实验室，广州市新港西路164号，510301

（2） 中国科学院大学，北京市玉泉路19号甲，100049.

（3）广东省生态气象中心，广州市东莞庄路312号，510080

（*） cqchen@scsio.ac.cn）；（**）hequanjunsx@163.com；(***)sltang@scsio.ac.cn

摘要

海面温度（SST）是上层海洋最重要参数，在水团研究、海气相互作用研究、海洋生态环境研究等众多领域具有非常广泛的应用。利用卫星热红外遥感数据反演海面温度已经成为探测海面温度的主要手段，大量的热红外卫星传感器用于探测地面（海面）温度。

本报告介绍利用中国第二代极轨气象卫星“ 风云三号气象卫星”的第二颗卫星（FY-3B）数据反演南海海面温度。FY-3B于2010年11月发射，其可见红外辐射计（Visible Infrared Radiometer-VIRR）具有与NOAA/AVHRR传感器类似10个波段，其中的热红外波段（4）和（5）的波段范围分别为10.3~11.3（μm）和11.5~12.5（μm）。

收集了2011-2012两年与FY-3B卫星数据准同步的南海及西太平洋海域的船舶站观测资料，经过对FY-3B数据与船舶实测数据的严格筛选，获得与FY-3B数据匹配的船舶测量温度有效数据共有20607组，其中白天数据数11419组，夜间数据9188组，利用这些匹配数据组建立了反演南海SST的非线性算法，反演了南海SST，并得到南海月平均SST分布图。从南海月平均SST分布图看，南海月平均SST最大值出现在6月，而不是太阳辐射强度更大的7月或8月，这可能与7月和8月南海受季风搅拌引起表层水温降低有关。

关键词：风云气象卫星三号，可见红外辐射计，海面温度，南海

Sentinel-1 A & B perform acquisitions over Taiwan island. To date, this data are not routinely processed up to Level-2 OCN product by Sentinel-1 ESA PDGS. This paper considers the complete archive of Sentinel-1 data acquired over Taiwan and proposes a new algorithm for wind inversion in coastal areas. It relies on a two steps process. The first one aims at extracting wind direction and filtering out non-wind related signatures in both co- and cross- polarized channels. The second one combines these information and the radar cross-section from the two channels to retrieve wind speed and direction at high resolution.

The complete Sentinel-1 SAR archive is processed with this new algorithm. Results are presented and will be made available to the public. The buoys network around Taiwan island is used as reference for the ocean surface wind. SAR wind speed and direction are compared to in-situ buoys and atmospheric model winds. The choice of the GMF to be used for wind inversion is discussed.

The occurrence and the location of the non-wind signature are also presented and tentatively related to other oceanic or meteorological phenomena such as rain or bloom.

During summer 2016, ESA set up SHOC (for Satellite Hurricane Observations Campaign) campaign dedicated to hurricane observations with Sentinel-1 SAR in both VV and VH polarizations acquired in wide swath modes. Among the 70 Sentinel-1 passes scheduled by ESA mission planning team, more than 20 observations over hurricane eyes were acquired and Tropical Cyclones (TC) were captured at different development stages. The sensitivity difference of VH and VV Normalized Radar Cross Section (NRCS) to the response of the ocean surface to tropical cyclones is analyzed. In particular, during Lester hurricane most intense phase (when the track files indicates wind speeds of 120 knots), the sensitivity of the VH-NRCS computed at 3-km resolution is found to be more than 3.5 times larger than in VV. In addition, taking opportunity of SAR high resolution, we also show that the decrease in resolution (up to 25 km) does not change dramatically the sensitivity difference between VV and VH polarizations. This clearly opens perspectives for MetOp-SG SCA, the next generation C-band scatterometer with co- and cross-polarization capability. VV and VH channels are combined to get ocean surface wind vectors from Sentinel-1 dual-polarized Level-1 products. SAR winds are compared at 40-km resolution against L-Band SMAP radiometer winds with co-locations less than 30 minutes. The wind speed obtained using both VH and VV polarization is found to be more consistent with SMAP than wind obtained in co-polarization for wind speeds larger than 25 m/s. Based on radiometer winds, a new GMF (MS1A) is proposed. It improves the consistency between 40-km SAR winds and SMAP winds. The method is applied to Megi and Lyonrock Typhoon and results presented in this paper.