Conference Agenda

Sea ice drift (SID) is the key to understanding sea ice dynamics and critical for navigation safety. This study focused on a comprehensive analysis of SID retrieval based on spaceborne synthetic aperture radar (SAR) data in the Arctic. A state-of-the-art method combining feature tracking and pattern matching techniques was applied to sequential Sentinel-1 (S1) SAR data to derive SID from the central Arctic to the Fram Strait in different seasons. The SAR retrievals were compared with drifting buoy data for validation. For temporal intervals of S1 data of 16 to 24 hours, 13,586 collocations were collected in the winter and spring seasons, yielding a 0.00 cm/s bias for the drift velocity magnitude and 0.32 degrees for direction with the corresponding root mean square error (RMSE) of 0.50 cm/s and 4.96 degrees. The applied method outperforms the maximum cross-correlation method for rapidly drifting sea ice. Using temporal intervals of S1 data of less than 16 hours, we retrieved SID in the summer and autumn seasons. 644 collocations yield a comparison with a bias of 0.52 cm/s and 4.62 degrees for the drift magnitude and direction, respectively. The corresponding RMSE values are 1.85 cm/s and 20.73 degrees. The comparisons present better results than the operational SAR-based SID product and consistent seasonal trends in drift velocity with the coarse-resolution product. We also analyzed the variations in SAR retrievals and further estimated appropriate temporal intervals, making it feasible to conduct long-term SID retrievals based on spaceborne SAR data at high spatial resolution in the Arctic.

Abstract

The rapid increase of offshore wind installations in the south-China sea near the coast triggers a new demand for studying the effects of horizontal wind speed gradients and the wind power variation within the coastal zone. The advent of Synthetic Aperture Radar (SAR) data offers an opportunity to map wind speed gradients and wind farm wakes with high spatial resolution. We have retrieved wind maps at 10 m above mean sea level (m.s.l.) from Sentinel-1 SAR and Envisat Advanced SAR observations. Generally, the speed of the prevailing south-easterly winds and wind power declined about 8% and 22%, respectively. Although the southern offshore wind farms (OWFs) were not in operation before December 2019, the wind velocity deficit at the upstream side of northern OWFs were between 8-12 %. After the southern OWFs became online, the region between OWFs is subjected to wind wakes and coastal upwelling effects. The coastal upwelling phenomena speeds up the wind at the downstream sides of OWFs that reduce the wind wakes up to 8-10%. The wind wakes extended 20 km beyond the southern OWFs.

1. Introduction

A Synthetic Aperture Radar (SAR) is a side-looking satellite sensor, which can be used to visualize the fine spatial details of the wind flow close to the coast. SAR observations have been utilized for a wide range of applications ranging from wind resource assessments [2], identifying offshore wind farm wakes and coastal wind speed gradients [3]. Several studies have validated the wind speeds retrieved from SAR observations with respect to other datasets and typically found the root mean square error to be less than 2 m/s. Ahsbahs et al. show good agreement through comparison of 15 Sentinel-1A wind maps against light detection and ranging (LiDAR) measurements at the west coast of Denmark [4].

Wind wakes are defined as areas of reduced wind speed at the downstream side of the offshore wind farms (OWFs) because of energy extraction by the wind turbines. Wakes can extend several tens of kilometers, therefore, the wakes can interact with adjacent OWFs and have severe consequences for the power production, as the wind power is proportional to the cube of the wind speed.

The wind speed within the coastal zone is also affected by (i) the surface discontinuity at the coastline, (ii) the influence of onshore topography, and (iii) thermal gradients [5]. The magnitude of wind velocity deficits at the downstream side of OWFs is thus a combination of wind wakes and the effects of horizontal coastal wind speed gradients. Hasager et al. concluded that the winds in the coastal zones have larger spatial gradients than further offshore and many other wind phenomena occur in coastal zones [6]. Owda et al. have studied the effects of coastal gradients for many OWFs in northern European seas and found strong gradients inversely proportional with the distance to shore. They decomposed the wind gradient effects from wind wakes based on SAR observations before commissioning of the OWFs [3].

2.1. Data

2.1.1. Sentinel 1A/B & Envisat

Sentinel 1A/B is a constellation of two different satellites, Sentinel-1A (2014-present) and Sentinel-1B (2016-2021), sharing the same orbital plane at the mean altitude 693 km. Envisat (2002-2012) carried an Advanced Synthetic Aperture Radar (ASAR) instrument at the altitude 800 km. The satellite data were acquired using C-band SAR sensors operating at 5.405 GHz. The satellites are in a near-polar, sun-synchronous orbit with a 6 and 35 day repeat cycle for the Sentinel-1 constellation and Envisat, respectively. In this study, VV co-polarized images with extra wide (EW) for Sentinel-1 and wide swath mode for Envisat were used.

2.2. Study area

Our study area is in southeast China and at 120 to 120.8 longitude and 34 to 34.6 latitude. The area has four OWFs (Figure 1). Table I presents the characteristics of each OWF. Table II presents available SAR scenes based on commissioning date of first operated southern OWFs (Datang Binhai). We refer southern OWFs term to Datang Binhat and Spic Binhat South H3 and northern OWFs term to SPIC Binhai North H1 and H2.

2.3. Methodology

2.3.1. SAR wind retrieval

The SAR radar observables relate to the local near-surface wind speed using an empirical equation called a geophysical model function (GMF)[7]. .

2.3.2 Mean wind speed, deficit and power variation calculation

A grid of 60×50 km is overlaid over the entire study area and used to retrieve SAR wind measurements with the regular grid spacing 1.5 km between each rectangular bin inside the grid. Figure 1 illustrates the boundary of the used grid. Based on the 86 available SAR scenes, the wind rose for a point close to the coast shows that the prevailing wind direction is from the southeast. In this study, we have taken in our analysis only the scenes with wind directions between 90 and 180 degrees “southeast”. The mean wind speed (U) is computed for our area of interest based on the selected scenes of each period in Table II. Furthermore, the relative wind speed difference to the mean upstream wind (∆U) is calculated for the entire grid. The wind power density (P) is estimated using a simple power equation. Equations 1, 2 refer to ∆U (%) and P (Watt/m²), respectively.

4.0 Results

the spatial variation of the wind speed is strong near the curved coast. It also shows the effects of natural wind speed gradients on the wind power potential. The high spatial resolution of SAR can provide valuable information about the wind speed variation from far offshore to the coastal zone. The results have shown strong coastal gradients as the winds approach the coastline. Along a transect line at the southern region of the area investigated, it shows the speed is reduced about 8 % over the 32 km distance. In terms of wind power estimation, the power reduction along the same transect line is about 22%. The region between the southern and northern OWFs within our area of interest is subject to different wind velocity deficits before and after commissioning southern OWFs

The German Wadden Sea is an area of great economic and ecological importance. Apart from being the largest German National Park, tourism and fishery benefit from the specific characteristics of this intertidal region. One of those specific characteristics are strong morphodynamics, which is why the bathymetry of the region undergoes frequent changes, including an eastward movement of islands. Major shipping routes to harbors such as Hamburg and Bremerhaven cross the German Wadden Sea; hence, a reliable monitoring of sand banks and shallow waters does not only give clues about the state of the region, but is also crucial for marine security. Since the German Wadden Sea is frequently overflown by satellites, radar images acquired at different water levels can be used for its surveillance. Algorithms for an automatic extrapolation of waterlines from these radar data exist, but they strongly depend on image quality, and they require careful manual optimization.

Neural networks that use images as input and also produce images as output are called U-Nets, because of their network architecture. A U-Net consists of encoding and decoding steps, shrinking the image size in the former, and reconstructing it in the latter. To maintain informational value, the number of channels rises with decreasing image size. Setting up a neural network to detect waterlines showed to be a promising approach. While no accurate predictions could be achieved, the use of additionally generated data could compensate for the small dataset of radar images.

The U-Net in this project consists of three encoding and two decoding steps and uses 128x128 images as input and output. Each step consists of a large (15x15) and a small (3x3) Convolutional Layer with Dropout, Batch Normalization, and a nonlinear Rectified Linear Unit (ReLU) function. During each encoding step, the number of channels is increased. After each of the first two encoding steps, a Maxpool Layer halves the image dimension, resulting in a virtual image size of 32x32 with 64 channels. Before each decoding step, an Upward Convolutional Layer reverts the downsizing of the Maxpool Layer, and the informational value from the comparable encoding step is concatenated to the decoding channels. With increasing virtual image size, the channels are reduced and finally transformed into an output mask consisting of only one channel.

Although further training with radar images improved the result both qualitatively and quantitatively, major problems detain an improvement with the given model and datasets: the size of the dataset of radar images is not sufficient to predict waterlines in a given Wadden Sea area at acceptable accuracy, and the improvement through augmentation is limited. Furthermore, the dependence of radar contrast on weather conditions may hinder the use of one single big image.

Coastal regions represent areas where a large portion of the world’s population lives. Hence, many people rely to some extent on coastal and marine ecosystems and resources for food, building materials, building sites, and agricultural and recreational areas, while utilizing coastal areas as a dumping ground for sewage, garbage, and toxic wastes. The human-induced phenomena, added to extreme natural events, lead to an ever-increasing pressure on such regions. As a result, harsh coastal environments can be formed where wetlands, mudflats, mangroves, marshes etc. are present altogether. Monitoring the impact of such phenomena on land-sea dynamic processes and supporting an effective coastal area management is a major scientific challenge, with an increasing importance due to growing urbanization, industrialization, and transportation. In this context, space-borne synthetic aperture radars (SARs) sensors gain great importance since they allow obtaining high-resolution imagery collected during almost all-weather conditions and captured during day and night. Moreover, the use of SAR multi-polarimetric imaging modes allows obtaining improved monitoring accuracy with respect to the optical and single-polarization cases.

In this study, a multi-frequency and multi-polarimetric approach is proposed to study the properties of harsh coastal environments. The multi-polarimetric approach allows studying the different scattering mechanism of the region of interest, while using different sensors at different frequencies allows discriminating among different aspects of the same scenario. The study area is the Solway Firth coastal region, located along the western coastal boundary between Scotland and England, that represents a very harsh coastal environment composed of marshes, mudflats, agricultural crops, hill farming and shallow water rich in sediments. Moreover, it is severely affected by erosion processes induced by storm surges during the rainy season. C- and X-band fine-resolution quad-polarimetric SAR satellite measurements, collected over the Solway Firth area by Radarsat-2 and Cosmo-SkyMed Second Generation (CSG) missions, respectively, are used. The purpose of the study is to analyze the considered scattering scenarios in terms of a two-fold analysis: an intensity-based multi-frequency backscattering analysis and a polarimetric analysis. The latter is performed using two properties, the polarization signature and the co-polarised phase difference.

The properties of three different scenarios, i.e., grasslands, mudflats and sea water are analyzed using the two different sensors and the two polarimetric properties. Preliminary results show that the proposed analysis allows improving the understanding of the harsh coastal environment scattering processes. Hence, this kind of techniques may support the development of advanced and robust scattering-based algorithms for coastal management purposes, in order to monitor and mitigate human- and nature-induced processes.

The Synthetic Aperture Radar (SAR), owing to its day-night and almost all-weather imaging capabilities together with its fine spatial resolution, is a valuable tool to observe the oceans and monitoring oil pollutions. Mineral oil films appear in the SAR image plane as spots darker than the sea surface background because of their suppression of capillary waves. However, mono-molecular biogenic surfactants, which are produced for instance by plankton or fishes, give rise to radar signatures similar to that of mineral oil films. Hence, to fully understand the link between the actual oil slick and the dark patch observed in the SAR image plane, it is necessary to analyze the underlying scattering process from theoretical aspects and distinguish mineral oil spills from such false alarms in SAR imagery in a robust way.

Mono-molecular oil films call for a resonance-type damping of short gravity and capillary waves that is well-described by the so-called “Marangoni damping”, which is used - together with a reduced input wind modeled through a reduced friction velocity to account for the effects on long wave part – to reduce the sea surface spectrum. In this study, sea surface scattering with and without surfactants is predicted using the two-scale boundary perturbation model (BPM) and the advanced integral equation model (AIEM) augmented with two different damping models, i.e., the Marangoni one and the model of local balance (MLB). Numerical predictions are showcased for both oil and biogenic slicks. The two scattering models result in significantly different predictions according to the slick type and the considered damping model.

Numerical predictions are contrasted with actual SAR measurements collected at X-band by the German TerraSAR-X sensor over oil and biogenic slicks of known origin. Experimental results show that: 1) When dealing with slick-free sea surface, the two-scale BPM and AIEM result in predicted NRCS values at both polarizations that exhibit non-negligible differences up to an incidence angle of about 40°. Those differences are negligible (less than 1 dB) at larger incidence angles. The NRCS predicted by BPM results in the best agreement with the measured one at low incidence angles, while AIEM results in a PR that best fits actual measurements; 2) the two-scale BPM augmented with the Marangoni damping model is more suitable for predicting the NRCS and the damping ratio of biogenic slicks; 3) the AIEM combined with the damping MLB results in a better agreement with SAR measurements collected over oil slicks.

This study is supported by the ESA-NRSCC Dragon-5 cooperation project “Monitoring harsh coastal environments and ocean surveillance using radar remote sensing sensors” (ID 57979).

Terra Nova Bay (TNB) is situated in the western Ross Sea, between the Drygalski ice tongue (75◦24’S, 163◦30’E) and Cape Washington (74◦39’S, 165◦25’E), Antarctica. The area is confined to a narrow strip of coastal waters to the south of Mario Zucchelli Station (MZS) (Italy), extending approximately 9.4 km in length and generally within 1.5 – 7 km of the shore, comprising an area of 29.4 km².

The site typically remains ice-free in summer, which is rare for coastal areas in the Ross Sea region, making it an ideal and accessible site for research into the near-shore of the region. The coastline of TNB is characterized predominantly by rocky cliffs, with large boulders forming occasional beaches. During the winter season, this coastline is the area where land ice meets the sea ice. Sometimes it is very difficult to determine a precise line that can be called “coastline,” due to the dynamic nature of the sea and the sea ice. In addition, these areas are often strongly affected by extreme weather and sea conditions and by the continuous fusion processes, therefore observing the coast from space in a continuous and effective way is of fundamental importance to support the planning and management of this coastal zone. In fact, polar coastal zone monitoring is an important task in sustainable development and environmental protection and the analysis of the time-variability of the coastline is a task of primarily importance.

This study focused on the analysis of the time variability of the morphology of the coastline of Terra Nova Bay (TNB), Antarctica, using a time series of C-band Sentinel-1 DP (HH-HV) Interferometric Wide (IW) swath mode single-look complex (SLC) SAR scenes collected from 2016 to 2022. The methodology to extract the coastline is based on a global threshold constant false alarm rate (CFAR) approach that is tested on both ice-free and ice-covered sea conditions. Once the optimal setting if found, the methodology is applied to a larger time series of S1 scenes to monitor the time-variability of selected features belonging to the extracted coastline.

The surface wave investigation and monitor- ing (SWIM) aboard the China-France Oceanography Satellite (CFOSAT), a pioneer conically scanning wave spectrometer, was successfully launched on October 29, 2018. Its innovative configuration composed of one nadir and five rotating near-nadir beams is designed to simultaneously observe the directional wave spectrum at a global scale. In this study, we systematically implement the spectral analysis of the radar backscattering with the periodogram technique to obtain the fluctuation spectrum for each azimuth direction. The 2-D fluctuation spectrum of the three spectral beams (θ = 6◦ , 8◦ , and 10◦ ) combines all the azimuth directions within one entire rotation of 360◦. The case study demonstrates that the wave features (peak wavelength and direction) are roughly consistent between the estimated fluctuation spectrum and the collocated WaveWatch III wave slope spectrum. A marked up-to-downwave asymmetry of the fluctuation spectrum with larger spectral level in the upwave direction for all the three spectral beams is observed. A ratio is defined between the fluctuation spectrum within the [0◦ , 180◦ ] sector relative to the [180◦ , 360◦ ] sector. Statistics display that this ratio is greater than 1 when it denotes the up-to-downwave ratio and smaller than 1 for the down-to-upwave ratio. This observed spectrum asymmetry is linked to the asymmetric modulation from upwind to downwind. In addition, we employ such finding to help remove the 180◦ wave direction ambiguity from a practical point of view. Preliminary results of the direction ambiguity removal display a bias of 41.3◦, 40.6◦, and 36.7◦ for the beams. The 10◦ beam shows slightly better performance compared to the other two beams in terms of bias and standard deviation. This shall lay a strong basis for the operational implementation of such algorithm to resolve the direction ambiguity.

The Surface Waves Investigation and Monitoring (SWIM) instrument onboard the China France Oceanography Satellite (CFOSAT) can retrieve directional wave spectra with a wavelength range of 70~500 m. This study aims to validate the partitioned integrated wave parameters (PIWPs) from SWIM, including partitioned significant wave height (PSWH), peak wave period (PPWP), and peak wave direction (PPWD), against those from National Data Buoy Center (NDBC) buoys. With quasi-simultaneous spectra from two NDBC buoys 13 km away from each other near Hawaii, the methods of comparing PIWPs from two sets of spectra were discussed first. After cross-assigning partitions according to the spectral distance, it is found that wrong cross-assignments lead to many outliers strongly impacting the estimate of error metrics. Three methods, namely comparing only the best-matched partition, changing the threshold of spectral distance during cross-assignment, and maximum likelihood estimation of root-mean-square error (RMSE) of PIWPs, were used to reduce the impact of potential wrong cross-assignments. Using these methods, the SWIM PIWPs were validated against NDBC buoys. The results show that SWIM performs well at finding the spectral peaks of different partitions with the RMSE of PPWPs and PPWDs of 0.9 s and 20°, respectively, which can be a useful complement for other wave observations. However, the accuracy of PSWH from SWIM is not that good at this stage, probably because the high noise level in the spectra impacts the result of the partitioning algorithm. Further improvement is needed to obtain better PSWH information.

Characterizing the uncertainties in buoy ocean wave records is critical not only for understanding the limitations of in situ wave measurements, but also for interpreting the implied accuracies of the remotely sensed products in which these buoy data are used as validation references. This letter preliminarily assesses the error of long-period swell heights (Hss) representing specific directional wave partition energy observed from deep-water buoys moored in the northeast Pacific. We propose a buoy Hss error estimation method by combining dual and triple collocation using data derived from buoys, two kinds of space-borne radars and numerical simulations. Compared to traditional methods, the proposed approach can reveal “absolute” errors (with respect to the underlying truth) from buoy Hss, accepting and then confirming that swell heights from buoy, satellite and model are all uncertain. This study simultaneously employs ocean swell products derived from synthetic/real aperture radars (Sentinel-1A/B and CFOSAT/SWIM) and WaveWatch III ocean wave model hindcasts to diagnose the accuracy of the Hss values observed by buoys of National Data Buoy Center (NDBC) and Coastal Data Information Program (CDIP) during the period from July 2019 to October 2021. We quantify that the NDBC’s 3-m heave-pitch-roll buoy (CDIP’s Waverider buoy) recorded Hss have root-mean-square error of 0.17 m (0.12 m), or have about 10.65% (7.06%) uncertainty relative to the mean Hss value (approximately 1.6 m). Our findings imply that the reference value uncertainties should be taken into account when understanding direct satellite Hss validation against buoy in situ.

Satellite altimetry has been developed for several decades and obtained abundant information on Marine environment change. At present, the SAR (Delay-Doppler) altimeter has become one of the main loads of satellite altimeters, such as the Sentinel-3 series and Cryosat-2 satellites. The measurement accuracy of SAR altimeter in the open sea is relatively high. However, due to the influence of land, island, and other factors, there are still some problems in the nearshore area, which limits the application of satellite altimeters in this area. Based on the waveform theory of SAR altimeter and the systematic analysis of different types of waveform retracking algorithms, this paper proposes a waveform retracking data processing strategy based on neural network waveform classification and discusses the waveform retracking algorithms suitable for different sea surface types. Based on the Sentinel-3 altimetry data, the accuracy of the algorithm and the change of altimetry sea level is analyzed by using the sea surface height data of the tide station and buoy.

The HY-2B satellite was launched in October 2018 as China second marine dynamic environmental satellite. It is equipped with a traditional dual-frequency altimeter, which can accurately observe marine dynamic environmental elements including sea surface height, wind field and significant wave height. In coastal areas, the precision of range corrections such as sea state bias, ionosphere delaying correction and tropospheric delaying correction provided by SGDR data have declined due to the influence of coastal "pollution" on the altimetry system. Aiming at this problem, this paper carries out a study about the improvement of the coastal altimetry range corrections for HY-2B altimeter. Based on the 20hz sea surface observation, the high-frequency sea state bias model is constructed, the deviation of the wet troposphere correction is modified by a composite method, and the error of the ionosphere correction and the dry troposphere correction are reduced by high-frequently filtering. Finally, the effectiveness of the new range correction is validated by comparing and analyzing the SSH before and after improving range corrections.

In July of 2020, the orbit of CryoSat-2 was modified to allow for repeated overlaps with ICESat-2. Following a year of coincident orbits with parallel observations by radar from CryoSat-2, and lidar from ICESat-2 allows for direct comparison between these systems. Using 136 orbit segments from the northern hemisphere, constrained to the Pacific and Atlantic oceans as well as the Bering Sea, we compare the significant wave height (SWH) observations. By utilizing the coincident orbits, we can compare observations between altimeters of the same sea state within a constrained time lag (less than four hours), allowing for comparison within longer stretches of the orbits. This is crucial to assess the level of agreement between observations, owing to the constantly changing ocean surface. With the comparison between the systems, as well as discussing the inherent benefit of each system, we can assess the possibilities of alternate methods for ocean surveying. From the available data, SWH up to 10 m has been used for the analysis, enabling this comparison to be done at various sea states.
We have used three methods with the ICESat-2 data in the comparison, with the first being the standard ocean data output (ATL12) as produced by the ICESat-2 team. This is compared with a method where modeling of the individual surface waves is used as an assessment of the SWH. It has been shown before to be possible to use the geolocated photons from ICESat-2 to assess these waves, which is then beneficial to compare with the radar altimeter of CryoSat-2. Functioning as a baseline for the wave approach, we are using the standard deviation of the ocean surface, the same method as in ATL12, however with the same filtering as for the wave-based model.
From this, we have described the differences between the altimeters and show a high correlation, with correlations between the models and CryoSat-2 SWH of 0.97 for ATL12, 0.95 for the observed waves model, and 0.97 for the standard deviation model. There has been found a mean deviation relative to the observed SWH for each model, deviating more at SWH larger than 2.5 m, but generally between -10 cm and 16 cm for SWH smaller than 2.5 m for all models. Compared with CryoSat-2 there was found an increasing deviation along with increasing SWH, along with a larger variance. In general, the SWH observed from ICESat-2 is found to agree with observations from CryoSat-2, within limitations due to cloud coverage. Observing the individual surface waves from ICESat-2 is therefore seen to provide additional observed properties of the sea state for global observations.