High-resolution satellite measurements of coastal wind field and sea state

Methods to derive wind speed and sea state by simple empirical models from synthetic aperture radar (SAR) data are presented and applied for use in high-resolution numerical modelling for coastal applications. The new radar satellite, TerraSAR-X (TS-X), images the surface of the sea with a high resolution up to 1 m. Therefore, not only wind information and integrated sea state parameters but also individual ocean waves with wavelengths down to 30 m are detectable. Two-dimensional information on the ocean surface retrieved using TS-X data is validated for different oceanographic applications: derivation of finely resolved wind fields (XMOD algorithm) and integrated sea state parameters (XWAVE algorithm). Both algorithms are capable of taking into account fine-scale effects in coastal areas. Wind and sea state information retrieved from SAR data are applied as the input for a wave numerical spectral model (wind forcing and boundary condition) running at a fine spatial horizontal resolution of 100 m. Results are compared to collocated buoy measurements. Studies are carried out for varying wind speeds and comparisons against wave height, simulated using original TS-X-derived wind data, showing the sensitivity of waves to local wind variation and thus the importance of local wind effects on wave behaviour in coastal areas. Examples for the German Bight (North Sea) are shown. The TS-X satellite scenes render well-developed ocean wave patterns of developed swell at the sea surface. Refraction of individual long swell waves at a water depth shallower than about 70 m, caused by the influence of underwater topography in coastal areas, is imaged on the radar scenes. A technique is developed for tracking wave rays depending on changes in swell wavelength and direction. We estimate the wave energy flux along wave tracks from deep water to the coastline based on SAR information: wave height and wavelength are derived from TS-X data.     

Improved statistics of sub-mesoscale eddies in the Baltic Sea retrieved from SAR imagery

In the present article the spatio-temporal distribution of sub-mesoscale eddies seen in Envisat Advanced Synthetic Aperture Radar (ASAR) imagery of the Baltic Sea is discussed. A total of 1250 ASAR images acquired between 2009 and 2011 form the basis of our studies and show imprints of almost 7000 sub-mesoscale eddies. Since the visibility of vortical structures in synthetic aperture radar (SAR) imagery significantly depends on the near-surface wind speed, wind data from a numerical model of the Baltic Sea were additionally used to obtain improved eddy statistics. Seasonally averaged fields of near-surface wind speed, surface currents, sea surface temperature (SST), and SST gradient were also analysed in order to reveal the role of these hydrophysical parameters in the observed spatial and temporal variation of sub-mesoscale eddies.     

Assessment of an analytical model for sea surface wind speed retrieval from spaceborne SAR

An analytical model based on radar backscatter theory was utilized to retrieve sea surface wind speeds from C-band satellite synthetic aperture radar (SAR) data at either vertical (VV) or horizontal (HH) polarization in transmission and reception. The wind speeds were estimated from several ENVISAT Advanced SAR (ASAR) images in Hong Kong coastal waters and from Radarsat-1 SAR images along the west coast of North America. To evaluate the accuracy of the analytical model, the estimated wind speeds were compared to coincident buoy measurements, as well as winds retrieved by C-band empirical algorithms (CMOD4, CMOD_IRF2 and CMOD5). The comparison shows that the accuracy of the analytical model is comparable to that of the C-band empirical algorithms. The results indicate the capability of the analytical model for sea surface wind speed retrieval from SAR images at both VV and HH polarization.     

Semi-automated feature-tracking of East Antarctic sea ice from Envisat ASAR imagery

While feature tracking of sea ice using cross-correlation methods on pairs of satellite Synthetic Aperture Radar (SAR) images has been extensively carried out in the Arctic, this is not the case in the Antarctic. This is due to the dynamic nature of Antarctic pack ice, its microwave signature, the tendency for SAR swath paths to be poorly aligned with the often narrow sea ice zone around the continent and inadequate satellite sampling. A semi-automated system, known as IPADS (IMCORR [IMageCORRelation] Processing, Analysis and Display System), has been developed to map fast ice and pack ice in Antarctica using multiple pairs of SAR images. The software processing pipeline uses overlapping image pairs which are geocoded and roughly registered using only data contained in the image headers. Next, fast ice maps are rapidly generated using zero motion features located within ocean regions. This also provides precise image registration. Finally, the same image pairs are re-examined for pack ice motion in a slower off-line batch process. The pack and fast ice are identified using a cluster-based search method which compares both location and motion information. Each image pair generates a NetCDF file which adds to a growing database of Antarctic sea ice motion and ice roughness. Five image-pair examples are presented to illustrate the methods used as well as their strengths and limitations. Substantial pack ice motion can often be detected in the marginal ice zone on SAR images only a few days apart.     

Multisource ERS-1 and optical data for vegetal cover assessment and monitoring in a semi-arid region of Algeria

This paper highlights advantages of using Synthetic Aperture Radar (SAR) data combined with multispectral data to improve vegetal cover assessment and monitoring in a semi-arid region of southern Algeria. We present a number of preprocessing and processing techniques using multidate optical data analysis alone and SAR ERS-1 and Landsat Thematic Mapper (TM) data integration due to aspects of radar image enhancement techniques and the study of roughness of different types of vegetation in steppic regions. Image data integration has become a valuable approach to integrate multisource satellite data. It has been found that image data from different spectral domains (visible, near-infrared, microwave) provides datasets with complementarity information content and can be used to improve the spatial resolution of satellite images. In this communication, we present a part of the cooperation research project which deals with fusing ERS-1 SAR geocoded images with Landsat TM data, investigating different combinations of integration and classification techniques. The methodology consists of several steps: (1) Speckle noise reduction by comparative performance of different filtering algorithms. Several filtering algorithms were implemented and tested with different window sizes, iterations and parameters. (2) Geometric superposition and geocoding of optical images regarding SAR ERS-1 image and resampling at unique resolution of 25 m. (3) Application of different numerical combinations of integration techniques and unsupervized classifications such as the Forgy method, the MacQueen method and other methods. The results were compared with vegetal cover mapping from aerial photographs of the region of Foum Redad in the south of the Saharian Atlas. The combinations proposed above allow us to distinguish different themes in the arid and semi-arid regions in the south of the Saharian Atlas using a colour composite image and show a good correlation between different types of land cover and land use and radar backscattering level in the SAR data which corresponds essentially to the roughness of the soil surface.     

The Bothnian Experiment in Preparation for ERS-1, 1988 BEPERS-88) — an overview

Abstract

BEPERS-88 was an extensive field campaign on the use of Synthetic Aperture Radar (SAR) in sea ice remote sensing in the Baltic Sea. This experiment was performed in order to study the possibilities of using the ERS-1 satellite SAR (and radar altimeter) in connection with the brackish ice in the Baltic Sea. The Canada Centre for Remote Sensing CV-580 C/X-band SAR was flown and an extensive validation programme was carried out. The data have been used for SAR image analysis, backscatter investigations, geophysical validation of SAR over sea ice, and evaluation of the potentials of SAR in operational ice information services. The results indicate that SAR can be used to discriminate between ice and open water, classify ice types into thrcc categories, quantify ice ridging intensity, and determine the ice drift. As an operational tool SAR is expected to be an excellent complement to NOAA imagery and ground truth.     

Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation

A massive floating green macroalgae bloom (GMB) has occurred for several years consecutively in the Yellow Sea since 2007. In view of the rapid growth of green macroalgae, early detection of its patches at first appearance by satellite imagery is of importance, and the central issue is the selection of appropriate satellite data. As a first step towards this goal, based on quasi-synchronous satellite images of HJ-1A/B (China Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting) charge-coupled devices (CCDs), Environmental Satellite (ENVISAT) Advanced Synthetic Aperture Radar (ASAR) and TERRA Moderate Resolution Imaging Spectroradiometer (MODIS), GMB monitoring abilities by these data were compared. The average percentage difference (APD) of the GMB areas derived by ASAR and CCD was less than 15%, which may be partly attributed to the inability of synthetic aperture radar (SAR) data to detect macroalgae suspended beneath the sea surface. The macroalgae area extracted by MODIS was over two times of that extracted by CCD, which was mainly explained by the difference in their spatial resolutions (250 vs 30 m). The effects of the configuration of sensor bands and the aerosol optical properties on the comparison result were found to be negligible, and the underlying reason is analysed by atmosphere radiative transfer modelling. With satellite images, the drifting velocity of macroalgae patches was estimated to be about 0.21 m s^–1, which was in agreement with the surface current field numerically simulated by the Hybrid Coordinate Ocean Model (HYCOM). It indicates that numerical modelling can aid in deduction of the situation of the patches when satellite data are not available, and on the other hand, satellite data can be used to estimate sea-surface currents through monitoring the movement of green algae. By a comprehensive comparison of available satellite data in operation, for the early detection of macroalgae patches and warning of a massive bloom, CCD data from the HJ-1A/B constellation was preferred, with 30 m spatial resolution, 700 km swath width and 2 day revisiting period. SAR data may be an effective supplement, which can avoid the effects of bad weather (cloud, fog and haze) on optical satellite monitoring.     

Fusion of multisensor multitemporal satellite data for land cover mapping

Efficient integration of remote sensing information with different temporal, spectral and spatial resolutions is important for accurate land cover mapping. A new temporal fusion classification (TFC) model is presented for land cover classification, based on statistical fusion of multitemporal satellite images. In the proposed model, the temporal dependence of multitemporal images is taken into account by estimating transition probabilities from the change pattern of a vegetation dynamics indicator (VDI). Extension of this model is applicable to Synthetic Aperture Radar (SAR) images and integration of multisensor multitemporal satellite images, concerning both temporal attributes and reliability of multiple data sources. The feasibility of the new method is verified using multitemporal Landsat Thematic Mapper (TM) and ERS SAR satellite images, and experimental results show improved performance over conventional methods.     

卷积神经网络和深度置信网络在SAR影像冰水分类的性能评估

目的 海冰分类是海冰监测的主要任务之一。目前基于合成孔径雷达SAR影像的海冰分类方法分为两类：一类是基于海冰物理特性与SAR成像特征等进行分类,这需要一定的专业背景;另一类基于传统的图像特征分类,需要人为设计特征,受限于先验知识。近年来深度学习在图像分类和目标识别方面取得了巨大的成功,为了提高海冰分类精度及海冰分类速度,本文尝试将卷积神经网络（CNN）和深度置信网络（DBN）用于海冰的冰水分类,评估不同类型深度学习模型在SAR影像海冰分类方面的性能及其影响因素。方法 首先根据加拿大海冰服务局（CIS）的冰蛋图构建海冰的冰水数据集;然后设计卷积神经网络和深度置信网络的网络架构;最后评估两种模型在不同训练样本尺寸、不同数据集大小和网络层数、不同冰水比例的测试影像以及不同中值滤波窗口的分类性能。结果 两种模型的总体分类准确率达到93%以上,Kappa系数0.8以上,根据分类结果得到的海冰区域密集度与CIS的冰蛋图海冰密集度数据一致。海冰的训练样本尺寸对分类结果影响显著,而训练集大小以及网络层数的影响较小。在本文的实验条件下,CNN和DBN网络的最佳分类样本尺寸分别是16×16像素和32×32像素。结论 利用CNN和DBN模型对SAR影像海冰冰水分类,并进行性能分析。发现深度学习模型用于SAR影像海冰分类具有潜力,与现有的海冰解译图的制作流程和信息量相比,基于深度学习模型的SAR影像海冰分类可以提供更加详细的海冰地理分布信息,并且减小时间和资源成本。     

Sea state in the Baltic Sea from space-borne high-resolution synthetic aperture radar imagery

In this work, remote sensing synthetic aperture radar (SAR) data from X-band TerraSAR-X and TanDEM-X (TS-X and TD-X) satellites have been used to adopt the algorithms for estimating sea state parameters in the specific condition of the Baltic Sea with archipelago islands and where short steep sea state dominates. Since the moving targets can be defocused and shifted in SAR images, sea state consisting of short windsea waves with strong local orbital velocities and wave breaking needs additional effort for accurate estimation of the total significant wave height that consists of swell and windsea parts. The XWAVE_C algorithm, developed for the North Sea, where the long swell waves coming from the Atlantic Ocean are present during storms, was further enhanced for the short steep windsea which dominates under ordinary storm conditions in the Baltics. For the empirical XWAVE_C model function, based on the spectral analysis of subscenes as well as on local wind information, an additional term was incorporated for assessment the minimal windsea significant wave height by applying JONSWAP wave spectra. A term to compensate spectral distortions triggered by windsea waves moving in SAR flight direction has also been introduced. In total, 95 TS-X/TD-X StripMap scenes between 2012 and 2017 were acquired in Eastern Baltic Sea, processed and analysed. The wave height results from SAR images were compared with collocated in situ data from 11 available buoys. The analysed data include both high and low windsea conditions. The comparison of SAR-derived wave heights with measured wave heights shows high agreement with a correlation coefficient r of 0.88. The wind speed, estimated from SAR images, was compared to measurements from 14 collocated in situ stations, yielding a high agreement with an r value of 0.90. This article is focused on the algorithm developments; however, it is also the first study of sea state retrieval in the Baltic Sea using high-resolution satellite-based techniques. The results show the local variability in the wave fields connected to atmospheric features. The observed local wave height can increase by 1–2 m in kilometre-size cells that are accompanied by wind gusts. The developed algorithms are installed in the German Aerospace Center’s (DLR) ground station Neustrelitz and can also be used in near-real-time.     

Model-based interpretation of ERS-1 SAR images of Arctic sea ice

Interpretation of Synthetic Aperture Radar (SAR) images of sea ice is complex because of the natural variability of sea ice and sensor-induced effects, such as speckle. Most of the research on SAR image interpretation has focused on the winter months and algorithms were developed to classify sea ice successfully under cold conditions. However, interpretation of SAR images during the seasonal transitions has proved difficult due to rapidly changing weather conditions. In this paper we address the application of SAR during the transition from summer to the fall freeze-up. This period is important because it signals the start of significant new ice growth, which affects the air-ocean heat exchange and injects brine into the upper layers of the ocean. We have interpreted SAR images of the sea ice in terms of the basic ice characteristics by using shipborne radar measurements of sea ice during the freeze-up and models derived from these measurements. We have shown that the model-based approach is effective in interpreting SAR images during this seasonal transition. This work also provides the physical mechanisms responsible for the large increase in backscatter observed at the end of the summer melt season.     

Application of a new algorithm using Doppler information to retrieve complex wind fields over the Black Sea from ENVISAT SAR images

Several algorithms have been proposed to retrieve near-surface wind fields from C-band synthetic aperture radar (SAR) images acquired over the ocean. They mainly differ in the way they retrieve the wind direction. Conventionally, the wind direction is taken from atmospheric models or is extracted from the linear features sometimes visible in SAR images. Recently, a new wind retrieval algorithm has been proposed, which also includes the Doppler shift induced by motions of the sea surface. In this article, we apply three wind retrieval algorithms, including the one using Doppler information, to three complex wind events encountered over the Black Sea and compare the SAR-derived wind fields with model wind fields calculated using the high-resolution weather research and forecasting (WRF) model. It is shown that the new algorithm is very efficient in resolving the 180° ambiguity in the wind direction, which is often a problem in the streak-based wind retrieval algorithms. However, the Doppler-based algorithm only yields good results for wind directions that have a significant component in the look direction of the SAR antenna. Furthermore, it is dependent on good separation of the contributions to the Doppler shift induced by surface currents and wind-related effects (wind drift and wind-sea components of the ocean wave spectrum). We conclude that an optimum wind retrieval algorithm should consist of a combination of the algorithms based on linear features and Doppler information.     

Correction of bottom influence in ocean colour satellite images of shallow water areas of the Baltic Sea

Ocean colour satellite measurements are mainly disturbed by the atmosphere, the sea surface and the sea bottom in shallow water areas. In such areas special features of bottom topography can be recognized in satellite images of the visible spectrum and the derived concentrations of water constituents are often misinterpreted. The influence of the sea bottom depends on the water depth, the transmission of the water column, the reflectance of the water, the reflectivity of the bottom materials and the used spectral channels of satellite sensors. The influence of the sea bottom on the spectral reflectance at the sea surface is discussed here on the basis of model computations. The calculations are realized for examples of shallow water areas of the Baltic Sea. Furthermore, a technique for the identification of sea bottom disturbed pixels in satellite images and for the elimination of bottom effects is presented. A linear regression analysis between the bottom depth and the spectral reflectance in the different satellite sensor channels is used in order to test the correlation between these two variables. If a relationship exists, the reflectances have to be corrected. This procedure and the elimination of the bottom influence will be explained for specific satellite systems.     

Retrieval of sea ice thickness distribution in the seasonal ice zone from airborne L‐band SAR

Although satellite data are known to be useful for obtaining ice thickness distribution for perennial sea ice or in stable thin sea ice areas, their use in the seasonal sea ice zone (SIZ) is still unresolved. In this study, we approached the problem of ice thickness retrieval by using L‐band Synthetic Aperture Radar (SAR). In the SIZ, ice thickness growth is closely related to ridging activity and therefore we expected surface roughness to be correlated to ice thickness. L‐band SAR is suitable for detecting such surface roughness and should be a useful tool for obtaining ice thickness distribution. To verify this correlation, we conducted shipborne electromagnetic (EM) inductive sounding and supersonic profiling observations with an icebreaker, coordinated with airborne L‐band SAR observations in the southern Sea of Okhotsk in February 2005. The surface elevation was estimated by representing the ship's motion with a low‐pass filter. Backscattering coefficients correlated well with ice thickness and surface roughness, defined by the standard deviation of the surface elevation. This result sheds light on the possibility of determining ice thickness distribution in the SIZ.     

Automatic detection of oil spills from SAR images

A probabilistic method has been developed that distinguishes oil spills from other similar sea surface features in synthetic aperture radar (SAR) images. It considers both the radiometric and the geometric characteristics of the areas being tested. In order to minimize the operator intervention, it adopts automatic selection criteria to extract the potentially polluted areas from the images. The method has an a priori percentage of correct classification higher than 90% on the training dataset; the performance is confirmed on a different dataset of verified slicks. Some analyses have been conducted using images with different radiometric and geometric resolutions to test its suitability with SAR images different from European Remote Sensing (ERS) satellite ones. The system and its ability to detect and classify oil and non‐oil surface features are described. Starting from a set of verified oil spills detected offshore and over the coastline, the ability of SAR to reveal oil spills is tested by analysing wind intensity, deduced from the image itself, and the distance from the coast.     

The study of ERS-1 SAR and Landsat TM synergism for land use classification

This work presents a comparative analysis of ERS-1 Synthetic Aperture Radar (SAR) and Landsat-5 Thematic Mapper (TM) images used for land use classification. The study area of 361 km² is located in the City of Campinas, Sao Paulo State, Brazil, and contains several classes of land use, including urban, agricultural and forests. The TM and SAR images were registered and transformed using the principal components transformation. SAR images were also filtered using an average filter. The principal components derived from SAR filtered, SAR, TM and coregistered TM/SAR and TM/SAR filtered images were classified using the maximum likelihood approach. Tests of 'goodness of fit' were also made to assess the statistical properties of the images. The results, confirmed by Kappa statistics, show a significant improvement when classifying the principal components of filtered SAR and TM images for urban, pasture and forest classes.     

High resolution observations of Weddell Sea surface currents using ERS-l SAR sea-ice motion vectors

Two areas of the Weddell Sea, one in the south and one in the west, were chosen for a preliminary investigation of sea-ice motion tracking from ERSt Synthetic Aperture Radar (SAR) images during the Austral summer. Only a small number of images were processed, so a manual tracking method was used. In the 3–day period between SAR images the atmosphere warmed near the surface, which led to significant changes in radar backscatter from, and thus in contrast between, ice floes and the areas between them. It was therefore not always possible to track features from one image to the next. The tracked features were clearly identified in images which were sub-sampled at onesixteenth of the full resolution available. In the southern Weddell Sea images, many large floes were present which allowed a quite detailed pattern of the surface water circulation to be mapped as the ice motion was predominantly forced by the ocean currents during a period of low surface wind speeds. The observed circulation pattern agreed well with previous observations from hydrographic surveys in this area north of the Filchner Ice Shelf. In the western Weddell Sea images good tracers were hard to find, but it was still possible to detect the edge of the western boundary current of the Weddell Gyre. Continuous monitoring of sea-ice motion in these two areas using SAR imagery could be a useful means of detecting changes in surface water flow which may be linked to the rate of formation of Antarctic bottom water.     

利用卫星SAR监测海上航行船舶

星载合成孔径雷达能够得到高分辨率的遥感图像。船舶目标以及船舶航迹在一些卫星ＳＡＲ海洋图像中清晰可见。文中就利用卫星ＳＡＲ对船舶目标和船舶航迹的监测问题进行了论述。首先简要介绍了船舶目标及其航迹的ＳＡＲ成像原理,然后对不同成像条件得到的航迹图像进行了分类,最后分析了船舶目标的检测、船舶航迹特征的检测以及船舶的相应参数的估计问题。通过对上述问题的讨论,认为利用卫星ＳＡＲ监测海上航行的船舶是可行的,而且是一项很有意义的工作。     

Eddy-advective effects on the temperature and wind speed of the sea surface in the Northwest Pacific Subtropical Countercurrent area from satellite observations

In this study, satellite microwave and altimeter data from 1998 to 2007 are used to quantify the eddy-induced meridional heat advection (EMHA) in the Northwest Pacific Subtropical Countercurrent area. Generally, from March to May, the robust EMHA is formed at the point where meridional currents of eddies cross a zonal front of climatological background sea surface temperature (SST). The EMHA shifts westwards with eddies and varies seasonally with the SST front. It warms (cools) the sea surface west of anticyclonic (cyclonic) eddies, inducing noticeable SST anomalies (SSTAs), which are westwardly phase shifted from the eddy-induced sea surface height anomalies by about 90°. Surface wind subsequently varies with the induced SSTAs: it blows faster (slower) over the warm (cold) SST regions than the surroundings. The spatial variations of SST and sea surface wind due to the EMHA shift westwards with eddy motion. These findings from satellite observations give us the possibility of studying the role of oceanic eddies in ocean–atmosphere interaction at the timescale of weather systems in an open ocean.     

SAR imaging of a topography-induced current front in a tidal channel

A quasi-linear dark-bright feature was observed on a TerraSAR-X synthetic aperture radar (SAR) image acquired in the area of Pearl River Estuary (PRE), China, on 25 October 2010. Examining the detailed local bathymetry chart, we find that the feature is collocated with major axis of Lingding Channel in the waterway of PRE. In the study, we first run a 3-D hydrodynamic model to simulate the tidal currents within PRE, and then used the simulated current and local wind data as input to run a radar simulation model to calculate the variation of normalized radar cross section induced by these parameters. Ocean model simulation shows that surface currents were parallel to the major axis of the channel at the satellite overpass time. Radar model simulation results show good agreement between the simulated and actual SAR images. The quasi-linear dark-bright feature on the SAR image was found to be due to the surface current convergence and divergence caused by the bathymetry-induced tidal current variation.