Figure 8-11 - uploaded by Ola M. Johannessen
Content may be subject to copyright.
(a) Illustration of ENVISAT ASAR geometry for the Wide-swath Mode, which consists of five narrow-swath beams, each covering a width between 70 and 100 km. Source: ESA 2002. The width of the wide-swath image is about 400 km. (b) Illustration and main terms of satellite SAR geometry. Source: Robinson 2004. Courtesy: Springer, Praxis Publishing Ltd.
Source publication
8.1 INTRODUCTION 8.1.1 The Role of Sea Ice in the Climate and Weather System Sea ice is a part of the cryosphere that interacts continuously with the underlying oceans and the overlaying atmosphere. The growth and decay of sea ice occur on a seasonal cycle at the surface of the ocean at high latitudes. As much as 30 million km 2 of the Earth's surf...
Similar publications
The Southern Ocean (in the region 60–180° E) south of the Indian Ocean, Australia, and the West Pacific is noted for the frequent occurrence and severity of its storms. These storms give rise to high‐amplitude secondary microseisms from sources, including the deep ocean regions, and primary microseisms where the swells impinge on submarine topograp...
In the summer of 2020, ESA changed the orbit of CryoSat‐2 to align periodically with NASA's ICESat‐2 mission, a campaign known as CRYO2ICE, which allows for near‐coincident CryoSat‐2 and ICESat‐2 observations in space and time over the Arctic until summer 2022, where the CRYO2ICE Antarctic campaign was initiated. This study investigates the Arctic...
The shrinking of Arctic-wide September sea ice extent is often cited as an indicator of modern climate change; however, the timing of seasonal sea ice retreat/advance and the length of the open-water period are often more relevant to stakeholders working at regional and local scales. Here we highlight changes in regional open-water periods at multi...
Owing to increase in snowfall, the Antarctic Ice Sheet surface mass balance is expected to increase by the end of the current century. Assuming no associated response of ice dynamics, this will be a negative contribution to sea-level rise. However, the assessment of these changes using dynamical downscaling of coupled climate model projections stil...
A distinctive feature of the Southern Hemisphere extratropical atmospheric circulation is the quasi-stationary zonal wave 3 pattern. This pattern is present in both the mean atmospheric circulation and its variability on daily, seasonal and interannual timescales. While the zonal wave 3 pattern has substantial impacts on meridional heat transport a...
Citations
... One reason is that sea ice plays an essential role in the polar ecosystem (Funder et al., 2010). Moreover, the knowledge about sea ice conditions is crucial for polar navigation, offshore operations, weather forecasting, and climate research (Sandven et al., 2006). The main sources of information about sea ice conditions and climatological studies are data from passive microwave radiometers (PMR), and synthetic aperture radars (SAR). ...
Remote sensing data acquired from various sensors have been used for decades to monitor sea ice conditions over polar regions. Sea ice plays an essential role in the polar environment and climate. Furthermore, sea ice affects anthropogenic activities, including shipping and navigation, the oil and gas industry, fisheries, tourism, and the lifestyle of the indigenous population of the Arctic. With the continuous decline of sea ice in the Arctic the presence of human-based activities will grow. Therefore, reliable information about sea ice conditions is of primary interest to protect the Arctic and to ensure safe and effective commercial activities and polar navigation. Currently, sea ice services produce operational ice charts manually using the knowledge of sea ice experts. However, with an increasing number of various data sources that provide different information regarding sea ice, it is important to develop automatic methods for sea ice characterization. Robust and automatic ice charting can not be achieved using only one satellite mission. It is fundamental to combine information from various remote sensors with different characteristics for more reliable sea ice monitoring and characterization. However, how do we know that all the information is actually relevant? It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms from the required processing time and accuracy point of view. Therefore, it is crucial to select an optimal set of features that provides the relevant information content to enhance the efficiency and accuracy of the image interpretation and retrieval of geophysical parameters. The work in this dissertation specifically focuses on the development of such a method. In this thesis, we employ a fully automatic, flexible, accurate, efficient, and interpretable information selection method that is based on the graph Laplacians. The proposed approach assesses relevant information on a global and local level using two metrics simultaneously and selects relevant features for different regions of an image according to their physical characteristics and observation conditions. Moreover, it is linked in a common scheme with a classification algorithm that helps to properly evaluate the performance of the information selection and provides sea ice classification maps as an output. Accordingly, in recent studies, we investigate and evaluate the robustness and effectiveness of the proposed method for sea ice classification by testing several data combinations with various sea ice conditions. Experiments illustrate the flexibility and efficiency of the proposed scheme and clearly indicate an advantage of combining various sensors. Moreover, the results demonstrate the potential for operational sea ice monitoring that should be further thoroughly examined in future studies.
... Sea ice affects the radiation balance through the sea ice-albedo feedback. Moreover, sea ice blocks the exchange of heat between the ocean and the atmosphere, lowering the vertical heat transfer by two orders of magnitude [ 26 ]. Antarctic sea-ice changes have been attributed to the adjustment of both the SAM [ 24 ] and the Amundsen Sea Low, which may be further driven by the teleconnections triggered by tropical interannual and decadal variabilities [ 28 , 34 -37 ]. ...
... In contrast, during the onset of summer, the seaice extent is near its maximum in November and the ocean-atmosphere heat exchange is greatly reduced [ 26 ] (Supplementary Fig. S2A-C). Therefore, other factors, such as atmospheric circulation, can play larger roles in determining air temperatures. ...
Antarctica's response to climate change varies greatly both spatially and temporally. Surface melting impacts mass balance and also lowers surface albedo. We use a 43-year record (from 1978 to 2020) of Antarctic snow melt seasons from spaceborne microwave radiometers with a machine learning algorithm to show that both the onset and the end of the melt season are being delayed. Granger-causality analysis shows that melt end is delayed due to increased heat flux from the ocean to the atmosphere at minimum sea ice extent from warming oceans. Melt onset is Granger-caused primarily by the turbulent heat flux from ocean to atmosphere that is in turn driven by sea ice variability. Delayed snowmelt season leads to a net decrease in the absorption of solar irradiance, as a delayed summer means that higher albedo occurs after the period of maximum solar radiation, which changes Antarctica's radiation balance more than sea ice cover.
... Microwave remote sensing has become the most effective means of sea ice monitoring and ice condition assessment because of its advantages of all-weather detection [4,5]. As a typical active microwave remote sensing payload, the scatterometer is most widely used in the sea ice type monitoring. ...
... The physical mechanism is that after repeated melting and freezing, the salt within the MYI precipitates continuously, leaving more bubbles in the ice layer. In contrast, the FYI generally has a higher salinity and dielectric constant, and the penetration depth is smaller, resulting in MYI having stronger volume scattering than FYI [4,6,7]. In addition, the surface of MYI is rougher than that of FYI, resulting in stronger surface scattering of MYI than FYI. ...
The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). A new algorithm for classification of Arctic sea ice types on CSCAT measurement data using a random forest classifier is presented. The random forest classifier is trained on the National Snow and Ice Data Center (NSIDC) weekly sea ice age and sea ice concentration product. Five feature parameters, including the mean value of horizontal and vertical polarization backscatter coefficient, the standard deviation of horizontal and vertical polarization backscatter coefficient and the copol ratio, are innovatively extracted from orbital measurement for the first time to distinguish water, first-year ice (FYI) and multi-year ice (MYI). The overall accuracy and kappa coefficient of sea ice type model are 93.35% and 88.53%, respectively, and the precisions of water, FYI, and MYI are 99.67%, 86.60%, and 79.74%, respectively. Multi-source datasets, including daily sea ice type from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), NSIDC weekly sea ice age, multi-year ice concentration (MYIC) provided by the University of Bremen, and SAR-based sea ice type released by Copernicus Marine Environment Monitoring Service (CMEMS) have been used for comparison and validation. It is shown that the most obvious difference in the distribution of sea ice types between the CSCAT results and OSI SAF sea ice type are mainly concentrated in the marginal zones of FYI and MYI. Furthermore, compared with OSI SAF sea ice type, the area of MYI derived from CSCAT is more homogeneous with less noise, especially in the case of younger multiyear ice. In the East Greenland region, CSCAT identifies more pixels as MYI with lower MYIC values, showing better accuracy in the identification of areas with obvious mobility of MYI. In conclusion, this research verifies the capability of CSCAT in monitoring Arctic sea ice classification, especially in the spatial homogeneity and detectable duration of sea ice classification. Given the high accuracy and processing speed, the random forest-based algorithm can offer good guidance for sea ice classification with FY-3E/RFSCAT, i.e., a dual-frequency (Ku and C band) scatterometer called WindRAD.
... It exists widely in high latitude areas and covers about 25 million square kilometers of ocean surface, which is one-tenth of the world's ocean. Sea ice plays an important role in atmospheric circulation [1], ocean water cycle [2], heat balance [3], and climate systems [4]. It also has a significant impact on the ecological environment and human economic activities [5], [6], [7], [8], [9]. ...
... Similarly, for the Liaodong Bay ENVISAT-CBERS data, C (4) B meets the termination condition, and thus, the fourth iteration is the terminal iteration. For the Liaodong Bay Sentinel-1 data, C (1) A meets the termination condition, and thus, the second iteration is the terminal iteration. For the Arctic Ocean Sentinel-1 data, C (2) B meets the termination condition, and thus, the second iteration is the terminal iteration. ...
In this paper, sea ice classification on a remote sensing image given just a small number of labeled pixels is investigated. Effective sea ice classification is rendered from two aspects. First, a feature extraction method is developed. It extracts the context feature from a classification map. Second, an iterative learning paradigm is established. The labeled pixels are divided into two training subsets. At each iteration, the context feature for one subset is extracted from the classification map which is obtained subject to the other subset. Therefore, the two subsets mutually guide each other for updating the context feature in an iterative manner, which finally renders effective sea ice classification. The above paradigm is referred to as mutually guided contexts. The advantages of the new paradigm are two-fold. First, the context feature enriches the sea ice image representation in a general manner regardless of the types of raw image data. Second, the two training subsets keep providing different refined classification maps for each other such that the comprehensiveness of the context feature is recursively enhanced. Therefore, the paradigm of mutually guided contexts comprehensively characterizes the sea ice image representation for training and classification even when only small training data are available. Experiments validate the effectiveness of the mutually guided contexts for sea ice classification.
... Sea ice in the Northern Hemisphere, which broke a record with a volume of 0.05 million km 3 , is almost twice the volume of ice formed in the Southern Ocean. The average thickness of sea ice in the Arctic Ocean is around 3 meters, while in Antarctica this ratio is between 1 and 1.5 meters (Sandven & Johannessen, 2006). While there is little data on Antarctica on these measurements, sea ice volume measurements in the Arctic Ocean have been made in research with submarines since the 1960s. ...
This year’s theme, “The Russian Arctic: Economics, Politics & Peoples” was chosen, at the turn of 2021-2022 and prior to Russia’s invasion of Ukraine, due to the high relevance of the Russian Arctic in every aspect of Arctic politics. The region comprises over half of the Arctic’s land surface area, mostly covered by permafrost, and almost half of the coastline and the Exclusive Economic Zone of the Arctic Ocean. Its population consists of almost 70% of the total number of Arctic inhabitants. The volume of its economy with multiple fields of exploitation is 73% of that of the Arctic. Despite being largely covered by permafrost, the Russian part of the Arctic contains large cities and numerous towns and villages, as well as road networks and even railways. These populated centers, many of them ‘mono-towns’, are surrounded by advanced infrastructure – both old and new – and consist of mines, smelters and other factories, harbors, airports and other transportation means, research stations, as well as navel and other military bases. Since the time of tzardom, Russian scientists and scholars have explored the Arctic and conducted field work studying geography, Arctic ecosystems, climate, cryospheric sciences, glaciers, the Arctic Ocean, and sea-ice. The Russian Arctic is also home to diverse groups of Indigenous peoples with their unique languages, cultures and livelihoods. Research done by and with Russian scientists, scholars and academic institutions is an invaluable part of international Arctic research.
The Russian Arctic is therefore an incredibly important part of the entire Arctic region to understand, not only because the Russian Federation is the biggest and largest of the eight Arctic states. And yet, the region is often either not known, and/or misunderstood to external audiences and stakeholders, with superficial characterizations proliferating due to a lack of up-to-date information. There is thus a need for sophisticated English-language scholarship on the Russian Arctic, especially from Russian authors themselves. That is the intent of the Arctic Yearbook 2022.
... Generally, SI forms, grows, and melts exclusively in the ocean [57]. Although SI can cover up to about 30 million square kilometers of the Earth's surface [58], many people might never directly encounter SI in their lives because SI is found primarily in the Arctic and Antarctic regions [57,58]. SI has direct and indirect effects on the climate, wildlife, and many human activities. ...
... Generally, SI forms, grows, and melts exclusively in the ocean [57]. Although SI can cover up to about 30 million square kilometers of the Earth's surface [58], many people might never directly encounter SI in their lives because SI is found primarily in the Arctic and Antarctic regions [57,58]. SI has direct and indirect effects on the climate, wildlife, and many human activities. ...
... Despite its crucial role, gathering in situ data for SI studies is very difficult due to their remote locations, extreme climate, and changing nature. Scientists have previously used ships, submarines, buoys, and field camps to gather data for SI monitoring over relatively small regions [58]. These methods are costly and labor-intensive. ...
As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.
... Sea ice observed from space has been widely investigated with intensive remote sensing data [8]. Specifically, the sea ice extent, drift, growth stage, concentration, and thickness can be estimated using different sensors, e.g., passive microwave [9,10], scatterometer [11,12], radar altimeter [4,13], and synthetic aperture radar (SAR) [14,15]. ...
... The SI coverage considerably fluctuates within a year. SI monitoring is essential for many environmental applications [58]. Particularly, SI can affect the global climate by altering the surface albedo and reducing solar radiation absorbed by the ocean surface water. ...
... Particularly, SI can affect the global climate by altering the surface albedo and reducing solar radiation absorbed by the ocean surface water. Additionally, melting SI, as a freshwater source, changes water constituents and, thus, influences the ocean circulation patterns [58]. SI presence and movement can threaten vessel navigations and impose severe limitations on vessel traffic. ...
Reliable, accurate, and timely information about oceans is important for many applications, including water resource management, hydrological cycle monitoring, environmental studies, agricultural and ecosystem health applications, economy, and the overall health of the environment. In this regard, Remote Sensing (RS) systems offer exceptional advantages for mapping and monitoring various oceanographic parameters with acceptable temporal and spatial resolutions over the oceans and coastal areas. So far, different methods have been developed to study oceans using various RS systems. This urges the necessity of having review studies that comprehensively discuss various RS systems, including passive and active sensors, and their advantages and limitations for ocean applications. In this paper, the goal is to review most remote sensing systems and approaches that have been worked on marine applications. This review paper is divided into two parts. Part 1 is dedicated to the passive RS systems for ocean studies. As such, four primary passive systems, including optical, Thermal Infrared (TIR) radiometers, microwave radiometers, and Global Navigation Satellite Systems (GNSS), are comprehensively discussed. Additionally, this paper summarizes the main passive RS sensors and satellites, which have been utilized for different oceanographic applications. Finally, various oceanographic parameters, which can be retrieved from the data acquired by passive RS systems, along with the corresponding methods, are discussed.
... Sea ice observations have a long history of more than a century. They were carried out visually from coastal stations, ships, and aircraft [3], while they were spatially and temporally limited. Regular sea ice monitoring over larger regions became possible in the late 1970s using image data from satellites [3]. ...
... They were carried out visually from coastal stations, ships, and aircraft [3], while they were spatially and temporally limited. Regular sea ice monitoring over larger regions became possible in the late 1970s using image data from satellites [3]. Since then, the technologies for acquiring and analyzing sea ice data have been considerably improved and extended. ...
... Passive microwave radiometers are another type of sensor that can be used for sea ice observations. However, in comparison to the aforementioned techniques, it has a significantly coarser spatial resolution and is, therefore, preferably used for global or large-scale observations [3]. The increasing amount of available satellite data together with more and more activities in sea ice covered waters requires a greater effort for supplementing the production of ice charts by employing fully automated methods of information selection and image analysis [3], [4]. ...
It is of considerable benefit to combine information obtained from different satellite sensors to achieve advanced and improved characterization of sea ice conditions. However, it is also true that not all the information is relevant. It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms. Therefore, it is crucial to select an optimal set of image attributes that provides the relevant information content to enhance the efficiency and accuracy of the image interpretation and retrieval of geophysical parameters. Comprehensive studies have been focused on the analysis of relevant features for sea ice analysis obtained from different sensors, especially synthetic aperture radar. However, the outcomes of these studies are mostly data and application-dependent, and can therefore rarely be generalized. In this article, we employ a feature selection method based on Graph Laplacians, which is fully automatic and easy to implement. The proposed approach assesses relevant information on a global and local level using two metrics and selects relevant features for different regions of an image according to their physical characteristics and observation conditions. In the recent study we investigate the effectiveness of this approach for sea ice classification, using different multi-sensor data combinations. Experiments show the advantage of applying multi-sensor data sets and demonstrate that the attributes selected by our method result in high classification accuracies. We demonstrate that our approach automatically considers varying technical, sensor-specific, environmental, and sea ice conditions by employing flexible and adaptive feature selection method as a pre-processing step.
... The existence of sea ice limits maritime operations at high latitudes in both hemispheres. Thus, it is essential to know the characteristics of sea ice and its formation, and monitoring and producing sea ice forecasts is crucial to support maritime operations [35]. The Polar Code (PC)'s efforts to mitigate the hazards and reduce risks to the environment elevate "seaworthiness" to a higher standard [36]. ...
... For this reason, the satellite era, which gained momentum at the beginning of the 1960s, has become the most crucial observation method for the polar regions. Data from the satellites are utilized widely in research and in monitoring the SIE and other parameters [35,53]. ...
Polar regions face increasing challenges resulting from the interactions between global climate change, human activities, and economic
and political pressures. As the sea ice extent trends diminish, maritime operations have started increasing in these regions. In this respect,
an international concern has arisen for the shrinking of sea ice, preserving the environment, and passengers’ and seafarers’ safety. The
International Maritime Organization has enforced the Polar Code (PC) for the ships navigating in these challenging Arctic and Antarctic
waters. Polar regions are similar in some aspects but exhibit significant differences in geographical conditions, maritime activities, and
legal status. Therefore, the PC that applies to both regions should be reconsidered, accounting for the differences between the areas for
further development. This study considers the Arctic and Antarctic geographical differences relevant to the PC’s scope. The emphasis is
placed on the changes regarding the sea ice extent and sea ice condition differences in the two regions, which are essential in maritime
safety. This study also addresses the aspects of the PC that need improvement.
