The Polar Oceans Program of the Alaska SAR Facility

The science plan for the Alaska SAR Facility (ASF) focuses on earth surface characteristics that are of interest within the overall concept of global change and that show significant regional, seasonal and interannual variations resulting in changes in the strength of their radar returns. The polar oceans, with the continuous motion and deformation of the pack ice and the changes in the surface state of the surrounding open seas, offer excellent opportunities for such research. Because such studies require both frequent and detailed analysis of Synthetic Aperture Radar (SAR) data, a Geophysical Processor System (GPS) has been developed to speed the extraction of useful geophysical information from SAR data sets. The system will initially produce three main types of products: (a) sets of ice motion vectors obtained by automated computer tracking of identifiable ice floes on sequential images, (b) the areal extent and location of several different ice types and open water and (c) a characterization of the wave state in ice-free regions as well as within the ice in the marginal ice zone at locations where significant wave penetration occurs. Details of these analysis procedures are described. Initially the GPS is planned to process 10 image pairs/day for ice motion, 20 images/day for ice type variations and 1 image/day for wave information, with a total estimated processing time of 13 hours. A variety of projects plan to utilize the SAR data stream in studies of ice, lead and polynya dynamics and thermodynamics. A common feature of these research programs will be attempts to provide, via the coupling of the SAR data with ice property and ice dynamics models, improved estimates of the heat and mass fluxes into both the atmosphere and the ocean as affected by the characteristics of the ice cover.Key words: SAR, radar, sea ice, image analysis, remote sensingMots clés: RAAS, radar, glace de mer, analyse d’images, télédétectio

Science plan for the Alaska SAR facility program. Phase 1: Data from the first European sensing satellite, ERS-1

Science objectives, opportunities and requirements are discussed for the utilization of data from the Synthetic Aperture Radar (SAR) on the European First Remote Sensing Satellite, to be flown by the European Space Agency in the early 1990s. The principal applications of the imaging data are in studies of geophysical processes taking place within the direct-reception area of the Alaska SAR Facility in Fairbanks, Alaska, essentially the area within 2000 km of the receiver. The primary research that will be supported by these data include studies of the oceanography and sea ice phenomena of Alaskan and adjacent polar waters and the geology, glaciology, hydrology, and ecology of the region. These studies focus on the area within the reception mask of ASF, and numerous connections are made to global processes and thus to the observation and understanding of global change. Processes within the station reception area both affect and are affected by global phenomena, in some cases quite critically. Requirements for data processing and archiving systems, prelaunch research, and image processing for geophysical product generation are discussed

Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error - MAE - of 2.28 %, anomaly correlation coefficient - ACC - of 0.98, root-mean-square error - RMSE - of 5.76 %, normalized RMSE - nRMSE - of 16.15 %, and NSE - Nash-Sutcliffe efficiency - of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics

Earth resources: A continuing bibliography with indexes (issue 62)

This bibliography lists 544 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1 and June 30, 1989. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Satellite radar altimetry of sea ice

The thesis concerns the analysis and interpretation of data from satellite borne radar altimeters over ice covered ocean surfaces. The applications of radar altimetry are described in detail and consider monitoring global climate change, the role that sea ice plays in the climate system, operational applications and the extension of high precision surface elevation measurements into areas of sea ice. The general nature of sea ice cover is discussed and a list of requirements for sea ice monitoring is provided and the capability of different satellite sensors to satisfy needs is examined. The operation of satellite borne altimeter over non-ocean surfaces is discussed in detail. Theories of radar backscatter over sea ice are described and are used to predict the radar altimeter response to different types of sea ice cover. Methods employed for analysis of altimeter data over sea ice are also described. Data from the Seasat altimeter is examined on a regional and global scale and compared with sea ice climatology. Data from the Geosat altimeter is compared with co-incident imagery from the Advanced Very High Resolution Radiometer and also from airborne Synthetic Aperture Radar. Correlations are observed between the altimeter data and imagery for the ice edge position, zones within the ice cover, new ice and leads, vast floes and the fast ice boundary. An analysis of data collected by the Geosat altimeter over a period of more than two years is used to derive seasonal and inter-annual variations in the total Antarctic sea ice extent. In addition the retrieval of high accuracy elevation measurements over sea ice areas is carried out. These data are used to produce improved maps of sea surface topography over ice- covered ocean and provide evidence of the ability of the altimeter to determine sea ice freeboard directly. In addition the changing freeboard of two giant Antarctic tabular icebergs, as measured by the Geosat altimeter, is presented. As a summary the achievements are reviewed and suggestions are made towards directions for further work on present data sets and for future data from the ERS-1 satellite

Multisensor Microwave Sea-Ice Classification

Understanding the polar ice regimes is fundamental to the understanding of global climate and other geophysical processes. Sea ice characteristics can be grouped into a number of .general sea ice classes. Multisensor data from NSCAT, ERS-2, and SSM/I is reconstructed into enhanced resolution imagery for use in ice type classification. The resulting 12-dimensional data set is linearly transformed through principal component analysis to reduce data dimensionality and noise levels. An iterative statistical data segmentation algorithm is developed using maximum a pasteriori techniques. The conditional probability distributions of observed vectors given the ice type are assumed to be Gaussian. The cluster centroids, covariance matrices, and a priori ·distributions are estimated from the classification of a previous temporal image set. An initial classification is produced using centroid training data and a weighted nearest neighbor classifier. Though validation is limited, the algorithm results in an ice classi1ication which is subjectively superior to a conventional k-means approach

NASA Sea Ice Validation Program for the Defense Meteorological Satellite Program Special Sensor Microwave Imager

The history of the program is described along with the SSM/I sensor, including its calibration and geolocation correction procedures used by NASA, SSM/I data flow, and the NASA program to distribute polar gridded SSM/I radiances and sea ice concentrations (SIC) on CD-ROMs. Following a discussion of the NASA algorithm used to convert SSM/I radiances to SICs, results of 95 SSM/I-MSS Landsat IC comparisons for regions in both the Arctic and the Antarctic are presented. The Landsat comparisons show that the overall algorithm accuracy under winter conditions is 7 pct. on average with 4 pct. negative bias. Next, high resolution active and passive microwave image mosaics from coordinated NASA and Navy aircraft underflights over regions of the Beaufort and Chukchi seas in March 1988 were used to show that the algorithm multiyear IC accuracy is 11 pct. on average with a positive bias of 12 pct. Ice edge crossings of the Bering Sea by the NASA DC-8 aircraft were used to show that the SSM/I 15 pct. ice concentration contour corresponds best to the location of the initial bands at the ice edge. Finally, a summary of results and recommendations for improving the SIC retrievals from spaceborne radiometers are provided

Geostatistical and statistical classification of sea-ice properties and provinces from SAR data

Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line