Multi-sensor data merging of sea ice concentration and thickness

With the rapid change in the Arctic sea ice, a large number of sea ice observations have been collected in recent years, and it is expected that an even larger number of such observations will emerge in the coming years. To make the best use of these observations, in this paper we develop a multi-sensor optimal data merging (MODM) method to merge any number of different sea ice observations. Since such merged data are independent on model forecast, they are valid for model initialization and model validation. Based on the maximum likelihood estimation theory, we prove that any model assimilated with the merged data is equivalent to assimilating the original multi-sensor data. This greatly facilitates sea ice data assimilation, particularly for operational forecast with limited computational resources. We apply the MODM method to merge sea ice concentration (SIC) and sea ice thickness (SIT), respectively, in the Arctic. For SIC merging, the Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) data are merged together with the Norwegian Ice Service ice chart. This substantially reduces the uncertainties at the ice edge and in the coastal areas. For SIT merging, the daily Soil Moisture and Ocean Salinity (SMOS) data is merged with the weekly-mean merged CryoSat-2 and SMOS (CS2SMOS) data. This generates a new daily CS2SMOS SIT data with better spatial coverage for the whole Arctic

SAR and Passive Microwave Fusion Scheme: A Test Case on Sentinel-1/AMSR-2 for Sea Ice Classification

The most common source of information about sea ice conditions is remote sensing data, especially images obtained from synthetic aperture radar (SAR) and passive microwave radiometers (PMR). Here we introduce an adaptive fusion scheme based on Graph Laplacians that allows us to retrieve the most relevant information from satellite images. In a first test case, we explore the potential of sea ice classification employing SAR and PMR separately and simultaneously, in order to evaluate the complementarity of both sensors and to assess the result of a combined use. Our test case illustrates the flexibility and efficiency of the proposed scheme and indicates an advantage of combining AMSR-2 89 GHz and Sentinel-1 data for sea ice mapping

Automatic Selection of Relevant Attributes for Multi-Sensor Remote Sensing Analysis: A Case Study on Sea Ice Classification

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 which 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

User requirements for a Copernicus polar mission

To monitor on a continuous basis the vast and harsh Arctic environment, considering the sparse population and the lack of transport links, space technologies are definitely essential tools including Earth observation, navigation and communication satellites. DG GROW asked for an Expert Group in spring 2017 with the mandate to update and/or complete the review and analysis of the Users’ needs, thus allowing the Commission to assess the relevance of the development of a "Copernicus expansion mission" dedicated to Polar and Snow monitoring.JRC.D.6-Knowledge for Sustainable Development and Food Securit

EPS/Metop-SG Scatterometer Mission Science Plan

89 pages, figures, tablesThis Science Plan describes the heritage, background, processing and control of C-band scatterometer data and its remaining exploitation challenges in view of SCA on EPS/MetOp-SGPeer reviewe

ST3 – Kartlegging av is- og snøforekomst i Barentshavet

Åpning av Barentshavet sørøst (heretter kalt Barentshavet SØ) er den første åpningen av et nytt område for petroleumsvirksomhet på 20 år. Dette området har nye og potensielt større utfordringer enn det vi tidligere har stått ovenfor i nyåpnede områder på sokkelen og i Barentshavet. Rapporten tar for seg isforholdene i Barentshavet med spesielt fokus på Barentshavet SØ. De forskjellige istypene forklares, og vi gir en oversikt over hvordan isen dannes og driver inn i Barentshavet. Vi ser også på historiske isobservasjoner spesielt i området Barentshavet SØ og viser hvordan trenden for is i området er. Det finnes flere modeller som brukes til simulering av framtidig isutbredelse, og reelle data sammen med simuleringer viser at utviklingen går raskere enn modellene viser, og at det kan bli isfritt i Barentshavet innen år 2060. Barentshavet SØ har vært isfritt siden 2003. Oppdragsgiver: Petroleumstilsyne

ST3 – Kartlegging av is- og snøforekomst i Barentshavet

Åpning av Barentshavet sørøst (heretter kalt Barentshavet SØ) er den første åpningen av et nytt område for petroleumsvirksomhet på 20 år. Dette området har nye og potensielt større utfordringer enn det vi tidligere har stått ovenfor i nyåpnede områder på sokkelen og i Barentshavet. Rapporten tar for seg isforholdene i Barentshavet med spesielt fokus på Barentshavet SØ. De forskjellige istypene forklares, og vi gir en oversikt over hvordan isen dannes og driver inn i Barentshavet. Vi ser også på historiske isobservasjoner spesielt i området Barentshavet SØ og viser hvordan trenden for is i området er. Det finnes flere modeller som brukes til simulering av framtidig isutbredelse, og reelle data sammen med simuleringer viser at utviklingen går raskere enn modellene viser, og at det kan bli isfritt i Barentshavet innen år 2060. Barentshavet SØ har vært isfritt siden 2003. Oppdragsgiver: PetroleumstilsynetST3 – Kartlegging av is- og snøforekomst i BarentshavetpublishedVersio

Electric Light; Light Fair 2012

Lighting devices -- Lamps -- Electric lamp

Climate change and phenological responses of two seabird species breeding in the high-Arctic

International audienceThe timing of breeding is a life-history trait that can greatly affect fitness, because successful reproduction depends on the match between the food requirements for raising young and the seasonal peak in food availability. We analysed phenology (hatch dates) in relation to climate change for 2 seabird species breeding in the high-Arctic, little auks Alle alle and black-legged kittiwakes Rissa tridactyla, for the periods 1963–2008 and 1970–2008, respectively. We show that spring climate has changed during the study period, with a strong increase in both air temperature (TEMP) and sea surface temperature (SST) and a decrease in sea ice concentration. Little auks showed a trend for earlier breeding over the study period, while kittiwakes showed a non-significant trend for later breeding, demonstrating different phenological responses in these 2 species. Little auks and kittiwakes adjusted their timing of breeding to different environmental signals. Spring TEMP was the best predictor of little auk phenology, with a significant negative effect. Spring SST was the strongest predictor of kittiwake phenology, with a non-significant negative effect. Spring sea ice concentration and the North Atlantic Oscillation (NAO) winter index had a low relative variable importance. Furthermore, in kittiwakes, years with late breeding were associated with low clutch size and mean annual breeding success, indicating poor investment and food availability. This study identifies some spring environmental factors important for regulating the timing of breeding in the high-Arctic, most likely through effects on snow cover limiting access to nest sites and the development of the polar marine food web. It remains to be investigated whether environmental factors are reliable predictors of marine prey phenology, and whether the decision to start breeding is constrained by food availability

Status of Earth Observation and Remote Sensing Applications in Svalbard

Remarkable developments in the fields of earth observation (EO) satellites and remote sensing (RS) technology over the past four decades have substantially contributed to spatial, spectral, and temporal sampling [...