Eirik Malnes

Cold Regions Hydrology High-Resolution Observatory for Snow and Cold Land Processes

Snow is a critical component of the global water cycle and climate system, and a major source of water supply in many parts of the world. There is a lack of spatially distributed information on the accumulation of snow on land surfaces, glaciers, lake ice, and sea ice. Satellite missions for systematic and global snow observations will be essential to improve the representation of the cryosphere in climate models and to advance the knowledge and prediction of the water cycle variability and changes that depend on snow and ice resources. This paper describes the scientific drivers and technical approach of the proposed Cold Regions Hydrology High-Resolution Observatory (CoReH2O) satellite mission for snow and cold land processes. The sensor is a synthetic aperture radar operating at 17.2 and 9.6 GHz, VV and VH polarizations. The dual-frequency and dual-polarization design enables the decomposition of the scattering signal for retrieving snow mass and other physical properties of snow and ice.

Record-low primary productivity and high plant damage in the Nordic Arctic Region in 2012 caused by multiple weather events and pest outbreaks

The release of cold temperature constraints on photosynthesis has led to increased productivity (greening) in significant parts (32–39%) of the Arctic, but much of the Arctic shows stable (57–64%) or reduced productivity (browning, <4%). Summer drought and wildfires are the best-documented drivers causing browning of continental areas, but factors dampening the greening effect of more maritime regions have remained elusive. Here we show how multiple anomalous weather events severely affected the terrestrial productivity during one water year (October 2011–September 2012) in a maritime region north of the Arctic Circle, the Nordic Arctic Region, and contributed to the lowest mean vegetation greenness (normalized difference vegetation index) recorded this century. Procedures for field data sampling were designed during or shortly after the events in order to assess both the variability in effects and the maximum effects of the stressors. Outbreaks of insect and fungal pests also contributed to low greenness. Vegetation greenness in 2012 was 6.8% lower than the 2000–11 average and 58% lower in the worst affected areas that were under multiple stressors. These results indicate the importance of events (some being mostly neglected in climate change effect studies and monitoring) for primary productivity in a high-latitude maritime region, and highlight the importance of monitoring plant damage in the field and including frequencies of stress events in models of carbon economy and ecosystem change in the Arctic. Fourteen weather events and anomalies and 32 hypothesized impacts on plant productivity are summarized as an aid for directing future research.

Investigating the feasibility of the globsnow snow water equivalent data for climate research purposes

This paper presents the efforts for creating two global scale snow dataset covering 15 and 30 years of satellite-based observations, one describing the extent of snow cover (SE) the other describing the snow water equivalent (SWE) characteristics. The main emphasis of the paper is describing the validation work carried out for the SWE product that will cover the non-mountainous regions of Northern Hemisphere on a daily basis starting from 1979. The work has been carried out within the ESA Globsnow project.

Snow covered area retrieval using Envisat ASAR wideswath in mountainous areas

It has previously been shown that wet snow can be detected using ERS SAR repeat pass imagery where a reference image is captured during cold dry snow conditions and subtracted from the image one want to classify. We have extended and validated this technique for retrieving snow covered area (SCA) using Envisat ASAR wide swath data (500 by 500 km swath coverage, 100 meter resolution), covering the mountainous regions of Southern Norway. The algorithm has also been extended to postulate dry snow above areas with wet snow, thus giving a total snow covered area that is comparable to SCA from optical sensors. A sliding window technique has been applied to facilitate the dry snow classification. The method has been implemented in a near-real time environment and has been run pre-operationally in Norway in 2004. The relatively large coverage allows SAR to become an operational tool for snow monitoring, as opposed to standard modes used in previous works. In order to improve snow classification we have used air temperature data from the Norwegian meteorological station network to create high-resolution surface air temperature maps. These maps are used to filter wet snow from reference images and prevent incorrect classification of dry snow. Snow covered area maps for South Norway has been derived for the spring melt season with a one-week temporal resolution. The results are validated against optical sensor retrievals (MODIS, ASTER) and high accuracy field measurements.

Multi-sensor monitoring of snow parameters in Nordic mountainous areas

Hydropower users require timely updated information about snow coverage and snow melting during the important snow-melting period in Nordic mountains. In this paper we report results from a series of experiments performed to map snow parameters with optical and radar remote sensing. A near real-time pre-operational system has been developed to provide timely snow cover mapping over Nordic mountainous areas for hydropower users. The multi sensor and multi temporal snow cover maps are based on single sensor snow maps from SAR and optical sensors. Each data acquisition over the area are classified into snow maps and projected on a common grid. A confidence raster in also produced where the accuracy of the classification of each pixel in the snow map is represented as a confidence value between 0 and 100% depending on incidence angle, probability of clouds and wet/dry. Each single sensor product is fused to the latest multisensor product with its associated confidence image to produce an updated snow map. The sensors used in the demonstration of the preoperational multi sensor snow mapping system are Envisat ASAR and Terra Modis. Testing has been done in 2003 and 2004 and continues in the melting season of 2005.

SAR firn line detection and correlation to glacial mass balance; Svartisen Glacier, northern Norway

The glacial firn line of the Svartisen Glacier has been detected using ERS II SAR and ENVISAT ASAR data from 1995 and up to today. The firn line is detected by first correcting the image backscatter intensity for topographic and geometric contributions using the Muhleman backscattering model. Then we discriminate between firn and ice facies based on the backscatter intensity since frozen firn has a much higher backscatter than ice. Transects across different areas of the glacier were chosen based on requirements of smoothness of topography, precipitation zones, as well as the availability of field data for validation and comparison. From having quite stable conditions during the nineties we have observed a substantial retreat of the firn line over the last few years. The equilibrium line derived from field measurements show a similar trend as the firn line changes, but has a much larger year to year variability. This indicates that the firn line may be a better indicator of climate change than the equilibrium line due to the smaller variance

Mapping surface-water with Radarsat at arbitrary incidence angles

Generally, the contrast between water and land in SAR images decreases with decreasing incidence angle. Thus, surface water detection by intensity thresholding requires high incidence angle data. In this work we demonstrate a texture based surface water detector that produces accurate results, independently of the incidence angle.

CoReH 2 O - Cold Regions Hydrology High-esolution Observatory

The COld REgions Hydrology High-resolution Observatory (CoRe-H2O) satellite mission has been selected for scientific and technical studies within the ESA Earth Explorer Programme. The mission addresses the need for spatially detailed snow and ice observations in order to improve the representation of the cryosphere in climate models and to improve the knowledge and prediction of water cycle variability and changes. CoRe-H2O will observe the extent, water equivalent and melting state of the snow cover, accumulation and diagenetic facies of glaciers, permafrost features, and sea ice types. The sensor is a dual frequency SAR, operating at 17 GHz and 9.6 GHz, VV and VH polarizations. This configuration enables the decomposition of the scattering signal for retrieving physical properties of snow and ice.

The development of new algorithms for remote sensing of snow conditions based on data from the catchment of Øvre Heimdalsvatn and the vicinity

The catchment of Øvre Heimdalsvatn and the surrounding area was established as a site for snow remote sensing algorithm development, calibration and validation in 1997. Information on snow cover and snowmelt are important for understanding the timing and scale of many lake ecosystem processes. Field campaigns combined with data from airborne sensors and spaceborne high-resolution sensors have been used as reference data in experiments over many years. Several satellite sensors have been utilised in the development of new algorithms, including Terra MODIS and Envisat ASAR. The experiments have been motivated by operational prospects for snow hydrology, meteorology and climate monitoring by satellite-based remote sensing techniques. This has resulted in new time-series multi-sensor approaches for monitoring of snow cover area (SCA) and snow surface wetness (SSW). The idea was to analyse, on a daily basis, a time series of optical and radar satellite data in multi-sensor models. The SCA algorithm analyses each optical and synthetic aperture radar (SAR) image individually and combines them into a day product based on a set of confidence functions. The SSW algorithm combines information about the development of the snow surface temperature and the snow grain size (SGS) in a time-series analysis. The snow cover algorithm is being evaluated for application in a global climate monitoring system for snow variables. The successful development of these algorithms has led to operational applications of snow monitoring in Norway and Sweden, as well as enabling the prediction of the spring snowmelt flood and thus the initiation of many lake production processes.

Spatial modelling of Arctic plant diversity

Habitat suitability and species distribution models have both become essential tools in biodiversity conservation and management. However, very few of these studies exist from Arctic habitats and hardly any on Arctic species diversity modelling. The basic goal of this study was to develop a statistical model based on vascular plant species’ spatial distribution data on the Svalbard archipelago and their dependence on a set of available environmental variables. The obtained model was then implemented into GIS, enabling us to calculate plant diversity indices for the Svalbard archipelago. Svalbard is easily accessible for research and contains well-known flora with plentiful ancillary data layers available. This location thus constitutes a suitable study area for analysing and modelling biodiversity. Georeferenced data on vascular plant species diversity were gathered from 184 study sites widely distributed on the archipelago. Thirteen environmental raster layers were generated based on a digital elevation model, a geological map, as well as climatic and remote sensing data. Environmental data were extracted from the raster layers at each of the 184 field study plots. Both field study plots and raster layers were studied at 1 km2 resolution. Analysis using forward stepwise multiple regression revealed that growth season temperature sum (GDD), mean July precipitation (PREC) and the vegetation indices ‘normalised deviation vegetation index’ (NDVI) are the best predictors of Svalbards vascular plant biodiversity. Despite a 48% precision of the statistical model in predicting Shannon diversity index (SDI), the output map seems to reflect well the expected distribution based on knowledge of the influence of the environmental variables considered. All variables in the model, and most other data tested in the model, are easily available and with global coverage.