Olli-Pekka Mattila

Structure and life cycle of supraglacial lakes in Dronning Maud Land

Abstract Supraglacial lakes form in Antarctic blue ice regions from penetration of solar radiation into the ice in summer. Three lakes were mapped for their structure in summers 2004–05 and 2010–11 in western Dronning Maud Land, and one was also examined for the radiation budget. The lake body consisted of two layers, each ∼1 m thick: an upper layer with a thin ice layer on top and main body of liquid water, and a lower layer containing slush and hard ice sub-layers. A sediment-rich slush pocket was found at the bottom. Hydraulic conductivity of the lake body was 0.25–30 cm s-1 depending on the stage of evolution, with 6.3 cm s-1 for closely packed slush. Albedo of the lake was 0.4–0.6 and light attenuation coefficient was 0.5–0.7 m-1. The formation and the depth scale of the lakes are determined by the light attenuation distance and thermal diffusion coefficient, limiting the growth to less than about 1.5 m in one summer. The potential winter growth is more and thus the lakes freeze up in winter in the present climatic conditions.

New approach for the global mapping of fractional snow coverage in boreal forest and tundra belt applicable to various sensors

A feasible method for estimating the areal fraction of snow cover for boreal forest and tundra belt from optical data is presented. The method SCAmod by the Finnish Environment Institute is based on a semi-empirical model where fractional snow cover is expressed as a function of at-satellite observed reflectance. The apparent forest transmissivity and reflectance of three major contributors (wet snow, forest canopy and snow-free ground) serve as model parameters. The forest transmissivity describes the visibility of the ground through forest canopy from above, and was previously determined from MODIS reflectance data with a great effort. Here we present a new method for transmissivity generation using global land cover map. Validation of gained FSC estimates as well as of NASA MOD10_L2 fractional snow product against Finnish ground truth data is presented.

Evaluation of Northern Hemisphere and regional snow extent products within ESA SnowPEx-project

The major results and findings made during the ESA SnowPEx project concerning the evaluation of the Earth Observation-based moderate resolution snow products is presented. Both the Northern Hemisphere (NH) Snow Extent (SE) daily products as well a few regionally available products by different data providers are addressed. Comparison against daily at-ground observed Snow Depth is made, after first converting all the snow products and the in-situ observations to binary ‘snow/no-snow’ information. We first introduce the datasets employed in the analyses, then describe the applied methodology and finally present the major findings obtained.

Continental use of SCAmod fractional snow cover mapping method in boreal forest and tundra belt

The method SCAmod for fractional snow cover (FSC) retrieval for Northern Eurasia boreal forest zone and tundra is presented, together with the accuracy assessment using high resolution Landsat TM/ETM+ data as a reference. Employing an apparent forest transmissivity, SCAmod accounts for forest canopy effect into the observed reflectance. Today, the method is implemented for Northern Hemisphere Snow extent production in European Space Agency DUE-project GlobSnow. It is therefore interesting to assess SCAmod performance in continental scale. We apply SCAmod to MODIS and compare the result with FSC derived from TM/ETM+ imagery over selected areas. Also validation against Finnish ground truth data is presented. Results indicate that high resolution data-derived FSC can be used to represent the ground truth, but this depends on the size and spatial distribution of snow-free patches as well as on the forest density in the target area. In dense forests, TM/ETM+ FSC showed strong underestimation, implying that these data cannot be used to represent the ground truth but only as suggestive reference.

Validation of ENVISAT ASAR based lake ice maps on Lake Päijänne

In this paper, we propose a general classification method for determining ice cover on lakes using ENVISAT ASAR data. The method assumes a Gaussian mixture model for feature vectors extracted from a local neighborhood around each pixel. We focus on Lake Päijänne in this work, and the classification results are validated against in situ observations of freeze up dates and a snow cover product from the optical MODIS sensor. The method is fully automatic, but it requires a manual post-processing step to remove poor results. The classification of the selected scenes compared fairly well to both sources of validation data. However, the manual selection step is time-consuming and subjective.