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Spatial snow grain size variability along the JASE 2007/2008 traverse route in Dronning Maud Land, Antarctica, and its relation to MOA NDSI index, MEDRIS and MODIS sattelite data
Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK).
2010 (English)In: Proceedings of ESA Living Planet Symposium: 28 June - 2 July 2010, Bergen, Norway / [ed] H. Lacoste-Francis, Noordwijk: ESA (European Space Agency) , 2010Conference paper, Published paper (Other academic)
Abstract [en]

Snow grain size is an important parameter for determining albedo of the ice sheets and for calibration of optical and microwave remote sensing scattering processes. Snow grain size is a function of the local climate determined by moisture content, air and snow temperature, their gradients within the snow and firn, and wind patterns. Furthermore, it is an indicator on snow metamorphism. We have developed The Digital Grain Size Properties method (DGSP-method) using object oriented image analysis of very high resolution snow grain size images. Commonly used methods are based on visual interpretation, which is a subjective method providing only mean grain size does not retrieve size distribution within each sample.

This is a first attempt to validate satellite information by the in situ measurements from JASE (Japanese Swedish Antarctic Expedition) 2007/2008 using digital image processing. The DSGP-method is based on in-field photography of snow and pixel-based object oriented image analysis. The results show shows decreasing grain size towards the centre of Antarctica and larger grains in the coastal areas. The data used to validate is three different products based on two different types of optic satellite sensors; MERIS (Medium Resolution Imaging Spectrometer) and MODIS (Moderate Resolution Imaging Spectroradiometer).  A first validation captures a cluster relation between grain size in the coastal and at the plateau and optical satellite reflection.

Place, publisher, year, edition, pages
Noordwijk: ESA (European Space Agency) , 2010.
National Category
Physical Geography
Research subject
Physical Geography
Identifiers
URN: urn:nbn:se:su:diva-53791ISBN: 978-92-9221-250-6 (print)OAI: oai:DiVA.org:su-53791DiVA: diva2:391284
Available from: 2011-01-24 Created: 2011-01-24 Last updated: 2011-10-03Bibliographically approved
In thesis
1. Snow particle size investigations using digital image analysis - implications for ground observations and remote sensing of snow
Open this publication in new window or tab >>Snow particle size investigations using digital image analysis - implications for ground observations and remote sensing of snow
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

During the past century climate warming has caused rapid changes in the Cryosphere. This has increased the need to accurately monitor rates of change in snow and ice in remote or sparsely populated areas where environmental observing capacity is limited. Monitoring snow cover requires understanding of the snow pack and the snow surface attributes. Snow particle size is an important parameter for characterization of snow pack properties. The size and shape of the snow particles affects the snow/air-ratio which in turn affect how energy is reflected on the snow surface. This governs the snow pack energy balance by changing the albedo or backscattering properties of the snow. Both the albedo and the snow particle size can be quantified by remote sensing. However, the snow particle size estimated by remote sensing, also called the optically equivalent particle size, represents only an approximation of the true or physical particle size of snow. Thus, there is demand for methods that relate both parameters and help to improve the interpretation of remote sensing data of snow at higher spatial and temporal scales. To address this demand the aim of this dissertation thesis is to improve existing sampling methods of the physical snow particle size to retrieve high-resolution, spatial and temporal data sets for validation of remote sensing data. A field sampling method based on object-oriented analysis of digital images was developed that allows measurements of various snow particle size parameters such as length, width, area, specific surface area and shape. The method generates a continuous snow particle size distribution that supports the detailed statistical characterization of a large number of samples. The results show its possibility to compare data from different existing methods. The sampling method was applied in field sites in Antarctica and in northern Sweden, to characterize the spatial variability in the physical snow particle size and to estimate correlations between various remote sensing products and the observed physical snow particle size. The results of the presented studies show that more detailed measurements of snow particle size in the field at higher temporal and spatial scales can improve the interpretation of active and passive satellite retrieved data.

Place, publisher, year, edition, pages
Stockholm: Department of Physical Geography and Quaternary Geology (INK), Stockholm University, 2011. 38 p.
Series
Dissertations from the Department of Physical Geography and Quaternary Geology, ISSN 1653-7211 ; 27
Keyword
snow, remote sensing, particle size, Antarctica, in-situ sampling, seasonal snow
National Category
Physical Geography
Research subject
Physical Geography
Identifiers
urn:nbn:se:su:diva-62800 (URN)978-91-7447-371-1 (ISBN)
Public defence
2011-11-11, De Geersalen, Geovetenskapens hus, Svante Arrhenius väg 14, Stockholm, 10:00 (English)
Opponent
Supervisors
Note
At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Manuscript. Paper 2: Submitted. Paper 4: Manuscript. Paper 5: Accepted. Available from: 2011-10-20 Created: 2011-09-30 Last updated: 2011-10-03Bibliographically approved

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