Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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Aalto University

in Cooperation with on an Cooperation-Score of 37%

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Publications (7/7 displayed)

  • 2024Comparing InSAR Snow Water Equivalent Retrieval Using ALOS2 With In Situ Observations and SnowModel Over the Boreal Forest Area1citations
  • 2023Atomic layer deposition (ALD) for environmental protection and whisker mitigation of electronic assemblies2citations
  • 2020Characterization and Implementation of a Piezoelectric Energy Harvester Configuration: Analytical, Numerical and Experimental Approach30citations
  • 2019Intra-spacecraft Optical Communication Solutions using discrete Transceiver5citations
  • 2006Assessing cows' welfare50citations
  • 2006Contactless measurement of cow behavior in a milking robot22citations
  • 2005Monitoring cow health in a milking robotcitations

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Nagler, Thomas
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Ruiz, Jorge Jorge
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Lemmetyinen, Juha
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Cohen, Juval
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Co-Authors (by relevance)

  • Nagler, Thomas
  • Ruiz, Jorge Jorge
  • Merkouriadi, Ioanna
  • Lemmetyinen, Juha
  • Cohen, Juval
  • Kontu, Anna
  • Pulliainen, Jouni
  • Wilcox, Geoffrey D.
  • Pudas, Marko
  • Hokka, Jussi
  • Kutilainen, Terho
  • Ashworth, Mark A.
  • Soldano, Caterina
  • Ali, Ahsan
  • Mughal, M. Rizwan
  • Gaudenzi, Paolo
  • Qayyum, Faisal
  • Elahi, Hassan
  • Israr, Asif
  • Eugeni, Marco
  • Munir, Khushboo
  • Ali, Anwar
  • Reyneri, Leonardo
  • Mughal, Muhammad
  • Grohn, H.
  • Veermae, I.
  • Ahokas, J.
  • Pastell, M.
  • Takko, H.
  • Hautala, M.
  • Kujala, M.
  • Poikalainen, Vaeino
  • Poikalainen, V.
  • Aisla, A. -M.
OrganizationsLocationPeople

article

Comparing InSAR Snow Water Equivalent Retrieval Using ALOS2 With In Situ Observations and SnowModel Over the Boreal Forest Area

  • Nagler, Thomas
  • Ruiz, Jorge Jorge
  • Merkouriadi, Ioanna
  • Lemmetyinen, Juha
  • Cohen, Juval
  • Kontu, Anna
  • Pulliainen, Jouni
  • Praks, Jaan
Abstract

<p>— Interferometric SAR (InSAR) is a promising tool for monitoring seasonal snow and for retrieving snow water equivalent (SWE) as the interferometric phase can be related to changes in SWE (1SWE). The boreal forest is a challenging landscape for the InSAR retrieval of SWE since it contributes to the signal by adding an undesired component originating from the vegetation. Although the technique has been validated extensively, most of these works are limited to discrete points. For comparison, we used snowpack simulations from the SnowModel, a high-resolution spatially distributed snow evolution model. This enables a better understanding of the limitations of L-band InSAR for SWE retrieval since it allows evaluating its performance under different conditions. We analyzed the effect on coherence caused by snow melt between acquisitions and the presence of wet snow at the acquisition time. The interferometric phase was inverted and compared to the simulated 1SWE from the SnowModel distributions for three interferometric pairs. The results indicate a good spatial match between SnowModel and InSAR estimations. However, an increased difference was observed over densely forested areas when the air temperature was close to zero in at least one of the interferometric pairs. We hypothesize that the increase in permittivity of the forest for close to zero temperatures also increases the contribution from the canopy, consequently inducing errors in the retrieval. Both ALOS2 and SnowModel 1SWE estimates were compared with in situ data including a snow scale, snow depth from an automatic weather station (AWS), a snow pit, and manual courses.</p>

Topics
  • impedance spectroscopy
  • simulation
  • melt