@article{IrrgangBendixenFarquharsonetal.2022,
  author    = {Irrgang, Anna M. and Bendixen, Mette and Farquharson, Louise M. and Baranskaya, Alisa and Erikson, Li H. and Gibbs, Ann E. and Ogorodov, Stanislav A. and Overduin, Pier Paul and Lantuit, Hugues and Grigoriev, Mikhail N. and Jones, Benjamin M.},
  title     = {Drivers, dynamics and impacts of changing Arctic coasts},
  series = {Nature reviews earth and environment},
  volume    = {3},
  journal   = {Nature reviews earth and environment},
  number    = {1},
  publisher = {Nature Research},
  address   = {London},
  issn      = {2662-138X},
  doi       = {10.1038/s43017-021-00232-1},
  pages     = {39 -- 54},
  year      = {2022},
  abstract  = {Arctic coasts are vulnerable to the effects of climate change, including rising sea levels and the loss of permafrost, sea ice and glaciers. Assessing the influence of anthropogenic warming on Arctic coastal dynamics, however, is challenged by the limited availability of observational, oceanographic and environmental data. Yet, with the majority of permafrost coasts being erosive, coupled with projected intensification of erosion and flooding, understanding these changes is critical. In this Review, we describe the morphological diversity of Arctic coasts, discuss important drivers of coastal change, explain the specific sensitivity of Arctic coasts to climate change and provide an overview of pan-Arctic shoreline change and its multifaceted impacts. Arctic coastal changes impact the human environment by threatening coastal settlements, infrastructure, cultural sites and archaeological remains. Changing sediment fluxes also impact the natural environment through carbon, nutrient and pollutant release on a magnitude that remains difficult to predict. Increasing transdisciplinary and interdisciplinary collaboration efforts will build the foundation for identifying sustainable solutions and adaptation strategies to reduce future risks for those living on, working at and visiting the rapidly changing Arctic coast.},
  language  = {en}
}
@article{VoglimacciStephanopoliWendlederLantuitetal.2022,
  author    = {Voglimacci-Stephanopoli, Jo{\"e}lle and Wendleder, Anna and Lantuit, Hugues and Langlois, Alexandre and Stettner, Samuel and Schmitt, Andreas and Dedieu, Jean-Pierre and Roth, Achim and Royer, Alain},
  title     = {Potential of X-band polarimetric synthetic aperture radar co-polar phase difference for arctic snow depth estimation},
  series = {Cryosphere},
  volume    = {16},
  journal   = {Cryosphere},
  number    = {6},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1994-0416},
  doi       = {10.5194/tc-16-2163-2022},
  pages     = {2163 -- 2181},
  year      = {2022},
  abstract  = {Changes in snowpack associated with climatic warming has drastic impacts on surface energy balance in the cryosphere. Yet, traditional monitoring techniques, such as punctual measurements in the field, do not cover the full snowpack spatial and temporal variability, which hampers efforts to upscale measurements to the global scale. This variability is one of the primary constraints in model development. In terms of spatial resolution, active microwaves (synthetic aperture radar - SAR) can address the issue and outperform methods based on passive microwaves. Thus, high-spatial-resolution monitoring of snow depth (SD) would allow for better parameterization of local processes that drive the spatial variability of snow. The overall objective of this study is to evaluate the potential of the TerraSAR-X (TSX) SAR sensor and the wave co-polar phase difference (CPD) method for characterizing snow cover at high spatial resolution. Consequently, we first (1) investigate SD and depth hoar fraction (DHF) variability between different vegetation classes in the Ice Creek catchment (Qikiqtaruk/Herschel Island, Yukon, Canada) using in situ measurements collected over the course of a field campaign in 2019; (2) evaluate linkages between snow characteristics and CPD distribution over the 2019 dataset; and (3) determine CPD seasonality considering meteorological data over the 2015-2019 period. SD could be extracted using the CPD when certain conditions are met. A high incidence angle (>30 circle) with a high topographic wetness index (TWI) (>7.0) showed correlation between SD and CPD (R2 up to 0.72). Further, future work should address a threshold of sensitivity to TWI and incidence angle to map snow depth in such environments and assess the potential of using interpolation tools to fill in gaps in SD information on drier vegetation types.},
  language  = {en}
}