@misc{RungeGrosse2019,
  author    = {Runge, Alexandra and Grosse, Guido},
  title     = {Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {767},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43866},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-438660},
  pages     = {29},
  year      = {2019},
  abstract  = {The Arctic-Boreal regions experience strong changes of air temperature and precipitation regimes, which affect the thermal state of the permafrost. This results in widespread permafrost-thaw disturbances, some unfolding slowly and over long periods, others occurring rapidly and abruptly. Despite optical remote sensing offering a variety of techniques to assess and monitor landscape changes, a persistent cloud cover decreases the amount of usable images considerably. However, combining data from multiple platforms promises to increase the number of images drastically. We therefore assess the comparability of Landsat-8 and Sentinel-2 imagery and the possibility to use both Landsat and Sentinel-2 images together in time series analyses, achieving a temporally-dense data coverage in Arctic-Boreal regions. We determined overlapping same-day acquisitions of Landsat-8 and Sentinel-2 images for three representative study sites in Eastern Siberia. We then compared the Landsat-8 and Sentinel-2 pixel-pairs, downscaled to 60 m, of corresponding bands and derived the ordinary least squares regression for every band combination. The acquired coefficients were used for spectral bandpass adjustment between the two sensors. The spectral band comparisons showed an overall good fit between Landsat-8 and Sentinel-2 images already. The ordinary least squares regression analyses underline the generally good spectral fit with intercept values between 0.0031 and 0.056 and slope values between 0.531 and 0.877. A spectral comparison after spectral bandpass adjustment of Sentinel-2 values to Landsat-8 shows a nearly perfect alignment between the same-day images. The spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to Landsat-8 very well in Eastern Siberian Arctic-Boreal landscapes. After spectral adjustment, Landsat and Sentinel-2 data can be used to create temporally-dense time series and be applied to assess permafrost landscape changes in Eastern Siberia. Remaining differences between the sensors can be attributed to several factors including heterogeneous terrain, poor cloud and cloud shadow masking, and mixed pixels.},
  language  = {en}
}
@article{RungeGrosse2019,
  author    = {Runge, Alexandra and Grosse, Guido},
  title     = {Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs11141730},
  pages     = {29},
  year      = {2019},
  abstract  = {The Arctic-Boreal regions experience strong changes of air temperature and precipitation regimes, which affect the thermal state of the permafrost. This results in widespread permafrost-thaw disturbances, some unfolding slowly and over long periods, others occurring rapidly and abruptly. Despite optical remote sensing offering a variety of techniques to assess and monitor landscape changes, a persistent cloud cover decreases the amount of usable images considerably. However, combining data from multiple platforms promises to increase the number of images drastically. We therefore assess the comparability of Landsat-8 and Sentinel-2 imagery and the possibility to use both Landsat and Sentinel-2 images together in time series analyses, achieving a temporally-dense data coverage in Arctic-Boreal regions. We determined overlapping same-day acquisitions of Landsat-8 and Sentinel-2 images for three representative study sites in Eastern Siberia. We then compared the Landsat-8 and Sentinel-2 pixel-pairs, downscaled to 60 m, of corresponding bands and derived the ordinary least squares regression for every band combination. The acquired coefficients were used for spectral bandpass adjustment between the two sensors. The spectral band comparisons showed an overall good fit between Landsat-8 and Sentinel-2 images already. The ordinary least squares regression analyses underline the generally good spectral fit with intercept values between 0.0031 and 0.056 and slope values between 0.531 and 0.877. A spectral comparison after spectral bandpass adjustment of Sentinel-2 values to Landsat-8 shows a nearly perfect alignment between the same-day images. The spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to Landsat-8 very well in Eastern Siberian Arctic-Boreal landscapes. After spectral adjustment, Landsat and Sentinel-2 data can be used to create temporally-dense time series and be applied to assess permafrost landscape changes in Eastern Siberia. Remaining differences between the sensors can be attributed to several factors including heterogeneous terrain, poor cloud and cloud shadow masking, and mixed pixels.},
  language  = {en}
}
@article{MusterRileyRothetal.2019,
  author    = {Muster, Sina and Riley, William J. and Roth, Kurt and Langer, Moritz and Aleina, Fabio Cresto and Koven, Charles D. and Lange, Stephan and Bartsch, Annett and Grosse, Guido and Wilson, Cathy J. and Jones, Benjamin M. and Boike, Julia},
  title     = {Size distributions of arctic waterbodies reveal consistent relations in their statistical moments in space and time},
  series = {Frontiers in Earth Science},
  volume    = {7},
  journal   = {Frontiers in Earth Science},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {2296-6463},
  doi       = {10.3389/feart.2019.00005},
  pages     = {15},
  year      = {2019},
  abstract  = {Arctic lowlands are characterized by large numbers of small waterbodies, which are known to affect surface energy budgets and the global carbon cycle. Statistical analysis of their size distributions has been hindered by the shortage of observations at sufficiently high spatial resolutions. This situation has now changed with the high-resolution (<5 m) circum-Arctic Permafrost Region Pond and Lake (PeRL) database recently becoming available. We have used this database to make the first consistent, high-resolution estimation of Arctic waterbody size distributions, with surface areas ranging from 0.0001 km(2) (100 m(2)) to 1 km(2). We found that the size distributions varied greatly across the thirty study regions investigated and that there was no single universal size distribution function (including power-law distribution functions) appropriate across all of the study regions. We did, however, find close relationships between the statistical moments (mean, variance, and skewness) of the waterbody size distributions from different study regions. Specifically, we found that the spatial variance increased linearly with mean waterbody size (R-2 = 0.97, p < 2.2e-16) and that the skewness decreased approximately hyperbolically. We have demonstrated that these relationships (1) hold across the 30 Arctic study regions covering a variety of (bio)climatic and permafrost zones, (2) hold over time in two of these study regions for which multi-decadal satellite imagery is available, and (3) can be reproduced by simulating rising water levels in a high-resolution digital elevation model. The consistent spatial and temporal relationships between the statistical moments of the waterbody size distributions underscore the dominance of topographic controls in lowland permafrost areas. These results provide motivation for further analyses of the factors involved in waterbody development and spatial distribution and for investigations into the possibility of using statistical moments to predict future hydrologic dynamics in the Arctic.},
  language  = {en}
}
@article{HeslopWinkelAnthonyetal.2019,
  author    = {Heslop, J. K. and Winkel, Matthias and Anthony, K. M. Walter and Spencer, R. G. M. and Podgorski, D. C. and Zito, P. and Kholodov, A. and Zhang, M. and Liebner, Susanne},
  title     = {Increasing organic carbon biolability with depth in yedoma permafrost},
  series = {Journal of geophysical research : Biogeosciences},
  volume    = {124},
  journal   = {Journal of geophysical research : Biogeosciences},
  number    = {7},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {2169-8953},
  doi       = {10.1029/2018JG004712},
  pages     = {2021 -- 2038},
  year      = {2019},
  abstract  = {Permafrost thaw subjects previously frozen organic carbon (OC) to microbial decomposition, generating the greenhouse gases (GHG) carbon dioxide (CO2) and methane (CH4) and fueling a positive climate feedback. Over one quarter of permafrost OC is stored in deep, ice-rich Pleistocene-aged yedoma permafrost deposits. We used a combination of anaerobic incubations, microbial sequencing, and ultrahigh-resolution mass spectrometry to show yedoma OC biolability increases with depth along a 12-m yedoma profile. In incubations at 3 degrees C and 13 degrees C, GHG production per unit OC at 12-versus 1.3-m depth was 4.6 and 20.5 times greater, respectively. Bacterial diversity decreased with depth and we detected methanogens at all our sampled depths, suggesting that in situ microbial communities are equipped to metabolize thawed OC into CH4. We concurrently observed an increase in the relative abundance of reduced, saturated OC compounds, which corresponded to high proportions of C mineralization and positively correlated with anaerobic GHG production potentials and higher proportions of OC being mineralized as CH4. Taking into account the higher global warming potential (GWP) of CH4 compared to CO2, thawed yedoma sediments in our study had 2 times higher GWP at 12-versus 9.0-m depth at 3 degrees C and 15 times higher GWP at 13 degrees C. Considering that yedoma is vulnerable to processes that thaw deep OC, our findings imply that it is important to account for this increasing GHG production and GWP with depth to better understand the disproportionate impact of yedoma on the magnitude of the permafrost carbon feedback.},
  language  = {en}
}