TY - JOUR A1 - Klemm, Juliane A1 - Herzschuh, Ulrike A1 - Pisaric, Michael F. J. A1 - Telford, Richard J. A1 - Heim, Birgit A1 - Pestryakova, Luidmila Agafyevna T1 - A pollen-climate transfer function from the tundra and taiga vegetation in Arctic Siberia and its applicability to a Holocene record JF - Palaeogeography, palaeoclimatology, palaeoecology : an international journal for the geo-sciences N2 - This study aims to establish, evaluate, and apply a modern pollen-climate transfer function from the transition zone between arctic tundra and light-needled taiga in Arctic Siberia. Lacustrine samples (n = 96) from the northern Siberian lowlands of Yakutia were collected along four north-to-south transects crossing the arctic forest line. Samples span a broad temperature and precipitation gradient (mean July temperature, T-July: 7.5-18.7 degrees C; mean annual precipitation, P-ann: 114-315 mm/yr). Redundancy analyses are used to examine the relationship between the modern pollen signal and corresponding vegetation types and climate. Performance of transfer functions for T-July and P-ann were cross-validated and tested for spatial autocorrelation effects. The root mean square errors of prediction are 1.67 degrees C for T-July and 40 mm/yr for P-ann. A climate reconstruction based on fossil pollen spectra from a Siberian Arctic lake sediment core spanning the Holocene yielded cold conditions for the Late Glacial (1-2 degrees C below present T-July). Warm and moist conditions were reconstructed for the early to mid Holocene (2 degrees C higher T-July than present), and climate conditions similar to modern ones were reconstructed for the last 4000 years. In conclusion, our modern pollen data set fills the gap of existing regional calibration sets with regard to the underrepresented Siberian tundra-taiga transition zone. The Holocene climate reconstruction indicates that the temperature deviation from modern values was only moderate despite the assumed Arctic sensitivity to present climate change. KW - Mean July temperature KW - Reconstruction KW - Weighted-average partial least squares KW - Autocorrelation KW - Yakutia Y1 - 2013 U6 - https://doi.org/10.1016/j.palaeo.2013.06.033 SN - 0031-0182 SN - 1872-616X VL - 386 SP - 702 EP - 713 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Walther, Sophia A1 - Guanter, Luis A1 - Heim, Birgit A1 - Jung, Martin A1 - Duveiller, Gregory A1 - Wolanin, Aleksandra A1 - Sachs, Torsten T1 - Assessing the dynamics of vegetation productivity in circumpolar regions with different satellite indicators of greenness and photosynthesis JF - Biogeosciences N2 - High-latitude treeless ecosystems represent spatially highly heterogeneous landscapes with small net carbon fluxes and a short growing season. Reliable observations and process understanding are critical for projections of the carbon balance of the climate-sensitive tundra. Space-borne remote sensing is the only tool to obtain spatially continuous and temporally resolved information on vegetation greenness and activity in remote circumpolar areas. However, confounding effects from persistent clouds, low sun elevation angles, numerous lakes, widespread surface inundation, and the sparseness of the vegetation render it highly challenging. Here, we conduct an extensive analysis of the timing of peak vegetation productivity as shown by satellite observations of complementary indicators of plant greenness and photosynthesis. We choose to focus on productivity during the peak of the growing season, as it importantly affects the total annual carbon uptake. The suite of indicators are as follows: (1) MODIS-based vegetation indices (VIs) as proxies for the fraction of incident photosynthetically active radiation (PAR) that is absorbed (fPAR), (2) VIs combined with estimates of PAR as a proxy of the total absorbed radiation (APAR), (3) sun-induced chlorophyll fluorescence (SIF) serving as a proxy for photosynthesis, (4) vegetation optical depth (VOD), indicative of total water content and (5) empirically upscaled modelled gross primary productivity (GPP). Averaged over the pan-Arctic we find a clear order of the annual peak as APAR <= GPP < SIF < VIs/VOD. SIF as an indicator of photosynthesis is maximised around the time of highest annual temperatures. The modelled GPP peaks at a similar time to APAR. The time lag of the annual peak between APAR and instantaneous SIF fluxes indicates that the SIF data do contain information on light-use efficiency of tundra vegetation, but further detailed studies are necessary to verify this. Delayed peak greenness compared to peak photosynthesis is consistently found across years and land-cover classes. A particularly late peak of the normalised difference vegetation index (NDVI) in regions with very small seasonality in greenness and a high amount of lakes probably originates from artefacts. Given the very short growing season in circumpolar areas, the average time difference in maximum annual photosynthetic activity and greenness or growth of 3 to 25 days (depending on the data sets chosen) is important and needs to be considered when using satellite observations as drivers in vegetation models. Y1 - 2018 U6 - https://doi.org/10.5194/bg-15-6221-2018 SN - 1726-4170 SN - 1726-4189 VL - 15 IS - 20 SP - 6221 EP - 6256 PB - Copernicus CY - Göttingen ER - TY - GEN A1 - Walther, Sophia A1 - Guanter, Luis A1 - Heim, Birgit A1 - Jung, Martin A1 - Duveiller, Gregory A1 - Wolanin, Aleksandra A1 - Sachs, Torsten T1 - Assessing the dynamics of vegetation productivity in circumpolar regions with different satellite indicators of greenness and photosynthesis T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - High-latitude treeless ecosystems represent spatially highly heterogeneous landscapes with small net carbon fluxes and a short growing season. Reliable observations and process understanding are critical for projections of the carbon balance of the climate-sensitive tundra. Space-borne remote sensing is the only tool to obtain spatially continuous and temporally resolved information on vegetation greenness and activity in remote circumpolar areas. However, confounding effects from persistent clouds, low sun elevation angles, numerous lakes, widespread surface inundation, and the sparseness of the vegetation render it highly challenging. Here, we conduct an extensive analysis of the timing of peak vegetation productivity as shown by satellite observations of complementary indicators of plant greenness and photosynthesis. We choose to focus on productivity during the peak of the growing season, as it importantly affects the total annual carbon uptake. The suite of indicators are as follows: (1) MODIS-based vegetation indices (VIs) as proxies for the fraction of incident photosynthetically active radiation (PAR) that is absorbed (fPAR), (2) VIs combined with estimates of PAR as a proxy of the total absorbed radiation (APAR), (3) sun-induced chlorophyll fluorescence (SIF) serving as a proxy for photosynthesis, (4) vegetation optical depth (VOD), indicative of total water content and (5) empirically upscaled modelled gross primary productivity (GPP). Averaged over the pan-Arctic we find a clear order of the annual peak as APAR ≦ GPP The detailed vegetation cover and structure information in the first two datasets are of use for ecological applications, on one hand for summergreen and evergreen needle-leaf forests and also for tundra-taiga ecotones. Datasets 1 and 2 further support the generation and validation of land cover remote-sensing products in radar and optical remote sensing. In addition to providing information on forest structure and vegetation composition of the vegetation plots, the third and fourth datasets are prepared as training and validation data for machine-learning purposes. For example, the synthetic tree-crown dataset is generated from the raw UAV images and optimized to be used in neural networks. Furthermore, the fourth SiDroForest dataset contains S-2 labeled image patches processed to a high standard that provide training data on vegetation class categories for machine-learning classification with JavaScript Object Notation (JSON) labels provided. The SiDroForest data collection adds unique insights into remote hard-to-reach circumboreal forest regions. Y1 - 2022 U6 - https://doi.org/10.5194/essd-14-4967-2022 SN - 1866-3508 SN - 1866-3516 VL - 14 IS - 11 SP - 4967 EP - 4994 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Stettner, Samuel A1 - Lantuit, Hugues A1 - Heim, Birgit A1 - Eppler, Jayson A1 - Roth, Achim A1 - Bartsch, Annett A1 - Rabus, Bernhard T1 - TerraSAR-X time series fill a gap in spaceborne snowmelt monitoring of small arctic catchments BT - a case study on qikiqtaruk (Herschel Island), Canada JF - Remote sensing N2 - The timing of snowmelt is an important turning point in the seasonal cycle of small Arctic catchments. The TerraSAR-X (TSX) satellite mission is a synthetic aperture radar system (SAR) with high potential to measure the high spatiotemporal variability of snow cover extent (SCE) and fractional snow cover (FSC) on the small catchment scale. We investigate the performance of multi-polarized and multi-pass TSX X-Band SAR data in monitoring SCE and FSC in small Arctic tundra catchments of Qikiqtaruk (Herschel Island) off the Yukon Coast in the Western Canadian Arctic. We applied a threshold based segmentation on ratio images between TSX images with wet snow and a dry snow reference, and tested the performance of two different thresholds. We quantitatively compared TSX- and Landsat 8-derived SCE maps using confusion matrices and analyzed the spatiotemporal dynamics of snowmelt from 2015 to 2017 using TSX, Landsat 8 and in situ time lapse data. Our data showed that the quality of SCE maps from TSX X-Band data is strongly influenced by polarization and to a lesser degree by incidence angle. VH polarized TSX data performed best in deriving SCE when compared to Landsat 8. TSX derived SCE maps from VH polarization detected late lying snow patches that were not detected by Landsat 8. Results of a local assessment of TSX FSC against the in situ data showed that TSX FSC accurately captured the temporal dynamics of different snow melt regimes that were related to topographic characteristics of the studied catchments. Both in situ and TSX FSC showed a longer snowmelt period in a catchment with higher contributions of steep valleys and a shorter snowmelt period in a catchment with higher contributions of upland terrain. Landsat 8 had fundamental data gaps during the snowmelt period in all 3 years due to cloud cover. The results also revealed that by choosing a positive threshold of 1 dB, detection of ice layers due to diurnal temperature variations resulted in a more accurate estimation of snow cover than a negative threshold that detects wet snow alone. We find that TSX X-Band data in VH polarization performs at a comparable quality to Landsat 8 in deriving SCE maps when a positive threshold is used. We conclude that TSX data polarization can be used to accurately monitor snowmelt events at high temporal and spatial resolution, overcoming limitations of Landsat 8, which due to cloud related data gaps generally only indicated the onset and end of snowmelt. KW - Snow Cover Extent (SCE) KW - TerraSAR-X KW - Landsat KW - wet snow KW - small Arctic catchments KW - satellite time series Y1 - 2018 U6 - https://doi.org/10.3390/rs10071155 SN - 2072-4292 VL - 10 IS - 7 PB - MDPI CY - Basel ER - TY - GEN A1 - Stettner, Samuel A1 - Lantuit, Hugues A1 - Heim, Birgit A1 - Eppler, Jayson A1 - Roth, Achim A1 - Bartsch, Annett A1 - Rabus, Bernhard T1 - TerraSAR-X time series fill a gap in spaceborne snowmelt monitoring of small Arctic catchments BT - a case study on Qikiqtaruk (Herschel Island), Canada T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - The timing of snowmelt is an important turning point in the seasonal cycle of small Arctic catchments. The TerraSAR-X (TSX) satellite mission is a synthetic aperture radar system (SAR) with high potential to measure the high spatiotemporal variability of snow cover extent (SCE) and fractional snow cover (FSC) on the small catchment scale. We investigate the performance of multi-polarized and multi-pass TSX X-Band SAR data in monitoring SCE and FSC in small Arctic tundra catchments of Qikiqtaruk (Herschel Island) off the Yukon Coast in the Western Canadian Arctic. We applied a threshold based segmentation on ratio images between TSX images with wet snow and a dry snow reference, and tested the performance of two different thresholds. We quantitatively compared TSX- and Landsat 8-derived SCE maps using confusion matrices and analyzed the spatiotemporal dynamics of snowmelt from 2015 to 2017 using TSX, Landsat 8 and in situ time lapse data. Our data showed that the quality of SCE maps from TSX X-Band data is strongly influenced by polarization and to a lesser degree by incidence angle. VH polarized TSX data performed best in deriving SCE when compared to Landsat 8. TSX derived SCE maps from VH polarization detected late lying snow patches that were not detected by Landsat 8. Results of a local assessment of TSX FSC against the in situ data showed that TSX FSC accurately captured the temporal dynamics of different snow melt regimes that were related to topographic characteristics of the studied catchments. Both in situ and TSX FSC showed a longer snowmelt period in a catchment with higher contributions of steep valleys and a shorter snowmelt period in a catchment with higher contributions of upland terrain. Landsat 8 had fundamental data gaps during the snowmelt period in all 3 years due to cloud cover. The results also revealed that by choosing a positive threshold of 1 dB, detection of ice layers due to diurnal temperature variations resulted in a more accurate estimation of snow cover than a negative threshold that detects wet snow alone. We find that TSX X-Band data in VH polarization performs at a comparable quality to Landsat 8 in deriving SCE maps when a positive threshold is used. We conclude that TSX data polarization can be used to accurately monitor snowmelt events at high temporal and spatial resolution, overcoming limitations of Landsat 8, which due to cloud related data gaps generally only indicated the onset and end of snowmelt. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 689 KW - Snow Cover Extent (SCE) KW - TerraSAR-X KW - Landsat KW - wet snow KW - small Arctic catchments KW - satellite time series Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-426810 SN - 1866-8372 IS - 689 ER -