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Institute
The Lena Delta in Siberia is the largest delta in the Arctic and as a snow-dominated ecosystem particularly vulnerable to climate change.
Using the two decades of MODerate resolution Imaging Spectroradiometer satellite acquisitions, this study investigates interannual and spatial variability of snow-cover duration and summer vegetation vitality in the Lena Delta.
We approximated snow by the application of the normalized difference snow index and vegetation greenness by the normalized difference vegetation index (NDVI). We consolidated the analyses by integrating reanalysis products on air temperature from 2001 to 2021, and air temperature, ground temperature, and the date of snow-melt from time-lapse camera (TLC) observations from the Samoylov observatory located in the central delta.
We extracted spring snow-cover duration determined by a latitudinal gradient. The 'regular year' snow-melt is transgressing from mid-May to late May within a time window of 10 days across the delta.
We calculated yearly deviations per grid cell for two defined regions, one for the delta, and one focusing on the central delta. We identified an ensemble of early snow-melt years from 2012 to 2014, with snow-melt already starting in early May, and two late snow-melt years in 2004 and 2017, with snow-melt starting in June. In the times of TLC recording, the years of early and late snow-melt were confirmed.
In the three summers after early snow-melt, summer vegetation greenness showed neither positive nor negative deviations. Whereas, vegetation greenness was reduced in 2004 after late snow-melt together with the lowest June monthly air temperature of the time series record. Since 2005, vegetation greenness is rising, with maxima in 2018 and 2021.
The NDVI rise since 2018 is preceded by up to 4 degrees C warmer than average June air temperature. The ongoing operation of satellite missions allows to monitor a wide range of land surface properties and processes that will provide urgently needed data in times when logistical challenges lead to data gaps in land-based observations in the rapidly changing Arctic.
TerraSAR-X time series fill a gap in spaceborne snowmelt monitoring of small arctic catchments
(2018)
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.
TerraSAR-X time series fill a gap in spaceborne snowmelt monitoring of small Arctic catchments
(2018)
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.
In this study, we analyze interactions in lake and lake catchment systems of a continuous permafrost area. We assessed colored dissolved organic matter (CDOM) absorption at 440 nm (a(440)(CDOM)) and absorption slope (S300-500) in lakes using field sampling and optical remote sensing data for an area of 350 km(2) in Central Yamal, Siberia. Applying a CDOM algorithm (ratio of green and red band reflectance) for two high spatial resolution multispectral GeoEye-1 and Worldview-2 satellite images, we were able to extrapolate the a()(CDOM) data from 18 lakes sampled in the field to 356 lakes in the study area (model R-2 = 0.79). Values of a(440)(CDOM) in 356 lakes varied from 0.48 to 8.35 m(-1) with a median of 1.43 m(-1). This a()(CDOM) dataset was used to relate lake CDOM to 17 lake and lake catchment parameters derived from optical and radar remote sensing data and from digital elevation model analysis in order to establish the parameters controlling CDOM in lakes on the Yamal Peninsula. Regression tree model and boosted regression tree analysis showed that the activity of cryogenic processes (thermocirques) in the lake shores and lake water level were the two most important controls, explaining 48.4% and 28.4% of lake CDOM, respectively (R-2 = 0.61). Activation of thermocirques led to a large input of terrestrial organic matter and sediments from catchments and thawed permafrost to lakes (n = 15, mean a(440)(CDOM) = 5.3 m(-1)). Large lakes on the floodplain with a connection to Mordy-Yakha River received more CDOM (n = 7, mean a(440)(CDOM) = 3.8 m(-1)) compared to lakes located on higher terraces.
In this study, we analyze interactions in lake and lake catchment systems of a continuous permafrost area. We assessed colored dissolved organic matter (CDOM) absorption at 440 nm (a(440)(CDOM)) and absorption slope (S300-500) in lakes using field sampling and optical remote sensing data for an area of 350 km(2) in Central Yamal, Siberia. Applying a CDOM algorithm (ratio of green and red band reflectance) for two high spatial resolution multispectral GeoEye-1 and Worldview-2 satellite images, we were able to extrapolate the a()(CDOM) data from 18 lakes sampled in the field to 356 lakes in the study area (model R-2 = 0.79). Values of a(440)(CDOM) in 356 lakes varied from 0.48 to 8.35 m(-1) with a median of 1.43 m(-1). This a()(CDOM) dataset was used to relate lake CDOM to 17 lake and lake catchment parameters derived from optical and radar remote sensing data and from digital elevation model analysis in order to establish the parameters controlling CDOM in lakes on the Yamal Peninsula. Regression tree model and boosted regression tree analysis showed that the activity of cryogenic processes (thermocirques) in the lake shores and lake water level were the two most important controls, explaining 48.4% and 28.4% of lake CDOM, respectively (R-2 = 0.61). Activation of thermocirques led to a large input of terrestrial organic matter and sediments from catchments and thawed permafrost to lakes (n = 15, mean a(440)(CDOM) = 5.3 m(-1)). Large lakes on the floodplain with a connection to Mordy-Yakha River received more CDOM (n = 7, mean a(440)(CDOM) = 3.8 m(-1)) compared to lakes located on higher terraces.
The Arctic is greatly impacted by climate change. The increase in air temperature drives the thawing of permafrost and an increase in coastal erosion and river discharge. This leads to a greater input of sediment and organic matter into coastal waters, which substantially impacts the ecosystems by reducing light transmission through the water column and altering the biogeochemistry, but also the subsistence economy of local people, and changes in climate because of the transformation of organic matter into greenhouse gases. Yet, the quantification of suspended sediment in Arctic coastal and nearshore waters remains unsatisfactory due to the absence of dedicated algorithms to resolve the high loads occurring in the close vicinity of the shoreline. In this study we present the Arctic Nearshore Turbidity Algorithm (ANTA), the first reflectance-turbidity relationship specifically targeted towards Arctic nearshore waters that is tuned with in-situ measurements from the nearshore waters of Herschel Island Qikiqtaruk in the western Canadian Arctic. A semi-empirical model was calibrated for several relevant sensors in ocean color remote sensing, including MODIS, Sentinel 3 (OLCI), Landsat 8 (OLI), and Sentinel 2 (MSI), as well as the older Landsat sensors TM and ETM+. The ANTA performed better with Landsat 8 than with Sentinel 2 and Sentinel 3. The application of the ANTA to Sentinel 2 imagery that matches in-situ turbidity samples taken in Adventfjorden, Svalbard, shows transferability to nearshore areas beyond Herschel Island Qikiqtaruk.
The Arctic is greatly impacted by climate change. The increase in air temperature drives the thawing of permafrost and an increase in coastal erosion and river discharge. This leads to a greater input of sediment and organic matter into coastal waters, which substantially impacts the ecosystems by reducing light transmission through the water column and altering the biogeochemistry, but also the subsistence economy of local people, and changes in climate because of the transformation of organic matter into greenhouse gases. Yet, the quantification of suspended sediment in Arctic coastal and nearshore waters remains unsatisfactory due to the absence of dedicated algorithms to resolve the high loads occurring in the close vicinity of the shoreline. In this study we present the Arctic Nearshore Turbidity Algorithm (ANTA), the first reflectance-turbidity relationship specifically targeted towards Arctic nearshore waters that is tuned with in-situ measurements from the nearshore waters of Herschel Island Qikiqtaruk in the western Canadian Arctic. A semi-empirical model was calibrated for several relevant sensors in ocean color remote sensing, including MODIS, Sentinel 3 (OLCI), Landsat 8 (OLI), and Sentinel 2 (MSI), as well as the older Landsat sensors TM and ETM+. The ANTA performed better with Landsat 8 than with Sentinel 2 and Sentinel 3. The application of the ANTA to Sentinel 2 imagery that matches in-situ turbidity samples taken in Adventfjorden, Svalbard, shows transferability to nearshore areas beyond Herschel Island Qikiqtaruk.