Feasibility Study for the Application of Synthetic Aperture Radar for Coastal Erosion Rate Quantification Across the Arctic

The applicability of optical satellite data to quantify coastal erosion across the Arctic is limited due to frequent cloud cover. Synthetic Aperture Radar (SAR) may provide an alternative. The interpretation of SAR data for coastal erosion monitoring in Arctic regions is, however, challenging due to issues of viewing geometry, ambiguities in scattering behavior and inconsistencies in acquisition strategies. In order to assess SAR applicability, we have investigated data acquired at three different wavelengths (X-, C-, L-band; TerraSAR-X, Sentinel-1, ALOS PALSAR 1/2). In a first step we developed a pre-processing workflow which considers viewing geometry issues (shoreline orientation, incidence angle relationships with respect to different landcover types). We distinguish between areas with foreshortening along cliffs facing the sensor, radar shadow along cliffs facing away and traditional land-water boundary discrimination. Results are compared to retrievals from Landsat trends. Four regions which feature high erosion rates have been selected. All three wavelengths have been investigated for Kay Point (Canadian Beaufort Sea Coast). C- and L-band have been studied at all sites, including also Herschel Island (Canadian Beaufort Sea Coast), Varandai (Barents Sea Coast, Russia), and Bykovsky Peninsula (Laptev Sea coast, Russia). Erosion rates have been derived for a 1-year period (2017–2018) and in case of L-band also over 11 years (2007–2018). Results indicate applicability of all wavelengths, but acquisitions need to be selected with care to deal with potential ambiguities in scattering behavior. Furthermore, incidence angle dependencies need to be considered for discrimination of the land-water boundary in case of L- and C-band. However, L-band has the lowest sensitivity to wave action and relevant future missions are expected to be of value for coastal erosion monitoring. The utilization of trends derived from Landsat is also promising for efficient long-term trend retrieval. The high spatial resolution of TerraSAR-X staring spot light mode (<1 m) also allows the use of radar shadow for cliff-top monitoring in all seasons. Derived retreat rates agree with rates available from other data sources, but the applicability for automatic retrieval is partially limited. The derived rates suggest an increase of erosion at all four sites in recent years, but uncertainties are also high

Surface water inundation in the boreal-Arctic: potential impacts on regional methane emissions

Northern wetlands may be vulnerable to increased carbon losses from methane (CH4), a potent greenhouse gas, under current warming trends. However, the dynamic nature of open water inundation and wetting/drying patterns may constrain regional emissions, offsetting the potential magnitude of methane release. Here we conduct a satellite data driven model investigation of the combined effects of surface warming and moisture variability on high northern latitude (>= 45 degrees N) wetland CH4 emissions, by considering (1) sub-grid scale changes in fractional water inundation (Fw) at 15 day, monthly and annual intervals using 25 km resolution satellite microwave retrievals, and (2) the impact of recent (2003-11) wetting/drying on northern CH4 emissions. The model simulations indicate mean summer contributions of 53 Tg CH4 yr(-1) from boreal-Arctic wetlands. Approximately 10% and 16% of the emissions originate from open water and landscapes with emergent vegetation, as determined from respective 15 day Fw means or maximums, and significant increases in regional CH4 efflux were observed when incorporating satellite observed inundated land fractions into the model simulations at monthly or annual time scales. The satellite Fw record reveals widespread wetting across the Arctic continuous permafrost zone, contrasting with surface drying in boreal Canada, Alaska and western Eurasia. Arctic wetting and summer warming increased wetland emissions by 0.56 Tg CH4 yr(-1) compared to the 2003-11 mean, but this was mainly offset by decreasing emissions (-0.38 Tg CH4 yr(-1)) in sub-Arctic areas experiencing surface drying or cooling. These findings underscore the importance of monitoring changes in surface moisture and temperature when assessing the vulnerability of boreal-Arctic wetlands to enhanced greenhouse gas emissions under a shifting climate

Surface water inundation in the boreal-Artic: potential impacts on regional methane emissions

Northern wetlands may be vulnerable to increased carbon losses from methane (CH4), a potent greenhouse gas, under current warming trends. However, the dynamic nature of open water inundation and wetting/drying patterns may constrain regional emissions, offsetting the potential magnitude of methane release. Here we conduct a satellite data driven model investigation of the combined effects of surface warming and moisture variability on high northern latitude (45° N) wetland CH4 emissions, by considering (1) sub-grid scale changes in fractional water inundation (Fw) at 15 day, monthly and annual intervals using 25 km resolution satellite microwave retrievals, and (2) the impact of recent (2003-11) wetting/drying on northern CH4 emissions. The model simulations indicate mean summer contributions of 53 Tg CH4 yr-1 from boreal-Arctic wetlands. Approximately 10% and 16% of the emissions originate from open water and landscapes with emergent vegetation, as determined from respective 15 day Fw means or maximums, and significant increases in regional CH4 efflux were observed when incorporating satellite observed inundated land fractions into the model simulations at monthly or annual time scales. The satellite Fw record reveals widespread wetting across the Arctic continuous permafrost zone, contrasting with surface drying in boreal Canada, Alaska and western Eurasia. Arctic wetting and summer warming increased wetland emissions by 0.56 Tg CH4 yr-1 compared to the 2003-1 mean, but this was mainly offset by decreasing emissions (-0.38 Tg CH4 yr-1) in sub-Arctic areas experiencing surface drying or cooling. These findings underscore the importance of monitoring changes in surface moisture and temperature when assessing the vulnerability of boreal-Arctic wetlands to enhanced greenhouse gas emissions under a shifting climate

Surface water inundation in the boreal- Arctic: potential impacts on regional methane emissions

Northern wetlands may be vulnerable to increased carbon losses from methane (CH4), a potent greenhouse gas, under current warming trends. However, the dynamic nature of open water inundation and wetting/drying patterns may constrain regional emissions, offsetting the potential magnitude of methane release. Here we conduct a satellite data driven model investigation of the combined effects of surface warming and moisture variability on high northern latitude (≥45° N) wetland CH4 emissions, by considering (1) sub-grid scale changes in fractional water inundation (Fw) at 15 day, monthly and annual intervals using 25 km resolution satellite microwave retrievals, and (2) the impact of recent (2003–11) wetting/drying on northern CH4 emissions. The model simulations indicate mean summer contributions of 53 Tg CH4 yr−1 from boreal-Arctic wetlands. Approximately 10% and 16% of the emissions originate from open water and landscapes with emergent vegetation, as determined from respective 15 day Fw means or maximums, and significant increases in regional CH4 efflux were observed when incorporating satellite observed inundated land fractions into the model simulations at monthly or annual time scales. The satellite Fw record reveals widespread wetting across the Arctic continuous permafrost zone, contrasting with surface drying in boreal Canada, Alaska and western Eurasia. Arctic wetting and summer warming increased wetland emissions by 0.56 Tg CH4 yr−1 compared to the 2003–11 mean, but this was mainly offset by decreasing emissions (−0.38 Tg CH4 yr−1) in sub-Arctic areas experiencing surface drying or cooling. These findings underscore the importance of monitoring changes in surface moisture and temperature when assessing the vulnerability of boreal-Arctic wetlands to enhanced greenhouse gas emissions under a shifting climate

The Cryosphere / Circumpolar patterns of potential mean annual ground temperature based on surface state obtained from microwave satellite data

Gap filling is required for temporally and spatially consistent records of land surface temperature from satellite data due to clouds or snow cover. Land surface state, frozen versus unfrozen conditions, can be, however, captured globally with satellite data obtained by microwave sensors. The number of frozen days per year has been previously proposed to be used for permafrost extent determination. This suggests an underlying relationship between number of frozen days and mean annual ground temperature (MAGT). We tested this hypothesis for the Northern Hemisphere north of 50N using coarse-spatial-resolution microwave satellite data (Metop Advanced SCATterometer ASCAT and Special Sensor Microwave Imager SSM/I; 12.5 and 25km nominal resolution; 20072012), which provide the necessary temporal sampling. The MAGT from GTN-P (Global TerrestrialNetworkfor Permafrost) borehole records at the coldest sensor depth was tested for validity in order to build a comprehensive in situ data set for calibration and validation and was eventually applied. Results are discussed with respect to snow water equivalent, soil properties, land cover and permafrost type. The obtained temperature maps were classified for permafrost extent and compared to alternative approaches.An R2 of 0.99 was found for correlation between and MAGT at zero annual amplitude provided in GTN-P metadata and MAGT at the coldest sensor depth. The latter could be obtained with an RMSE of 2.2C from ASCAT and 2.5C from SSM/I surface state records using a linear model. The average deviation within the validation period is less than 1C at locations without glaciers and coastlines within the resolution cell in the case of ASCAT. The exclusion of snow melt days (available for ASCAT) led to better results. This suggests that soil warming under wet snow cover needs to be accounted for in this context. Specifically Scandinavia and western Russia are affected. In addition, MAGT at the coldest sensor depth was overestimated in areas with a certain amount of organic material and in areas of cold permafrost. The derived permafrost extent differed between the used data sets and methods. Deviations are high in central Siberia, for example. We show that microwave-satellite-derived surface state records can provide an estimation of not only permafrost extent but also MAGT without the need for gap filling. This applies specifically to ASCAT. The deviations among the tested data sets, their spatial patterns as well as in relation to environmental conditions, revealed areas which need special attention for modelling of MAGT.(VLID)278661

Recent coastline evolution along the Yukon Coast, western Canadian Arctic

Soft sediment permafrost coasts are well known for their very dynamic nature. In some places their erosion can reach tens of meters, even though the erosion time is restricted to the short open water season of three to four months per year. Due to its high ground ice content, the Yukon coast in the western Canadian Arctic is particularly prone to erosion. Building on results from Irrgang et al., 2018, we continued analyzing shoreline movements along the Yukon Coast using Pleiades satellite imagery covering the whole Yukon Coast from 2018 and 2022, as well as very highly resolved data from UAV overflights covering long term monitoring sites in 2019 and 2022. Using the Digital Shoreline Analysis System (DSAS) Esri ArcMap extension tool, we quantified shoreline movements for the time periods 2011-2018, and 2018-2022 for the entire coastline and for 2015-2019 and 2019- 2022 for long term monitoring sites. We used the same transects and shoreline proxies as in Irrgang et al., 2018, to ensure comparability of our results and elongate our observation series. We will show how recent shoreline position changes differ from past ones and will provide possible reasoning for these detected changes. We are using our multi-time-step shoreline change rate dataset of the Yukon Coast for training and validation purposes within the Earth Observation for Permafrost Coasts (EO4PAC) project. The increasing usage of machine learning approaches for automated shoreline delineation and shoreline change rate retrieval opens up new pathways – especially if it comes to exploring large and remote areas. Such datasets which contain on site derived shoreline change rates and manually derived shorelines from (very) high resolution airborne and spaceborne data are crucial for training algorithms, validation of results and thus for the quality improvement of machine learning techniques