Lake Tarfala, Northern Sweden - Remote Sensing of Ice Phenology Using Sentinel-1 Backscatter and Coherence Time Series

Author(s):  
Abhay Prakash ◽  
Saeed Aminjafari ◽  
Nina Kirchner ◽  
Tarmo Virtanen ◽  
Jan Weckström ◽  
...  

<p>Lake Tarfala is a small (~0.5 km<sup>2</sup>) glacier-proximal lake in the Kebnekaise Mountains in Northern Sweden, located at an altitude of 1162 meters above sea level, and close to Tarfala Research Station run by Stockholm University. Only very limited direct monitoring of lake ice phenology using ground observations is available so far, and, long polar nights and often persistent cloud cover at such altitude limit the use of optical remote sensing. However, active microwave radar signals illuminate the target and penetrate through the cloud cover allowing to monitor the lake independent of weather or time of day. In this study, we opt for the Level-1 GRD (Ground Range Detected) and SLC (Single Look Complex) products from the twin Sentinel-1 satellites which provide a coverage of Lake Tarfala at a very high spatial and temporal resolution. We aim to make use of a total of 60 scenes (June 2020 - May 2021) to create the backscatter and coherence time series. Further, we aim to associate the variation in intensity seen in the backscatter time series to the backscattering potential of the medium. It has been shown [1] that an increase in intensity is observed when transitioning from ice-free waters to the initial freeze-up (ice-on) stage. Around ice-on, the intensity would, however, be comparatively low as the ice cover would be very thin and not yet fully developed. The availability of in-situ high-resolution time-lapse imagery and air temperature data from a pilot project carried out during the fall of 2020 [2] will be exploited to assist in the detection of the initial ice formation and freeze-up. Over the course of winter, ice will continue to thicken and a subsequent increase in backscatter intensity is expected until it reaches a saturation point where it stabilises, until the onset of melt in the subsequent spring/summer, when finally, the detection of ice-off (water free of ice) can be characterised by low backscatter values. Furthermore, loss of interferometric coherence upon the onset of melt will aid the backscatter time series when it fails to show a clear signal. We expect to track and provide a complete timeline of the different ice-phenology stages, namely the onset of freezing and the date of complete ice-on, the ice-thickening, the onset of surface melt and the date of complete ice-off. We expect that this study will provide a basis for Arctic lake ice monitoring for various applications such as management of winter water resources, understanding the seasonal and inter-annual land-atmosphere greenhouse gases and energy flux exchanges and biological productivity.</p><p>References:</p><p>1. Morris, K., Jeffries, M.O., Weeks, W.F. Ice processes and growth history on Arctic and sub-Arctic lakes using ERS-1 SAR data. Polar Rec. 1995, 31, 115-128.</p><p><br>2. Weckström, J., Korhola, A. Kirchner, N., Virtanen, T., Schenk, F., Granebeck, A., Prakash, A. “Lake Thermal and Mixing Dynamics under Changing Climate” and “Towards a multi-approach detection and classification of ice phenology at Lake Tarfala”. Pilot projects funded by Arctic Avenue (a spearhead research project between the University of Helsinki and Stockholm University).</p>

2020 ◽  
Vol 12 (3) ◽  
pp. 382 ◽  
Author(s):  
Justin Murfitt ◽  
Claude R. Duguay

Lake ice is a dominant component of Canada’s landscape and can act as an indicator for how freshwater aquatic ecosystems are changing with warming climates. While lake ice monitoring through government networks has decreased in the last three decades, the increased availability of remote sensing images can help to provide consistent spatial and temporal coverage for areas with annual ice cover. Synthetic aperture radar (SAR) data are commonly used for lake ice monitoring, due to the acquisition of images in any condition (time of day or weather). Using Sentinel-1 A/B images, a high-density time series of SAR images was developed for Lake Hazen in Nunavut, Canada, from 2015–2018. These images were used to test two different methods of monitoring lake ice phenology: one method using the first difference between SAR images and another that applies the Otsu segmentation method. Ice phenology dates determined from the two methods were compared with visual interpretation of the Sentinel-1 images. Mean errors for the pixel comparison of the first difference method ranged 3–10 days for ice-on and ice-off, while average error values for the Otsu method ranged 2–10 days. Mean errors for comparisons of different sections of the lake ranged 0–15 days for the first difference method and 2–17 days for the Otsu method. This research demonstrates the value of temporally consistent image acquisition for improving the accuracy of lake ice monitoring.


2019 ◽  
Vol 11 (14) ◽  
pp. 1718 ◽  
Author(s):  
Shuai Zhang ◽  
Tamlin M. Pavelsky

Remote sensing of ice phenology for small lakes is hindered by a lack of satellite observations with both high temporal and spatial resolutions. By merging multi-source satellite data over individual lakes, we present a new algorithm that successfully estimates ice freeze and thaw timing for lakes with surface areas as small as 0.13 km2 and obtains consistent results across a range of lake sizes. We have developed an approach for classifying ice pixels based on the red reflectance band of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, with a threshold calibrated against ice fraction from Landsat Fmask over each lake. Using a filter derived from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) surface air temperature product, we removed outliers in the time series of lake ice fraction. The time series of lake ice fraction was then applied to identify lake ice breakup and freezeup dates. Validation results from over 296 lakes in Maine indicate that the satellite-based lake ice timing detection algorithm perform well, with mean absolute error (MAE) of 5.54 days for breakup dates and 7.31 days for freezeup dates. This algorithm can be applied to lakes worldwide, including the nearly two million lakes with surface area between 0.1 and 1 km2.


2021 ◽  
Author(s):  
Yubao Qiu ◽  
Xingxing Wang ◽  
Matti Leppäranta ◽  
Bin Cheng ◽  
Yixiao Zhang

<p>Lake-ice phenology is an essential indicator of climate change impact for different regions (Livingstone, 1997; Duguay, 2010), which helps understand the regional characters of synchrony and asynchrony. The observation of lake ice phenology includes ground observation and remote sensing inversion. Although some lakes have been observed for hundreds of years, due to the limitations of the observation station and the experience of the observers, ground observations cannot obtain the lake ice phenology of the entire lake. Remote sensing has been used for the past 40 years, in particular, has provided data covering the high mountain and high latitude regions, where the environment is harsh and ground observations are lacking. Remote sensing also provides a unified data source and monitoring standard, and the possibility of monitoring changes in lake ice in different regions and making comparisons between them. The existing remote sensing retrieval products mainly cover North America and Europe, and data for Eurasia is lacking (Crétaux et al., 2020).</p><p>Based on the passive microwave, the lake ice phenology of 522 lakes in the northern hemisphere during 1978-2020 was obtained, including Freeze-Up Start (FUS), Freeze-Up End (FUE), Break-Up Start (BUS), Break-Up End (BUE), and Ice Cover Duration (ICD). The ICD is the duration from the FUS to the BUE, which can directly reflect the ice cover condition. At latitudes north of 60°N, the average of ICD is approximately 8-9 months in North America and 5-6 months in Eurasia. Limited by the spatial resolution of the passive microwave, lake ice monitoring is mainly in Northern Europe. Therefore, the average of ICD over Eurasia is shorter, while the ICD is more than 6 months for most lakes in Russia. After 2000, the ICD has shown a shrinking trend, except northeastern North America (southeast of the Hudson Bay) and the northern Tibetan Plateau. The reasons for the extension of ice cover duration need to be analyzed with parameters, such as temperature, the lake area, and lake depth, in the two regions.</p>


1980 ◽  
Vol 7 (4) ◽  
pp. 243-246 ◽  
Author(s):  
C. T. Swift ◽  
W. L. Jones ◽  
R. F. Harrington ◽  
J. C. Fedors ◽  
R. H. Couch ◽  
...  

2016 ◽  
Vol 8 (11) ◽  
pp. 903 ◽  
Author(s):  
Sofia Antonova ◽  
Claude Duguay ◽  
Andreas Kääb ◽  
Birgit Heim ◽  
Moritz Langer ◽  
...  

2021 ◽  
pp. 1-19
Author(s):  
Xingxing Wang ◽  
Yubao Qiu ◽  
Yixiao Zhang ◽  
Juha Lemmetyinen ◽  
Bin Cheng ◽  
...  

Author(s):  
Shuai Zhang ◽  
Tamlin M Pavelsky ◽  
Christopher D. Arp ◽  
Xiao Yang

2021 ◽  
Vol 13 (9) ◽  
pp. 1626
Author(s):  
Georg Pointner ◽  
Annett Bartsch

Seepage of geological methane through sediments of Arctic lakes might contribute conceivably to the atmospheric methane budget. However, the abundance and precise locations of such seeps are poorly quantified. For Lake Neyto, one of the largest lakes on the Yamal Peninsula in Northwestern Siberia, temporally expanding regions of anomalously low backscatter in C-band SAR imagery acquired in late winter and spring have been suggested to be related to seepage of methane from hydrocarbon reservoirs. However, this hypothesis has not been verified using in-situ observations so far. Similar anomalies have also been identified for other lakes on Yamal, but it is still uncertain whether or how many of them are related to methane seepage. This study aimed to document similar lake ice backscatter anomalies on a regional scale over four study regions (the Yamal Peninsula and Tazovskiy Peninsulas; the Lena Delta in Russia; the National Petroleum Reserve Alaska) during different years using a time series based approach on Google Earth Engine (GEE) that quantifies changes of σ0 from the Sentinel-1 C-band SAR sensor over time. An algorithm for assessing the coverage that takes the number of acquisitions and maximum time between acquisitions into account is presented, and differences between the main operating modes of Sentinel-1 are evaluated. Results show that better coverage can be achieved in extra wide swath (EW) mode, but interferometric wide swath (IW) mode data could be useful for smaller study areas and to substantiate EW results. A classification of anomalies on Lake Neyto from EW Δσ0 images derived from GEE showed good agreement with the classification presented in a previous study. Automatic threshold-based per-lake counting of years where anomalies occurred was tested, but a number of issues related to this approach were identified. For example, effects of late grounding of the ice and anomalies potentially related to methane emissions could not be separated efficiently. Visualizations of Δσ0 images likely reflect the temporal expansions of anomalies and are expected to be particularly useful for identifying target areas for future field-based research. Characteristic anomalies that clearly resemble the ones observed for Lake Neyto could be identified solely visually in the Yamal and Tazovskiy study regions. All data and algorithms produced in the framework of this study are openly provided to the scientific community for future studies and might potentially aid our understanding of geological lake seepage upon the progression of related field-based studies and corresponding evaluations of formation hypotheses.


2021 ◽  
Vol 13 (7) ◽  
pp. 1389
Author(s):  
Lei Su ◽  
Tao Che ◽  
Liyun Dai

Ice phenology data of 22 large lakes of the Northern Hemisphere for 40 years (1979−2018) have been retrieved from passive microwave remote sensing brightness temperature (Tb). The results were compared with site-observation data and visual interpretation from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectivity products images (MOD09GA). The mean absolute errors of four lake ice phenology parameters, including freeze-up start date (FUS), freeze-up end date (FUE), break-up start date (BUS), and break-up end date (BUE) against MODIS-derived ice phenology were 2.50, 2.33, 1.98, and 3.27 days, respectively. The long-term variation in lake ice phenology indicates that FUS and FUE are delayed; BUS and BUE are earlier; ice duration (ID) and complete ice duration (CID) have a general decreasing trend. The average change rates of FUS, FUE, BUS, BUE, ID, and CID of lakes in this study from 1979 to 2018 were 0.23, 0.23, −0.17, −0.33, −0.67, and −0.48 days/year, respectively. Air temperature and latitude are two dominant driving factors of lake ice phenology. Lake ice phenology for the period 2021−2100 was predicted by the relationship between ice phenology and air temperature for each lake. Compared with lake ice phenology changes from 1990 to 2010, FUS is projected to be delayed by 3.1 days and 11.8 days under Representative Concentration Pathways (RCPs) 2.6 and 8.5 scenarios, respectively; BUS is projected to be earlier by 3.3 days and 10.7 days, respectively; and ice duration from 2080 to 2100 will decrease by 6.5 days and 21.9 days, respectively.


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