Deriving a Frozen Area Fraction From Metop ASCAT Backscatter Based on Sentinel-1

Bergstedt, Helena; Bartsch, Annett; Neureiter, Anton; Höfler, Angelika; Widhalm, Barbara; Pepin, Nicholas; Hjort, Jan

doi:10.1109/tgrs.2020.2967364

Cited by 10 publications

(7 citation statements)

References 31 publications

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“…Additionally, Synthetic Aperture Radar (SAR) missions such as e.g. C-band ERS-1 [54], L-band Phase Array type L-band Synthetic Aperture Radar (PALSAR) [55], dual-pol C-band Sentinel-1 [56], and airborne P-band [57], as well as C-band MetOp (Meteorological Operational) ASCAT (Advanced Scatterometer) [58] have been operated for F/T detection using backscattered signals properties.…”

Section: Final Discussionmentioning

confidence: 99%

Mapping Freezing and Thawing Surface State Periods With the CYGNSS Based F/T Seasonal Threshold Algorithm

Carreño-Luengo

Ruf

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Freeze/Thaw (F/T) surface state detection and imaging has been demonstrated as a capability of NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission. The objective of the research presented here is the application of an existing F/T retrieval algorithm to multiple target areas over several years in order to demonstrate the ability to provide long term trending of F/T behavior. Metrics are developed and evaluated to characterize F/T trends related to global warming. They include the annual fraction of time spent frozen and thawed as a function of location and year, and the earliest Fall freeze and earliest Spring thaw as a function of location and year. The results demonstrate the capability of Global Navigation Satellite Systems Reflectometry (GNSS-R) to provide long term trending of F/T behavior relevant to studies of climate change.

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Section: Final Discussionmentioning

confidence: 99%

Mapping Freezing and Thawing Surface State Periods With the CYGNSS Based F/T Seasonal Threshold Algorithm

Carreño-Luengo

Ruf

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…There are, however, also other mechanisms than snow structure change which lead to higher values at C-band. Comparisons with soil and air temperature records under frozen conditions suggest a dependence of C-band backscatter (Bergstedt et al, 2020). The lower the temperature the higher the backscatter.…”

Section: Considerations For C-and L-band Usementioning

confidence: 94%

“…Naeimi et al (2012)). For scatterometer data this can be observed to take several weeks as topography within the footprint may vary (Bergstedt et al, 2020). When freeze-up is winter can be used as a representation for surface roughness (Bartsch et al, 2016).…”

Section: Considerations For C-and L-band Usementioning

confidence: 99%

“…Late October 2020, after freeze-up, is considered for the retrieval of the frozen reference. This reference is compared to acquisitions from December 2020, right after the last potential ROS event before temperatures dropped below -10°C in order to account for temperature effects (Bergstedt et al, 2020). In case of Sodankylä, only one scene before and one after a specific event in 2016 was processed (only pair in Sentinel-1 archive with snow pit data as well as event in between).…”

Section: Assessment Of Polarization Differences At C-band Using Sarmentioning

confidence: 99%

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Towards long-term records of rain-on-snow events across the Arctic from satellite data

Bartsch¹,

Bergstedt²,

Pointner³

et al. 2022

Preprint

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Abstract. Rain-on-Snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. Snow pack properties are changing and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. Specifically, satellite microwave observations have been shown to provide insight into known events. Only Ku-band radar (scatterometer) has been applied so far across the entire Arctic. Data availability at this frequency is limited, however. The utility of other frequencies from passive and active systems need to be explored to develop a concept for long-term monitoring. Active (radar) records have been shown to capture the associated snow structure change based on time series analyses. This approach is also applicable when data gaps exist and bears capabilities to evaluate the impact severity of events. Active as well as passive microwave sensors can also detect wet snow at the timing of a ROS, if an acquisition is available. Wet snow retrieval methodology is, however, rather mature, compared to identification of snow structure change which needs consideration of ambiguous scattering behaviour. C-band radar is of special interest due to good data availability including a range of nominal spatial resolution (10 m–12.5 km) A combined approach is therefore considered and tested for C-band (active, snow structure change) and L-band (passive, wet snow). Results were compared to in situ observations (snow pit records, caribou migration data) and Ku-band products. Ice crusts were found in the snow pack after detected events. The more crusts (events) the higher the winter season backscatter increase at C-band. ROS events captured on the Yamal and Seward peninsulas have had severe impacts on reindeer and caribou, respectively, due to crust formation. Temperature dependence of C-band backscatter observable down to -40 °C is identified as a major issue for ROS retrieval, but can be addressed by combination with passive microwave wet snow indicators (demonstrated for Metop ASCAT and SMOS). Synthetic Aperture Radar (SAR) from specifically Sentinel-1 (C-band) is promising regarding ice layer identification at better spatial details for all available polarizations. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record, but the consideration of performance differences due to spatial and temporal cover as well as microwave frequency is crucial. Retrieval is most robust north of 65° N, in the tundra biome, where records can be used to identify extremes and to apply the results for impact studies at regional scale.

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“…In early winter, the temperature gradually drops and the soil surface starts to freeze, causing water molecules to accumulate in the lattice, which will cause a rapid decrease in the soil dielectric constant, in turn causing the backscatter value to decrease [44]. Subsequently, the stabilization of the soil freezing fraction and the gradual increase in the volume scattering of snow on the soil surface led to a better correlation between CR and SD [45,46].…”

Section: Effect Of Soil Surface Freeze-thaw Cyclesmentioning

confidence: 99%

Quantitative Evaluation of the Soil Signal Effect on the Correlation between Sentinel-1 Cross Ratio and Snow Depth

et al. 2021

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High-resolution Synthetic Aperture Radar (SAR), as an efficient Earth observation technology, can be used as a complementary means of observation for snow depth (SD) and can address the spatial heterogeneity of mountain snow. However, there is still uncertainty in the SD retrieval algorithm based on SAR data, due to soil surface scattering. The aim of this study is to quantify the impact of soil signals on the SD retrieval method based on the cross-ratio (CR) of high-spatial resolution SAR images. Utilizing ascending Sentinel-1 observation data during the period from November 2016 to March 2020 and a CR method based on VH- and VV-polarization, we quantitatively analyzed the CR variability characteristics of rock and soil areas within typical thick snow study areas in the Northern Hemisphere from temporal and spatial perspectives. The correlation analysis demonstrated that the CR signal in rock areas at a daily timescale shows a strong correlation (mean value > 0.60) with snow depth. Furthermore, the soil areas are more influenced by freeze-thaw cycles, such that the monthly CR changes showed no or negative trend during the snow accumulation period. This study highlights the complexity of the physical mechanisms of snow scattering during winter processes and the influencing factors that cause uncertainty in the SD retrieval, which help to promote the development of high-spatial resolution C-band data for snow characterization applications.

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Deriving a Frozen Area Fraction From Metop ASCAT Backscatter Based on Sentinel-1

Cited by 10 publications

References 31 publications

Mapping Freezing and Thawing Surface State Periods With the CYGNSS Based F/T Seasonal Threshold Algorithm

Mapping Freezing and Thawing Surface State Periods With the CYGNSS Based F/T Seasonal Threshold Algorithm

Towards long-term records of rain-on-snow events across the Arctic from satellite data

Quantitative Evaluation of the Soil Signal Effect on the Correlation between Sentinel-1 Cross Ratio and Snow Depth

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