Parsing Synthetic Aperture Radar Measurements of Snow in Complex Terrain: Scaling Behaviour and Sensitivity to Snow Wetness and Landcover

Manickam, Surendar; Barros, A. P.

doi:10.20944/preprints202001.0300.v1

Cited by 6 publications

(2 citation statements)

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“…Additionally, high-resolution satellite-derived snow distributions could support development of improved downscaling techniques for existing coarsely gridded products (Manickam and Barros, 2020).…”

Section: Scientific Objectives Of Global Remote Sensing Of Swe and Spatial And Temporal Requirementsmentioning

confidence: 99%

“…More work is needed to improve our understanding of the physical mechanisms that cause C-band sensitivity to snow mass. Manickam and Barros (2020) showed that Sentinel-1 backscatter exhibit different scaling behavior depending on land-cover and landform and specifically topography that is dynamic at sub-seasonal scale reflecting changes in weather. Specifically they identified an ubiquitous scaling break at scales of 180-360 m that separates the small-scale range with invariant single scaling from scaling highly sensitive to snow wetness at larger scales.…”

Section: C-band Sarmentioning

confidence: 99%

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Review Article: Global Monitoring of Snow Water Equivalent using High Frequency Radar Remote Sensing

Tsang

Durand

Derksen

et al. 2021

Preprint

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Abstract. Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 million square km of Earth's surface (31 % of the land area) each year, and is thus an important expression of and driver of the Earth’s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (~ −13 %/decade) as Arctic summer sea ice. More than one-sixth of the world’s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth’s cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of snow stored on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations will not be able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high socio-economic value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-Band Synthetic Aperture Radar (SAR) for global monitoring of SWE. We describe radar interactions with snow-covered landscapes, characterization of snowpack properties using radar measurements, and refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimetre-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modelling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, densities, and layering. We describe radar interactions with snow-covered landscapes, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and applications communities on progress made in recent decades, and sets the stage for a new era in SWE remote-sensing from SAR measurements.

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Section: Scientific Objectives Of Global Remote Sensing Of Swe and Spatial And Temporal Requirementsmentioning

confidence: 99%

Section: C-band Sarmentioning

confidence: 99%

Review Article: Global Monitoring of Snow Water Equivalent using High Frequency Radar Remote Sensing

Tsang

Durand

Derksen

et al. 2021

Preprint

View full text Add to dashboard Cite

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Applications Optimal Math Model to Solve Difficult Problems for Businesses Producing and Processing Agricultural Products in Vietnam

Nguyen

2021

Axioms

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During the months of the COVID-19 epidemic, the number of processing and manufacturing agricultural products enterprises in Vietnam faced many difficulties in the consumption of agricultural products, even when they were unable to sell. Therefore, the enterprises are more and more difficult. Many enterprises are not strong enough to restore production, so it is necessary to find solutions to overcome this difficult period. In this research, the author used the application of modern statistical techniques, along with the Grey method, to predict enterprises’ business results in the future, combined with the model of Super-slacks-based-measure efficiency (Super-SBM) to help businesses select the right partners in a supply chain to achieve their business goals. By our proposed approach, the selected solution (AG6 combined with AG10) should be implemented in the future to upgrade efficiency to help stabilize output and raise productivity; thus, both parties are able not only to improve their product quality but also achieve business goals and sustainable development. In fact, it is necessary to further this study, in combination with these factors and other quantitative models, to give investors a more comprehensive view, helping them to make the right decisions and appropriately develop their businesses and social and economic development.

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Weather-Dependent Nonlinear Microwave Behavior of Seasonal High-Elevation Snowpacks

Cao

Barros

2020

Remote Sensing

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Ensemble predictions of the seasonal snowpack over the Grand Mesa, CO (~300 km2) for the hydrologic year 2016–2017 were conducted using a multilayer snow hydrology model. Snowpack ensembles were driven by gridded atmospheric reanalysis and evaluated against SnowEx’17 measurements. The multi-frequency microwave brightness temperatures and backscattering behavior of the snowpack (separate from soil and vegetation contributions) show that at sub-daily time-scales, the ensemble standard deviation (i.e., weather variability at 3 × 3 km2) is < 3 dB for dry snow, and increases to 8–10 dB at mid-day when there is surficial melt that also explains the wide ensemble range (~20 dB). The linear relationship of the ensemble mean backscatter with SWE (R2 > 0.95) depends on weather conditions (e.g., 5–6 cm/dB/month in January; 2–2.5 cm/dB/month in late February as melt-refreeze cycles modify the microphysics in the top 50 cm of the snowpack). The nonlinear evolution of ensemble snowpack physics translates into seasonal hysteresis in the mesoscale microwave behavior. The backscatter hysteretic offsets between accumulation and melt regimes are robust in the L- and C-bands and collapse for wet, shallow snow at Ku-band. The emissions behave as a limit-cycles with weak sensitivity in the accumulation regime, and hysteretic behavior during melt that is different for deep (winter-spring transition) and shallow snow (spring-summer), and offsets that increase with frequency. These findings suggest potential for multi-frequency active-passive remote-sensing of high-elevation SWE conditional on snowpack regime, particularly suited for data-assimilation using coupled snow hydrology-microwave models extended to include snow-soil and snow-vegetation interactions.

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Parsing Synthetic Aperture Radar Measurements of Snow in Complex Terrain: Scaling Behaviour and Sensitivity to Snow Wetness and Landcover

Cited by 6 publications

References 50 publications

Review Article: Global Monitoring of Snow Water Equivalent using High Frequency Radar Remote Sensing

Review Article: Global Monitoring of Snow Water Equivalent using High Frequency Radar Remote Sensing

Applications Optimal Math Model to Solve Difficult Problems for Businesses Producing and Processing Agricultural Products in Vietnam

Weather-Dependent Nonlinear Microwave Behavior of Seasonal High-Elevation Snowpacks

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