Considerations for Ku-Band Radar Retrieval of Snow Water Equivalent at Mid-Latitude Ontario Agricultural Sites

Thompson, Aaron; Kelly, Richard

doi:10.1080/07038992.2021.1898938

Cited by 1 publication

(3 citation statements)

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“…The degree to which prior information is applied to a problem can be thought of as a spectrum: the most minimal use of prior information is to remove the prior from the objective function but to specify a range of possible values for each prior, as done by Thompson and Kelly (2021a). Specification of a prior on either grain size or single-scattering albedo can be considered moderate use of a priori information.…”

Section: Constraining the Retrieval Problem With Prior Informationmentioning

confidence: 99%

“…However, other approaches have indicated that microstructure information may not be required to be so precise. Thompson and Kelly (2021a) showed a successful inversion of SWE from in situ radar measurements in a prairie snow environment using a cost function specified with only minimal prior information (as defined in the previous paragraph). The algorithm of Zhu et al (2018) requires only the specification of whether the single-scattering albedo is high or low (i.e., because the specification of a priori information is changed from a continuous to a categorical problem, the burden of a high-precision prior is much alleviated).…”

Section: Constraining the Retrieval Problem With Prior Informationmentioning

confidence: 99%

“…Recently, Thompson and Kelly (2021a) applied a new algorithm using no prior information in the cost function, in which an iterative search algorithm was run using the MEMLS3&a model (Proksch et al, 2015a). They applied the algorithm in both a one-and two-layer model on Canadian prairie snowpacks near agricultural fields.…”

Section: Solving the Retrieval Problem: Algorithmsmentioning

confidence: 99%

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Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing

et al. 2022

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Abstract. Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth's surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth's climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per 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 water stored as snow 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 are not 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. 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 millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, 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, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, 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 application 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: Constraining the Retrieval Problem With Prior Informationmentioning

confidence: 99%

Section: Constraining the Retrieval Problem With Prior Informationmentioning

confidence: 99%

Section: Solving the Retrieval Problem: Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing

et al. 2022

Self Cite

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Considerations for Ku-Band Radar Retrieval of Snow Water Equivalent at Mid-Latitude Ontario Agricultural Sites

Cited by 1 publication

References 56 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

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