Modelling the L-Band Snow-Covered Surface Emission in a Winter Canadian Prairie Environment

Roy, Alexandre; Leduc-Leballeur, Marion; Picard, Ghislain; Royer, A.; Toose, Peter; Derksen, Chris; Lemmetyinen, Juha; Berg, Aaron; Schwank, Mike

doi:10.3390/rs10091451

Cited by 9 publications

(5 citation statements)

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“…It also enables measurements of layer boundary roughness, particularly of the snowair and snow-ground interfaces. Other methods to characterize snow-air surface roughness use photographic image contrast analysis of dark boards placed behind snow (Fassnacht et al, 2009;Anttila et al, 2014) and subnivean roughness of areas cleared of snow using pin profilers, lidar scanning (Chabot et al, 2018;Roy et al, 2018), or structure from motion photogrammetry (Meloche et al, 2020). The subnivean roughness between snow and soil gives significant contributions of rough surface scattering because of the contrast of dielectric constants between snow and soil.…”

Section: Planning a Satellite Missionmentioning

confidence: 99%

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: Planning a Satellite Missionmentioning

confidence: 99%

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

et al. 2022

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“…It also enables measurements of layer boundary roughness, particularly of the snow-air and snow-ground interfaces. Other methods to characterise snow-air surface roughness use photographic image contrast analysis of dark boards placed behind snow (Fassnacht et al, 2009;Anttila et al, 2014) and subnivean roughness of areas cleared of snow using pin profilers, LiDAR scanning (Chabot et al, 2018;Roy et al, 2018) or structure from motion photogrammetry (Meloche et al, 2020). The subnivean roughness between snow and soil give significant contributions of rough surface scattering because of the contrast of dielectric constants between snow and soil.…”

Section: Spatial Variability Of Field Measurementsmentioning

confidence: 99%

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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“…Leduc-Leballeur et al [10] proposed the wave approach for low-frequency microwave emission in snow (WALOMIS) model, and experiments in Antarctica showed that the wave approach was more suitable than the radiative transfer model to simulate the L-band TB. Roy et al [11] applied WALOMIS to snow in a prairie environment and compared it with the DMRT-ML and LS-MEMLS-1L models for L-band TB simulation, and the results showed that the root-mean-square errors of the three model simulations were similar at about 7.2~10.5 k.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Application of SMRT Model for L-Band Brightness Temperature Simulation in Arctic Sea Ice

Chen

et al. 2023

Remote Sensing

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Using L-band microwave radiative transfer theory to retrieve ice and snow parameters is one of the focuses of Arctic research. At present, due to limitations of frequency and substrates, few operational microwave radiative transfer models can be used to simulate L-band brightness temperature (TB) in Arctic sea ice. The snow microwave radiative transfer (SMRT) model, developed with the support of the European Space Agency in 2018, has been used to simulate high-frequency TB in polar regions and has obtained good results, but no studies have shown whether it can be used appropriately in the L-band. Therefore, in this study, we systematically evaluate the ability of the SMRT model to simulate L-band TB in the Arctic sea ice and snow environment, and we show that the results are significantly optimized by improving the simulation method. In this paper, we first consider the thermal insulation effect of snow by adding the thermodynamic equation, then use a reasonable salinity profile formula for multi-layer model simulation to solve the problem of excessive L-band penetration in the SMRT single-layer model, and finally add ice lead correction to resolve the large influence it has on the results. The improved SMRT model is evaluated using Operation IceBridge (OIB) data from 2012 to 2015 and compared with the snow-corrected classical L-band radiative transfer model for Arctic sea ice proposed in 2010 (KA2010). The results show that the SMRT model has better simulation results, and the correlation coefficient (R) between SMRT-simulated TB and Soil Moisture and Ocean Salinity (SMOS) satellite TB is 0.65, and the RMSE is 3.11 K. Finally, the SMRT model with the improved simulation method is applied to the whole Arctic from November 2014 to April 2015, and the simulated R is 0.63, and the RMSE is 5.22 K. The results show that the SMRT multi-layer model is feasible for simulating L-band TB in the Arctic sea ice and snow environment, which provides a basis for the retrieval of Arctic parameters.

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Modelling the L-Band Snow-Covered Surface Emission in a Winter Canadian Prairie Environment

Cited by 9 publications

References 41 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

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

Evaluation and Application of SMRT Model for L-Band Brightness Temperature Simulation in Arctic Sea Ice

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