Improvement of microwave emissivity parameterization of frozen Arctic soils using roughness measurements derived from photogrammetry

Meloche, Julien; Royer, A.; Langlois, Alexandre; Rutter, Nick; Sasseville, Vincent

doi:10.1080/17538947.2020.1836049

Cited by 14 publications

(10 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Planning a Satellite Missionmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…(3b) and (4) in Vargel et al (2020) with the improved Born approximation (IBA-Exp) configuration. Soil emission was simulated using the Wegmüller and Mätzler (1999) model with permittivity and roughness values from a field study of frozen soil emission based in CB (Meloche et al, 2020). The soil parameters from CB (Meloche et al, 2020) closely match values from a study in TVC (King et al, 2018) and were used for both site simulations.…”

Section: Measured Brightness Temperatures and Snowmentioning

confidence: 99%

Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

et al. 2022

Self Cite

View full text Add to dashboard Cite

Abstract. Topography and vegetation play a major role in sub-pixel variability of Arctic snowpack properties but are not considered in current passive microwave (PMW) satellite SWE retrievals. Simulation of sub-pixel variability of snow properties is also problematic when downscaling snow and climate models. In this study, we simplified observed variability of snowpack properties (depth, density, microstructure) in a two-layer model with mean values and distributions of two multi-year tundra dataset so they could be incorporated in SWE retrieval schemes. Spatial variation of snow depth was parameterized by a log-normal distribution with mean (μsd) values and coefficients of variation (CVsd). Snow depth variability (CVsd) was found to increase as a function of the area measured by a remotely piloted aircraft system (RPAS). Distributions of snow specific surface area (SSA) and density were found for the wind slab (WS) and depth hoar (DH) layers. The mean depth hoar fraction (DHF) was found to be higher in Trail Valley Creek (TVC) than in Cambridge Bay (CB), where TVC is at a lower latitude with a subarctic shrub tundra compared to CB, which is a graminoid tundra. DHFs were fitted with a Gaussian process and predicted from snow depth. Simulations of brightness temperatures using the Snow Microwave Radiative Transfer (SMRT) model incorporating snow depth and DHF variation were evaluated with measurements from the Special Sensor Microwave/Imager and Sounder (SSMIS) sensor. Variation in snow depth (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. SMRT simulations using a CVsd of 0.9 best matched CVsd observations from spatial datasets for areas > 3 km2, which is comparable to the 3.125 km pixel size of the Equal-Area Scalable Earth (EASE)-Grid 2.0 enhanced resolution at 37 GHz.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Improvement of microwave emissivity parameterization of frozen Arctic soils using roughness measurements derived from photogrammetry

Cited by 14 publications

References 51 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

Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

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

Contact Info

Product

Resources

About