Airborne Ku-Band Polarimetric Radar Remote Sensing of Terrestrial Snow Cover

Yueh, Simon; Dinardo, S.; Akgiray, Ahmed; West, Richard; Cline, D.; Elder, Kelly

doi:10.1109/tgrs.2009.2022945

Cited by 89 publications

(38 citation statements)

References 17 publications

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“…While the intensity of such sampling may have practical limitations, targeted submetre-scale sampling as part of wider ground-based catchment-scale snow measurement campaigns is highly relevant for evaluation of current and future satellite sensors that operate over resolutions on the scale of metres to tens of metres (e.g. Cline et al, 2009;Yueh et al, 2009;King et al, 2018). Rapid acquisition of vertical profiles of snowpack properties using snow micropenetrometers (Schneebeli et al, 1999;Proksch et al, 2015) may increasingly provide the enhanced field measurement capacity required to achieve this spatial sampling resolution.…”

Section: Discussionmentioning

confidence: 99%

“…1; Derksen et al, 2009). As depth hoar and wind slab have strongly diverging microwave scattering properties (Hall et al, 1991), the relative proportion of each strongly influences Ku-band radar backscatter (Yueh et al, 2009;King et al, 2015King et al, , 2018. Knowledge of how layers vary within Arctic snowpacks is therefore critical to the assessment of uncertainty in radar-based retrievals of snow water equivalent (SWE) and forward models of snow radiative transfer.…”

Section: Introductionmentioning

confidence: 99%

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Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

Rutter

Sandells²,

Derksen

et al. 2019

The Cryosphere

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Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

Rutter

Sandells²,

Derksen

et al. 2019

The Cryosphere

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“…[]. Field measurements at large scales are logistically difficult and expensive, but there have been some intensive field studies conducted to validate remotely sensed SWE with observation such as the Cold Land Processes Experiment [ Cline et al ., , ], the SnowSTAR2002 Transect [ Shi et al ., ], and others [e.g., Langlois et al ., ; Yueh et al ., ; Derksen , ; Derksen and Walker , ; Mote et al ., ; Chang et al ., ]. Using measurements from snow course transects in the former Soviet Union, Armstrong and Brodzik [] found that nearly all retrieval algorithms (e.g., horizontal‐based polarization algorithm of Chang et al .…”

Section: Introductionmentioning

confidence: 99%

Changes in North American snowpacks for 1979–2007 detected from the snow water equivalent data of SMMR and SSM/I passive microwave and related climatic factors

et al. 2013

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[1] Changes to the North American snowpacks for 1979-2007 were detected from snow water equivalent (SWE) retrieved empirically from horizontally polarized brightness temperature (T B ) of a scanning multichannel microwave radiometer (18 and 37 GHz) and special sensor microwave imager (19 and 37 GHz) passive microwave data using the nonparametric Kendall's test. The predominant SWE trends detected agree with negative anomalies in snow cover observed in Northern Hemisphere since the 1980s, and both the SWE and snow cover results should be related to the significant increase in the surface temperature of North America (NA) observed since the 1970s. About 30% of detected decreasing trends of SWE for 1979-2007 are statistically significant, which is three times more than the significant increasing trends of SWE detected in NA. Significant decreasing SWE trends are more extensive in Canada than in the United States. The mean trend magnitudes detected for December-April are À0.4 to À0.5 mm/yr, which means an overall reduction of snow depth of about 5-8 cm in 29 years (assuming a snowpack density between 200 and 250 kg/m 3 ), which can impact regions relying on spring snowmelt for water supply. From detected increasing (decreasing) trends of gridded temperature (precipitation) based on the North American Regional Reanalysis data set and the University of Delaware data set for NA, their respective correlations with SWE data and other findings, such as global scale decline of snow cover, longer rainfall seasons, etc., it seems the extensive decreasing trends in SWE detected mainly in Canada are more caused by increasing temperatures than by decreasing precipitation. However, climate anomalies could also contribute to the detected trends, such as PC1 of NA's SWE, which is found to be correlated to the Pacific Decadal Oscillation index and marginally correlated to the Pacific North American pattern.

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“…Radar retrievals have shown potential for direct and inferred retrievals of SWE (Shi and 154 Dozier, 2000b;Shi and Dozier, 2000a;Yueh et al, 2009). For mountain snow cover, radar 155 retrievals have particular sensitivities to grain size stratigraphy, liquid water in the snowpack 156 absorbing the radar signal, and terrain layover.…”

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confidence: 99%

The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo

Painter

Berisford

Boardman

et al. 2016

Remote Sensing of Environment

398

412

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39 Snow cover and its melt dominate regional climate and water resources in many of the 40 world's mountainous regions. Snowmelt timing and magnitude in mountains are controlled 41 predominantly by absorption of solar radiation and the distribution of snow water equivalent 42 (SWE), and yet both of these are very poorly known even in the best-instrumented mountain 43 regions of the globe. Here we describe and present results from the Airborne Snow 44 Observatory (ASO), a coupled imaging spectrometer and scanning lidar, combined with 45 distributed snow modeling, developed for the measurement of snow spectral albedo/broadband 46 albedo and snow depth/SWE. Snow density is simulated over the domain to convert snow 47 depth to SWE. The result presented in this paper is the first operational application of remotely 48 sensed snow albedo and depth/SWE to quantify the volume of water stored in the seasonal 49 snow cover. The weekly values of SWE volume provided by the ASO program represent a 50 critical increase in the information available to hydrologic scientists and resource managers in 51 mountain regions. 52 53 3 54 55Introduction 56Snow cover and its melt dominate sources in many of the world's mountainous regions, and 57 in adjacent areas dependent on river flows originating from mountain basins. In the western 58 United States, snowmelt runoff dominates the surface water hydrology, providing more than 59 75% of the total freshwater (Bales et al., 2006). However, the region faces significant water 60 resource challenges due to the intersection of increasing demand from population growth and 61 changes in runoff volume and timing due to climate change (Christensen et al., 2004; 62 Christensen and Lettenmaier, 2007). 63Observations indicate an ongoing reduction in the seasonal duration of mountain snowpacks 64 Mote et al., 2005;Hamlet et al., 2007;Clow, 2010), a trend likely to 65 continue under unimpeded warming associated with climate change (Christensen and 66 Lettenmaier, 2007;Deems et al., 2013b). Moreover, increasing temperatures in desert systems 67 will increase dust loading to mountain snow cover (Munson et al., 2011), thus reducing the 68 snow cover albedo and accelerating snowmelt runoff (Painter et al., 2007;Painter et al., 2010; 69 Skiles et al., 2012). 70The two most critical properties for understanding timing and magnitude of snowmelt runoff 71 are the spatial distributions of snow albedo and snow water equivalent (SWE) (Blöschl, 1991; 72 Kirnbauer et al., 1994). Despite the importance of these properties in controlling volume and 73 timing of runoff, the mountain snowpack remains poorly quantified around the globe (Bales et 74 al., 2006) (Barnett et al., 2005, leaving runoff and climate models poorly constrained and our 75 physical understanding of mountain snowmelt driven systems incomplete. 76 4In the western US, we have relatively sparse measurements of SWE, mostly at lower and 77 middle elevations and only a few per basin (Bales et al., 2006). These measurements are used as 78 in...

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Airborne Ku-Band Polarimetric Radar Remote Sensing of Terrestrial Snow Cover

Cited by 89 publications

References 17 publications

Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

Changes in North American snowpacks for 1979–2007 detected from the snow water equivalent data of SMMR and SSM/I passive microwave and related climatic factors

The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo

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