The active and passive microwave response to snow parameters: 1. Wetness

Stiles, W. H.; Ulaby, F. T.

doi:10.1029/jc085ic02p01037

Cited by 256 publications

(153 citation statements)

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“…The value of 1.59 was found by assuming a grain size of 0.35 mm (Chang et al 1982), but as snow grains change through the winter an assumption of constant grain size will cause errors (Kelly et al 2003). Liquid water affects the snow-pack's dielectric constant, increasing attenuation, potentially preventing the retrieval of SWE (Stiles and Ulaby 1980). Both AMSR-E and SSM/I take measurements at night when it is hoped that the snow has refrozen (AMSR-E crosses the equator at 1:30 am and SSM/I at 6 am, within 20 minutes depending on which platform (NSIDC 2012), Globsnow uses these same sensors, see section 2.4); however, any melt and refreeze is likely to alter the grain size and could lead to errors.…”

Section: Microwave Measurement Of Snowmentioning

confidence: 99%

Evaluating global snow water equivalent products for testing land surface models

Hancock

Baxter

Evans³

et al. 2013

Remote Sensing of Environment

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(2013) 'Evaluating global snow water equivalent products for testing land surface models.', Remote sensing of environment., 128 . pp. 107-117. Further information on publisher's website:http://dx.doi.org/10.1016/j.rse. 2012.10.004 Publisher's copyright statement: NOTICE: this is the author's version of a work that was accepted for publication in Remote sensing of environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be re ected in this document. Changes may have been made to this work since it was submitted for publication. A de nitive version was subsequently published in Remote sensing of environment, 128, 2013, 10.1016/j.rse.2012.10.004 Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract This paper compares three global snow water equivalent (SWE) products, SSM/I (NSIDC), AMSR-E (NSIDC) and Globsnow (v1.0, ESA) to each other, snow covered area (SCA), ground measures of snow depth and meteorological data in an attempt to determine which might be most suitable for testing and developing land surface models. Particular attention is payed to which gives the most accurate peak accumulation, seasonal SWE changes and first and last dates of snow cover.SSM/I and AMSR-E are pure earth observation (EO) derived products whilst Globsnow is a combination of EO and ground data. The results suggest that the pure EO products can saturate in deeper snow (SWE > 80 -150 mm), can show spurious features during melt and can overestimate SWE due to strong thermal gradients and erroneous forest cover correction factors. Along with the comparison to ground data (only a single point) this suggests that Globsnow is the more accurate product for determining peak accumulation and seasonal SWE cycle.The snow start and end dates of the three SWE products were compared to an optically derived SCA (MOD10C1, taken as truth) and found to give large errors of snow start date (root mean square error of 20+ days, though SSM/I was correct on average). The snow end dates had lower errors (a bias of 1-6 days) although the spread was still on the order of three weeks.During the investigation, occasional abrupt changes in Globsnow were observed (in the v1.0 and v1.2 daily and v1.2 weekly products). These only occurred in around 1% of cases examined and seem to be spurious. Care should be taken to correct or avoid these jumps if using Globsnow to validate land surface models or in an assimilation scheme.

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Section: Microwave Measurement Of Snowmentioning

confidence: 99%

Evaluating global snow water equivalent products for testing land surface models

Hancock

Baxter

Evans³

et al. 2013

Remote Sensing of Environment

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“…However, these ground truth measurements are usually laborious and thus very difficult to acquire as time series. Several experiences have been reported in the literature on the millimeter wave interaction with snowcover [8][9][10][11][12][13] and the diurnal variation of radar signatures of snow [14][15][16]. Although these investigations indicate that the backscatter of snowcover is strongly influenced by the presence of liquid water, electromagnetic modeling of snow undergoing internal changes, due to metamorphism and phase change, still lacks a systematic study.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Millimeter Wave Backscatter of Time-Varying Snowcover

Shih¹,

Ding²,

Kong³

et al. 1997

PIER

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“…The onset of surface melt is characterized by a rapid decrease in backscatter values (corresponding to no.6 in Fig. 2, Stiles and Ulaby, 1980;Smith et al, 1997;Wolken et al, 2009), most likely introduced by warm and wet weather conditions (Rotschky et al, 2011). The lowest backscatter values in the ablation zone are found when wet snow covers the glacier (corresponding to no.…”

Section: Seasonal Melt Patternsmentioning

confidence: 99%

“…Wet snow absorbs most of the microwave signal, returning little energy back to the sensor (Stiles and Ulaby, 1980). The roughness of snow/ice and incidence angle of the SAR satellite sensor also affects the backscatter signal strength (Shi and Dozier, 1995).…”

Section: Seasonal Melt Patternsmentioning

confidence: 99%

Using SAR satellite data time-series for regional glacier mapping

Winsvold¹,

Kääb²,

Nuth³

et al. 2017

Preprint

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With dense SAR satellite data time-series it is possible to map surface and subsurface glacier properties that vary in time. On 10 Sentinel-1A and Radarsat-2 backscatter images over mainland Norway and Svalbard, we have used descriptive methods for outlining the possibilities of using SAR time-series for mapping glaciers. We present five application scenarios, where the first shows potential for tracking transient snow lines with SAR backscatter time-series, and correlates with both optical satellite images (Sentinel-2A and Landsat 8) and equilibrium line altitudes derived from in situ surface mass balance data. In the second application scenario, time-series representation of glacier facies corresponding to SAR glacier zones shows 15 potential for a more accurate delineation of the zones and how they change in time. The third application scenario investigates the firn evolution using dense SAR backscatter time-series together with a coupled energy balance and multilayer firn model. We find strong correlation between backscatter signals with both the modeled firn air-content and modeled wetness in the firn. In the fourth application scenario, we highlight how winter rain events can be detected in SAR timeseries, revealing important information about the area extent of internal accumulation. Finally, in the last application 20 scenario, averaged summer SAR images were found to have potential in assisting the process of mapping glaciers outlines, especially in the presence of seasonal snow. Altogether we present examples of how to map glaciers and to further understand glaciological processes using the existing and future massive amount of multi-sensor time-series data. Our results reveal the potential of satellite imagery for automatically derived products as important input in modeling assessments and glacier change analysis. 25

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The active and passive microwave response to snow parameters: 1. Wetness

Cited by 256 publications

References 3 publications

Evaluating global snow water equivalent products for testing land surface models

Evaluating global snow water equivalent products for testing land surface models

Modeling of Millimeter Wave Backscatter of Time-Varying Snowcover

Using SAR satellite data time-series for regional glacier mapping

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