Quantifying Surface Melt and Liquid Water on the Greenland Ice Sheet using L-band Radiometry

Houtz, Derek; Mätzler, Christian; Naderpour, Reza; Schwank, Mike; Steffen, Konrad

doi:10.1016/j.rse.2021.112341

Cited by 26 publications

(19 citation statements)

References 48 publications

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“…Surface melting is extensive on the GrIS, and spatial variation in the effects of this melting [3] enabled us to divide the snow pack of the GrIS into distinct diagenetic facies, or zones [4], such as dry-snow facies, percolation facies, wet-snow facies (WSF), and ice facies, etc. Wet snow is an import indicator of snow melting in an area [5], accurate acquisition of time-series of WSF information is crucial in investigating the uncertainty of GrIS mass balance and assessing global sea level change [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…Using variations in C-band SAR image intensity to map zones of dry snow, percolation, wet snow, and bare ice [4], extending the early facies work of Benson [3] into the satellite era [8]. A large number of GrIS surface melting monitoring methods were developed by using active microwaves data [9]- [11], passive microwave data [7], [12], [13] and scatterometer data [10], [14], [15] in subsequent studies. However, these methods are difficult to satisfy the requirements of large scale, high spatial resolution and time series at the same time.…”

Section: Introductionmentioning

confidence: 99%

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Extracting Time-Series of Wet-Snow Facies in Greenland Using Sentinel-1 SAR Data on Google Earth Engine

Zhang

Zhou

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extracting Time-Series of Wet-Snow Facies in Greenland Using Sentinel-1 SAR Data on Google Earth Engine

Zhang

Zhou

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Remote sensing observations from polar orbiting satellites have global coverage and frequent sampling over the boreal Arctic and provide an opportunity to observe snow processes at moderate resolution. Passive microwave observations, in particular, are useful because brightness temperature measurements from both lower frequency (1-2 GHz) and higher frequency (18-37 GHz) radiometers are highly sensitive to liquid water content (LWC) changes within the snowpack [16][17][18]. Further, clouds and polar darkness have little influence over the passive microwave (PMW) retrievals due to the high atmospheric transparency to land surface microwave emissions at these frequencies, which do not rely on incoming solar energy [19].…”

Section: Introductionmentioning

confidence: 99%

Snow Phenology and Hydrologic Timing in the Yukon River Basin, AK, USA

et al. 2021

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The Yukon River basin encompasses over 832,000 km2 of boreal Arctic Alaska and northwest Canada, providing a major transportation corridor and multiple natural resources to regional communities. The river seasonal hydrology is defined by a long winter frozen season and a snowmelt-driven spring flood pulse. Capabilities for accurate monitoring and forecasting of the annual spring freshet and river ice breakup (RIB) in the Yukon and other northern rivers is limited, but critical for understanding hydrologic processes related to snow, and for assessing flood-related risks to regional communities. We developed a regional snow phenology record using satellite passive microwave remote sensing to elucidate interactions between the timing of upland snowmelt and the downstream spring flood pulse and RIB in the Yukon. The seasonal snow metrics included annual Main Melt Onset Date (MMOD), Snowoff (SO) and Snowmelt Duration (SMD) derived from multifrequency (18.7 and 36.5 GHz) daily brightness temperatures and a physically-based Gradient Ratio Polarization (GRP) retrieval algorithm. The resulting snow phenology record extends over a 29-year period (1988–2016) with 6.25 km grid resolution. The MMOD retrievals showed good agreement with similar snow metrics derived from in situ weather station measurements of snowpack water equivalence (r = 0.48, bias = −3.63 days) and surface air temperatures (r = 0.69, bias = 1 day). The MMOD and SO impact on the spring freshet was investigated by comparing areal quantiles of the remotely sensed snow metrics with measured streamflow quantiles over selected sub-basins. The SO 50% quantile showed the strongest (p < 0.1) correspondence with the measured spring flood pulse at Stevens Village (r = 0.71) and Pilot (r = 0.63) river gaging stations, representing two major Yukon sub-basins. MMOD quantiles indicating 20% and 50% of a catchment under active snowmelt corresponded favorably with downstream RIB (r = 0.61) from 19 river observation stations spanning a range of Yukon sub-basins; these results also revealed a 14–27 day lag between MMOD and subsequent RIB. Together, the satellite based MMOD and SO metrics show potential value for regional monitoring and forecasting of the spring flood pulse and RIB timing in the Yukon and other boreal Arctic basins.

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“…However, these methods cannot quantify daily melt flux, mainly because (a) the lack of accurate in-situ daily melt flux measurement in the regression model, and (b) the response of microwave measurements to meltwater is nonlinear and varies spatially and temporally due to the changes in snow properties Zheng et al, 2018). Recently, Houtz et al (2019Houtz et al ( , 2021 have developed an algorithm that can estimate GrIS liquid water content from L-band satellite radiometer observations using a physically deterministic emission model. However, liquid water content does not represent melt flux because of the refereeing, migration and retention of meltwater which is widely observed over the GrIS (Miller et al, 2020).…”

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confidence: 99%

Greenland Ice Sheet Daily Surface Melt Flux Observed From Space

Zheng

Cheng

Shang

et al. 2022

Geophysical Research Letters

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Greenland Ice Sheet (GrIS) surface melt has contributed to the global sea‐level rise and the ongoing warming is expected to promote this process. This study provides a new strategy for the quantitative estimate of GrIS daily surface melt at enhanced resolution (3.125 km) from a remote sensing perspective beyond traditional regional climate models (RCMs). Daily melt flux is estimated from spaceborne radiometer observations with a back‐propagation neural network model. The network is trained with melt fluxes that are calculated using detailed in‐situ atmospheric and snow observations and a surface energy balance model. Our results provide details about the extreme melt in mid‐July 2012 when surface melt occurred at Summit and the meltwater volume exceeded 20 Gt as a result of anomalous warming. Meltwater volume from the satellite is very close to that from RCMs.

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Quantifying Surface Melt and Liquid Water on the Greenland Ice Sheet using L-band Radiometry

Cited by 26 publications

References 48 publications

Extracting Time-Series of Wet-Snow Facies in Greenland Using Sentinel-1 SAR Data on Google Earth Engine

Extracting Time-Series of Wet-Snow Facies in Greenland Using Sentinel-1 SAR Data on Google Earth Engine

Snow Phenology and Hydrologic Timing in the Yukon River Basin, AK, USA

Greenland Ice Sheet Daily Surface Melt Flux Observed From Space

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