Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions

Abstract. Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of: i) the snow melt onset; ii) the snow gliding and wet-snow avalanches; iii) the release of snow contaminants and iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to be obtained. On the other hand, active microwave sensors such as the Synthetic Aperture Radar (SAR) mounted on board of satellites, are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 days over several places in the world. In this paper we analyze the correlation between the multi-temporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering two hydrological years. We found that the multi-temporal SAR measurements allow the identification of the three melting phases that characterize the melting process i.e., moistening, ripening and runoff. In detail, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water, and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease, but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially-distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.

Download Full-text

Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions

The Cryosphere ◽

10.5194/tc-14-935-2020 ◽

2020 ◽

Vol 14 (3) ◽

pp. 935-956 ◽

Cited By ~ 3

Author(s):

Carlo Marin ◽

Giacomo Bertoldi ◽

Valentina Premier ◽

Mattia Callegari ◽

Christian Brida ◽

...

Keyword(s):

Snow Water Equivalent ◽

Melting Process ◽

Liquid Water Content ◽

Sar Images ◽

Distributed Information ◽

Snow Modeling ◽

Alpine Regions ◽

Snow Properties ◽

Wet Snow ◽

Spatially Distributed

Abstract. Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of (i) the snowmelt onset, (ii) the snow gliding and wet-snow avalanches, (iii) the release of snow contaminants, and (iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to obtain. On the other hand, active microwave sensors such as the synthetic aperture radar (SAR) mounted on board satellites are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 d over several places in the world. In this paper we analyze the correlation between the multitemporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering 2 hydrological years. We found that the multitemporal SAR measurements allow the identification of the three melting phases that characterize the melting process, i.e., moistening, ripening and runoff. In particular, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow us to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.

Download Full-text

Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

Water Resources Research ◽

10.1029/2018wr024431 ◽

2019 ◽

Vol 55 (5) ◽

pp. 4465-4487 ◽

Cited By ~ 9

Author(s):

Franziska Koch ◽

Patrick Henkel ◽

Florian Appel ◽

Lino Schmid ◽

Heike Bach ◽

...

Keyword(s):

Time Delay ◽

Water Content ◽

Liquid Water ◽

Snow Water Equivalent ◽

Liquid Water Content ◽

Signal Attenuation ◽

Wet Snow ◽

Snow Water

Download Full-text

Assessing the Glacier Boundaries in the Qinghai-Tibetan Plateau of China by Multi-Temporal Coherence Estimation with Sentinel-1A InSAR

Remote Sensing ◽

10.3390/rs11040392 ◽

2019 ◽

Vol 11 (4) ◽

pp. 392

Author(s):

Yueling Shi ◽

Guoxiang Liu ◽

Xiaowen Wang ◽

Qiao Liu ◽

Rui Zhang ◽

...

Keyword(s):

Time Series ◽

Mean Value ◽

Sar Images ◽

Temperature And Precipitation ◽

Alpine Glaciers ◽

Wet Snow ◽

Multi Temporal ◽

The Mean ◽

Coherence Estimation ◽

Coherence Level

The sensitivity of synthetic aperture radar (SAR) coherence has been applied in delineating the boundaries of alpine glaciers because it is nearly unaffected by cloud coverage and can collect data day and night. However, very limited work with application of SAR data has been performed for the alpine glaciers in the Qinghai-Tibetan Plateau (QTP) of China. In this study, we attempted to investigate the change of coherence level in alpine glacier zone and access the glacier boundaries in the QTP using time series of Sentinel-1A SAR images. The DaDongkemadi Glacier (DDG) in the central QTP was selected as the study area with land cover mainly classified into wet snow, ice, river outwash and soil land. We utilized 45 Sentinel-1A C-band SAR images collected during October of 2014 through January of 2018 over the DDG to generate time series of interferometric coherence maps, and to further extract the DDG boundaries. Based on the spatiotemporal analysis of coherence values in the selected sampling areas, we first determined the threshold as 0.7 for distinguishing among different ground targets and then extracted the DDG boundaries through threshold-based segmentation and edge detection. The validation was performed by comparing the DDG boundaries extracted from the coherence maps with those extracted from the Sentinel-2B optical image. The testing results show that the wet snow and ice present a relatively low level of coherence (about 0.5), while the river outwash and the soil land present a higher level of coherence (0.8–1.0). It was found that the coherence maps spanning between June and September (i.e., the glacier ablation period) are the most suitable for identifying the snow- and ice-covered areas. When compared with the boundary detected using optical image, the mean value of Jaccard similarity coefficient for the total areas within the DDG boundaries derived from the coherence maps selected around July, August and September reached up to 0.9010. In contrast, the mean value from the coherence maps selected around December was relatively lower (0.8862). However, the coherence maps around December were the most suitable for distinguishing the ice from the river outwash around the DDG terminus, as the river outwash areas could hardly be differentiated from the ice-covered areas from June through September. The correlation analysis performed by using the meteorological data (i.e., air temperature and precipitation records) suggests that the air temperature and precipitation have a more significant influence on the coherence level of the ice and river outwash than the wet snow and soil land. The proposed method, applied efficiently in this study, shows the potential of multi-temporal coherence estimation from the Sentinel-1A mission to access the boundaries of alpine glaciers on a larger scale in the QTP.

Download Full-text

Snow Wetness Influence on Impulse Radar Snow Surveys Theoretical and Laboratory Study

Hydrology Research ◽

10.2166/nh.2000.0007 ◽

2000 ◽

Vol 31 (2) ◽

pp. 89-106 ◽

Cited By ~ 23

Author(s):

A. Lundberg ◽

H. Thunehed

Keyword(s):

Water Content ◽

Liquid Water ◽

Snow Water Equivalent ◽

Liquid Water Content ◽

Late Winter ◽

Propagation Time ◽

Reservoir Water ◽

A Value ◽

Wet Snow ◽

Snow Water

The snow-water equivalent of late-winter snowpack is of utmost importance for hydropower production in areas where a large proportion of the reservoir water emanates from snowmelt. Impulse radar can be used to estimate the snow-water equivalent of the snowpack and thus the expected snowmelt discharge. Impulse radar is now in operational use in some Scandinavian basins. With radar technology the radar wave propagation time in the snowpack is converted into snow-water equivalent with help of a parameter usually termed the a-value. Use of radar technology during late winter brings about risk for measurements on wet snow. The a-value for dry snow cannot be used directly for wet snow. We have found that a liquid-water content of 5% (by volume) reduces the a-value by approximately 20%. In this paper an equation, based on snow density and snow liquid water content, for calculation of wet-snow a-value is presented.

Download Full-text

Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing

Remote Sensing ◽

10.3390/rs13214223 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4223

Author(s):

Randall Bonnell ◽

Daniel McGrath ◽

Keith Williams ◽

Ryan Webb ◽

Steven R. Fassnacht ◽

...

Keyword(s):

Water Content ◽

Liquid Water ◽

Electromagnetic Waves ◽

Snow Water Equivalent ◽

Canopy Cover ◽

Liquid Water Content ◽

Spatiotemporal Variability ◽

Snow Density ◽

Wet Snow ◽

Observation Method

Radar instruments have been widely used to measure snow water equivalent (SWE) and Interferometric Synthetic Aperture Radar is a promising approach for doing so from spaceborne platforms. Electromagnetic waves propagate through the snowpack at a velocity determined by its dielectric permittivity. Velocity estimates are a significant source of uncertainty in radar SWE retrievals, especially in wet snow. In dry snow, velocity can be calculated from relations between permittivity and snow density. However, wet snow velocity is a function of both snow density and liquid water content (LWC); the latter exhibits high spatiotemporal variability, there is no standard observation method, and it is not typically measured by automated stations. In this study, we used ground-penetrating radar (GPR), probed snow depths, and measured in situ vertically-averaged density to estimate SWE and bulk LWC for seven survey dates at Cameron Pass, Colorado (~3120 m) from April to June 2019. During this cooler than average season, median LWC for individual survey dates never exceeded 7 vol. %. However, in June, LWC values greater than 10 vol. % were observed in isolated areas where the ground and the base of the snowpack were saturated and therefore inhibited further meltwater output. LWC development was modulated by canopy cover and meltwater drainage was influenced by ground slope. We generated synthetic SWE retrievals that resemble the planned footprint of the NASA-ISRO L-band InSAR satellite (NISAR) from GPR using a dry snow density model. Synthetic SWE retrievals overestimated observed SWE by as much as 40% during the melt season due to the presence of LWC. Our findings emphasize the importance of considering LWC variability in order to fully realize the potential of future spaceborne radar missions for measuring SWE.

Download Full-text

Fuzzy set approach to calibrating distributed flood inundation models using remote sensing observations

Hydrology and Earth System Sciences ◽

10.5194/hess-11-739-2007 ◽

2007 ◽

Vol 11 (2) ◽

pp. 739-752 ◽

Cited By ~ 97

Author(s):

F. Pappenberger ◽

K. Frodsham ◽

K. Beven ◽

R. Romanowicz ◽

P. Matgen

Keyword(s):

Flood Hazard ◽

Evaluation Methodology ◽

Flood Inundation ◽

Fuzzy Evaluation ◽

Sar Images ◽

Distributed Information ◽

Inundation Maps ◽

Spatially Distributed ◽

Inundation Extent ◽

Traditional Approaches

Abstract. The paper presents a methodology for the estimation of uncertainty of inundation extent, which takes account of the uncertainty in the observed spatially distributed information and implements a fuzzy evaluation methodology. The Generalised Likelihood Uncertainty Estimation (GLUE) technique and the 2-D LISFLOOD-FP model were applied to derive the set of uncertain inundation realisations and resulting flood inundation maps. Conditioning of the inundation maps on fuzzified Synthetic Aperture Radar (SAR) images results in much more realistic inundation risk maps which can better depict the variable pattern of inundation extent than previously used methods. It has been shown that the evaluation methodology compares well to traditional approaches and can produce flood hazard maps that reflect the uncertainties in model evaluation.

Download Full-text

Fuzzy set approach to calibrating distributed flood inundation models using remote sensing observations

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-3-2243-2006 ◽

2006 ◽

Vol 3 (4) ◽

pp. 2243-2277 ◽

Cited By ~ 6

Author(s):

F. Pappenberger ◽

K. Frodsham ◽

K. Beven ◽

R. Romanowicz ◽

P. Matgen

Keyword(s):

Flood Hazard ◽

Evaluation Methodology ◽

Flood Inundation ◽

Fuzzy Evaluation ◽

Sar Images ◽

Distributed Information ◽

Inundation Maps ◽

Spatially Distributed ◽

Inundation Extent ◽

Traditional Approaches

Abstract. The paper presents a methodology for the estimation of uncertainty of inundation extent, which takes account of the uncertainty in the observed spatially distributed information and implements a fuzzy evaluation methodology. The Generalised Likelihood Uncertainty Estimation (GLUE) technique and the 2-D LISFLOOD-FP model were applied to derive the set of uncertain inundation realisations and resulting flood inundation maps. Conditioning of the inundation maps on fuzzified Synthetic Aperture Radar (SAR) images results in much more realistic inundation risk maps which can better depict the variable pattern of inundation extent than previously used methods. It has been shown that the methodology compares well to traditional approaches and can produce flood hazard maps that reflect the uncertainties in model evaluation.

Download Full-text

In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications

Remote Sensing ◽

10.3390/rs13224617 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4617

Author(s):

Ryan W. Webb ◽

Adrian Marziliano ◽

Daniel McGrath ◽

Randall Bonnell ◽

Tate G. Meehan ◽

...

Keyword(s):

Snow Water Equivalent ◽

Low Frequency ◽

Liquid Water Content ◽

Seasonal Snow ◽

Wet Snow ◽

Primary Driver ◽

Snow Water ◽

Snow Conditions ◽

Ground Penetrating

Extensive efforts have been made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at global scales is challenging. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately constrained. The dielectric constant (k) determines the velocity of a radar wave through snow, which is a critical component of time-of-flight radar techniques such as ground penetrating radar and interferometric synthetic aperture radar (InSAR). However, equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work was to further understand the dielectric permittivity of seasonal snow under both dry and wet conditions. We utilized extensive direct field observations of k, along with corresponding snow density and liquid water content (LWC) measurements. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We present empirical relationships based on 146 snow pits for dry snow conditions and 92 independent LWC observations in naturally melting snowpacks. Regression results had r2 values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in the shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, suggesting that further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ measurements. Many previous equations assume a background (dry snow) k that we found to be inaccurate, as previously stated, and is the primary driver of resulting uncertainty. Our results suggest large errors in SWE (10–15%) or LWC (0.05–0.07 volumetric LWC) estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future InSAR opportunities such as NISAR and ROSE-L.

Download Full-text