On The Estimation of Temporal Changes of Snow Water Equivalent by Spaceborne Sar Interferometry: A New Application for the Sentinel-1 Mission

Conde, Vasco; Nico, Giovanni; Mateus, Pedro; Catalão, João; Kontu, Anna; Gritsevich, Maria

doi:10.2478/johh-2018-0003

Cited by 38 publications

(24 citation statements)

References 22 publications

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“…Besides MODIS and AVHRR optical remote sensing snow cover maps, LANDSAT snow cover maps with high spatial resolution (30 m) were used to reconstruct glacier mass balance in selected glaciers in the ASB (Barandun et al 2018;Kronenberg et al 2016). Satellite radar systems, e.g., Sentinel-1, are being tested for their capability to detect snow water equivalent (Conde et al 2019). Overall, snow cover maps, obtained from remote sensing, can significantly improve understanding of the hydrological processes in the ASB.…”

Section: Alpine Snow Covermentioning

confidence: 99%

The Aral Sea Basin

2019

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• The alpine cryosphere, including snow, glaciers and permafrost, is critical to water management in the Aral Sea Basin (ASB) and larger Central Asia (CA) under the changing climate, as it stores large amounts of water in its solid forms. Most cryospheric components in the Aral Sea Basin are close to melting point, and hence very vulnerable to a slight increase in air temperature with significant consequences to long-term water availability and to water resources variability and extremes. The alpine cryosphere 101 • Current knowledge about different components of the cryosphere and their connection to climate in the Basin and in the entire Central Asia region varies. While it is advanced in the topics of snow and glaciers, knowledge on permafrost is rather limited. • Observed trends in runoff point in the direction of increasing water availability in July and August at least until mid-century and increasing possibility for water storage in reservoirs and aquifers. However, eventually this will change as glaciers waste away. Future runoff may change considerably after mid-century and start to decline if not compensated by increasing precipitation. • Cryosphere monitoring systems are the basis for sound estimates of water availability and water-related hazards associated with snow, glaciers and permafrost. They require a well-distributed observational network for all cryospheric variables. Such systems need to be re-established in the Basin after the breakup of the Soviet Union in the early 1990s. This process is slowly emerging in the region. Collaboration between local operational hydro-meteorological services and the academic sector, and with international research networks, may improve the observational capabilities in high-mountain regions of CA in general and in the ASB in particular.

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Section: Alpine Snow Covermentioning

confidence: 99%

The Aral Sea Basin

2019

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“…Sun et al [28] used microwave scattering models to analyze the C-band SAR scattering characteristics of snow-covered areas and estimated the distribution of the SWE using SAR data and snow cover data measured in the field. Conde et al [29] presented a methodology for mapping the temporal variation of SWE through the SAR Interferometry technique and Sentinel-1 data.…”

Section: Introductionmentioning

confidence: 99%

Improving SWE Estimation by Fusion of Snow Models with Topographic and Remotely Sensed Data

et al. 2019

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This paper presents a new concept to derive the snow water equivalent (SWE) based on the joint use of snow model (AMUNDSEN) simulation, ground data, and auxiliary products derived from remote sensing. The main objective is to characterize the spatial-temporal distribution of the model-derived SWE deviation with respect to the real SWE values derived from ground measurements. This deviation is due to the intrinsic uncertainty of any theoretical model, related to the approximations in the analytical formulation. The method, based on the k-NN algorithm, computes the deviation for some labeled samples, i.e., samples for which ground measurements are available, in order to characterize and model the deviations associated to unlabeled samples (no ground measurements available), by assuming that the deviations of samples vary depending on the location within the feature space. Obtained results indicate an improved performance with respect to AMUNDSEN model, by decreasing the RMSE and the MAE with ground data, on average, from 154 to 75 mm and from 99 to 45 mm, respectively. Furthermore, the slope of regression line between estimated SWE and ground reference samples reaches 0.9 from 0.6 of AMUNDSEN simulations, by reducing the data spread and the number of outliers.

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“…Obtaining accurate estimation of the SD and SWE is quite challenging depending upon the data availability, variety, and quality, parameterisation method, mathematical model selection, and the hydrometeorological conditions. Hence, it is considered to be an important research element in the cryosphere paradigm (Leinss et al, 2014(Leinss et al, , 2015(Leinss et al, , 2016Conde et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Leinss et al (2014) introduced the use of spaceborne PolSAR for snow height determination, wherein the relationship between the copolar phase difference (CPD) and fresh snow depth is quantitatively analysed by deriving a theoretical model. Moreover, InSAR techniques find significant usage in the cryosphere domain and have been used to measure dry snow depth and SWE in several studies (Conde et al, 2019;Guneriussen et al, 2001;Leinss et al, 2015;Liu et al, 2017). In this context, the Pol-InSAR technique works on the coherent combination of both PolSAR and InSAR observations, thereby enabling the interferogram generation in arbitrary transmit and receive channels (Papathanassiou & Cloude, 2001;Cloude, 2005Cloude, , 2010.…”

Section: Introductionmentioning

confidence: 99%

Snow Depth and Snow Water Equivalent Estimation in the Northwestern Himalayan Watershed using Spaceborne Polarimetric SAR Interferometry

Majumdar¹,

Thakur²,

Chang³

et al. 2019

Preprint

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Snow depth (SD) and Snow Water Equivalent (SWE) constitute essential physical properties of snow and find extensive usage in the hydrological modelling domain. However, the prominent impact of the hydrometeorological conditions and difficult terrain conditions inhibit accurate measurement of the SD and SWE— an ongoing research problem in the cryosphere paradigm. In this context, spaceborne synthetic aperture radar (SAR) systems benefit from global coverage at sufficiently high spatial and temporal resolutions. Still, existing polarimetric and interferometric SAR techniques are susceptible to high volume scattering resulting from the increased snow grain sizes due to the standing (or old) snow formation driven by the temperature induced snow metamorphosis process. Hence, to model this volume decorrelation, the polarimetric SAR interferometry (Pol-InSAR) technique can be effectively applied. In this work, the standing snow depth (SSD) and its corresponding standing snow water equivalent (SSWE) are estimated using the single-baseline Pol-InSAR based hybrid Digital Elevation Model (DEM) differencing and coherence amplitude inversion model. To achieve this, six TerraSAR-X, TanDEM-X Coregistered Single look Slant range Complex (CoSSC) bistatic quad-pol acquisitions between December 2015 and January 2016 over Dhundi (situated in the Beas watershed, northwestern Himalayas, India) are used. Due to the associated problems of model parameter tuning, complex topographical conditions, and limited ground-truth measurements, appropriate sensitivity analyses have been carried out for the parameter optimisation. Furthermore, the uncertainty sources are identified by performing a summer (June 8, 2017) and wintertime (January 8, 2016) comparative analysis of the study area which quantitatively highlights the changes in the percentages of the surface and volume scatterings. Evidently, the improved model displays sufficiently high overall SSD accuracy with coefficient of determination (R^2) ≈ 0.96, Mean Absolute Error (MAE) ≈ 1.61 cm, and Root Mean Square Error (RMSE) ≈ 2.16 cm. Additionally, the respective SSWEs have been calculated by assuming a fixed snow density for each epoch wherein the overall error metrics are R^2 ≈ 0.71, MAE ≈ 5.19 mm, and RMSE ≈ 6.84 mm. Therefore, this research successfully demonstrates the practicability of the improved Pol-InSAR model for SD estimation over rugged terrains.

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On The Estimation of Temporal Changes of Snow Water Equivalent by Spaceborne Sar Interferometry: A New Application for the Sentinel-1 Mission

Cited by 38 publications

References 22 publications

The Aral Sea Basin

The Aral Sea Basin

Improving SWE Estimation by Fusion of Snow Models with Topographic and Remotely Sensed Data

Snow Depth and Snow Water Equivalent Estimation in the Northwestern Himalayan Watershed using Spaceborne Polarimetric SAR Interferometry

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