Algorithm for retrieval of snow mass from Ku- and X-band radar backscatter measurements

Rott, Helmut; Nägler, Thomas; Voglmeier, Karl; Kern, Michael; Macelloni, Giovanni; Gai, Marco; Cortesi, Ugo; Scheiber, Rolf; Hajnsek, Irena; Pulliainen, Jouni; Flach, Dominic

doi:10.1109/igarss.2012.6350911

Cited by 6 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In satellite-based SWE products, mountain ranges are masked out due to the low sensor resolution of several kilometers [Rott et al, 2004;Liang et al, 2008;Clifford, 2010;Nolin, 2010;Dietz et al, 2012]. Recent developments based on high-resolution Synthetic Aperture Radar satellites allow for mapping wet snow cover [Rott et al, 2004[Rott et al, , 2012Pettinato et al, 2013;Nagler et al, 2016;Rondeau-Genesse et al, 2016]. However, no information related to the mass of the snowpack like SD or SWE can be retrieved.…”

Section: Introductionmentioning

confidence: 99%

Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing

Schattan

Baroni

Oswald

et al. 2017

Water Resources Research

View full text Add to dashboard Cite

The characteristics of an aboveground cosmic‐ray neutron sensor (CRNS) are evaluated for monitoring a mountain snowpack in the Austrian Alps from March 2014 to June 2016. Neutron counts were compared to continuous point‐scale snow depth (SD) and snow‐water‐equivalent (SWE) measurements from an automatic weather station with a maximum SWE of 600 mm (April 2014). Several spatially distributed Terrestrial Laser Scanning (TLS)‐based SD and SWE maps were additionally used. A strong nonlinear correlation is found for both SD and SWE. The representative footprint of the CRNS is in the range of 230–270 m. In contrast to previous studies suggesting signal saturation at around 100 mm of SWE, no complete signal saturation was observed. These results imply that CRNS could be transferred into an unprecedented method for continuous detection of spatially averaged SD and SWE for alpine snowpacks, though with sensitivity decreasing with increasing SWE. While initially different functions were found for accumulation and melting season conditions, this could be resolved by accounting for a limited measurement depth. This depth limit is in the range of 200 mm of SWE for dense snowpacks with high liquid water contents and associated snow density values around 450 kg m−3 and above. In contrast to prior studies with shallow snowpacks, interannual transferability of the results is very high regardless of presnowfall soil moisture conditions. This underlines the unexpectedly high potential of CRNS to close the gap between point‐scale measurements, hydrological models, and remote sensing of the cryosphere in alpine terrain.

show abstract

Section: Introductionmentioning

confidence: 99%

Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing

Schattan

Baroni

Oswald

et al. 2017

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…The other one is built with a priori constraint, as shown in Equation (14), that was also used in the proposed CoReH2O SWE retrieval algorithm, where an a priori constraint was applied for the effective grain radius [41]: …”

Section: Cost Functionmentioning

confidence: 99%

Estimating Snow Water Equivalent with Backscattering at X and Ku Band Based on Absorption Loss

et al. 2016

View full text Add to dashboard Cite

Snow water equivalent (SWE) is a key parameter in the Earth's energy budget and water cycle. It has been demonstrated that SWE can be retrieved using active microwave remote sensing from space. This necessitates the development of forward models that are capable of simulating the interactions of microwaves and the snow medium. Several proposed models have described snow as a collection of sphere-or ellipsoid-shaped ice particles embedded in air, while the microstructure of snow is, in reality, more complex. Natural snow usually forms a sintered structure following mechanical and thermal metamorphism processes. In this research, the bi-continuous vector radiative transfer (bi-continuous-VRT) model, which firstly constructs snow microstructure more similar to real snow and then simulates the snow backscattering signal, is used as the forward model for SWE estimation. Based on this forward model, a parameterization scheme of snow volume backscattering is proposed. A relationship between snow optical thickness and single scattering albedo at X and Ku bands is established by analyzing the database generated from the bi-continuous-VRT model. A cost function with constraints is used to solve effective albedo and optical thickness, while the absorption part of optical thickness is obtained from these two parameters. SWE is estimated after a correction for physical temperature. The estimated SWE is correlated with the measured SWE with an acceptable accuracy. Validation against two-year measurements, using the SnowScat instrument from the Nordic Snow Radar Experiment (NoSREx), shows that the estimated SWE using the presented algorithm has a root mean square error (RMSE) of 16.59 mm for the winter of 2009

show abstract

“…We present a multi-frequency approach, in which we decouple the background soil contribution using C-band satellite data, from the snow volume scattering contribution at Ku-band. By illustrating the successful forward simulation of measured Ku-band backscatter using an open source electromagnetic model, we successfully demonstrate a crucial component of the cost function SWE retrieval concepts described in the literature (Rott et al, 2012).…”

Section: Introductionmentioning

confidence: 92%

“…In this study, we utilized measurements from this UMass instrument, deployed during the 2018/19 winter over the Trail Valley Creek study area in the Northwest Territories, Canada, to advance science readiness activities of TSMM (Siqueira et al, 2021). This paper focuses on the forward radiative transfer modeling component of a future SWE retrieval algorithm similar to what is proposed by (Rott et al, 2012;Pan et al, 2023). The data collected during the TVC experiment are used to validate SMRT as the forward model used for such SWE algorithm and will provide insight on the model parameterization needed to link snow properties to the Ku-Band signal, i.e.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of airborne Ku-Band SAR Using Forward Radiative Transfer Modeling to Estimate Snow Water Equivalent: The Trail Valley Creek 2018/19 Snow Experiment

Montpetit,

King,

Meloche

et al. 2024

Preprint

View full text Add to dashboard Cite

Abstract. Accurate snow information at high spatial and temporal resolution is needed to support climate services, water resource management, and environmental prediction services. However, snow remains the only element of the water cycle without a dedicated Earth Observation mission. The snow scientific community has shown that Ku-Band radar measurements provide quality snow information with its sensitivity to snow water equivalent and the wet/dry state of snow. With recent developments of tools like the Snow MicroPenetrometer (SMP) to retrieve snow microstructure data in the field and radiative transfer models like the Snow Microwave Radiative Transfer Model (SMRT), it becomes possible to properly characterize the snow and how it translates into radar backscatter measurements. An experiment at Trail Valley Creek (TVC), Northwest Territories, Canada was conducted during the winter of 2018/19 in order to characterize the impacts of varying snow geophysical properties on Ku-Band radar backscatter at a 100-m scale. Airborne Ku-Band data was acquired using the University of Massachusetts radar instrument. This study shows that it is possible to calibrate SMP data to retrieve statistical information on snow geophysical properties and properly characterize a representative snowpack at the experiment scale. The tundra snowpack measured during the campaign can be characterize by two layers corresponding to a rounded snow grain layer and a depth hoar layer. Using Radarsat-2 and TerraSAR-X data, soil background roughness properties were retrieved (msssoil = 0.010±0.002) and it was shown that a single value could be used for the entire domain. Microwave snow grain size polydispersity values of 0.74 and 1.11 for rounded and depth hoar snow grains, respectively, was retrieved. Using the Geometrical Optics surface backscatter model, the retrieved effective soil permittivity increased from C-Band (εsoil = 2.47) to X-Band (εsoil = 2.61), to Ku-Band (εsoil = 2.77) for the TVC domain. Using SMRT and the retrieved soil and snow parameterizations, an RMSE of 2.6 dB was obtained between the measured and simulated Ku-Band backscatter values when using a global set of parameters for all measured sites. When using a distributed set of soil and snow parameters, the RMSE drops to 0.9 dB. This study thus shows that it is possible to link Ku-Band radar backscatter measurements to snow conditions on the ground using a priori knowledge of the snow conditions to retrieve SWE at the 100 m scale.

show abstract

Algorithm for retrieval of snow mass from Ku- and X-band radar backscatter measurements

Cited by 6 publications

References 0 publications

Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing

Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing

Estimating Snow Water Equivalent with Backscattering at X and Ku Band Based on Absorption Loss

Retrieval of airborne Ku-Band SAR Using Forward Radiative Transfer Modeling to Estimate Snow Water Equivalent: The Trail Valley Creek 2018/19 Snow Experiment

Contact Info

Product

Resources

About