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

Abstract

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

Retrieval of Effective Correlation Length and Snow Water Equivalent from Radar and Passive Microwave Measurements

“…Similar retrieval results for SWE were obtained by using an absorption-loss-based methodology using the same NoSREx dataset [45]; that study also showed that a priori definition of snow-scattering characteristics (in the form of single scattering albedo) was essential to achieve reasonable retrievals. Nevertheless, further improvement may be achieved by applying more sophisticated retrieval schemes, including balancing or constraining the parameterization of the retrieval (following e.g., [45]), as well as by estimating the microstructural evolution of snow based on a physical model.…”

“…Nevertheless, further improvement may be achieved by applying more sophisticated retrieval schemes, including balancing or constraining the parameterization of the retrieval (following e.g., [45]), as well as by estimating the microstructural evolution of snow based on a physical model. Direct coupling with physical snow models has been previously demonstrated using the same dataset [46], including retrieval of SWE using a coupled model.…”

The Potential of Earth Observation for the Analysis of Cold Region Land Surface Dynamics in Europe—A Review

“…They found that the satisfactory SWE results can only be obtained in sparse forested (<30%) areas. Cui et al [234] developed a bi-continuous-VRT model to estimate SWE from X-and Ku-band SAR data based on absorption loss. Meanwhile, the authors discovered good relationships between the single scattering albedo and snow optical thickness at X-and Ku-band [234].…”

Evaluation of Sub-Kilometric Numerical Simulations of C-Band Radar Backscatter over the French Alps against Sentinel-1 Observations

“…The exploitation of C-band SAR signals over snow can proceed further than mapping of wet snow. Inferring other snow properties, such as snow depth, mass or internal properties, can either be performed through geophysical retrievals [14][15][16][17][18] or data assimilation using snowpack models [4,19]. In both cases, this requires a quantitative understanding of the relationships between snow properties and SAR signal.…”