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

Abstract

Current methods for retrieving SWE (snow water equivalent) from space rely on passive microwave sensors. Observations are limited by poor spatial resolution, ambiguities related to separation of snow microstructural properties from the total snow mass, and signal saturation when snow is deep (~>80 cm). The use of SAR (Synthetic Aperture Radar) at suitable frequencies has been suggested as a potential observation method to overcome the coarse resolution of passive microwave sensors. Nevertheless, suitable sensors operating from space are, up to now, unavailable. Active microwave retrievals suffer, however, from the same difficulties as the passive case in separating impacts of scattering efficiency from those of snow mass. In this study, we explore the potential of applying active (radar) and passive (radiometer) microwave observations in tandem, by using a dataset of co-incident tower-based active and passive microwave observations and detailed in situ data from a test site in Northern Finland. The dataset spans four winter seasons with daily coverage. In order to quantify the temporal variability of snow microstructure, we derive an effective correlation length for the snowpack (treated as a single layer), which matches the simulated microwave response of a semi-empirical radiative transfer model to observations. This effective parameter is derived from radiometer and radar observations at different frequencies and frequency combinations (10.2, 13.3 and 16.7 GHz for radar; 10.65, 18.7 and 37 GHz for radiometer). Under dry snow conditions, correlations are found between the effective correlation length retrieved from active and passive measurements. Consequently, the derived effective correlation length from passive microwave observations is applied to parameterize the retrieval of SWE using radar, improving retrieval skill compared to a case with no prior knowledge of snow-scattering efficiency. The same concept can be applied to future radar satellite mission concepts focused on retrieving SWE, exploiting existing methods for retrieval of snow microstructural parameters, as employed within the ESA (European Space Agency) GlobSnow SWE product. Using radar alone, a seasonally optimized value of effective correlation length to parameterize retrievals of SWE was sufficient to provide an accuracy of <25 mm (unbiased) Root-Mean Square Error using certain frequency combinations. A temporally dynamic value, derived from e.g., physical snow models, is necessary to further improve retrieval skill, in particular for snow regimes with larger temporal variability in snow microstructure and a more pronounced layered structure.

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

“…Monitoring of snow cover area is well established based on optical data, whereas current Snow Water Equivalent (SWE, i.e., the total water mass in liquid equivalent) products mostly rely on passive microwave measurements [114]. Active microwave measurements at high frequency (Ku-band) are sensitive to volume scattering of the dry snow as well as the wet or dry status of the snow cover [115]. They are therefore also relevant for future mission concepts dedicated to accurate snow water equivalent retrieval from space.…”

Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

“…Field data presented in this study were collected between 4-9 April 2013 and 14-22 March 2018 within the research basin of Trail Valley Creek (TVC), NWT, Canada (68 • 44 N, 133 • 33 W), located at the southern edge of the Arctic tundra. Measurements of snow microstructure were made mainly on graminoid tundra, which dominates the land cover, as well as in patches of taller shrubs (willow or alder) found on south-facing slopes and in proximity to drainage channels and water features (Marsh et al, 2010). Snow pit and snow trench locations (Fig.…”

“…The Snow Microwave Radiative Transfer (SMRT) backscatter and emission model (Picard et al, 2018) was used to illustrate potential retrieval error from a dual Ku-band radar system (cf. King et al, 2018;Lemmetyinen et al, 2018). Three layers were assumed within the snowpack for snow up to 0.7 m in depth: depth hoar (DH), wind slab (WS), and surface snow (SS), with layer thickness dependent on total snow depth and based on relationships derived from snow trenches.…”

“…Derksen et al, 2014). However, recent experimental work using Ku-band radar suggests using two frequencies, each with different sensitivities to wind slab and depth hoar, may mitigate this primary source of uncertainty (King et al, 2018;Lemmetyinen et al, 2018). In order to apply this two-frequency approach in a distributed manner, we need an understanding of layer length scales: horizontal distances over which the physical properties of each layer decouple and become statistically uncorrelated to each other.…”

Mapping snow depth at very high spatial resolution with RPAS photogrammetry

“…CC BY 4.0 License. active microwave sensors offer the capacity to retrieve estimates of snow water equivalent directly from space-borne platforms, but also suffer substantial limitations, including coarse spatial resolution in the case of passive microwave sensors, and complexities in successfully processing snow signals and accounting for complex terrain in the case of both passive and active sensors (Lemmetyinen et al, 2018).…”