Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US

Abstract

A large amount of fresh water resources are stored in the snowpack, which is the primary source of water for streamflow in many places at middle-to-high latitude areas. Therefore, snow water equivalent (SWE) is a key parameter in the water cycle. Active and passive microwave remote sensing methods have been used to retrieve SWE due to relatively poor resolution of current in situ interpolated maps with good accuracy. However, estimation of SWE has proved challenging, despite several decades of efforts to develop retrieval approaches. Active sensors provide higher-resolution observations. Two recent promising retrieval algorithms using active data are dual frequency dual polarization backscattered power and differential interferometry. These retrieval algorithms have some restrictions on snow characteristics, the environment, and instrument properties. The restrictions limit the snow that is suitable for the specific retrieval algorithm. In order to better understand how much of the snowpack satisfies the precondition of these retrieval approaches, we use a 4 km gridded snowpack product over the contiguous US for years 1997 and 2015. We use a simple scattering model to simulate the scattering characteristics of snow. The snow property maps, simulated scattering characteristics of snow, and environmental conditions are used to filter the suitable snow for each retrieval algorithm. We show that snow wetness and vegetation coverage are the two main limiting conditions for these retrieval algorithms. We show that 39% and 44% of the grid-points with snow satisfy the preconditions of dual polarization dual frequency retrieval algorithms at 13.5 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important limiting factors for dual polarization dual frequency retrieval method are dryness of snow, penetration depth, and vegetation-free constraints. The backscattered power in dual polarization dual frequency method is more sensitive to snow density and grain radius rather than to snow depth. We also show that 55% and 53% of the grid-points with snow satisfy the precondition of differential interferometry retrieval algorithms at 1 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important precondition-limiting factors for differential interferometry are dryness of snow and vegetation-free constraints. The differential interferometry phase retrieval algorithm is equally sensitive to snow height and snow density variations and is independent of snow grain radius.

Weather-Dependent Nonlinear Microwave Behavior of Seasonal High-Elevation Snowpacks

“…Because of nonlinear dynamic sensitivity and dramatic changes between early and late melt regimes, and because of sensitivity to seasonal weather at local and regional scales (e.g., Figure 15 and Figure S19), finding unique relationships for retrieval is challenging (i.e., calibration of model parameters may not be robust). Whereas soil emissions below the snow-soil interface are not accounted for here, previous point-scale studies of microwave emissions including the contribution of the soil substrate [53,54] indicate that between December 1 at the time the soil freezes and March 1 in this study, one would expect to see an increase in brightness temperatures on the order of 10K, followed by a decrease of the same order of magnitude in the spring when the soil substrate is fully thawed, which potentially amplifies the limit-cycles shown in Figure 16c and thus reduces ambiguity (Figure S23). Nevertheless, coupling snow hydrology and microwave models provides a framework for physically-based interpretation and disambiguation of microwave measurements of snowpack properties toward a monitoring system that can be achieved by data-assimilation.…”

“…This approach explains the spatial variability of accumulation rates (2-10 cm/dB) from scatterometer data matching different snow types in Antarctica [26]. Using a 1-layer snow model and one-parameter at a time sensitivity analysis, Oveigharan et al [53] showed that the backscattered power in dual-polarization dual-frequency retrievals at C-and Ku-bands is more sensitive to snow density and grain radius than to snow depth. This raises the possibility of retrieving information on snowpack stratigraphy.…”

A Snow Water Equivalent Retrieval Framework Coupling 1D Hydrology and Passive Microwave Radiative Transfer Models

“…The above studies used only microwave remote sensing data to retrieve SWE. However, due to complex environmental conditions in different regions of the world, the retrieval of SWE from microwave remote sensing observations suffers from ill-posed problems and low sensitivity issues under various snow conditions, such as thin, thick, and wet snow [17,18]. The uncertainty in SWE retrieval comes from the fact that there is limited knowledge about snow vertical profiles, snow/soil interfaces, plant coverages, and other snow hydrological processes, which leads to the ambiguity issue.…”