Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing

Abstract

Abstract. Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth's surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth's climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world's population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth's cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements.

Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast System

“…Overall, STC‐MODSCAG fSCA data provide significant, if incremental, additional WSF skill in specific hydroclimatic environments during specific stages of the forecast season, which seems consistent with the notion that no one remotely sensed snow product is universally optimal at this time (Tsang et al., 2022). NRCS operational experience has typically been that long‐lead/early‐season and short‐lead/late‐season forecasts, before substantial winter snowpack accumulation has occurred and after much of the winter snowpack has melted, and when snow cover tends to be patchy, are often challenging for WSF models driven primarily by snow data.…”

The Rise and Fall of Alaska and Yukon Glaciers Detected by TOPEX/Poseidon and Jason‐2 Altimeters Using a Novel Glacier‐Threshold Method

“…The bias between the altimeter-detected surface and the actual surface can cause an error in the estimated glacier elevation. Specifically, ice surface processes such as blowing snow, firn compaction, surface melting, and refreezing can change the surface scattering properties, leading to elevation measurement errors (Gray et al, 2013(Gray et al, , 2019Hugonnet et al, 2021;Nilsson et al, 2015;Tsang et al, 2022). Previous studies have shown that the penetration effect of radar altimeter data is related to waveform parameters and corrected by a waveform retracking algorithm (Legresy et al, 2005;Nilsson et al, 2015).…”

Snowpack relative permittivity and density derived from near‐coincident lidar and ground‐penetrating radar

“…Currently, no single method or ensemble of methods has proven capable of measuring SWE to the high standard of accuracy established for global monitoring (Dozier et al, 2016). Recent campaigns, such as the U.S.‐based National Aeronautics and Space Agency (NASA) Snow Experiment (SnowEx; Durand et al, 2018) and the Europe‐based NoSREx and APRESS (Tsang et al, 2022), evaluated a suite of remote sensing approaches (e.g., lidar, radar) for SWE‐mapping applications. At the watershed scale, light detection and ranging (lidar) operations, such as the Airborne Snow Observatory (ASO), have demonstrated operational feasibility (Deems et al, 2013; Painter et al, 2016), but can be cost prohibitive.…”

“…backscatter approaches rely on empirical models that are at least partly dependent on the snowpack relative permittivity (Tsang et al, 2022).…”

“…Not all radar SWE retrieval approaches utilize the signal path length through the snow. Some approaches, such as the Ka‐band interferometry for wet snow conditions (Moller et al, 2017), may be better described as depth‐based approaches, whereas Synthetic Aperture Radar (SAR) backscatter approaches rely on empirical models that are at least partly dependent on the snowpack relative permittivity (Tsang et al, 2022).…”