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 million square km of Earth's surface (31 % of the land area) each year, and is thus an important expression of and driver of the Earth’s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (~ −13 %/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 snow stored 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 will not be 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. We describe radar interactions with snow-covered landscapes, characterization of snowpack properties using radar measurements, and refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. 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 millimetre-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modelling, 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, densities, and layering. We describe radar interactions with snow-covered landscapes, 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 applications communities on progress made in recent decades, and sets the stage for a new era in SWE remote-sensing from SAR measurements.

Estimating snow accumulation and ablation with L-band InSAR

“…To account for the significant issue of cloud cover, future investigations should leverage optical sensor fusion and interpolation methods (Rittger et al, 2021;Dozier et al, 2008) and focus on how to best combine SAR and optical data for SWE change monitoring. Any future SAR-derived SWE product such as the Ku-and X-band approach (Tsang et al, 2022), or the P-band Signals of Opportunity (SoOp) (Yueh et al, 2021) will require optical data to delineate snow covered pixels in midlatitude mountain environments, making this sensor fusion research applicable for radars other than NISAR. Continued work on how to best fuse disparate sensors through cloud computing and machine learning will be key to progressing our knowledge of mountain snowpack monitoring.…”

Exploring the Use of Multi-source High-Resolution Satellite Data for Snow Water Equivalent Reconstruction over Mountainous Catchments

“…showing an increase of the backscattering in correspondence of a snowfall (e.g., Lievens et al, 2022;Tsang et al, 2021). The poor temporal resolution represents however a strong limitation for a practical application.…”

“…Moreover, all these techniques work only in dry conditions while the scarce penetration of the electromagnetic signal in wet conditions is invalidating their applicability in monitoring the SWE evolution during the melting season. Several review articles are available for more details about SWE retrieval using SAR acquisitions (e.g., Tsang et al, 2021).…”

Investigation of Environmental Effects on Coherence Loss in SAR Interferometry for Snow Water Equivalent Retrieval

“…While initiatives to reprocess passive microwave radiometry datasets ( [10], [11]), as well as improved sensors [12], will provide an improvement to spatial resolution, these will still fall short of the requirements of many applications. Observation of radar backscattering intensity has been proposed as a solution to address both the accuracy and limited spatial resolution of passive microwave observations [13]; however, despite progress in physically-based SWE retrieval methods for radar [14], [15], challenges still remain in particular concerning the quantification of the role of snow microstructure in the radar response. Moreover, no satellite sensors using the required high frequencies have been launched to date.…”