NASA Stand-alone Passive Microwave Snow Products

Overview

Passive microwave brightness temperatures can be used to approximate equivalent depth of snow water (SWE). As passively sensed microwave radiation travels through snow its signal attenuates due to scatter out of the line of sight. The amount of scatter is related to the volume of snow grains, and hence to the snowpack depth but also to its density, liquid water content, microstructure characteristics (e.g., snow grain structure), and stratigraphy. Initial algorithms created to retrieve snow depth from passive microwave temperature input (e.g., Chang et al., 1987) was designed for shallow to medium depth (< 150 mm) dry snow (without liquid water in the snowpack) occurring in low relief regions with low forest cover (~< 20%). Under such conditions reasonable accuracy is obtained (e.g., Vuyovich et al., 2014). Updates to the original algorithm (Kelly et al., 2003; Tedesco and Jeyaratnam, 2016) have made use of ancillary data (e.g., forest fraction, climatological snow density fields) aiming to improve retrievals of both snow depth and SWE over a broader range of snow conditions. Nonetheless comparisons of these updated algorithms with in situ data and other gridded SWE products still identify substantial performance issues throughout the snow season (Mortimer et al., 2020). In particular their spatial patterns of hemispheric climatological snow depth and SWE, as well as sub-seasonal integrated measures (i.e. daily/monthly continental snow mass) are not realistic.

While versions of these products are provided with hemispheric coverage for the entire snow season, they are not recommended for casual/non-expert use. Consider how snow conditions will limit seasonal and geographical applicability carefully.

Examples of specific products:

AMSR-E/Aqua L3 Global Snow Water Equivalent EASE-Grids, Version 2, 2002-2011
- Daily output, doi: 10.5067/AMSR-E/AE_DYSNO.002
- 5-Day output, doi: 10.5067/AMSR-E/AE_5DSNO.002
- Monthly output, doi: 10.5067/AMSR-E/AE_MOSNO.002)
AMSR-E/AMSR2 Unified L3 Global 25 km EASE-Grid Snow Water Equivalent, Version 1, 2012-present
- Daily output, doi: 10.5067/8AE2ILXB5SM6
- 5-Day output, doi: 10.5067/0PX911G6417E
- Monthly output, doi: 10.5067/43NH9LHM9YRK
Global Monthly EASE-Grid Snow Water Equivalent Climatology, Version 1, November 1978 through May 2007, doi: 10.5067/KJVERY3MIBPS

Spatial coverage and resolution:

Northern Hemisphere
Products provided on EASE2 grid

Temporal coverage and resolution

Product dependent

Limitations and strengths for application in North Canada

While large portions of snow cover in northern Canada may be dry and free of forest cover, its microstructure and stratigraphy may produce additional inaccuracies in applying the algorithms discussed above. Caution is warranted.

Note that the limitations discussed in this document pertain specifically to the determination of snow depth and/or SWE from passive microwave brightness temperature. The algorithms do accurately determine dry snow presence. Hence a threshold can be applied the SWE fields to determine accurate estimates of spring time variability in snow cover (e.g., Brown et al., 2007, 2010). In addition, abrupt or diurnal changes in PM brightness temperature (the source data for SWE retrievals) can be used as an indicator of melt onset/ wet snow (e.g., Semmens et al., 2013).

References to documents describing the methodology or/and the dataset

Brown, R., C. Derksen, and L. Wang, 2007: Assessment of spring snow cover duration variability over northern Canada from satellite datasets, Remote Sensing of Environment, 111, 367:381, https://doi.org/10.1016/j.rse.2006.09.035.

Brown, R., C. Derksen, and L. Wang, 2010: A multi‐data set analysis of variability and change in Arctic spring snow cover extent, 1967–2008, J. Geophys. Res., 115, D16111, https://doi.org/10.1029/2010JD013975.

Chang, A.T.C., J.L. Foster and D.K. Hall, 1987: Nimbus-7 derived global snow cover parameters, Annals of Glaciology, 9, 39-44.

Kelly, Richard. E. J., A. T. C. Chang, L. Tsang, and J. L. Foster, 2003: A Prototype AMSR-E Global Snow Area and Snow Depth Algorithm. IEEE Transactions on Geoscience and Remote Sensing 41(2): 230-242.

Mortimer, C., L. Mudryk, C. Derksen, K. Luojus, R. Brown, R. Kelly, and M.Tedesco, 2020: Evaluation of long-term Northern Hemisphere snow water equivalent products, The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020.

Tedesco, M., and J. Jeyaratnam, 2016: A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures (ATBD). Remote Sensing, 8(12) 1037.

Semmens, K.A., J. Ramage, A. Bartsch, and G.E Liston, 2013: Early snowmelt events: detection, distribution, and significance in a major subarctic watershed. Environ. Res. Lett. 8 014020, https://doi.org/10.1088/1748-9326/8/1/014020.

Vuyovich, C. M., J. M. Jacobs, and S. F. Daly, 2014: Comparison of passive microwave and modelled estimates of total watershed SWE in the continental United States, Water Resour. Res., 50, 9088– 9102, https://doi.org/10.1002/2013WR014734.