A Joint Landsat- and MODIS-Based Reanalysis Approach for Midlatitude Montane Seasonal Snow Characterization

Steven A Margulis,Elisabeth Baldo,Yufei Liu

doi:10.3389/feart.2019.00272

Steven A Margulis, Elisabeth Baldo + Show 1 more

Open Access

https://doi.org/10.3389/feart.2019.00272

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Abstract

A new snow reanalysis method is presented that is designed to jointly assimilate Landsat- and MODIS-derived (MODSCAG) fractional snow covered area (fSCA) to characterize seasonal snow in remote regions like High Mountain Asia (HMA) where in situ data is severely lacking. The method leverages existing readily available global datasets for forcing a snow model and uses the fSCA retrievals along with the ensemble prior model estimates to derive posterior estimates using a Bayesian framework. The method addresses MODIS viewing-geometry effects on the fSCA retrievals by accounting for viewing angle dependent measurement errors and using a CDF-matching technique to improve the joint fSCA measurement consistency before assimilation. The method was verified through comparison with the Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates over the Tuolumne River watershed in California. The posterior SWE estimates were shown to be much more consistent with the independent ASO estimates across the three WYs examined. Tests over Tuolumne showed that in cases where sufficient Landsat observations are available (i.e. with multiple sensors and in areas of overlapping Landsat tiles), assimilation of only Landsat data may be optimal, which is attributable primarily to the higher spatial resolution of the raw Landsat data, but that in cases with fewer Landsat measurements (i.e. with single Landsat tiles and/or significant reduction due to clouds), the additional screened and CDF-matched MODIS-based measurements can have a positive (albeit marginal) impact. Illustrative results are presented for nine HMA test tiles to illustrate how the method can provide posterior estimates of the space-time climatology of SWE storage in areas where in situ data does not generally exist. Ongoing work is being conducted to use the method outlined herein to generate an HMA-wide reanalysis dataset that will provide an opportunity for a more thorough characterization of HMA seasonal snow storage and dynamics over the joint Landsat-MODIS era. The method is generalizable to any midlatitude montane region where seasonal snow is important.

Highlights

Midlatitude montane seasonal snowpacks are a vital part of the global water and energy budget and the resulting snowmelt-driven runoff provides fresh water to a significant fraction of the global population (Barnett et al, 2005; Mankin et al, 2015)
The method was verified through comparison with the Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates over the Tuolumne River watershed in California
The posterior SWE estimates were shown to be much more consistent with the independent ASO estimates across the three water year (WY) examined

Summary

Introduction

Midlatitude montane seasonal snowpacks are a vital part of the global water and energy budget and the resulting snowmelt-driven runoff provides fresh water to a significant fraction of the global population (Barnett et al, 2005; Mankin et al, 2015). Accurate and generally applicable SWE retrieval algorithms based on these measurements are complicated by many factors including (Li et al, 2017): coarse-scale measurements (in the case of passive microwave sensors) that do not capture sub-grid variations, decreasing sensitivity with respect to deep SWE, and a lack of sensitivity altogether to SWE in forest-covered or wet snow conditions. All of these factors lead to significant retrieval errors, especially in mountainous terrain (Tedesco et al, 2010; Frei et al, 2012)

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