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Open AccessArticle

Mapping of Snow Depth by Blending Satellite and In-Situ Data Using Two-Dimensional Optimal Interpolation—Application to AMSR2

1
Earth System Science Interdisciplinary Center (ESSIC), University of Maryland College Park, College Park, MD 20740, USA
2
National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite Information Center (NESDIS), Madison, WI 53706, USA
3
National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite Information Center (NESDIS), College Park, MD 20740, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(24), 3049; https://doi.org/10.3390/rs11243049
Received: 4 November 2019 / Revised: 3 December 2019 / Accepted: 13 December 2019 / Published: 17 December 2019
(This article belongs to the Collection Feature Papers for Section Environmental Remote Sensing)
The development of a snow depth product over North America is investigated by applying two-dimensional optimal interpolation to passive microwave satellite-derived and in-situ measured snow depth. At each snow-covered satellite footprint, the technique computes a snow depth increment as the weighted average of data increments, and updates the satellite-derived snow depth accordingly. Data increments are computed as the difference between the in-situ-measured and satellite snow depth at station locations surrounding the satellite footprint. Calculation of optimal weights is based on spatial lag autocorrelation of snow depth increments, modelled as functions of horizontal distance and elevation difference between pairs of observations. The technique is applied to Advanced Microwave Scanning Radiometer 2 (AMSR2) snow depth and in-situ snow depth obtained from the Global Historical Climatology Network. The results over North America during January–February 2017 indicate that the technique greatly enhances the performance of the satellite estimates, especially over mountain terrain, albeit with an accuracy inferior to that over low elevation areas. Moreover, the technique generates more accurate output compared to that from NOAA’s Global Forecast System, with implications for improving the utilization of satellite data in snow assessments and numerical weather prediction. View Full-Text
Keywords: snow depth; optimal interpolation; passive microwave satellite remote sensing; in-situ station measurements snow depth; optimal interpolation; passive microwave satellite remote sensing; in-situ station measurements
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MDPI and ACS Style

Kongoli, C.; Key, J.; Smith, T.M. Mapping of Snow Depth by Blending Satellite and In-Situ Data Using Two-Dimensional Optimal Interpolation—Application to AMSR2. Remote Sens. 2019, 11, 3049.

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