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Remote Sens. 2016, 8(12), 1037; doi:10.3390/rs8121037

A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

1
Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, NY 10964, USA
2
NASA Goddard Institute for Space Studies, New York, NY 10025, USA
3
The City College of New York, New York, NY 10031, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Gabriel Senay, Xiaofeng Li and Prasad S. Thenkabail
Received: 21 May 2016 / Revised: 19 November 2016 / Accepted: 6 December 2016 / Published: 21 December 2016
View Full-Text   |   Download PDF [8063 KB, uploaded 21 December 2016]   |  

Abstract

Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer—Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration’s (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size. View Full-Text
Keywords: snow; passive microwave; AMSR-E snow; passive microwave; AMSR-E
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Tedesco, M.; Jeyaratnam, J. A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures. Remote Sens. 2016, 8, 1037.

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