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Remote Sens. 2017, 9(6), 523; doi:10.3390/rs9060523

Possibility of Estimating Seasonal Snow Depth Based Solely on Passive Microwave Remote Sensing on the Greenland Ice Sheet in Spring

Earth Observation Research Center, Japan Aerospace Exploration Agency, 2-1-1 Sengen, Tsukuba, Ibaraki 305-8505, Japan
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Authors to whom correspondence should be addressed.
Academic Editors: Claudia Notarnicola, Xiaofeng Li and Prasad S. Thenkabail
Received: 1 February 2017 / Revised: 21 April 2017 / Accepted: 27 April 2017 / Published: 25 May 2017
(This article belongs to the Special Issue Snow Remote Sensing)
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Abstract

Sea level rise related to the melting and thinning of the Greenland Ice Sheet (GrIS), a subject of growing concern in recent years, will eventually affect the global climate. Although the melting of snow on the GrIS is actively monitored by passive microwave remote sensing, very few studies have estimated the seasonal GrIS snow depth using this technique. In this study, to estimate seasonal snowpack on GrIS, we investigated the microwave property and optimum physical parameters. We used our microwave radiative transfer model to create a lookup table and a simple satellite retrieval algorithm to estimate seasonal snow depth on GrIS in spring, based on the microwave satellite brightness temperature from AMSR-E and AMSR2. Our research suggests there is potential for estimating snow depth based solely on GrIS passive microwave remote sensing data. We validated these estimates against in situ snow depths at several sites and compared them with the snow spatial distributions over the entire GrIS of several major products (ERA-interim, MAR ver. 5.3.1 and GLDAS-CLM) that evaluate snow depth. View Full-Text
Keywords: seasonal snow depth; Greenland Ice Sheet; passive microwave remote sensing; AMSR-E; AMSR2 seasonal snow depth; Greenland Ice Sheet; passive microwave remote sensing; AMSR-E; AMSR2
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Tsutsui, H.; Maeda, T. Possibility of Estimating Seasonal Snow Depth Based Solely on Passive Microwave Remote Sensing on the Greenland Ice Sheet in Spring. Remote Sens. 2017, 9, 523.

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