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Remote Sens. 2017, 9(7), 723; https://doi.org/10.3390/rs9070723

Evaluation of the Multi-Scale Ultra-High Resolution (MUR) Analysis of Lake Surface Temperature

1
Department of Atmospheric Sciences, University of Utah, 135 South 1460 East, Rm 819, Salt Lake City, UT 84112, USA
2
National Aeronautics and Space Administration Jet Propulsion Laboratory, California Institute of Technology, M/S 300/323 4800 Oak Grove Dr., Pasadena, CA 91109, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Xiaofeng Li
Received: 25 May 2017 / Revised: 30 June 2017 / Accepted: 7 July 2017 / Published: 13 July 2017
(This article belongs to the Collection Sea Surface Temperature Retrievals from Remote Sensing)
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Abstract

Obtaining accurate and timely lake surface water temperature (LSWT) analyses from satellite remains difficult. Data gaps, cloud contamination, variations in atmospheric profiles of temperature and moisture, and a lack of in situ observations provide challenges for satellite-derived LSWT for climatological analysis or input into geophysical models. In this study, the Multi-scale Ultra-high Resolution (MUR) analysis of LSWT is evaluated between 2007 and 2015 over a small (Lake Oneida), medium (Lake Okeechobee), and large (Lake Michigan) lake. The advantages of the MUR LSWT analyses include daily consistency, high-resolution (~1 km), near-real time production, and multi-platform data synthesis. The MUR LSWT versus in situ measurements for Lake Michigan (Lake Okeechobee) have an overall bias (MUR LSWT-in situ) of −0.20 °C (0.31 °C) and a RMSE of 0.86 °C (0.91 °C). The MUR LSWT versus in situ measurements for Lake Oneida have overall large biases (−1.74 °C) and RMSE (3.42°C) due to a lack of available satellite imagery over the lake, but performs better during the less cloudy 15 July–30 September period. The results of this study highlight the importance of calculating validation statistics on a seasonal and annual basis for evaluating satellite-derived LSWT. View Full-Text
Keywords: lake surface temperature; sea surface temperature (SST); surface state; lake modeling; numerical weather prediction; surface analysis lake surface temperature; sea surface temperature (SST); surface state; lake modeling; numerical weather prediction; surface analysis
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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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Crosman, E.; Vazquez-Cuervo, J.; Chin, T.M. Evaluation of the Multi-Scale Ultra-High Resolution (MUR) Analysis of Lake Surface Temperature. Remote Sens. 2017, 9, 723.

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