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Spatiotemporal Variability of Lake Water Quality in the Context of Remote Sensing Models

Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT 84112, USA
Department of Geography, University of Utah, Salt Lake City, UT 84112, USA
Department of Civil and Environmental Engineering, Brigham Young University, Provo, UT 84602, USA
Author to whom correspondence should be addressed.
Academic Editors: Yunlin Zhang, Claudia Giardino, Linhai Li, Deepak R. Mishra and Prasad S. Thenkabail
Remote Sens. 2017, 9(5), 409;
Received: 25 February 2017 / Revised: 7 April 2017 / Accepted: 21 April 2017 / Published: 26 April 2017
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
PDF [2575 KB, uploaded 26 April 2017]


This study demonstrates a number of methods for using field sampling and observed lake characteristics and patterns to improve techniques for development of algae remote sensing models and applications. As satellite and airborne sensors improve and their data are more readily available, applications of models to estimate water quality via remote sensing are becoming more practical for local water quality monitoring, particularly of surface algal conditions. Despite the increasing number of applications, there are significant concerns associated with remote sensing model development and application, several of which are addressed in this study. These concerns include: (1) selecting sensors which are suitable for the spatial and temporal variability in the water body; (2) determining appropriate uses of near-coincident data in empirical model calibration; and (3) recognizing potential limitations of remote sensing measurements which are biased toward surface and near-surface conditions. We address these issues in three lakes in the Great Salt Lake surface water system (namely the Great Salt Lake, Farmington Bay, and Utah Lake) through sampling at scales that are representative of commonly used sensors, repeated sampling, and sampling at both near-surface depths and throughout the water column. The variability across distances representative of the spatial resolutions of Landsat, SENTINEL-2 and MODIS sensors suggests that these sensors are appropriate for this lake system. We also use observed temporal variability in the system to evaluate sensors. These relationships proved to be complex, and observed temporal variability indicates the revisit time of Landsat may be problematic for detecting short events in some lakes, while it may be sufficient for other areas of the system with lower short-term variability. Temporal variability patterns in these lakes are also used to assess near-coincident data in empirical model development. Finally, relationships between the surface and water column conditions illustrate potential issues with near-surface remote sensing, particularly when there are events that cause mixing in the water column. View Full-Text
Keywords: spatiotemporal variability; water quality; chlorophyll-a; near-coincident remote sensing spatiotemporal variability; water quality; chlorophyll-a; near-coincident remote sensing

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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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Hansen, C.H.; Burian, S.J.; Dennison, P.E.; Williams, G.P. Spatiotemporal Variability of Lake Water Quality in the Context of Remote Sensing Models. Remote Sens. 2017, 9, 409.

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