Abstract
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.
Highlights
Over the past decade, remote sensing of water quality has become more widely used and the extent of applications has grown tremendously, especially in non-coastal environments
Remote Sens. 2017, 9, 409 correction and conversion from digital numbers to reflectance at the near-surface of the water body), collecting coincident field measurements of chlorophyll-a, and using regression or other statistical modeling techniques to develop a relationship between the field-measured concentrations and remotely sensed reflectance from the corresponding pixel or group of pixels
While there are many additional considerations for water quality this paper focuses on the three issues outlined above: (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
Summary
Remote sensing of water quality has become more widely used and the extent of applications has grown tremendously, especially in non-coastal environments. Increased availability of imagery data and processed data products has facilitated increased use and application. Despite all of these advances, there are a number of issues that remain to be addressed to support more effective and accurate remote sensing model development and application. Many of these issues stem from traditional assumptions associated with the use and application of remote sensing data, and do not consider conditions and processes that are specific to the water bodies of interest
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