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

Abstract

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.

Temporal and Spatial Variations of Secchi Depth and Diffuse Attenuation Coefficient from Sentinel-2 MSI over a Large Reservoir

“…In addition to the high spatial resolution of 10-60 m, it presents a high re-visitation, with systematic global coverage in the Alqueva reservoir region of two to three days, considering the two twin satellites in operation. Landsat also has a high spatial resolution (30 m), but much shorter temporal resolution, with 16 days difference between each image, and this revisit time may be problematic for the detection of short events [38]. MODIS has high revisitation (every 1-2 days), but lower spatial resolution of 250-1000 m. Thus, Sentinel-2 represents the best choice for satellite monitoring of Alqueva reservoir compared with these two satellites.…”

Excessive Fertilizer Usage Drives Agriculture Growth but Depletes Water Quality

“…Studying of the nutrient contamination across the spatial spread of large water bodies is extremely difficult using traditional methods like field monitoring (Ritchie et al, 2003). Remote sensing (RS) approaches (Hansen et al, 2017) can become a valuable tool for detecting water contamination and also record Global Water distribution its temporal variations, depending on the spectral and spatial resolution of these sensors. This paper focuses on estimation of nutrient contamination in a large water body in India using remote sensing techniques.…”

Phosphorus Distribution in Delta Sediments: A Unique Data Set from Deer Creek Reservoir

“…The reservoir was completed in the early 1940s [27]. Owing to DCR's importance as a water supply reservoir, researchers and government agencies have performed several water quality studies [28][29][30][31][32].…”