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Geology topics

Wilson B. Salls

Publications and source records attributed to Wilson B. Salls.

2 recordsLinked to original sources

Advancements in satellite observations of inland and coastal waters: Building towards a global validation network

The use of satellite-based remote sensing imagery for water quality monitoring of inland and coastal waters has become widespread over the last few decades, with the expansion of, and investment in, operational Earth-observing missions. Satellite-based sensors are uniquely suited to provide synoptic, system-wide water quality parameter estimates that supplement traditional field-based sampling methods. The remote sensing of water quality parameter estimates is particularly valuable in systems with high temporal and spatial variability, as well as in areas that are difficult to access, or where agencies lack funding for routine monitoring. However, optically complex inland and coastal waters pose additional challenges for developing robust remote sensing retrieval models for optical properties and water quality parameters. One of the biggest challenges is collecting high quality field measurements that are used to calibrate and validate the retrieval algorithms. Here, we present the current status of satellite missions, field methods that include instruments used and commonly measured parameters, and repositories of historical field data that are relevant to inland and coastal water studies. We then present data requirements for model validation and highlight gaps in validation coverage. Finally, we provide suggestions for future field campaigns to improve coordination with remote sensing data collection and to ensure that field data is well suited for use in model or algorithm development.

Remote Sensing

Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales

Cyanobacterial blooms can have negative effects on human health and local ecosystems. Field monitoring of cyanobacterial blooms can be costly, but satellite remote sensing has shown utility for more efficient spatial and temporal monitoring across the United States. Here, satellite imagery was used to assess the annual frequency of surface cyanobacterial blooms, defined for each satellite pixel as the percentage of images for that pixel throughout the year exhibiting detectable cyanobacteria . Cyanobacterial frequency was assessed across 2,196 large lakes in 46 states across the continental United States (CONUS) using imagery from the European Space Agency’s Ocean and Land Colour Instrument for the years 2017 through 2019. In 2019, across all satellite pixels considered, annual bloom frequency had a median value of 4% and a maximum value of 100%, the latter indicating that for those satellite pixels, a cyanobacterial bloom was detected by the satellite sensor for every satellite image considered. In addition to annual pixel-scale cyanobacterial frequency, results were summarized at the lake- and state-scales by averaging annual pixel-scale results across each lake and state. For 2019, average annual lake-scale frequencies also had a maximum value of 100%, and Oregon and Ohio had the highest average annual state-scale frequencies at 65% and 52%. Pixel-scale frequency results can assist in identifying portions of a lake that are more prone to cyanobacterial blooms, while lake- and state-scale frequency results can assist in the prioritization of sampling resources and mitigation efforts. Satellite imagery is limited by the presence of snow and ice, as imagery collected in these conditions are quality flagged and discarded. Thus, annual bloom frequencies within nine climate regions were investigated to determine whether missing data biased results in climate regions more prone to snow and ice, given that their annual summaries would be weighted toward the summer months when cyanobacterial blooms tend to occur. Results were unbiased by the time period selected in most climate regions, but a large bias was observed for the Northwest Rockies and Plains climate region. Moderate biases were observed for the Ohio Valley and the Southeast climate regions. Finally, a clustering analysis was used to identify areas of high and low cyanobacterial frequency across CONUS based on average annual lake-scale cyanobacterial frequencies for 2019. Several clusters were identified that transcended state, watershed, and eco-regional boundaries. Combined with additional data, results from the clustering analysis may offer insight regarding large-scale drivers of cyanobacterial blooms.

Ecological Indicators