Matching Satellite and Public Data To Assess Water Quality

9 Dec 2019 by Staff - Water Diplomat
FORT COLLINS CO, United States

New research from Colorado State University (CSU) in the United States has matched large public datasets of water quality observations with satellite imagery to enable more efficient, cost-effective measurement of water quality.

Historically, water quality has been verified through field sampling that, at best, is minimal and often is unreliable. Thorough examination of all water bodies would be very expensive and is probably not highly reliable and, as a consequence, reliable and comprehensive water quality datasets are rare.

Threats to water quality and quantity are numerous and difficult to measure as they are so varied: nutrients from agricultural runoff support algae blooms; sedimentation in reservoirs cause distribution challenges; and dissolved carbon from decaying leaves interrupt chemical reactions that keep water clean and safe for drinking.

The researchers examined the use of remote sensing from satellite imagery to create datasets at a continental scale.

Water’s color reveals its contents. For instance green swirls might indicate algae bloom and high levels of chlorophyll; tan water is evidence of sediment; brown water contains dissolved organic carbon compounds.

By using satellite imagery, the color variations can be revealed every two weeks. "These satellites have fundamentally changed how we understand long-term changes in agriculture, forests, fires, and other land cover changes," explained team leader Matt Ross. "However, there has been less use of the Landsat archive for understanding inland water quality changes".

The next step is to match the satellite images with on-the-ground observations. By coordinating the field sampling with the images, new algorithms can be developed to predict water quality.

The result is a novel dataset of more than 600,000 matchups between water quality field measurements and Landsat imagery. The water quality data came from two public sources, which combined provide more than 6 million water quality observations.

Using open-source software, the authors merged the water quality data with the Landsat archive from 1984-2019 and created a new dataset under the name of AquaSat. This dataset is available for future users to update, change, and improve it.

"We're hoping these tools will help build national-scale water quality estimates for large rivers and lakes," said Ross. "These data would dramatically improve our understanding of water quality change at the macro-scale and allow the remote sensing community to compare methods and collectively improve our approach."

In the future, Ross's team expects to go beyond the U.S. to employ these same methods to improve water quality monitoring in other places with little or no field observations.