MD - New NASA software could help predict water quality problems in the Chesapeake Bay
A NASA team is training and verifying machine learning algorithms that will be used to identify areas of poor water quality in satellite images—starting with the Chesapeake Bay.
Excess nutrients and pollutants washed off land by rainstorms can degrade water quality and harm marine life and humans. Identifying these problems before they can affect us is a constant challenge for water resource managers.
NASA, the National Oceanic and Atmospheric Administration (NOAA), and the Maryland Department of the Environment (MDE) are teaming up to create a software program to help identify impaired water quality more easily. The software is being designed to aid resource managers monitoring the Chesapeake Bay, which is home to a vibrant aquaculture industry that contributes more than $9 million annually to Maryland’s state economy, according to the Chesapeake Bay Foundation.
This collaboration between federal and state agencies – which provided an opportunity to leverage NASA sensors mounted to MDE boats and a US Coast Guard tower – began a complex software development process that includes collecting in-situ observations for training and verifying machine learning algorithms that will ultimately be used to identify areas of poor water quality in satellite images.
Though still in the early stages of development, the research team has already reached several important milestones. In particular, they demonstrated that their software is able to successfully train machine learning algorithms to identify precursors to poor water quality in satellite images over the Chesapeake Bay collected by NASA satellite MODIS-Aqua and the Sentinel 3 OLCI satellite, which is managed by the European Space Agency.
This software prepares scientists to maximize the utility of future missions – such as NASA’s upcoming Surface Biology and Geology (SBG) and Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) missions – and to help coastal communities that depend on pollution-free seas for their livelihoods.
“Maryland and Virginia have decades of observational data, making the Chesapeake Bay the perfect place to develop and train our algorithms to find patterns too subtle for human eyes to detect on their own,” said Stephanie Schollaert Uz, project Principal Investigator and Applied Science Manager at NASA Goddard Space Flight Center.
Schollaert Uz explains that as climate change leads to wetter, more energetic storms, a larger amount of nutrients, sediments, and pollutants like raw sewage will find their way from urban environments into the sea. The excess runoff not only jeopardizes the health of aquatic ecosystems, but also the health of human beings.
“For example, sewage leaks or spills can contaminate an entire crop of oysters, and if the problem goes undetected, people can get sick,” said Schollaert Uz.
Resource managers with the MDE work tirelessly to ensure the Chesapeake Bay is safe for aquaculture and recreation. But with hundreds of test sites to check each month, it’s difficult to find every instance of polluted water before it can endanger people and the environment.
NASA satellites, with their broad coverage and multiple looks within a month, could be an excellent tool for augmenting state water quality monitoring programs. But many pollutants don’t emit an optical signature that can be identified from satellite sensors, which use light to identify sediments, phytoplankton pigments, and dissolved organics. Instead, machine learning algorithms must detect secondary – or “proxy” – patterns indicative of invisible pollutants.