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Description

Monitoring water quality is vital for managing drinking water treatment and public health and ecosystem advisories. In particular, monitoring harmful algal blooms and water transparency are crucial for assessing health and productivity of freshwater and saltwater fisheries. Conventional in situ measurements of water quality parameters are expensive and have limited spatial and temporal coverage. Remote sensing provides a cost-effective way to assess water quality in thousands of lakes and on coastal waters. 

NASA has developed the Satellite-based Tool for Rapid Evaluation of Aquatic Environments (STREAM) — an interactive web tool that enables high-resolution (20–30 meter) monitoring of water quality in inland lakes and coastal waters across the U.S. and selected other countries. STREAM provides both past (since 2018) and near real-time maps of chlorophyll-a concentration, Secchi disk depth, and total suspended solids based on data from the Landsat 8/9 Operational Land Imager (OLI) and the Sentinel 2 Multispectral Instrument (MSI). Additionally, an open-source machine learning model based on a Mixture Density Network (MDN) is available to estimate water quality parameters for any inland or coastal water body worldwide (minimum size: 100m x 100m). 

This two-part training led by NASA's Applied Remote Sensing Training Program (ARSET) introduces the STREAM web tool to visualize maps of water quality parameters over lakes and coastal areas and how to use STREAM application programming interface (API) to discover and download satellite images and water quality data for a selected water body. The training demonstrates how to assess changes in water quality in a given lake using QGIS. The training also demonstrates the application of a MDN model to estimate water quality parameters from satellite images.

Prerequisites

Objectives

By the end of this training attendees will be able to:

  • Identify the purpose, capabilities, and benefits of the STREAM tool for analyzing inland and coastal water bodies.
  • Identify the process to use STREAM to monitor chlorophyll-a concentration, Secchi disk depth, and total suspended solids in lakes and coastal waters.
  • Identify the steps to use STREAM API to search and download chlorophyll-a concentration, Secchi disk depth, and total suspended solids data for a specific time period.
  • Examine time series of chlorophyll-a concentration, Secchi disk depth, and total suspended solids using QGIS.
  • Identify how an open-source machine-learning model based on Mixture Density Network (MDN) enables users to estimate water quality parameters for any inland or coastal water body (greater than 100m x 100m) worldwide.

Target Audience

  • Primary target audience: National and international water resources managers at local, state, and federal levels. Public and private water utilities, natural resources conservation organizations, and fisheries and aquaculture organizations.
  • Secondary target audience: Academic faculty and students.

Course Format

  • Two 1.5-hour parts
  • Two identical sessions will be hosted at two different times of day:
    • Session A: 10:00 a.m. to 11:30 p.m. EST (UTC-5)
    • Session B: 2:00 p.m. to 3:30 p.m. EST (UTC-5)
  • Each part will include a 30-minute live Q&A.
  • Those who attend all parts and complete the homework by the due date will receive a certificate of attendance.

Sessions

Part 1: Introduction and Demonstration of STREAM

Tuesday, Feb. 10, 2026

  • Introduction to STREAM webtool and API
  • Demonstration of mapping water quality using STREAM
  • Demonstration of making time series of water quality parameters
  • Q&A Session

ARSET trainer: Amita Mehta (NASA's Goddard Space Flight Center)

Guest instructor: William Wainwright (NASA's Goddard Space Flight Center)

Part 2: Introduction to a Machine Learning Model to Estimate Water Quality Parameters Based on Satellite Observations

Tuesday, Feb. 17, 2026

  • Overview: Access and process satellite images for the machine learning model for estimating water quality parameters
  • Overview of the Mixture Density Network (MDN) based model
  • Demonstration of deriving water quality parameters using the model and satellite data
  • Q&A session

ARSET trainer: Amita Mehta (NASA's Goddard Space Flight Center)

Guest Instructor: Ryan O’Shea (NASA's Goddard Space Flight Center)

Citation

(2026). ARSET - Monitoring Water Quality in Lakes and Coastal Regions Using STREAM. NASA Applied Remote Sensing Training Program (ARSET). https://www.earthdata.nasa.gov/learn/trainings/monitoring-water-quality-lakes-coastal-regions-using-stream

Details

Last Updated

Dec. 19, 2025

Published

Dec. 17, 2025

Data Center/Project

Applied Remote Sensing Training Program (ARSET)