Monitoring water quality provides an understanding of the physical, chemical, biological, and microbiological characteristics of water, both inland and coastal, to support decision making on health and environmental issues. For example, toxic algal blooms in lakes and coastal areas can impact humans by affecting drinking water or recreation and animals by contaminating shellfish and killing other fish and marine mammals. A combination of ground- and satellite-based tools provides a regional to global understanding of the impacts of water quality, by providing information on ocean color, which is based on constituents within the water (like sediment, phytoplankton, organic matter, etc.). Ocean color data from satellites allows us not only to identify where an algal bloom is forming, but also to predict where it might drift in the future. Ocean color also provides information on the impact of floods along the coast and in the detection of river plumes.
This data pathfinder provides measurements from satellite, airborne, and in-situ sensors that can aid in this understanding of water quality conditions. In addition, the pathfinder provides information to assess Sustainable Development Goals, specifically SDG 6: Clean Water and Sanitation and SDG 14: Life Below Water.
New to using NASA Earth science data? This pathfinder is designed to help guide you through the process of selecting and using applicable datasets, with guidance on resolutions and direct links to the data sources. After getting started here, there are numerous NASA resources that can help develop your skills further. If you are new to remote sensing, check out What is Remote Sensing? or view the Applied Remote Sensing Training on Fundamentals of Remote Sensing.
About the Data
NASA, in collaboration with other organizations (NOAA, USGS, and the European Space Agency [ESA]), has various instruments that provide information for understanding a number of phenomena associated with water quality. NASA's Earth science data products are validated, meaning the accuracy has been assessed over a widely distributed set of locations and time periods via several ground-truth and validation efforts.
Satellites and sensors referenced in this pathfinder include:
Satellite imagery, coupled with in-situ data, aids in the assessment of water quality by distinguishing between parameters, such as dissolved organic matter, sediments, plankton, and algal blooms. Some of these parameters affect optical properties which allows for their detection by remote sensors. For example, colored dissolved organic matter (CDOM) is a mixture of organic substances produced as organic matter decays, such as tannins; tannins stain the water (making it look black) affecting light absorption.
Scientists, researchers, water resource managers, decision makers, and others use remotely sensed data in various ways, including to help meet the 17 sustainable development goals put forth by the United Nations. Specifically, goal 6 states that countries should ensure availability and sustainable management of water and sanitation for all. (To see the data in use, view our Data User Profiles or our Freshwater Feature Articles.)
Ocean color remote sensing uses remote-sensing reflectance, which is based on the properties of the materials in the water. When light interacts with water, it can be absorbed or scattered. Light is absorbed by a combination of phytoplankton, non-algal properties (NAP), CDOM and water itself.
Through a series of complex algorithms, the relationship between this absorption and scattering (in forward and backward directions), can provide a remote-sensing reflectance value for the water-leaving radiance.
One important note is that satellite sensors measure the top of atmosphere radiances. These radiances result from a combination of surface and atmospheric conditions (effects of clouds and aerosol particles); in order to look at just the water-leaving reflectance, the data must be atmospherically corrected; the goal is to subtract the atmospheric path and the surface-reflected spectrum and retain only the water-leaving radiance spectrum. There are a number of free products which help do this (see Find Ocean Color Data, below).
Once atmospheric correction has been applied, ocean color reflectance can be visualized and a qualitative interpretation made based on color. Generally, chlorophyll appears as green, water as blue, CDOM as black, and sediments/NAP as browns and whites. Every element has a spectral fingerprint, a unique absorption/reflectance pattern at varying wavelengths of the electromagnetic spectrum, which aids in the identification of these different parameters.
For a more quantitative measurement of ocean color data, different algorithms combine atmospherically corrected satellite images and in-situ measurements. Having in-situ data for validation is critical to getting a quantitative measure.
Earthdata Search is a tool for data discovery of Earth Observation data collections from NASA's Earth Observing System Data and Information System (EOSDIS), as well as U.S and international agencies across the Earth science disciplines.
Users (including those without specific knowledge of the data) can search for and read about data collections, search for data files by date and spatial area, preview browse images, and download or submit requests for data files, with customization for select data collections.
In the project area, for some datasets, you can customize your granule. You can reformat the data and output as HDF, NetCDF, ASCII, KML, or GeoTIFF format. You can also choose from a variety of projection options. Lastly, you can subset the data, obtaining only the bands that are needed.
Files in HDF and NetCDF format can be viewed in Panoply, a cross-platform application that plots geo-referenced and other arrays. Panoply offers additional functionality, such as slicing and plotting arrays, combining arrays, and exporting plots and animations.
Data recipe for downloading a Giovanni map in NetCDF format, and converting its data to quantifiable map data in the form of latitude-longitude-data value ASCII text.
NASA's EOSDIS Worldview visualization application provides the capability to interactively browse over 1,000 global, full-resolution satellite imagery layers and then download the underlying data. Many of the available imagery layers are updated within three hours of observation, essentially showing the entire Earth as it looks "right now." This supports time-critical application areas such as wildfire management, air quality measurements, and flood monitoring. Imagery in Worldview is provided by NASA's Global Imagery Browse Services (GIBS). Worldview now includes nine geostationary imagery layers from GOES-East, GOES-West and Himawari-8 available at ten minute increments for the last 30 days. These layers include Red Visible, which can be used for analyzing daytime clouds, fog, insolation, and winds; Clean Infrared, which provides cloud top temperature and information about precipitation; and Air Mass RGB, which enables the visualization of the differentiation between air mass types (e.g., dry air, moist air, etc.). These full disk hemispheric views allow for almost real-time viewing of changes occurring around most of the world.
View current natural hazards and events using the Events tab which reveals a list of natural events, including wildfires, tropical storms, and volcanic eruptions. You can animate the imagery over time or do a screen-by-screen comparison of data for different time periods or a comparison of different datasets.
NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Data Analysis System (SeaDAS) is a comprehensive software package for the processing, display, analysis, and quality control of ocean color data. While the primary focus of SeaDAS is ocean color data, it is applicable to many satellite-based earth science data analyses.
Within SeaDAS, you can visualize data, and re-project, crop and create land, water and coastline masks. You can perform mathematical and statistical operations, such as band math (band additions and subtractions, band ratios), plot histograms, scatter plots and correlation plots, and you can incorporate in-situ data.
If you only have reflectance values, you can use the band ratio and algorithm coefficients within SeaDAS to derive chlorophyll-a. If using Landsat data, you need to convert Level 1 to Level 2 data. To do this, make sure your data processors within SeaDAS are updated.
In-situ data can be incorporated as well; this is critical for data validation. To integrate in-situ data, whether from the SeaWiFS Bio-optical Archive and Storage System (SeaBASS) or from another source, the data must be in a specific format. The file must be tab-delimited with fields of data, time, station (with the stations defined in the file), lat, lon, and depth. Date and time are relevant as well. They need to be defined as YYYYMMDD and time as HH:MM:SS. If not defined properly, the file must be reworked to make it readable.
Once the tab-delimited file is complete, you can select Vector/Import and then select your data source. Remember in order to validate your remotely sensed data, you only want to look at the in-situ data at the surface (depth of 0).
For more detailed tutorials:
SeaDAS Video tutorials and demos—NASA's Ocean Biology DAAC (OB.DAAC) recommends viewing the first few in the order they are shown. The core videos are listed first, followed by multi-tool case studies; everything below that appears in chronological order by release date.
SeaDAS FAQs—Frequently asked questions from SeaDAS users.
The ARSET Program has numerous training resources focused on water quality. There are resources on integrating remote sensing into water quality management, water quality in freshwater systems/inland bodies, harmful algal blooms (HAB), and addressing sustainable development goal 6.
For questions regarding ocean color data from NASA and processing or analysis of that data, OB.DAAC has an Ocean Color User Forum.
The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled to launch in 2023, contains an Ocean Color Instrument (OCI), which will measure properties of light at finer wavelength resolution than previous NASA satellite sensors, extending key system ocean color data records for climate studies.
The multi-agency (EPA, NASA, NOAA, and USGS) Cyanobacteria Assessment Network, or CyAN, has developed an app to alert officials and members of the public when a HAB could be forming, depending on specific changes in the color of the water observed by satellites. An article on this effort is available as NASA Helps Warn of HABs.
Cyanobacteria Index from CyAN:
Cyanobacteria HABs are a big problem in lake bodies. Not only do they produce excessive biomass, which consumes oxygen needed by other living organisms, but they also produce toxins. Many of these cyanobacteria species produce surface scum and so can be detected using remote sensing reflectance. Calculating the cyanobacteria index provides an indication of cyanobacteria bloom. The algorithm plots slope between 665-709 nm and then looks at the difference between the slope and the bloom spectral fingerprint. If the spectrum is below the slope line, it's a negative, indicating a cyanobacteria bloom.
CyAN provides information on the cyanobacteria index.
ESA's Sentinel Hub provides technical information on the missions, access to the data, user guides and tutorials, and news related to Sentinel data. In addition, their Water Quality Monitoring page provides information on their suite of products that can be used for monitoring.
NOAA's Great Lakes Environmental Research Laboratory (GLERL) forecasts HABs within Lake Erie with their experimental HAB Tracker. They also have a GLERL YouTube Channel, which provides information on 2019 conditions and outlooks, harmful algal blooms including cyanobacteria, and climate-based projections.
NOAA's National Centers for Coastal Ocean Science developed the Algal Bloom Monitoring System to routinely deliver near real-time products for use in locating, monitoring, and quantifying algal blooms in coastal and lake regions of the US.
UNESCO's Water Quality Portal is a free visualizer of satellite-derived water quality information for worldwide lakes and rivers. A global set of parameters in 90 m spatial resolution is provided on a continental base.
Benefits and Limitations of Remote Sensing Data
The United States is fortunate to have numerous in-situ measurements for assessing water quality parameters, yet in-situ measurements have limited sample collection and so are not representative of the entire water body. In other countries and in more rural areas of the United States, sampling is even more limited or non-existent. Satellite data provide more regional to global spatial coverage; some information is available in near real-time, allowing for a more efficient response. Satellite data have also been collected for a longer period of time, providing for data continuity and trend analyses. With satellite data, assessments can be made regarding ocean color, but this provides only qualitative measures. For quantitative water quality monitoring analysis, in-situ measurements are required; the combination of satellite observations with in-situ makes for a more robust and integrated forecasting and response system.
While satellite data provide a more global view, it is important to note that satellite measurements are made through the atmosphere and not at the water level. As such, atmospheric correction algorithms must be run before water quality assessments can be made.
Also note that the sensors all have varying spatial, temporal and radiometric resolutions. For example, many of the polar-orbiting satellites only pass over the same location every 1-2 days, but have a coarser spatial resolution, while others pass over every 16+ days, but have a much finer spatial resolution. Finding the right instrument or understanding the modeling processes for your area of interest is key.
Other challenges include the difficulty in separating water quality parameters of CDOM, NAP, and chlorophyll content when all three are present. Also, remote sensing observations alone are unable to discern between algal types or toxins.
Ocean color and the factors that may influence ocean color are used for answering fundamental questions about phytoplankton blooms, the aquatic food web, and fisheries, as well as the storage of carbon in the ocean.
High quality in situ measurements are prerequisite for satellite data product validation, algorithm development, and many climate-related inquiries. NASA's SeaWiFS Bio-optical Archive and Storage System (SeaBASS) is a local repository of in situ data.