Water Quality Data Pathfinder

Water quality can be remotely sensed and monitored by instruments aboard satellites and aircraft as well as sensors deployed on and under the water's surface.
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Image of Florida with red along coast indicating areas of harmful bacteria.
High concentrations of microscopic plants called phytoplankton (red regions) along the Florida coast and in Tampa Bay are an indicator of ocean health and change as seen in this SeaWiFS image from October 2004. Credit: SeaWiFS Project.

The U.S. has one of the safest water supplies in the world, and more than 90% of Americans get their tap water from community water systems that are subject to national standards for treatment and quality. Water quality, however, depends on the source of the water being consumed. While U.S. potable water is strictly regulated, water sources can become contaminated through many sources, including agricultural runoff, algal blooms, land use practices, chemical spills, and the failure of treatment systems.

Data collected remotely from sensors aboard satellites and aircraft or deployed in and under water are an important tool for addressing the need to better understand factors impacting water quality. Water quality data in NASA's Earth science collection cover the globe, and are fully and openly available. This Data Pathfinder will help you explore, analyze, and evaluate datasets related to water quality, as well as events impacting water quality. If you are new to remote sensing, the What is Remote Sensing? Backgrounder provides a good overview. In addition, NASA's Applied Remote Sensing Training Program (ARSET) provides numerous training modules, including Fundamentals of Remote Sensing and Fundamentals of Aquatic Remote Sensing.

If you have specific questions about how to use data, tools, or resources mentioned in this Data Pathfinder, please visit the Earthdata Forum. Here, you can interact with other data users and NASA subject matter experts on a variety of Earth science research and applications topics.

The datasets in this Data Pathfinder can be discovered using Earthdata Search (an Earthdata Login is required to download data). Learn more about how to use Earthdata Search and other tools to visualize and explore data in the Tools for Data Access and Visualization section below.

An Overview of Water Quality and Water Color

Water, from watersheds to vapor in Earth’s atmosphere and everywhere in between, has characteristics that are visible to the naked eye as well as invisible. The physical, chemical, biological, and microbiological constituents of water determine its suitability for particular uses; this is referred to as its "quality."

Factors affecting water quality include the following:

  • The amount of nutrients in the water, especially excess nutrients 
  • Pollution
  • Water temperature (warmer water affects algal blooms)
  • Food web changes
  • Introduced species
  • Changes in water flow, such as after events like hurricanes, drought, or floods

Traditional methods of evaluating and monitoring coastal water quality require scientists to use boats to gather water samples, typically on a monthly basis because of the high costs of these surveys. This method is sufficient for collecting data about episodic events impacting water quality but does not provide the breadth of data over time necessary for developing a holistic understanding of the hydrospheric system, such as the specific factors affecting water quality and rates of change. This is one benefit of using remotely sensed data for studying and assessing water quality. ​Remotely sensed data enable water quality observations across large distances along with frequent and consistent measurements. Remote sensing imagery is used for a variety of water quality applications:

Observation Application
Chlorophyll-a Phytoplankton biomass, primary productivity, biogeochemical cycling
Water Turbidity Water quality, human and ecosystem health
Colored Dissolved Organic Matter (CDOM) Water quality, biogeochemical cycling, human and ecosystem health
Sea Surface Temperature (SST) Currents, primary productivity, climate studies, biogeochemistry, temperature flux
Surface Winds Currents, mixing, air-sea flux of gases

While water’s characterization is based on many measurable parameters including temperature, pH, and density, not all of these properties are observable by remote sensors.​ Remotely sensed data should be used to complement in-situ data, as opposed to replacing them.

For more information about optical oceanography and ocean color remote sensing, explore Ocean Optics Web Book.​

Common Measurements at a Glance

NASA collaborates with other federal entities and international space organizations, including NOAA, the USGS, and the European Space Agency (ESA), to acquire and distribute data relevant to water quality. Datasets referenced in this Data Pathfinder are from sensors shown in the table below (which lists the primary sensor used in collecting the specified measurement). When available, near real-time data from NASA's Land, Atmosphere Near real-time Capability for EO (LANCE) are available within three hours of a satellite observation, which allows for monitoring and decision making as an event is occurring.

Note: This is not an exhaustive list of datasets related to water quality measurements, and only includes datasets in NASA's Earth Observing System Data and Information System (EOSDIS) collection.

Satellites and sensors referenced in this Data Pathfinder include:

Measurement Satellite Sensor Spatial Resolution Temporal Resolution Temporal Extent
Spectral Reflectance Landsat 7 Enhanced Thematic Mapper (ETM+) 15, 30, 60 m (185 km swath) 16 days 1999 to Present
Top of Atmosphere Radiances Landsat 8 Operational Land Imager (OLI) 15, 30, 60 m (185 km swath) 16 days 2013 to Present
Spectral Reflectance, Chlorophyll-a Concentration, Temperature, Colored Dissolved Organic Matter (CDOM), Turbidity, Euphotic Depth, Inherent Optical Properties (IOP),Fraction of Photosynthetically Active Radiation  Terra, Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) 250, 500, 1000, 5600 m
(2330 km swath)
1 to 2 days, 8 days, monthly 1999 to Present
Spectral Reflectance, Temperature
Chlorophyll Concentration, Inherent Optical Properties,  Fraction of Photosynthetically Active Radiation
Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) 500, 1000, 5600 m
(3040 km swath)
1 to 2 days 2017 to Present
Ocean Color (e.g., remote sensing reflectances, chlorophyll-a concentration, etc.) Sentinel-2A and 2B Multispectral Instrument (MSI) 10, 20, 60 m
(3040 km swath)
5 days 2015 to Present
Ocean Color (e.g., remote sensing reflectances, chlorophyll-a concentration, etc.) Sentinel-3A Ocean and Land Colour Instrument (OLCI) 300 m
(1270 km swath)
27 days 2016 to Present

Looking ahead, two scheduled missions will add significantly to water color and water quality data in the NASA collection: the Surface Water and Ocean Topography (SWOT) mission and the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.

SWOT

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Logo for the SWOT mission showing a spacecraft sensing Earth over a body of water with the word

SWOT, which is scheduled for launch in late-2022, was jointly developed by NASA and Centre National D'Etudes Spatiales (CNES, the French space agency) with contributions from the Canadian Space Agency (CSA) and the United Kingdom Space Agency. SWOT will provide the first comprehensive view of Earth's freshwater bodies from space and will allow scientists to determine changing volumes of fresh water across the globe at an unprecedented resolution. SWOT data will be used to measure water quality changes as related to hydrodynamic conditions and will prove helpful for surveying the evolution of salinity gradients and high turbidity zones, oxygen levels in estuarine environments, as well as turbidity plumes into the sea.​

For more information about SWOT data products, and data samples, visit SWOT's datasets landing page.​

PACE

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PACE logo with words PACE and the acronym spelled out over a multi-colored background

NASA's PACE mission, scheduled to launch in 2024, will extend and improve NASA's 20+ year record of satellite observations of global ocean biology, aerosols, and clouds. It will measure phytoplankton distribution and key atmospheric variables related to air quality and Earth's climate. PACE's unprecedented spectral coverage will provide the first-ever global measurements designed to identify phytoplankton community composition. This will significantly improve our ability to understand Earth's changing marine ecosystems, manage natural resources such as fisheries, and identify harmful algal blooms.​

The primary science instruments planned for PACE are:​

  • Ocean Color Instrument (OCI): Spectrometer used to measure intensity of light over portions of the electromagnetic spectrum: ultraviolet (UV), visible, near infrared, and several shortwave infrared bands. The OCI will enable continuous measurement of light at a finer wavelength resolution than previous NASA ocean color sensors, providing detailed information on our global ocean. ​
  • Multi-angle Polarimeters: Radiometers used to measure how the oscillation of sunlight within a geometric plane, known as its polarization, is changed by passing through clouds, aerosols, and the ocean. Measuring polarization states of UV-to-shortwave light at various angles provides detailed information on the atmosphere and ocean, such as particle size and composition.​

Find the Data

water-color
Ocean color assessments are used for answering fundamental questions about phytoplankton blooms, the aquatic food web, fisheries, and the storage of carbon in the ocean.
PAR
PAR is important for understanding biogeochemical cycles and addressing global climate change issues
water surface temperature
Water surface temperature is important for the health of microscopic organisms and controls algal blooms.
precipitation
The amount and intensity of precipitation is a key aspect of soil moisture, runoff, turbidity, and water availability.
Soil Moisture
The amount of moisture in the top levels of soil determines vegetation health and productivity.
runoff
Runoff consists of the chemicals, pollutants, minerals, and sediments picked up and transported by water.
turbidity
The rate at which water flows and the nutrients carried in this flow impact water quality and color.
socioeconomic map
NASA socioeconomic data can help identify populations that may be more vulnerable to or most impacted by compromised water quality.
Use the Data

The following use case examples show how water quality data are being used and provide relevant real-world examples of work being done with these data. While this is not an exhaustive list, it may provide some examples to stimulate your work with these data.

Water Color

Feature Articles and StoryMaps​:

Environmental Justice-Related Examples:

Relevant Data User Profiles:

  • Adnan Al-Azri studies why periodic harmful blooms of algae occur in the Sea of Oman and Arabian Sea
  • Dr. Bridget Seegers develops new ways to study water quality and track harmful algal blooms
  • John Lehrter studies the water quality of estuarine and coastal environments

 

 

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Photosynthetically Available Radiation (PAR)

Feature Articles and StoryMaps:

Environmental Justice-Related Examples:

Relevant Data User Profiles:

 

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Turbidity

Feature Articles and StoryMaps:

Environmental Justice-Related Examples:

Relevant Data User Profiles:

  • Dr. Priscila Kienteca Lange uses NASA ocean biology data to study the biomass and distribution of phytoplankton
  • Dr. Brian Barnes uses ocean color data in his investigations into the health of optically shallow waters
  • Mark Trice monitors the health of Chesapeake Bay

 

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Runoff

Feature Articles and StoryMaps:

Relevant Data User Profiles:

  • Katherine Pitts studies the impacts of climate change and analyzes remotely-sensed geophysical data
  • Ben Holt studies polar sea ice, coastal oceanography, and marine pollution
  • Mark Trice monitors the health of Chesapeake Bay

 

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Soil Moisture

Feature Articles and StoryMaps:

Relevant Data User Profiles:

  • David Mocko uses land-surface models to study drought
  • Dr. David Thoma determines how climate change will impact natural resources on National Park System lands
  • Thomas Parris assesses the impact of water stress on agriculture, industries, and people around the globe

 

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Precipitation

Feature Articles and StoryMaps:

Relevant Data User Profiles:

  • Mark Trice monitors the health of Chesapeake Bay
  • John Lehrter studies the water quality of estuarine and coastal environments

 

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Surface Temperature

Feature Articles and StoryMaps:

Relevant Data User Profiles:

  • John Lehrter studies the water quality of estuarine and coastal environments
  • Ben Holt studies polar sea ice, coastal oceanography, and marine pollution
  • Mark Trice monitors the health of Chesapeake Bay
  • Dr. Brian Barnes uses ocean color data in his investigations into the health of optically shallow waters

 

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Socioeconomic and Human Impacts

Environmental Justice-Related Examples:

Relevant Data User Profiles:

  • Thomas Parris assesses the impact of water stress on agriculture, industries, and people around the globe
  • Dr. Eric Sproles uses Earth observing data as part of his studies into water’s eco-social effects
  • Dr. Faisal Hossain uses NASA Earth observing data to improve water management and accelerate economic development in Asia and Southeast Asia

 

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Surface Reflectance

Feature Articles and StoryMaps:

 

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Additional Resources

NASA Resources

NASA's Applied Remote Sensing Training (ARSET) Program has numerous training resources focused on water quality, including resources related to integrating remote sensing into water quality management, water quality in freshwater systems/inland bodies, and harmful algal blooms (HAB).

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logo for the Western Water Applications Office with initials WWAO over an icon of mountains and water.

NASA's Western Water Applications Office (WWAO) works  to get NASA science, data, and technology into the hands of water managers and decision makers. They identify pressing water issues and deliver solutions to those issues based on NASA capabilities with the goal of improving how water is managed in the Western U.S. The WWAO's Water Portal is an information hub for water-related resources and provides interactive catalogs of Water Data Needs and NASA Water-Related Capabilities.

External Resources

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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 based on specific changes in the color of the water observed by satellites.

NOAA offers a number of resources for tracking water quality:

  • CoastWatch/OceanWatch: Provides links to satellite data products and services for understanding and managing the ocean and coasts.
  • 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 with dozens of water quality-related videos focusing on the Great Lakes.
  • NOAA's National Centers for Coastal Ocean Science developed the Harmful Algal Bloom Monitoring System to routinely deliver near real-time products for use in locating, monitoring, and quantifying algal blooms in U.S. coastal and lake regions.
  • The NOAA-sponsored Caribbean Coastal & Ocean Observing System (CARICOOS) platform features an interactive Ecosystem and Water Quality Map visualizing the latest water quality results for beaches in Puerto Rico and the U.S. Virgin Islands.

The U.S. EPA's EJScreen: Environmental Justice Screening and Mapping Tool features Wastewater Dischargers Indicator (Stream Proximity and Toxic Concentration) data, among other environmental and social data.

The National Water Quality Monitoring Council (NWQMC) Portal is a cooperative service sponsored by the USGS, EPA, and NWQMC. It serves in-situ data collected by over 400 state, federal, tribal, and local agencies.

The ESA (European Space Agency) Sentinel Hub provides technical information on the Sentinel series of missions, access to 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 assessing water quality.

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.

CyanoLakes is a global initiative to monitor water quality as it relates to drinking water. Note, this program may provide data and related resources to support operational activities, as opposed to research.​

Connection of Sustainable Development Goals to Water Quality

The Sustainable Development Goals (SDGs) are a collection of 17 interlinked global goals designed to be a blueprint for a sustainable future for all of Earth’s inhabitants. The SDGs are part of the 2030 Agenda for Sustainable Development, an international plan signed by all United Nations (UN) member states in 2015 and underpinned by the foundational components of People, Planet, and Prosperity.

The 17 SDGs in the Agenda are made up of 169 objectives that include specific social, economic, and environmental targets. These targets provide a blueprint for developing a more sustainable global future.

Data acquired remotely by sensors aboard satellites and aircraft or installed on the ground play a unique role in tracking the progress toward achieving the SDGs. These remotely sensed Earth observations provide consistent and continuous information on the state of Earth processes and their change over time. These data also are integral components of socioeconomic metrics that provide a measure of how humans co-exist with the environment and the stresses they encounter through natural and human-caused changes to the environment.

NASA Earth observation data are available without restriction to all data users, a policy that is being adopted by other international space agencies and one that reduces the cost of monitoring the SDGs and provides developing countries a means to acquire and utilize these data for other policy-making purposes.

NASA’s datasets are organized by topics that help users to locate, access, and apply relevant and complementary datasets for each SDG. The Water Quality Data Pathfinder addresses (but is not limited to) the following SDGs:

SDG SDG Goals Relevant to Water Quality
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blue square with number 6 and words clean water and sanitation

Goal 6. Ensure access to water and sanitation for all

  • Achieve universal and equitable access to safe and affordable drinking water for all
  • Improve water quality by reducing pollution, eliminating dumping and minimizing release of hazardous chemicals and materials, halving the proportion of untreated wastewater, and substantially increasing recycling and safe reuse globally
  • Protect and restore water-related ecosystems, including mountains, forests, wetlands, rivers, aquifers, and lakes
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Brown square with number 12 and words Responsible Consumption and Production.

Goal 12. Ensure sustainable consumption and production patterns

  • Achieve the environmentally sound management of chemicals and all wastes throughout their life cycle, in accordance with agreed international frameworks, and significantly reduce their release to air, water, and soil in order to minimize their adverse impacts on human health and the environment
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Blue square with 14 in upper left and words Life Below Water with icon of water and fish below

Goal 14. Conserve and sustainably use the oceans, seas, and marine resources for sustainable development

  • Prevent and significantly reduce marine pollution of all kinds, in particular from land-based activities, including marine debris and nutrient pollution
  • Sustainably manage and protect marine and coastal ecosystems to avoid significant adverse impacts, including by strengthening their resilience, and take action for their restoration in order to achieve healthy and productive oceans
The opportunities to connect NASA data to the SDGs are infinite; therefore, the datasets included in specific Data Pathfinders are not intended to be comprehensive. Additionally, NASA datasets are not official indicators for SDG monitoring and decision-making, but are complementary.

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Tools for Data Access and Visualization

Earthdata Search | Panoply | Giovanni | Worldview | SeaDASAppEEARS | Soil Moisture VisualizerMODIS/VIIRS Subsetting Tools SuiteUSGS EarthExplorer

This section provides links to tools and applications relevant to analyzing and visualizing water quality data referenced in this Data Pathfinder. NASA's Earth Science Data Systems (ESDS) Program maintains many more resources for data analysis that may be helpful. Explore the full list on the NASA Earthdata Data Tools page.

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.

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searchEarthdata


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.

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Earthdata Search customization tools diagram.

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Panoply

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.

 

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Giovanni

Giovanni is an online environment for the display and analysis of geophysical parameters. There are many options for analysis. The following are the more popular ones.

  • Time-averaged maps are a simple way to observe the variability of data values over a region of interest.
  • Map animations are a means to observe spatial patterns and detect unusual events over time.
  • Area-averaged time series are used to display the value of a data variable that has been averaged from all the data values acquired for a selected region for each time step.
  • Histogram plots are used to display the distribution of values of a data variable in a selected region and time interval.

For more detailed tutorials:

  • Giovanni How-To's on GES DISC's YouTube channel.
  • 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.

 

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Worldview

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.

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Worldview data visualization of the nighttime lights in Puerto Rico pre- and post- Hurricane Maria, which made landfall on September 20, 2017. The post-hurricane image on the left shows widespread outages around San Juan, including key hospital and transportation infrastructure.​
Worldview Suomi NPP/VIIRS nighttime lights comparison image showing power outages caused by Hurricane Irma in September 2017. The right image (acquired September 1) shows the island before Hurricane Irma. The left image (acquired September 9) shows power outages across the island after the storm. Credit: NASA Worldview.

 

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SeaDAS

NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Data Analysis System (SeaDAS), available through NASA's Ocean Biology Distributed Active Archive Center (OB.DAAC), 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 other satellite-based Earth science data analyses.

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SeaDAS is a comprehensive software package for the processing, display, analysis, and quality control of ocean color data. This image shows ocean color, sea surface temperature and non-algal material plus colored dissolved organic matter.
SeaDAS is a comprehensive software package for the processing, display, analysis, and quality control of ocean color data. This image shows ocean color, sea surface temperature, and non-algal material plus colored dissolved organic matter. Credit: NASA OB.DAAC.

SeaDAS enables users to visualize data and re-project, crop, and create land, water, and coastline masks. Mathematical and statistical operations can be performed within the application, such as band math (band additions and subtractions, band ratios), plot histograms, scatter plots, and correlation plots.

In-situ data can be incorporated as well, which 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 tab-delimited with fields of data, time, station (with the stations defined in the file), lat, lon, and depth. Date needs 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, select Vector/Import and then select the data source. In order to validate remotely sensed data, you only want to look at the in-situ data at the surface (depth of 0).

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SeaDAS allows for the integration of in-situ data in order to validate satellite measurements.
SeaDAS allows for the integration of in-situ data for validating satellite measurements. Credit: NASA OB.DAAC.

For more detailed tutorials and more information about SeaDAS:

 

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AppEEARS

AppEEARS, available through NASA's Land Processes DAAC (LP DAAC), offers a simple and efficient way to access and transform geospatial data from a variety of federal data archives. AppEEARS enables users to subset geospatial datasets using spatial, temporal, and band/layer parameters. Two types of sample requests are available: point samples for geographic coordinates and area samples for spatial areas via vector polygons.

Performing Area Extractions

After requesting an area extraction, users are taken to the Extract Area Sample page where they specify a series of parameters that are used to extract data for areas of interest.

Spatial Subsetting

Define the region of interest in one of three ways:

  • Upload a vector polygon file in shapefile format (a single file with multiple features or multipart single features can be uploaded); the .shp, .shx, .dbf, or .prj files must be zipped into a file folder to upload
  • Upload a vector polygon file in GeoJSON format (users can upload a single file with multiple features or multipart single features)
  • Draw a polygon on the map by clicking on the Bounding box or Polygon icons (single feature only)

Select the date range for the time period of interest.

Specify the range of dates for which data are desired for extraction by entering a start and end date (MM-DD-YYYY) or by clicking on the Calendar icon and selecting dates a start and end date in the calendar.

Adding Data Layers

Enter the product short name (e.g., MOD09A1, ECO3ETPTJPL), keywords from the product long name, a spatial resolution, a temporal extent, or a temporal resolution into the search bar. A list of available products matching the query will be generated. Select the layer(s) of interest to add to the Selected layers list. Layers from multiple products can be added to a single request. Be sure to read the list of available products available through AppEEARS.

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Extracting an area in AppEEARS

Selecting Output Options

Two output file formats are available:

  • GeoTIFF
  • NetCDF4

If GeoTIFF is selected, one GeoTIFF will be created for each feature in the input vector polygon file for each layer by observation. If NetCDF4 is selected, outputs will be grouped into .nc files by product and by feature.

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If GeoTIFF is selected, you must select a projection

Interacting with Results

From the Explore Requests page, click the View icon to view and interact with results. This will take users to the View Area Sample page.

The Layer Stats plot provides time series boxplots for all of the sample data for a given feature, data layer, and observation. Each input feature is renamed with a unique AppEEARS ID (AID). If the feature contains attribute table information, users can view the feature attribute table data by clicking on the Information icon to the right of the Feature dropdown. To view statistics from different features or layers, select a different aid from the Feature dropdown and/or a different layer of interest from the Layer dropdown.

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Interpreting Results in AppEEARS

Please see the AppEEARS documentation to learn more about downloading the output as GeoTIFF or NetCDF4 files.

 

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Soil Moisture Visualizer

NASA's Oak Ridge National Laboratory DAAC (ORNL DAAC) developed a Soil Moisture Visualizer tool (read about it at Soil Moisture Data Sets Become Fertile Ground for Applications) that integrates a variety of North American soil moisture datasets. The visualization tool incorporates in-situ, airborne, and remote sensing data into one easy-to-use platform. This integration helps to validate and calibrate the data and provides spatial and temporal data continuity. It also facilitates exploratory analysis and data discovery for different groups of users. The Soil Moisture Visualizer offers the capability to geographically subset and download time series data in .csv format. For more information on the available datasets and use of the visualizer, view the Soil Moisture Visualizer Guide.

To use the visualizer, select a dataset of interest under Data. Depending on the dataset chosen, the visualizer provides the included latitude/longitude or an actual site location name and relative time frames of data collection. Upon selecting a parameter, the tool displays a time series with available datasets. All measurements are volumetric soil moisture. Surface soil moisture is the daily average of measurements at 0-5 cm depth and root zone soil moisture (RZSM) is the daily average of measurements at 0-100 cm depth. The visualizer also provides data sources for download.

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ORNL DAAC Soil Moisture Visualizer
The Soil Moisture Visualizer allows users to compare soil moisture measurements from multiple sources at the same location. In this screenshot, Level 4 Root Zone Soil Moisture (L4 RZSM) data acquired by NASA’s Soil Moisture Active Passive (SMAP) satellite are shown with data from in situ sensors across the 9-kilometer Equal-Area Scalable Earth (EASE) grid cell encompassing the Tonzi Ranch Fluxnet site in the Sierra Nevada foothills of California, USA. Daily precipitation values for the site (purple spikes) are also provided for reference. Credit: NASA ORNL DAAC.

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MODIS/VIIRS Subsetting Tools Suite

ORNL DAAC also has several tools for subsetting data from the MODIS and VIIRS instruments:

  • With the Global Subset Tool, users can request a subset for any location on Earth as a GeoTiff and in text format, including interactive time-series plots and more. Users specify a site by entering the site's geographic coordinates and the area surrounding that site, from one pixel up to 201 x 201 km. From the available datasets, users can specify a date and then select from MODIS Sinusoidal Projection or Geographic Lat/Long. Note: An Earthdata Login is required to request data.
  • With the Fixed Sites Subsets Tool, users can download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products. The goal of the Fixed Sites Subsets Tool is to prepare summaries of selected data products for the community to characterize field sites. It includes sites from networks such as National Ecological Observatory Network, Forest Global Earth Observatory network, Phenology Camera network, and Long Term Ecological Research Network.
  • With the Web Service, users can retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service. Web service client and libraries are available in multiple programming languages, allowing integration of subsets into a workflow.
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Directions for subsetting data with the ORNL DAAC MODIS and VIIRS subset tool

Top image: The Global Subsets Tool enables users to download available products for any location on Earth. Bottom image: The Fixed Sites Subsets Tool provides spatial subsets for established field sites for site characterization and validation of models and remote sensing products. Credit: NASA ORNL DAAC.

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USGS EarthExplorer

The EarthExplorer provides online search, browse display, metadata export, and data download for Earth science data distributed by the USGS, including Landsat data. Note: An EarthExplorer login is required to download data. Learn more about working with EarthExplorer:

Benefits and Limitations of Remote Sensing Data

Satellite data provide coverage over a much larger area with some data and imagery available 1 to 3 hours after a satellite observation. These near real-time data facilitate the monitoring of specific areas and facilitate rapid response during emergencies. For quantitative water quality monitoring analysis, however, in-situ measurements are required. The combination of satellite observations with in-situ data makes for a more robust and integrated forecasting and response system.​

While satellite data provide a more global view, these observations have limitations. Satellite measurements are made through the atmosphere and not at the water level, which means some measurements may be missed due to clouds, haze, and other conditions that might impede a sensor. Also, it can be difficult to separate water quality parameters when sediments, dissolved matter, and chlorophyll-a are all present. Additionally, remote sensing observations alone are unable to discern between algal types or toxins. As such, atmospheric correction algorithms must be run before water quality assessments can be made.

An important consideration when evaluating satellite data is that sensors have varying spatial, temporal, and radiometric resolutions. For example, while polar-orbiting satellites might pass over the same location every 1-2 days, their sensors may have a low spatial resolution. On the other hand, some polar-orbiting satellites pass over the same location 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.​  Additionally, the timing of satellite overpasses limits the events and processes that can be measured. For example, temporal processes such as the effects of tides and the migration of HABs are not always discernible in satellite imagery as the specific sensor may not pass over the location at the right time.

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