Water Quality Data Pathfinder - Find Data

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.

Ocean Color

Image of land in brown with water in blue; green swirls in water near land indicate phytoplankton
The green swirls of color in this Suomi NPP/VIIRS image of the northern Arabian Sea are a massive algal bloom, most likely a species called Noctiluca scintillans. Image acquired February 20, 2022. Credit: NASA Ocean Color Image Gallery.

Ocean color is a measure of sunlight that is scattered back out of the ocean after interacting with water and its components, such as phytoplankton, sediments, and colored dissolved organic matter (CDOM). Ocean color can be used as a substitute for examining water quality in any body of water. For example, estimates of chlorophyll-a in phytoplankton concentrations calculated from ocean color data are used as an indicator for harmful algal blooms (HABs), which occur when algae containing toxins grow out of control. These blooms can wreak havoc on the organisms that live in or depend on that ecosystem and can contaminate seafood.

The primary instruments for measuring ocean color are MODIS aboard NASA’s Terra and Aqua satellites, VIIRS aboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA-20 satellites, and the Ocean and Land Colour Instrument (OLCI) aboard the ESA (European Space Agency) Sentinel series of satellites. The joint NASA/USGS Landsat series of satellites and the ESA Sentinel-2 satellites each carry a high-resolution sensor that is designed primarily for land observation (the Operational Land Imager, OLI, aboard Landsat and MultiSpectral Instrument, MSI, aboard Sentinel-2), but which can be used for observations of coastal waters and lakes.

Near-real time data can be visualized and accessed using NASA Worldview:

Data products can be visualized using Giovanni: 

Research-quality data products can be accessed using Earthdata Search:

Ocean color data products also are available through NASA’s Ocean Biology DAAC (OB.DAAC), which is managed by NASA’s Ocean Biology Processing Group (OBPG). OBPG supports the collection, processing, calibration, validation, archive, and distribution of ocean-related products from several missions that are supported within the framework and facilities of the NASA Ocean Data Processing System (ODPS):

  • Level 1 and 2 Data Browser: Algorithms are available through SeaDAS to derive ocean color products from Level 1 and 2 data
  • Level 3 Data Browser: Data products include chlorophyll-a concentration, sea surface temperature, reflectance, and related measurements from MODIS and VIIRS at 4 km and 9 km resolution; these data products are provided in five temporal resolutions: daily, 8-day, monthly, seasonally, and annually

Light measurements collected from Earth-facing satellite sensors include radiance values from Earth’s surface as well as from the atmosphere (radiance is the amount of light the instrument senses from the object being observed). Once these data are atmospherically corrected, the ocean color reflectance can be visualized and a qualitative interpretation made based on color (see image below).

Generally, chlorophyll (a pigment present in all phytoplankton) appears as green, water as blue, dissolved organic matter as red/brown or black, and sediments/organic particles as whites and browns. Many ocean color images use a color scale to indicate chlorophyll-a concentration visually. Every component 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.

Cartoon showing land in green, water in blue, sun in yellow with three images showing water properties and a bar showing the color spectrum from purple to red in the upper right corner.
Satellite instruments measure light reflected back to the sensor at different wavelengths and create emission spectra graphs (inset, top right). Differences in the shape of the spectra can be used to determine what is in the water, such as sediments (orange line), chlorophyll (green line), or clear water (blue line). Brighter objects (e.g., sediments) reflect more light of all wavelengths while darker objects absorb more, thus the values are higher across the spectrum for sediment. Credit: NASA/My NASA Data.

NASA also acquires ocean color data from a series of small CubeSats. The HawkEye imager aboard the SeaHawk CubeSat captures images of ocean and land areas with 120-meter-per-pixel resolution.


Map of Australia with colored area outlining the country in colors ranging from red to blue.
Colors surrounding this map of Australia indicate percent change in chlorophyll-a concentrations from 1998-2000 to 2005-2007. Yellow and red indicate greater percent change. Credit: NASA SEDAC.

Indicators of Coastal Water Quality is a collection of datasets by NASA’s Socioeconomic Data and Applications Center (SEDAC) that assess coastal water quality and its change over time by providing chlorophyll concentrations as an indicator of algae biomass.

SEDAC used chlorophyll-a concentrations derived from NASA’s Sea-viewing Wide Field-of-view Sensor (SeaWiFS) to analyze trends over a 10-year period (1998 to 2007). Raster data are available for download in ESRI GRID and GeoTiff format, and the indicators of change are provided in tabular format. Ancillary data are provided in ESRI GRID and Shapefile formats.


Image of a lake with a huge algal bloom in bright green along southern and northern edges.
On July 28, 2015, the Operational Land Imager (OLI) aboard Landsat 8 acquired this image of an algal bloom in Lake St. Clair and in western Lake Erie. Credit: NASA Earth Observatory images by Joshua Stevens, using Landsat data from the U.S. Geological Survey.

Water quality in freshwater systems is impacted by a variety of factors, including extreme weather events, human-induced erosion and sedimentation, eutrophication, and the dumping of waste. The quality of freshwater systems may have direct implications on drinking and recreational waters intended for human use, domestic animals, wildlife, and other ecosystems. To track these environmental conditions, scientists monitor the color of freshwater reservoirs to assess cyanobacteria, pathogens such as E. coli, human-created pollutants, nutrient inputs, and water clarity.  

Freshwater bodies tend to be smaller and often are surrounded by land. As a result, the size of the water body along with sensor qualities must be considered before selecting specific data. For large lakes like Lake Victoria or one of the Great Lakes, MODIS and VIIRS data are adequate since they provide more observations over time, which is useful in a dynamic system. For smaller lakes, data from sensors aboard Landsat and Sentinel-2 are best as they provide fine spatial resolution. For example, the Operational Land Imager (OLI) aboard Landsat 8 provides 30 m resolution, which is useful for assessing water quality parameters of inland water bodies of small sizes. A drawback of data from Landsat and Sentinel, however, is that they have fewer observations over time, allowing for only monthly and seasonal monitoring.

In-situ water quality sampling refers to the direct measurement of physical, chemical, and biological parameters in a water body. High quality in situ measurements are a prerequisite for satellite data product validation, algorithm development, and many climate-related analyses. The most common method of measuring in situ water quality is with a multi-parameter water quality instrument, which has a collection of probes for measuring individual water parameters. The most common parameters measured are pressure (used to derive depth), temperature, electrical conductivity (used to derive salinity), turbidity, chlorophyll, pH, and dissolved oxygen.

The SeaWiFS Bio-optical Archive and Storage System (SeaBASS) is the NASA archive of in-situ oceanographic and atmospheric data maintained by NASA’s OBPG. SeaBASS contains in-situ measurements of apparent and inherent optical properties, phytoplankton pigment concentrations, and other oceanographic and atmospheric data.
If you are planning to collect your own in-situ data timed to when a specific satellite passes over your location, use the Overpass Prediction Tool.

The Bio-Optical in situ Data Discovery and Access with SeaBASS webinar covers how SeaBASS can be leveraged for data search, discovery, and access, and demonstrates how SeaBASS supports NASA’s ocean color satellite products and the broader scientific community.

Photosynthetically Available Radiation

Global map with land in black. Colored bands over ocean indicate PAR values.
Global PAR acquired by Aqua/MODIS for October 1, 2022. Warmer colors (yellow, orange, red), such as along the equator, indicate higher values of PAR. Cooler colors (green, blue, purple), such as at the poles, indicate lower PAR values. Image downloaded from the OB.DAAC Level 3 Browser. Credit: NASA Aqua/MODIS; OB.DAAC.

The growth of phytoplankton is affected by incoming radiation (sunlight). The solar energy available for photosynthesis, known as photosynthetically available radiation (PAR), is one of the important factors controling the growth of phytoplankton and regulates the composition and evolution of marine ecosystems. PAR refers to the wavelength range of incoming sunlight (roughly 400-700 nanometers) that can be absorbed by plants for photosynthesis. Knowing the spatial and temporal distribution of PAR is critical to understanding biogeochemical cycles of carbon, nutrients, and oxygen, all of which can impact water quality by stimulating or inhibiting the growth of organisms in the water.

An algorithm, available through NASA’s Ocean Biology DAAC (OB.DAAC), can be used to estimate daily PAR at the ocean surface. The algorithm is applicable to MODIS, MERIS, SeaWiFS, and VIIRS, but it can be operated on data from all ocean color sensors. For more information on this data product, see the NASA Making Earth System Data Records for use in Research Environments (MEaSUREs) project A Time Series of Photo-Synthetically Available Radiation at the Ocean Surface from SeaWiFS and MODIS Data.

Near real-time PAR data can be accessed using NASA Worldview:

Data products can be visualized in Giovanni: 

  • Aqua MODIS PAR: Data products at 4 km resolution provided at both 8-day and monthly temporal resolutions
  • SeaWiFS PAR: Data products at 9 km resolution provided at 8-day temporal resolution from 1997-2010

Research-quality data products can be accessed using Earthdata Search:

Level 3 data products from OB.DAAC: Data products include PAR, chlorophyll-a concentration, sea surface temperature (SST), reflectance, and other related measurements from MODIS and VIIRS at 4 km and 9 km resolution. These data products are provided in five temporal resolutions: daily, 8-day, monthly, seasonally, and annually.

Water Surface Temperature

Map of US East Coast with colors indicating sea surface temperature; orange and red up to Chesapeake Bay, then transition to yellow, green, and blue indicating cooling SSTs
Sea surface temperature of the Atlantic Ocean off the U.S. East Coast for September 30, 2022. Red indicates temperatures of 80F or warmer; blue indicates temperatures of 60F or cooler. Credit: NASA SOTO/ PO.DAAC.

Sea surface temperature (SST) or water skin temperature (the temperature of water just below the water’s surface at a depth of 10-20 μm) provides fundamental information on the global climate system and is an essential parameter in weather prediction and atmospheric model simulations. SST also is a valuable parameter in evaluating the composition and biological activity in a water body.

Temperature affects the rates of metabolism and growth of aquatic organisms, regulates photosynthesis in phytoplankton, influences the solubility of oxygen in the water, and more. For example, marine phytoplankton bloom as cold upwelling waters bring iron and other nutrients from the seafloor.

SST has one of the longest-satellite records of any Earth climate parameter, with a data record dating back to 1981. Surface temperature of water bodies is derived from the emitted thermal radiation detected by satellite sensors.

Near real-time data can be accessed using NASA Worldview:

Data products can be visualized in Giovanni: 

  • Aqua MODIS SST: Data products at 4 km resolution provided at both 8-day and monthly temporal resolutions

Research-quality data products can be accessed using Earthdata Search:

  • MODIS and VIIRS SST, Level 2: These datasets are available at 1 km spatial resolution (MODIS) and 0.75 km at nadir/1.5 km at swath edge spatial resolution (VIIRS) in NetCDF format and can be opened with SeaDAS or other NASA tools

Level 3 data products from OB.DAAC: Data products include chlorophyll-a concentration, SST, reflectance, and other related measurements from MODIS and VIIRS at 4 km and 9 km resolution. These data products are provided in five temporal resolutions: daily, 8-day, monthly, seasonally, and annually.

Global, Level 4 SST data products are available from the NASA’s MEaSUREs project Multi-scale Ultra-high Resolution (MUR) Sea Surface Temperature (MEaSUREs-MUR). The MUR dataset is among the highest resolution SST analysis datasets currently available: 0.25º (Latitude) x 0.25° (Longitude), hourly-daily, from 2002 to present.


Global map with colors indicating areas of detected rain; heavier rain is indicated in yellow and red; lighter rain is indicated in green; snow/frozen precipitation is indicated in blue
IMERG latest half-hourly image of global precipitation from 12:30 p.m., EDT [UTC -4] on October 3, 2022. Credit: NASA GPM/IMERG.

Rain and snow provide the freshwater upon which life depends. Our ability to remotely sense freshwater resources helps advance our ability to understand the global hydrologic cycle; improve forecasts of extreme events such as flooding, landslides, and drought; and predict and mitigate events that compromise water quality. 

NASA’s Precipitation Measurement Missions (PMM) provide a continuous record of precipitation data starting with the Tropical Rainfall Measuring Mission (TRMM; operational 1997 to 2015) and continuing with the Global Precipitation Measurement mission (GPM; launched in 2014). GPM, the TRMM successor mission, provides more accurate measurements, improved detection of light rain and snow, and extended spatial coverage.

Data products from TRMM and GPM are available individually and have been integrated with data from a global constellation of satellites to yield precipitation estimates with improved spatial coverage and temporal resolution. The first integrated product was the TRMM Multi-satellite Precipitation Analysis (TMPA), which has been superseded by the Integrated Multi-satellitE Retrievals for GPM (IMERG). IMERG’s multiple runs accommodate different user requirements for accuracy and latency (Early = 4 hours, e.g., for flash flood events; Late = 12 hours, e.g., for crop forecasting; and Final = 3 months, with the incorporation of rain gauge data, for research).

TRMM and GPM data can be accessed using Earthdata Search:

NASA’s Hydrological Sciences Laboratory, in collaboration with other agencies, has developed land surface models incorporating satellite precipitation estimates with ground-based data. These models are part of the Land Data Assimilation System (LDAS), which includes a global collection (GLDAS) and a North American collection (NLDAS). LDAS uses inputs of measurements including precipitation, soil texture, topography, and leaf area index to model high quality fields of land surface states (e.g., soil moisture, temperature) and fluxes (e.g., evapotranspiration, runoff).

GLDAS has a spatial resolution of 1° and 0.25°, with data available for all land north of 60° south latitude. GLDAS data are available from January 1948 to present. NLDAS is currently running operationally in near real-time (with an approximate four-day lag) on a 1/8° grid with an hourly timestep over central North America (between approximately 25° to 53° north latitude and -125° to -67° west longitude). Retrospective hourly/monthly NLDAS datasets are available from January 1979 to present.

A lack of precipitation can lead to drought. In developing nations and in areas with limited hydrologic data, it can be difficult to proactively assess conditions leading to drought, which, in turn, can contribute to food security issues. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) is a custom instance of NASA’s Land Information System (LIS) that has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing-country settings. Adopting LIS allows FEWS NET to leverage existing land surface models and generate ensembles of soil moisture, evapotranspiration (ET), and other variables based on multiple meteorological inputs or land surface models. The goal of FLDAS is to achieve more effective use of limited available hydroclimatic observations. FLDAS data have a spatial resolution of 0.1° and are available for all land north of 60° south latitude. Daily FLDAS data are available in 15-minute time steps with a data record starting in January 1981.

Near real-time data can be visualized and interactively explored using NASA Worldview:

Various NASA precipitation products can be visualized in Giovanni:

Another NASA source for precipitation data is Daymet, which can be accessed through NASA’s Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). Daymet is a collection of gridded estimates of daily weather parameters including minimum and maximum temperature, precipitation, vapor pressure, radiation, snow water equivalent, and day length at 1 km resolution over North America, Puerto Rico, and Hawaii. Daymet data are available from 1980 to present (North America and Hawaii) and from 1950 to present (Puerto Rico). Along with daily data, annual Daymet climatologies also are available.

Research-quality Daymet data products can be accessed using Earthdata Search or through NASA partner websites:

Precipitation also impacts food security, which is most clearly seen in a lack of precipitation leading to drought conditions impacting agricultural production. NASA’s Socioeconomic and Data Applications Center (SEDAC) archives and distributes NASA socioeconomic data and provides datasets relevant to precipitation:

Soil Moisture

Map of South America with colors indicating soil moisture values.
SMAP radiometer image from September 3, 2022, showing soil moisture across South America, with yellow/orange indicating areas of low soil moisture and green/blue areas indicating areas of higher soil moisture. Interactively explore this map. Credit: NASA Worldview.

The amount of moisture in the soil controls the amount of water the soil can retain, with excess water leaving the soil as runoff. This runoff can transport pollutants present in the soil long distances and carry these pollutants into creeks, lakes, rivers, and the ocean, which can impact water quality. While satellite-based soil moisture data provide excellent coverage over large areas, they can be limited by their relatively coarse resolution. Ground-based data, on the other hand, have a higher temporal resolution, but have much more limited coverage. Utilizing a combination of both satellite-based and ground-based data helps ensure the best spatial and temporal resolution.

NASA’s Soil Moisture Active Passive (SMAP) satellite measures the moisture in the top five cm of soil globally every 2 to 3 days at a resolution of 9 to 36 km. NASA, in collaboration with other agencies, has also developed models of soil moisture content, incorporating satellite information with ground-based data when available. These models are part of the Land Data Assimilation System (LDAS), which includes a global collection (GLDAS) and a North American collection (NLDAS). LDAS takes inputs of measurements like precipitation, soil texture, topography, and leaf area index and then uses these inputs to model output estimates of soil moisture and evapotranspiration.

Data can be visualized using NASA Worldview:

ORNL DAAC’s Soil Moisture Visualizer integrates ground-based, SMAP, and other soil moisture data into a visualization and data distribution tool. LP DAAC’s AppEEARS offers another option to extract subsets, transform, and visualize SMAP data products. See the Tools for Data Access and Visualization section on the Water Quality Pathfinder landing page for additional information about these resources.

Researchers, meteorologists, farmers, and other stakeholders have access to high-resolution NASA soil moisture data thanks to a tool developed by the USDA’s National Agricultural Statistics Service, NASA, and George Mason University. The Crop Condition and Soil Moisture Analytics geospatial application provides access to high-resolution data from the SMAP mission and MODIS instrument in an easy-to-use format.

Soil moisture data products can be visualized using Giovanni: 

Research-quality data products can be accessed using Earthdata Search (datasets are available in HDF5 format, which are also customizable to GeoTIFF):


Oval illustration of the water cycle with arrows going around the edge showing the flow.
Runoff is an integral part of the water cycle, as seen in the lower right side of this image. Credit: NASA AIRS.

Runoff is related to water quality, although it is not a direct water quality characteristic. As precipitation falls, water moves over, under, and through a variety of media within the watershed. Runoff is the volume of water that flows on the surface and through the subsurface, and transports surface and subsurface chemicals, pollutants, minerals, and sediments.

Satellites cannot measure runoff directly; however, information that can be used to assess runoff can be measured remotely, such as precipitation amount and intensity. These data can then be input into land surface models to estimate runoff and its potential to impact water quality. For example, results from a multi-year Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis simulation demonstrated the potential to estimate runoff values from satellite rainfall for the globe and for medium to large river basins.

NASA’s Land Data Assimilation System (LDAS), which includes a global collection (GLDAS) and a North American collection (NLDAS), uses inputs of measurements like precipitation, soil texture, topography, and leaf area index to model output estimates of runoff and evapotranspiration. LDAS runoff data can be analyzed and visualized using Giovanni, with results downloaded as GeoTIFFs:

    • Select a map plot (you can generate a time-averaged map, an animation, or seasonal maps), date range, and region; select your variable and then plot the data
    • Select a map plot (you can generate a time-averaged map, an animation, or seasonal maps) and date range, then select your variable and plot the data (data are in multiple temporal resolutions and multiple temporal coverages, so be sure to note the start and end date to ensure you access the desired dataset)

NASA’s Socioeconomic Data and Applications Center (SEDAC) distributes data on past runoff anomalies derived from the Water Security Indicator Model (WSIM) GLDAS Monthly Grids. This dataset characterizes 67 years (1948 to 2014) of anomalous freshwater surpluses and deficits across the global terrestrial surface along with the parameters determining them.

Finally, the NASA Applied Remote Sensing Training (ARSET) Program resource Using Earth Observations to Monitor Water Budgets for River Basin Management II provides additional information about the use of water budget calculations for assessing runoff.


Fresh, pure water is clear, and light can penetrate deep into the water column. The presence of suspended solids in the water, including silts, clays, solid and dissolved organic matter, industrial wastes, sewage, and plankton, reduce the passage of light through the water column and add color to the water. Turbidity is an optical measurement of the amount of light scattered by material in the water when light passes through. The more that light is scattered, the higher the turbidity. Turbidity is used as a proxy for water quality—in general, the higher the turbidity, the more particles and other materials are present to reduce the clarity of the water. Solid particles, such as sediment suspended in the water, can block light that aquatic plants and organisms need. Suspended solids also can absorb heat from sunlight and raise the temperature of the water. As the water becomes warmer, it loses its ability to hold oxygen. This causes dissolved oxygen levels to drop, further reducing the number of plants and animals that can live in the water.

Three side by side images showing decreasing water clarity from left to right.
Use of a Secchi disk for assessing water turbidity. Credit: NASA Earth Observatory using photographs courtesy of the Minnesota Pollution Control Agency.

A simple method for assessing water turbidity is the use of a Secchi disk, which is a scientific tool for measuring the relative clarity of deep water. The disk has a black and white pattern on it and is suspended from a line as it is lowered into the water. The length of line at which the disk can no longer be seen is measured; this is the Secchi depth. The clearer the water, the lower the turbidity and the deeper the Secchi depth; the murkier the water, the higher the turbidity and the shallower the Secchi depth. A closely related quantity is the euphotic depth, which is the maximum depth to which light can penetrate a water body.

A more precise parameter that scientists use as an indicator of turbidity is Suspended Particulate Matter (SPM). This variable describes the mass of solid material in the water column when nanoscale to sand-size particles are suspended. While remotely sensed SPM data are useful in characterizing surface water, in situ measurements (such as from a Secchi disk) should be included in examining water quality throughout the column.

Global map with blue/purple and green areas indicating light attenuation.
Aqua/MODIS Kd490 global image acquired October 2, 2022. Yellow/red areas (such as along the southern coast of South America) are areas of higher light attenuation; blue/green areas have less light attenuation. Click on image for larger view. Credit: NASA Aqua/MODIS; NASA OBPG.

An additional variable used for monitoring turbidity of the water column is the Diffuse Attenuation Coefficient at 490 nm (Kd490 or K490). This value indicates the penetration of visible light in the blue to green region of the spectrum within the water column. The higher the coefficient, the more quickly the light attenuates (weakens) as it passes through the water column. 

Kd490 data are available through several sensors, including MODIS, VIIRS, and SeaWiFS. High optical resolution data are available through data acquired by the OLI and OLI-2 instruments aboard Landsat-8 and Landsat-9, the MSI aboard Sentinel-2, and the OLCI aboard Sentinel-3. Kd/Kd490 data from multiple instruments are available through the Level 3 Browser at NASA’s OceanColor Web (select Kd/Kd490 in the Product window).

Diffuse attenuation coefficient for downwelling irradiance at 490 nm data products can be visualized using Giovanni: 

  • Aqua MODIS: Data products at 4 km resolution provided at both 8-day and monthly temporal resolutions.
  • SeaWiFS: Data products at 9 km resolution provided at 8-day temporal resolution. From 1997-2010

Research-quality data products can be accessed using Earthdata Search:

Data products also can be accessed through NASA’s OB.DAAC and through NASA partner websites:

Human Impacts/Socioeconomic

Many environmental and human factors impact water quality, including deforestation, agricultural development, urbanization, pollution, and climate change. Understanding the ways in which humans are interacting with the environment and how these interactions impact Earth systems is important to monitoring and protecting water quality.

Impacts to human health related to water quality are preventable. However, prevention requires a knowledge of where vulnerable populations reside and the interventions needed in these communities. Combining socioeconomic data with water quality data can provide a more accurate analysis of populations facing greater exposure and vulnerability from water quality issues.

The following tabs show how socioeconomic data can be incorporated with remotely sensed data.
Several socioeconomic factors need to be considered when analyzing communities impacted by water quality issues. These include:
  • Population
  • Poverty
  • Unemployment 
  • Academic attainment
  • Housing burden
  • Social vulnerability
Map of Eastern U.S. with shades of blue indicating higher social vulnerability areas; large cluster in Deep South and along Atlantic Coast.
Map from the SEDAC U.S. Social Vulnerability Index (SVI) Grids showing overall social vulnerability of the Eastern U.S. Darker colors are higher SVI values and the location of more vulnerable communities. Click on image for larger view. Credit: CIESIN - Columbia University. 2021. Palisades, NY: NASA SEDAC.
NASA’s Socioeconomic Data and Applications Center (SEDAC) is the home for socioeconomic data in NASA’s Earth Observing System Data and Information System (EOSDIS) collection. Hosted at Columbia University’s Center for International Earth Science Information Network (CIESIN), SEDAC serves as an “Information Gateway” between the socioeconomic and Earth science data and information domains. General SEDAC services, data collections, and datasets that can be incorporated with water quality data include:
SEDAC also offers quantitative metrics for evaluating a country’s environmental performance in different policy categories, including water resources:
NASA data can be visualized and interactively explored using NASA Worldview:
Research-quality data products can be accessed using Earthdata Search:
Brown indicates land; black indicates water; greenish area around Nile river indicates Nile Delta and the path of the Nile River
Aqua/MODIS true color surface reflectance image of the Nile Delta, Egypt, and Israel acquired on September 30, 2022. Explore this image in NASA Worldview. Credit: NASA Worldview.
Surface reflectance is useful for measuring the greenness of vegetation along with the health of agriculture, both of which can impact water quality, water quantity, runoff, and turbidity. Moderate resolution instruments that are primarily used for surface reflectance measurement include NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) aboard both the Terra and Aqua satellites and the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA-20 satellites. MODIS reflectance products are available at 250 m, 500 m, 1,000 m, and 5,600 m spatial resolution and provides global coverage every one to two days, depending on latitude. VIIRS reflectance products are available at 500 m and 1,000 m spatial resolution and provide daily global coverage.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)—a cooperative effort between NASA and Japan’s Ministry of Economy Trade and Industry—is a high-resolution instrument that acquires visible and near-infrared (VNIR) reflectance data at 15 m resolution and short wave infrared (SWIR) reflectance data at 30 m resolution. As a tasked sensor, ASTER only acquires data when it is directed to do so over specific targets, making its temporal resolution variable depending on the target region of interest. In addition, ASTER surface reflectance products are processed on-demand and so must be requested with additional parameters (note that there is a limit of 2,000 data granules per processing order).

Research quality surface reflectance data products can be accessed using Earthdata Search; all data are available as HDF files, but are also customizable to GeoTIFF:
MODIS Terra and Aqua surface reflectance imagery can be visualized and interactively explored using NASA Worldview (a surface reflectance layer is not available in Worldview for VIIRS Suomi NPP or NOAA-20; however, corrected reflectance imagery is available as a near real-time product):
Surface reflectance data also are available through the Data Pool at NASA’s Land Processes Distributed Active Archive Center (LP DAAC).

Along with ASTER, higher resolution surface reflectance products are available through the joint NASA/USGS Landsat 8 Operational Land Imager (OLI) and the Landsat 9 OLI-2 instruments. Both instruments acquire data at 30 m spatial resolution in VNIR every 16 days (or less as you move away from the equator). The joint NASA/USGS Landsat 7 satellite with its 30 m spatial resolution Enhanced Thematic Mapper (ETM+) sensor is currently in extended operations at a lower altitude as part of the satellite’s decommissioning activities.

Landsat 7 and 8 data can be discovered using Earthdata Search (a USGS Earth Explorer login is required to download data):
Another high-resolution option is imagery from the Harmonized Landsat and Sentinel-2 (HLS) project, which provides consistent surface reflectance and top of atmosphere brightness data from the Landsat 8 OLI and the Multi-Spectral Instrument (MSI) aboard the ESA (European Space Agency) Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global land observations every 2 to 3 days at 30 m spatial resolution. Data from the Landsat 9 OLI-2 soon will be incorporated into the HLS data stream.

HLS imagery can be visualized and interactively explored using NASA Worldview:
Research-quality HLS data products can be accessed using Earthdata Search:
Map of Asia with areas with higher nitrogen from manure indicated in yellow; best seen throughout China and India
Higher levels of manure use in Asia are depicted in yellow. Knowing global centers of manure use in agriculture enables the tracking and assessment of potential impacts to water sources. Credit:  2011. The Trustees of Columbia University in the City of New York. Data distributed by NASA SEDAC.
Agricultural activities are major drivers of global environmental impacts on water resources. To maintain crop production levels, farmers apply pesticides, fertilizer, and manure to croplands. As these wastes and chemicals make their way into water bodies, the addition of excessive nutrients can lead to explosive growth of plants and microorganisms, which can lead to oxygen starvation and death (eutrophication). Agricultural practices such as the digging of irrigation canals, the drawdown of surface and subsurface water supplies, and the draining of wetlands can be monitored remotely. For more information on datasets and resources, see the Agriculture and Water Resources Data Pathfinder.

The following NASA SEDAC datasets and resources provide a better understanding of the levels and spatial patterns of agricultural applications, improve the ability to assess human and ecosystem exposure to potential and recognized toxicants, and identify areas that might be sensitive to compromised water quality:
Last Updated
Nov 5, 2021