Floods Data Pathfinder - Find Data

Common Precipitation Data at a Glance

Flooding can occur in any location where precipitation occurs. Measuring rainfall helps advance our understanding of Earth's water cycle, improving forecasts of extreme events such as flooding.

Relevant datasets are from sensors shown in the table below. This is not intended to be an exhaustive list of all sensors or missions producing data relevant to this Data Pathfinder. An asterisk (*) by an entry indicates that near real-time (NRT) data products are available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE). LANCE data products are available within three hours of a satellite observation. If low-latency is not a primary concern, users are encouraged to use standard science products, which are produced using the best available calibration, ancillary, and ephemeris information. To learn more about the difference between NRT and Standard Science Products, see Near Real Time versus Standard Products.

Use the Precipitation Data

Use Cases and Articles

• User Profile: Dr. Pierre Kirstetter
• GES DISC Data in Action: Combining NASA Precipitation Data With GIS to Assess Hurricane Ida Flood Impact
• GES DISC Data in Action: Precipitation data provides a detailed view of extraordinary rainfall event in Australia
• Blending CHIRPS Data And GEFS Forecasts For An Enhanced Rainfall Forecast Product
• Heavy Rains Hit Drought-Stricken Horn of Africa
• Warming Makes Droughts, Extreme Wet Events More Frequent, Intense
• Using GPM Data for Disasters and Risk Management

Tutorials

• NASA Applied Remote Sensing Training (ARSET): Applications of GPM IMERG Reanalysis for Assessing Extreme Dry and Wet Periods
• ARSET: Water Resource Management Using NASA Earth Science Data
• ARSET: Introduction to Global Precipitation Measurement (GPM) Data and Applications
• Data Access and Visualization of Model Data at the NASA GES DISC
• NetCD-what? An Ecologist’s Guide to Working with Daymet and other NetCDF-formatted Data 
• International Precipitation Working Group (IPWG) and Global Precipitation Measurement (GPM) Applications Training
• Introductory Webinar: Overview and Applications of Integrated Multi-Satellite Retrievals for GPM (IMERG) Long-term Precipitation Data Products
• Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) Precipitation Products and Services at GES DISC 
• GES DISC How-To's: How to access the GES DISC IMERG ArcGIS Image Service using the ArcGIS Enterprise Map Viewer (nasa.gov)
• All IMERG/GPM Trainings

Data Visualizations

• IMERG Daily Climatology
• IMERG Monthly Climatology
• Precipitation Accumulation and Anomalies
• Life of the Monsoon
• GPM - related Visualizations
• A Map of Freshwater
• How Does NASA Model Atmospheric Patterns?

GIS-Ready Tools and Tutorials

• Tutorial: How to Import MERRA Surface Product Data into ArcGIS
• Tutorial: Toward Analysis Ready Data - Programmatically Discover, Access, and Subset Daymet V4 Data with Python and ArcGIS
• Tutorial: ArcGIS Image Service for IMERG Early and IMERG Final
• Tutorial: Using NASA’s GPM IMERG ArcGIS Image Service in ArcGIS Pro
• Tutorial: Using NASA’s GPM IMERG ArcGIS Image Service in Jupyter Notebook: Assessing Hurricane Impact
• Tutorial: Accessing ORNL DAAC OGC services in popular GIS tools
• The NASA Prediction of Worldwide Energy Resources (POWER) Data Access Viewer provides MERRA-2 reanalysis meteorological parameters through a web mapping application with the capability for data subsetting, charting, and visualizing
• For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
• NASA GIS Tutorials and How-Tos

Data Access Tools

• Users may access and visualize data directly through ClimateSERV. Datasets include IMERG, Evaporative Stress Index, SMAP Soil Moisture, Rainfall, and NDVI
• The Data Rods Explorer (DRE) interface enables the visualization and downloading of NASA observation retrievals and land surface model (LSM) outputs by space, time, and variable

Data Customizing Tools

• Daymet Single Pixel Data Extraction—Web Services 
• 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
• AMSR-2 Tools and Libraries
• How to use AMSR Earth Observation Data
• AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
• For information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page

Programming Tools

• NASA Earthdata Application Programming Interfaces (APIs)
• Toward Analysis Ready Data—Programmatically Discover, Access, and Subset Daymet V4 Data with Python and ArcGIS
• Agrometeorological data using R-software
• DaymetPy: A python library for accessing Daymet surface weather data
• A Programmatic Interface to the Daymet Web Services: DaymetR
• AMSR-2 Libraries
• NASAaccess is an R package that can generate gridded ASCII tables of climate (CIMP5) and weather data (GPM, TRMM, GLDAS) needed to drive various hydrological models (e.g., SWAT, VIC, RHESSys)
• ClimateSERV offers an API for those who wish to incorporate their data into a separate application or script

Earth Observation Data by Sensor

AIRS

The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer aboard NASA's Aqua satellite. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. 

The AIRS Precipitation Estimate is an estimate of daily precipitation measured in millimeters using cloud-related parameters of cloud-top pressure, fractional cloud cover, and cloud-layer relative humidity. The precipitation algorithm is a regression between these parameters and observed precipitation data. It is an estimate from AIRS and is intended for merging into the precipitation product of the Global Precipitation Climatology Project (GPCP).

Create and share layered maps with AIRS data using the AIRS Applications Browse Tool.

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

• AIRS/Aqua L2G Precipitation Estimate

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AMSR2

The Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument collects data that indicate the rate at which precipitation is falling on the ocean surface in millimeters per hour (mm/hr). 

Data can be accessed and interactively explored using NASA Worldview:

• AMSR2 Surface Precipitation Rate
  • Offered in Day and Night

Create and share layered maps with AMSR2 data using the AIRS Applications Browse Tool.

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

• AMSR2 Surface Precipitation

Near real-time (NRT) Surface Precipitation products are generated within three hours by the Land, Atmosphere Near real-time Capability for EOS (LANCE). The NRT products are generated in HDF-EOS-5 augmented with netCDF-4/CF metadata and are available via HTTPS. If data latency is not a primary concern, please use science quality products, which are an internally consistent, well-calibrated record of Earth's geophysical properties to support science. AMSR2 NRT data also are available through Earthdata Search:

• NRT AMSR2 Surface Precipitation Rate

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GPM IMERG

NASA's Precipitation Measurement Missions (PMM) provide a continuous record of precipitation data through the Tropical Rainfall Measuring Mission (TRMM; operational 1997 to 2015) and 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).

GPM data can be visualized using NASA Worldview:

• IMERG Precipitation Rate
• GPM Precipitation Rate

The Short-term Prediction Research and Transition (SPoRT) project at NASA's Marshall Space Flight Center in Huntsville, Alabama, is a NASA- and NOAA-funded activity to transition experimental and quasi-operational satellite observations and research capabilities to the operational weather community to improve short-term weather forecasts on a regional and local scale. SPoRT offers a Near Real-Time Viewer for the following IMERG data products:

• GPM IMERG Early
• GPM IMERG Late
• IMERG-Tropics

Users may access and visualize these data directly through ClimateSERV. Additional datasets include IMERG, Evaporative Stress Index, Soil Moisture Active Passive (SMAP) Soil Moisture, Rainfall, and Normalized Difference Vegetation Index (NDVI).

An online interactive tool called Giovanni enables users to map visualizations of IMERG data products. These visualizations can be downloaded as PNG images or as GeoTIFF or KMZ files; time-series data are available as CSV data files, and animations are in either webm video format or animated GIF images:

• IMERG Final
  • Data are available from 2000-present
• GPM
  • Includes all IMERG runs: Early, Late, and Final
• TMPA

NASA Earthdata GIS Products:

• Early IMERG Precipitation Rate Image service
• Early IMERG Precipitation Rate Web Map
• Final IMERG Precipitation Rate Image service
• Final IMERG Precipitation Rate Web Map

Near-real time (NRT) data:

• IMERG Early Run Half-Hourly 
  • The Early Run product at NASA's Global Precipitation Measurement website is generated every half hour with a 6-hour latency from the time of data acquisition

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

• TMPA
  • Rainfall estimates at three hours, 1 day, or NRT and accumulated rainfall at 3 hours and 1 day; data are available from 1997
• IMERG
  • Early, Late, and Final precipitation data on the half-hour or 1-day timeframe; data are available from 2000

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Model Data

Daymet

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). Data can be retrieved in a variety of ways, including: Earthdata Search; an API available through ORNL DAAC; ORNL DAAC tools; and through the Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) application. Along with daily data, annual Daymet climatologies also are available.

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GEOS-5

NASA's Goddard Earth Observing System, Version 5 (GEOS-5) is an atmospheric model used to study the physics of the atmosphere. GEOS-5 has a series of weather maps that can be used to predict parameters such as wind speed up to 240 hours out, which can be used to forecast the movement of a smoke plume over time.

• GEOS-5 Weather Maps
  • Within the viewer, select the parameter or field of interest, the area of interest, and indicate the forecast time and the forecast lead hour. Selecting "Animate" shows the forecast for the given parameter over the time period indicated (it may take time to load the images to animate). For wind speed near the surface, select 850 as your level (850 hPa is approximately 5,000 ft/1,500 m above sea level)

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NEX-GDDP-CMIP6

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of high-resolution, bias-corrected global downscaled climate projections derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across all four “Tier 1” greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). 

This dataset provides a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions. Uses include: air temperature, precipitation volume, humidity, stellar radiation, and atmospheric wind speed.

NEX-GDDP-CMIP6 data are available through the NASA Center for Climate Simulation (NCCS):

• NEX-GDDP-CMIP6 data at NCCS

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CHIRPS-GEFS

SERVIR (a joint initiative of NASA, USAID, and geospatial organizations in Asia, Africa, and Latin America) and the Climate Hazards Group (CHG) at University of California at Santa Barbara have developed an improved rainfall forecast dataset that merges two highly recognized datasets: Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and the NCEP’s Global Ensemble Forecasting System (GEFS). GEFS is a weather forecast system that provides daily forecasts out to 16 days at 1º x 1º resolution at six-hour intervals.The combined CHIRPS-GEFS dataset uses the higher spatial resolution of CHIRPS and the advanced forecasting ability of GEFS to provide up to a 16-day forecast updated every five days at a global spatial resolution of 5 km.

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GPCP

The Global Precipitation Climatology Project (GPCP) is the precipitation component of an internationally coordinated set that combines observations and satellite precipitation data. 

Using an online interactive tool called Giovanni, users can map visualizations of GPCP Precipitation data products and download these visualizations as PNG images or as GeoTIFF or KMZ files; time-series data are available as CSV data files, and animations are in either webm video format or animated GIF images:

• GPCP Precipitation data

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LDAS

The Land Data Assimilation System (LDAS) includes a global collection (GLDAS), a North American collection (NLDAS), National Climate Assessment-Land Data Assimilation System (NCA-LDAS), and a Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). LDAS uses measurements including air temperature, precipitation, soil texture, and topography. GLDAS modeled data are available from January 1948 to present. NLDAS modeled datasets are available from January 1979 to present. FLDAS modeled datasets are available from January 1982 to present.

Data can be accessed and interactively explored using NASA Worldview:

• GLDAS Precipitation Total
  • Data are only available between 1947 and 2022
• NLDAS Precipitation Total

Using an online interactive tool called Giovanni, data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series, as well as downloaded in .CSV format:

• LDAS Precipitation data
  • FLDAS Rainfall data
  • NLDAS Precipitation data
  • NCA-LDAS Precipitation data
  • GLDAS Precipitation data

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Reanalysis Data

MERRA-2

The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns as well as anomalies. There are several options available: 1-hourly, 3-hourly, 6-hourly, daily, and monthly. These options provide information on precipitation.

Data can be accessed and interactively explored using NASA Worldview:

• MERRA-2 Precipitation data

Using an online interactive tool called Giovanni, map visualizations of MERRA-2 Precipitation data products can be downloaded as PNG images or as GeoTIFF or KMZ files; time-series data are available as CSV data files, and animations are in either webm video format or animated GIF images:

• MERRA-2 Precipitation data

Geographic Information Systems (GIS) products using MERRA-2 reanalysis data are produced by NASA's Prediction of Worldwide Energy Resources (POWER) project:

• POWER Monthly Meteorology
• POWER Annual Meteorology
• POWER Climatology Meteorology

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

• MERRA-2 Precipitation data

The NASA POWER Data Access Viewer provides MERRA-2 meteorological parameters through a web mapping application with the capability for data subsetting, charting, and visualizing. The data are also available via API as well as via OPeNDAP:

•  POWER meteorological data

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Soil moisture is an important factor in flood prediction, mitigation, and preparedness efforts since soil moisture content is critical in determining the susceptibility of an area to flooding. Areas with high soil moisture levels reduce the soil's capacity to absorb additional water, increasing the risk of flooding during heavy rainfall events. Satellite data can provide a global view of soil moisture. Although ground-based measurements provide data at a higher resolution, these data are often sparse and have limited coverage. 

Common Soil Moisture Data at a Glance

Relevant datasets are from sensors shown in the table below. This is not intended to be an exhaustive list of all sensors or missions producing data relevant to this Data Pathfinder. An asterisk (*) by an entry indicates that near real-time (NRT) data products are available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE). LANCE data products are available within three hours of a satellite observation. If low-latency is not a primary concern, users are encouraged to use standard science products, which are produced using the best available calibration, ancillary, and ephemeris information. To learn more about the difference between NRT and Standard Science Products, see Near Real Time versus Standard Products.

Use the Soil Moisture Data

Tutorials

• NASA Applied Remote Sensing Training (ARSET): Water Resource Management Using NASA Earth Science Data
• ARSET: Applications of Remote Sensing to Soil Moisture and Evapotranspiration
• Learn how to Search, Order, and Customize SMAP Data using Earthdata Search (PDF)
• SPORT Training Module: NASA Land Information System (LIS):
  • Soil Moisture Percentile
    • Background information in training modules: 
      • LIS Primer 
      • LIS Applications

GIS-Ready Tools and Tutorials

• How to Import MERRA Surface Product Data into ArcGIS
• Visualize NSIDC data as WMS layers with ArcGIS and Google Earth
• How to Import SMAP HDF Data Into ArcGIS
• Accessing ORNL DAAC OGC services in popular GIS tools
• How to Import AMSR-E Daily Soil Moisture data into ArcGIS
• Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox
• HEG: HDF-EOS to GeoTIFF Conversion Tool
• For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
• NASA GIS Tutorials and How-Tos

Data Access Tools

• Users can visualize data directly through ClimateSERV. Additional datasets include IMERG, Evaporative Stress Index, Rainfall, and NDVI
• SMAP Soil Moisture Level 3, 9 km Day and Night Data are available through the State of the Ocean (SOTO) Visualization Tool
• Soil Moisture Visualizer Guide
• Data Rods Explorer (DRE) enables the visualization and downloading of NASA observation retrievals and land surface model (LSM) outputs by space, time, and variable. The key variables are precipitation, wind, temperature, surface downward radiation flux, heat flux, humidity, soil moisture, groundwater, runoff, and evapotranspiration. These variables describe the main components of the water cycle over land masses
• SMAP Data Access Tool
• Programmatic Data Access Guide (SMAP)

Data Customizing Tools

• Soil Moisture Visualizer integrates a variety of North American soil moisture datasets. For more information on the available datasets and use of the visualizer, view the Soil Moisture Visualizer Guide
• AppEEARS enables users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
• MODIS/VIIRS Subsetting Tools Suite
  • Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
  • Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
  • Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service; web service clients and libraries are available in multiple programming languages, allowing integration of subsets into a workflow
• For more information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page

Programming Tools

• Analysis of Soil Moisture at USFS Rangeland Monitoring Sites with Python
• NASA Earthdata Application Programming Interfaces (APIs)
• Download and Visualize SMAP Data using Python 
• Learn how to work with SMAP data at NSIDC DAAC 
• ClimateSERV offers an API for those who wish to incorporate their data into a separate application or script

Earth Observation Data by Sensor

SMAP

NASA's Soil Moisture Active Passive satellite (SMAP), launched in 2015, measures the moisture in the top 5 cm of soil globally daily and every two to three days at a resolution of 9 to 36 km. 

Near real-time SMAP imagery can be interactively explored using NASA Worldview:

• SMAP
  • includes root zone and surface soil moisture values

The Soil Moisture Visualizer, available at NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC), integrates ground-based, SMAP, and other soil moisture data into a visualization and data distribution tool.

Research quality data products can be accessed using Earthdata Search:

• SMAP Soil Moisture Data

Near real-time (NRT) SMAP data are available through NASA’s LANCE within 60 to 125 minutes after a satellite observation:

• SMAP NRT Data

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AMSR2

The Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) and the AMSR2 instruments provide volumetric soil moisture data. AMSR2 provides global passive microwave measurements of Surface Soil Moisture. Near real-time (NRT) products are generated within 3 hours of the last observations in the file.

Data can be visualized using NASA Worldview:

• AMSR2 Surface Soil Moisture (offered in Day and Night)

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series using a NASA interactive tool called Giovanni:

• AMSR2 volumetric soil moisture data

Near-real time data:

• AMSR2 NRT data are available through LANCE (global data from 2018 to present)

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

• AMSR2 Surface Soil Moisture data

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Model Data

SPoRT-LiS

The Short-term Prediction Research and Transition (SPoRT) project at NASA's Marshall Space Flight Center in Huntsville, Alabama, is a NASA- and NOAA-funded activity to transition experimental and quasi-operational satellite observations and research capabilities to the operational weather community to improve short-term weather forecasts on a regional and local scale.

The SPoRT-Land Information System (SPoRT-LiS) provides real-time 3 km LiS data on the following parameters: volumetric soil moisture, relative soil moisture, column-integrated relative soil moisture, and green vegetation fraction.

SPoRT offers a Near Real-Time Viewer that includes SMAP datasets for the following regions:

• Enhanced Alaska (9 km)
• Enhanced Continental U.S. (CONUS) (9 km)
• Enhanced East Africa (9 km)
• Level 2 CONUS
• Level 2 East Africa

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LDAS

NASA, in collaboration with other agencies, has developed models of soil moisture content that incorporate satellite information with ground-based data (when ground-based data are available). These models are part of the Land Data Assimilation System (LDAS), which includes a global collection (GLDAS) and a North American collection (NLDAS). The Famine Early Warning Systems Network (FEWS NET) LDAS (FLDAS) datasets offer global monthly data with a 0.1º x 0.1º spatial resolution covering the period from January 1982 to present. FLDAS Soil Moisture data are available from the surface to the depth of 10 cm and 100 cm, and re-expressed as volumetric water content percent. LDAS takes inputs of measurements like precipitation, soil texture, topography, and leaf area index and uses these inputs to model output estimates of soil moisture and evapotranspiration.

FLDAS datasets also are available on the Early Warning eXplorer (EWX): Next Generation Viewer.

LDAS data are available through NASA Worldview:

• NLDAS Soil Moisture Data
• GLDAS Soil Moisture Data

The NLDAS experimental drought monitor is derived from near real-time soil moisture output model data: 

• NLDAS Drought Monitor Soil Moisture

Soil MERGE (SMERGE) is a root-zone soil moisture product developed by merging NLDAS land surface model output with surface satellite retrievals from the ESA (European Space Agency) Climate Change Initiative. This data product contains root-zone soil moisture of 0-40 cm layer, Climate Change Initiative (CCI)-derived soil moisture anomalies of 0-40 cm layer, and a soil moisture data estimation flag:

• SMERGE in Earthdata Search

NLDAS, GLDAS, and SMERGE data products can be visualized as a time-averaged map, an animation, seasonally-averaged maps, scatter plots, or a time series using a NASA interactive data analysis tool called Giovanni:

• NLDAS (North American) Soil Moisture
• GLDAS (Global) Soil Moisture
• SMERGE Root Zone Soil Moisture 

LDAS Soil Moisture data from GLDAS, NLDAS and FLDAS are available in Earthdata Search:

• LDAS components Soil Moisture data

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GRACE-DA-DM

Weekly soil moisture and groundwater drought indicators are available each week based on terrestrial water storage observations derived from GRACE satellite data and integrated with other observations, using a sophisticated numerical model of land surface water and energy processes, referred to as GRACE Data Assimilation for Drought Monitoring (GRACE-DA-DM). GRACE-DA-DM data are available through Earthdata Search:

• Groundwater and Soil Moisture Conditions from GRACE and GRACE-FO Data Assimilation

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GMAO SMAP

NASA’S Global Modeling And Assimilation Office (GMAO), in collaboration with The University of Montana and NASA's Jet Propulsion Laboratory, provides value-added Level 4 data products. These Level 4 datasets rely on the merger of SMAP observations into physically-based numerical models of the land surface water, energy, and carbon cycles. Available Level 4 data include global, 9-km, 3-hourly estimates of surface and root zone soil moisture, surface and soil temperature, and land surface fluxes, along with algorithm diagnostics from the ensemble-based data assimilation system. Level 4 data also include global, 9-km, daily estimates of net ecosystem carbon dioxide (CO²) exchange, component carbon stocks and fluxes, and sub-grid information broken down by plant functional types.

Near real-time SMAP imagery can be interactively explored using NASA Worldview:

• GEOS-5 SMAP soil moisture data

These data products are available from Earthdata Search:

• GEOS-5 SMAP soil moisture data

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Reanalysis Data

MERRA-2

The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns, as well as anomalies. Hourly and monthly data options are available.

MERRA -2 Soil Moisture data products can be visualized as a time-averaged map, an animation, seasonally-averaged maps, scatter plots, or a time series using a NASA interactive data analysis tool called Giovanni:

• MERRA-2 soil moisture data

MERRA-2 Soil Moisture data in Earthdata Search:

• MERRA-2 Soil Moisture

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Soil moisture is important in forecasting fire events as the dryness of the soil contributes to fire potential. Satellite data can provide a global view of soil moisture. Although ground-based measurements provide data at a higher resolution, these data are often sparse and have limited coverage. The preferred measurement (satellite vs. ground-based) should be chosen based upon your needs.

Common Measurements at a Glance

Relevant datasets are from sensors shown in the table below. This is not intended to be an exhaustive list of all sensors or missions producing data relevant to wildfires. An asterisk (*) by an entry indicates that near real-time (NRT) data products are available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE). LANCE data products are available within three hours of a satellite observation. If low-latency is not a primary concern, users are encouraged to use standard science products, which are produced using the best available calibration, ancillary, and ephemeris information. To learn more about the difference between NRT and Standard Science Products, see Near Real Time versus Standard Products.

Earth Observation Data by Sensor

SMAP

NASA's Soil Moisture Active Passive satellite (SMAP), launched in 2015, measures the moisture in the top 5 cm of soil globally daily and every 2 to 3 days at a resolution of 9 to 36 km. 

Near real-time SMAP imagery can be interactively explored using NASA Worldview:

• SMAP
  • includes root zone and surface soil moisture values

The Soil Moisture Visualizer, available at NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC), integrates ground-based, SMAP, and other soil moisture data into a visualization and data distribution tool.

Research quality data products can be accessed using Earthdata Search:

• SMAP Soil Moisture Data

Near real-time (NRT) SMAP data are available through NASA’s LANCE within 60 to 125 minutes after a satellite observation:

• SMAP NRT Data

AMSR-2

The Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) and the AMSR-2 instruments provide volumetric soil moisture data. AMSR2 provides global passive microwave measurements of Surface Soil Moisture. Near real-time (NRT) products are generated within 3 hours of the last observations in the file.

Data can be visualized using NASA Worldview:

• AMSR-2 Surface Soil Moisture (offered in Day and Night)

Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series using a NASA interactive tool called Giovanni:

• AMSR-2 volumetric soil moisture data

Near-real time data:

• AMSR-2 NRT data are available through LANCE (global data from 2018 to present)

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

• AMSR-2 Surface Soil Moisture data

Model Data

SPoRT-LiS

The Short-term Prediction Research and Transition (SPoRT) project at NASA's Marshall Space Flight Center in Huntsville, Alabama, is a NASA- and NOAA-funded activity to transition experimental and quasi-operational satellite observations and research capabilities to the operational weather community to improve short-term weather forecasts on a regional and local scale.

The SPoRT-Land Information System (SPoRT-LiS) provides real-time 3 km LiS data on the following parameters: volumetric soil moisture, relative soil moisture, column-integrated relative soil moisture, and green vegetation fraction.

SPoRT offers a Near Real-Time Viewer that includes SMAP datasets for the following regions:

• Enhanced Alaska (9 km)
• Enhanced Continental U.S. (CONUS) (9 km)
• Enhanced East Africa (9 km)
• Level 2 CONUS
• Level 2 East Africa

LDAS

NASA, in collaboration with other agencies, has developed models of soil moisture content that incorporate satellite information with ground-based data (when ground-based data are available). These models are part of the Land Data Assimilation System (LDAS), which includes a global collection (GLDAS) and a North American collection (NLDAS). The Famine Early Warning Systems Network (FEWS NET) LDAS (FLDAS) datasets offer global monthly data with a 0.1º x 0.1º spatial resolution covering the period from January 1982 to present. FLDAS Soil Moisture data are available from the surface to the depth of 10 cm and 100 cm, and re-expressed as volumetric water content percent. LDAS takes inputs of measurements like precipitation, soil texture, topography, and leaf area index and uses these inputs to model output estimates of soil moisture and evapotranspiration.

FLDAS datasets also are available on the Early Warning eXplorer (EWX): Next Generation Viewer.

LDAS data are available through NASA Worldview:

• NLDAS Soil Moisture Data
• GLDAS Soil Moisture Data

The NLDAS experimental drought monitor is derived from near real-time soil moisture output model data: 

• NLDAS Drought Monitor Soil Moisture

Soil MERGE (SMERGE) is a root-zone soil moisture product developed by merging NLDAS land surface model output with surface satellite retrievals from the ESA (European Space Agency) Climate Change Initiative. This data product contains root-zone soil moisture of 0-40 cm layer, Climate Change Initiative (CCI)-derived soil moisture anomalies of 0-40 cm layer, and a soil moisture data estimation flag:

• SMERGE in Earthdata Search

NLDAS, GLDAS, and SMERGE data products can be visualized as a time-averaged map, an animation, seasonally-averaged maps, scatter plots, or a time series using a NASA interactive data analysis tool called Giovanni:

• NLDAS (North American) Soil Moisture
• GLDAS (Global) Soil Moisture
• SMERGE Root Zone Soil Moisture 

LDAS Soil Moisture data from GLDAS, NLDAS and FLDAS are available in Earthdata Search:

• LDAS components Soil Moisture data

GRACE-DA-DM

Weekly soil moisture and groundwater drought indicators are available each week based on terrestrial water storage observations derived from GRACE satellite data and integrated with other observations, using a sophisticated numerical model of land surface water and energy processes, referred to as GRACE Data Assimilation for Drought Monitoring (GRACE-DA-DM). GRACE-DA-DM data are available through Earthdata Search:

• Groundwater and Soil Moisture Conditions from GRACE and GRACE-FO Data Assimilation

GMAO SMAP

NASA’S Global Modeling And Assimilation Office (GMAO), in collaboration with The University of Montana and NASA's Jet Propulsion Laboratory, provides value-added Level 4 data products. These Level 4 datasets rely on the merger of Soil Moisture Active Passive (SMAP) observations into physically-based numerical models of the land surface water, energy, and carbon cycles. Available Level 4 data include global, 9-km, 3-hourly estimates of surface and root zone soil moisture, surface and soil temperature, and land surface fluxes, along with algorithm diagnostics from the ensemble-based data assimilation system. Level 4 data also include global, 9-km, daily estimates of net ecosystem carbon dioxide (CO²) exchange, component carbon stocks and fluxes, and sub-grid information broken down by plant functional types.

Near real-time SMAP imagery can be interactively explored using NASA Worldview:

• GEOS-5 SMAP soil moisture data

These data products are available from Earthdata Search:

• GEOS-5 SMAP soil moisture data

Reanalysis Data

MERRA-2

The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns, as well as anomalies. Hourly and monthly data options are available.

MERRA -2 Soil Moisture data products can be visualized as a time-averaged map, an animation, seasonally-averaged maps, scatter plots, or a time series using a NASA interactive data analysis tool called Giovanni:

• MERRA-2 soil moisture data

MERRA-2 Soil Moisture data in Earthdata Search:

• MERRA-2 Soil Moisture

Seasonal water runoff from snowpack and glaciers, when combined with rainfall, can affect the timing and magnitude of river flows and significantly impact the risk of flooding events. Snow Water Equivalent (SWE) is the amount of water contained in snowpack. It is analogous to melting the snow and measuring the depth of the resulting pool of water. SWE measurements are useful for assessing both the potential surface runoff from snowmelt and the water availability for regions in lower elevations. Snow cover and SWE are critical variables for flood prediction and mitigation efforts in these regions.

Common Snow Cover/SWE Data at a Glance

Relevant datasets are from sensors shown in the table below. This is not intended to be an exhaustive list of all sensors or missions producing data relevant to this Data Pathfinder. An asterisk (*) by an entry indicates that near real-time (NRT) data products are available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE). LANCE data products are available within three hours of a satellite observation. If low-latency is not a primary concern, users are encouraged to use standard science products, which are produced using the best available calibration, ancillary, and ephemeris information. To learn more about the difference between NRT and Standard Science Products, see Near Real Time versus Standard Products.

Use the Snow Cover/SWE Data

Tutorials

• Let It Snow! Accessing and Analyzing Snow Data at NSIDC DAAC
• Discovering and Differentiating Data with NSIDC Search 
• NetCD-what? An Ecologist’s Guide to Working with Daymet and other NetCDF-formatted Data 

Data Visualizations

• NASA Investigates Water Supply in Snow
• Connect the Drops with NASA Data

GIS-Ready Tools and Tutorials

• Tutorial: Accessing ORNL DAAC OGC services in popular GIS tools
• Tutorial: Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox
• HEG: HDF-EOS to GeoTIFF Conversion Tool
• For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
• NASA GIS Tutorials and How-Tos

Data Access Tools

• Snow Today, available through the National Snow and Ice Data Center (NSIDC), is a NASA-supported scientific analysis website that provides a snapshot and interpretation of snow conditions in near real-time across the Western U.S. Snow Today updates daily images on snow conditions and relevant data and also provides monthly scientific analyses from January to May, or more frequently as conditions warrant

Data Customizing Tools

• Daymet Single Pixel Data Extraction—Web Services 
• AppEEARS offers a simple and effective way to extract, transform, visualize, and download Snow cover data products and allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
• MODIS/VIIRS Subsetting Tools Suite
• For more information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page

Programming Tools

• Snow Depth and Snow Cover Data Tutorial
• NASA Earthdata Application Programming Interfaces (APIs)
• Tutorial: Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox

Earth Observation Data by Sensor

MODIS

Near real-time snow cover data can be interactively explored using NASA Worldview:

• MODIS Snow Cover in Worldview

Research quality data products can be accessed using Earthdata Search:

• MODIS Snow Cover
  • Data are available daily, 8-day, or monthly at various resolutions; datasets are also customizable to GeoTIFF

Near real-time (NRT) MODIS snow cover data are available through LANCE 60 to 125 minutes after a satellite observation. 

GIS layer displaying water states (ice, snow, water, etc.):

• Water States - Corrected Reflectance (MODIS/Terra) - Overview (arcgis.com)

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AMSR2

The Advanced Microwave Scanning Radiometer (AMSR) for EOS (AMSR-E) instrument and the AMSR2 instrument provide SWE data.

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

• AMSR2 SWE in Worldview

Research quality data products can be accessed using Earthdata Search:

• AMSR-E SWE
  • Data are available from 2002 to 2011 in daily, 5-day, and monthly timeframes; datasets are also customizable to GeoTIFF
• AMSR2 SWE
  • Data are available from February 2018

Near real-time (NRT) AMSR2 snow water equivalent data are available through LANCE 60 to 125 minutes after a satellite observation. 

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Model Data

ASO

NASA's Airborne Snow Observatory (ASO) mission collects data on snowmelt flowing out of major water basins in the Western U.S. The mission began in April 2013 as a collaboration between NASA's Jet Propulsion Laboratory (JPL) and the California Department of Water Resources, with weekly flights over the Tuolumne River Basin in California and monthly flights over the Uncompahgre River Basin in Colorado during the snowmelt season. Current data collection is undertaken by Airborne Snow Observatories, Inc., a private company working in partnership with Esri and the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro) team of the National Center for Atmospheric Research.

Image

• ASO Snow Depth and SWE from Earthdata Search

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Daymet

Another NASA source for SWE data is Daymet, which can be accessed through NASA's 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 the North America, Puerto Rico, and Hawaii from 1980 to present (North America and Hawaii) and from 1950 to present (Puerto Rico). 

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LDAS: NLDAS, GLDAS, FLDAS

NASA, in collaboration with other agencies, has developed models of snow cover and depth that incorporate satellite information with ground-based data (when ground-based data are available). These models are part of the Land Data Assimilation System (LDAS), which includes a global collection (GLDAS) and a North American collection (NLDAS). 

Snow depth data products are are available from Giovanni. These data can be visualized as a time-averaged map, an animation, seasonally-averaged maps, scatter plots, or a time series using an online interactive data analysis tool called Giovanni:

• Snow Depth
  • NLDAS (North American), 0.125° from 1975 to current at monthly and hourly resolutions
  • GLDAS (Global), 0.25° and 1° from 1948 to 2022 at monthly, daily, and 3-hourly temporal resolutions
  • FLDAS, 0.1° and 0.01° from about 1982 - current in daily and monthly temporal resolutions
• Snow Water Equivalent
  • NLDAS (North American), 0.125° from 1975 to present at monthly and hourly resolutions
  • GLDAS (Global), 0.25° and 1° from 1948 to 2022 at monthly, daily, and 3-hourly temporal resolutions

GLDAS model data are available from NASA's GES DISC:

• Snow and Snow Water Equivalent

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Reanalysis Data

MERRA-2

The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns, as well as anomalies. There are several options available: hourly and monthly from 1980.

MERRA -2 Snow data products can be visualized as a time-averaged map, an animation, seasonally-averaged maps, scatter plots, or a time series using a NASA interactive data analysis tool called Giovanni:

• Snow depth data 

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Surface runoff is the volume of available surface water after a precipitation event that has been partitioned into evapotranspiration (ET) and stored as soil moisture. Droughts are indicated by lower surface runoff anomaly values showing that water reservoirs have received less than normal amounts of surface runoff, while elevated total runoff anomaly values may indicate flooding events. 

Common Runoff Data at a Glance

Relevant datasets are from sensors shown in the table below. This is not intended to be an exhaustive list of all sensors or missions producing data relevant to this Data Pathfinder. 

Use the Runoff Data

GIS-Ready Tools and Tutorials

• How to Import MERRA Surface Product Data into ArcGIS
• NASA GIS Tutorials and How-Tos
• For more information about NASA resources for GIS users, visit the GIS Data Pathfinder

Data Access Tools

• Data Rods Explorer (DRE) is a web client app that enables users to browse several NASA-hosted datasets
• AppEEARS offers a simple and effective way to extract, transform, visualize, and download data products; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
• For more information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page

Programming Tools

• NASA Earthdata Application Programming Interfaces (APIs)
• NASAaccess is an R package that can generate gridded ASCII tables of climate (CIMP5) and weather data (GPM, TRMM, GLDAS) needed to drive various hydrological models (e.g., SWAT, VIC, RHESSys)

Model Data

LDAS

Runoff potential is important data for water resources and agricultural management, especially after storm events and wildfires. Runoff can impact water quality as chemicals from fertilizers and stormwater runoff, debris, and waste products enter water bodies. Satellites cannot measure runoff directly; however, information that can be used to predict runoff can be measured remotely. These data are then input, along with ground-based data, into land surface models to estimate runoff. 

NASA's Land Data Assimilation System (LDAS), of which there is a global collection (GLDAS) and a North American collection (NLDAS), takes inputs of measurements like precipitation, soil texture, topography, and leaf area index and then uses those inputs to model output estimates of runoff and evapotranspiration:

• NLDAS (North American) Runoff Data in Giovanni
• FLDAS (Famine) Runoff Data in Giovanni
• GLDAS (Global) Runoff Data in Giovanni

Research quality LDAS Total Runoff data are available in Earthdata Search:

• Total Runoff data

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Reanalysis Data

MERRA-2

Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version. The dataset covers the period of 1980-present with the latency of approximately three weeks after the end of a month:

• MERRA-2 Runoff Data in Giovanni
  • Data are in multiple temporal resolutions and multiple temporal coverages; be sure to note the starting and end date to ensure you access the desired dataset

Research quality MERRA-2 Total Runoff datasets are available in Earthdata Search:

• Total Runoff data

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NEX-GDDP-CMIP6

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of high-resolution, bias-corrected global downscaled climate projections derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across all four “Tier 1” greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). 

This dataset provides a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions. Uses include: air temperature, precipitation volume, humidity, stellar radiation, and atmospheric wind speed.

NEX-GDDP-CMIP6 data are available through the NASA Center for Climate Simulation (NCCS):

• NEX-GDDP-CMIP6 data at NCCS

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Aboveground biomass density (AGBD) includes living and dead plant mass per unit area located above Earth's surface. Incorporating and maintaining healthy vegetation in flood-prone areas and considering aboveground biomass in land use planning can help reduce the impact of flooding events. These benefits may include but not limited to reducing surface runoff, promoting infiltration, stabilizing soil, and regulating water flow. 

Common Aboveground Biomass Data at a Glance

Relevant datasets are from sensors shown in the table below. This is not intended to be an exhaustive list of all sensors or missions producing data relevant to this Data Pathfinder. 

Use the Aboveground Biomass Density Data

Use Cases and Articles

• Biomass Earthdata Dashboard: The Biomass Harmonization Activity
• New, Gridded Level 3 Data Product Facilitates the Use of GEDI Mission Data
• Earth in the Third Dimension: First GEDI Data Available
• ORNL DAAC Releases GEDI Level 4B Dataset Offering Gridded Estimates of Aboveground Biomass Density
• Mapping Carbon Beyond Forests: New Harmonized Global Maps of Above and Belowground Biomass Carbon

Tutorials

• Getting Started with NASA Global Ecosystems Dynamics Investigation (GEDI) Lidar Data
• Explore NASA GEDI Aboveground Biomass Datasets, Services, and Tools at NASA's ORNL DAAC
• Meet GEDI! Overview of GEDI Mission and Measurements

Data Visualization

• Meet GEDI! The Global Ecosystem Dynamics Investigation Sensor Aboard the ISS
• Earth Observing Dashboard: Clearing the Air: A view from space monitors land cover and trees to track biomass and carbon content

GIS-Ready Tools and Tutorials

• Spatial Data Access Tool (SDAT) Geospatial Data Visualization/Download based on OGC Standards
• Accessing ORNL DAAC OGC services in popular GIS tools
• For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
• NASA GIS Tutorials and How-Tos

Data Access Tools

• Spatial Data Access Tool

Data Customizing Tools

• AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
• MODIS/VIIRS Subsetting Tools Suite
  • Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
  • Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
  • Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service. Web service clients and libraries are available in multiple programming languages, allowing integration of subsets into a workflow
• ORNL DAAC Subsetting Tools
• For more information on a suite of NASA data tools for searching and ordering, data handling, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page

Programming Tools

• Tutorials on GEDI Science Data Products (GEDI L3 and L4)
  • Jupyter Notebooks
  • Getting Started with GEDI L1B, L2A, and L2B Version 2 Data in Python Tutorial Series
• A collection of R and Python Data Prep Scripts are available at NASA's Land Processes Distributed Active Archive Center (LP DAAC). These Data Prep Scripts can be used to download data and perform basic data processing functions such as georeferencing, reprojecting, converting, and reformatting data. LP DAAC also offers an E-Learning section, with resources to learn more about LP DAAC products, how to interact with LP DAAC products in R and Python, and how to use AppEEARS
• Jupyter Notebook: Getting Started with the AppEEARS API: Submitting and Downloading an Area Request
• NASA Earthdata Application Programming Interfaces (APIs)

Earth Observation Data by Sensor

GEDI

The Global Ecosystem Dynamics Investigation (GEDI) is a joint mission between NASA and the University of Maryland, with the instrument installed on the International Space Station. Data acquired using the instrument’s three lasers are used to construct detailed 3D maps of forest canopy height and the distribution of branches and leaves. GEDI data play an important role in understanding the amounts of biomass and carbon forests store and how much they lose when disturbed. GEDI data also can be used to study plant and animal habitats and biodiversity along with how these change over time. 

Research quality Level 3 data can be accessed using Earthdata Search:

• GEDI Aboveground Biomass (AGBD) data

Model Data

GEDI

GEDI Level 4B data provide gridded 1km x 1km estimates of mean aboveground biomass density (AGBD). This dataset provides gridded estimates of aboveground biomass density at a greater accuracy and resolution than previously available.

Model GEDI AGBD data can be accessed using Earthdata Search:

• GEDI Aboveground Biomass (AGBD) data

Land cover plays an important role in influencing flooding events and their impacts. To effectively manage and mitigate these events, it is important to consider how changes in land cover impact local hydrology and incorporate these considerations into land use planning, urban design, and water management strategies. 

Common Land Cover Data at a Glance

Relevant datasets are from sensors shown in the table below. This is not intended to be an exhaustive list of all sensors or missions producing data relevant to this Data Pathfinder. 

Use the Land Cover Data

Use Cases and Articles

• Estimating Urban Flood Susceptibility, Historical Flooding Extent, and Land Cover Change in Fairfax County, Virginia to Aid in Flood Mitigation Planning
• Assessing Climatology and Land Cover Trends and Evaluating Flood Risk of the Cheat River
• Using NASA Earth Observations to Assess Coastal Flood Risk with Measures of Land Cover Change, Flood Extent, and Vulnerability for Adaptation and Mitigation Planning on Hawai‘i Island
• Analyzing Precipitation and Land Cover Data to Refine the Assessment of Urban Flood Vulnerability
• Mapping Past Floods to Help Predict Future Ones

Tutorials

• NASA Applied Remote Sensing Training (ARSET): Land Cover Classification with Satellite Imagery 
• ARSET: Change Detection for Land Cover Mapping
• ARSET: Remote Sensing for Monitoring Land Degradation and Sustainable Cities SDGs
• ARSET: Using Google Earth Engine for Land Monitoring Applications

Data Visualizations

• Water Cycle Extremes: Droughts and Pluvials
• GPM observes Washington, D.C., flooding
• Historic Floods Inundate Nebraska
• El Nino Fueled Rains Swamp South America

GIS-Ready Tools and Tutorials

• Accessing ORNL DAAC OGC services in popular GIS tools
• HEG: HDF-EOS to GeoTIFF Conversion Tool
• For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
• NASA GIS Tutorials and How-Tos

Data Access Tools

• NLCD 2019: USGS National Land Cover Database, 2019 release (Google Earth Engine)
• NLCD EVA (Enhanced Visualization and Analysis) tool 
• National Land Cover Database Viewer web application, a platform for data visualization, side-by-side image analysis and comparisons, and a custom tool enabling users to select a region of interest for download
• MRLC NLCD Mapping Tool
• NOAA’s Coastal Change Analysis Program (C-CAP).

Data Customizing Tools

• AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest. Output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
• MODIS/VIIRS Subsetting Tools Suite
  • Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
  • Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
  • Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service; web service clients and libraries are available in multiple programming languages, allowing integration of subsets into a workflow
• For more information on a suite of NASA data tools for searching and ordering, data handling, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page

Programming Tools

• R and Python Data Prep Scripts available through NASA's Land Processes Distributed Active Archive Center (LP DAAC) can be used to download data and perform basic data processing functions such as georeferencing, reprojecting, converting, and reformatting data. LP DAAC also offers an E-Learning section, with resources to learn more about LP DAAC products, how to interact with LP DAAC products in R and Python, and how to use AppEEARS. In addition to the user interface, AppEEARS also provides a publicly accessible API
• Tutorial: Access the MODIS web service and perform quality filtering using Python
• Tutorial: Access the MODIS web service and perform quality filtering using R
• NASA Earthdata Application Programming Interfaces (APIs)
• MODISTools R Package: Programmatic interface to the 'MODIS Land Products Subsets' web services. Allows for easy downloads of 'MODIS' time series directly to your R workspace or your computer
• MODIS/VIIRS Python Client: Python interface to the ORNL REST web service for MODIS and VIIRS-NPP data, and a basic class for handling small amounts of satellite data

Earth Observation Data by Sensor

MODIS

The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Land Cover Type data product (MCD12Q1) provides global land cover types at yearly intervals. This product is derived using supervised classifications of MODIS surface reflectance data. The supervised classifications then undergo additional post-processing that incorporates prior knowledge and ancillary information to further refine specific land type classes.

These data can be accessed and downloaded several ways:

• MODIS Land Cover Type in NASA Worldview
• MODIS Land Cover Type in Google Earth Engine
• MODIS Land Cover Type from Earthdata Search

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GLanCE

NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Land Cover Mapping and Estimation (GLanCE) annual 30m Version 1 data product provides global land cover and land cover change data derived from Landsat TM, ETM+, and OLI data. These maps provide the user community with land cover type, land cover change, metrics characterizing the magnitude and seasonality of greenness of each pixel, and the magnitude of change. This dataset is useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management. 

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NLCD

The National Land Cover Database (NLCD) is a comprehensive land cover database that utilizes 30-meter resolution Landsat satellite imagery. It covers eight different time periods, or epochs, spanning from 2001 to 2019. This product was created out of the Multi-Resolution Land Characteristics (MRLC) project to provide multi-resolution land cover data of the conterminous United States from local to regional scales. A major component of MRLC is an objective to develop a national 30-meter land cover characteristics data base using Landsat thematic mapper (TM) data. This is a cooperative effort among six programs within four U.S. Government agencies, including the U.S. Environmental Protection Agency's (EPA) Environmental Monitoring and Assessment Program; the U.S. Geological Survey's (USGS) National Water Quality Assessment Program; the National Biological Service's Gap Analysis Program; the USGS' Earth Resources Observation Systems (EROS) Center; the National Oceanic and Atmospheric Administration's Coastal Change Analysis Program; and the EPA's North American Landscape Characterization project.

• NLCD 2019: USGS National Land Cover Database, 2019 release (Google Earth Engine)
• NLCD EVA (Enhanced Visualization and Analysis) tool 
• National Land Cover Database Viewer web application, a platform for data visualization, side-by-side image analysis and comparisons, and a custom tool enabling users to select a region of interest for download.
• NOAA’s Coastal Change Analysis Program (C-CAP)

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Leaf Area Index (LAI) is the ratio of leaf surface area to ground surface area. Understanding the role of LAI in land use planning and water management strategies helps managers maintain areas with dense, healthy vegetation with the goal of reducing the risk of flooding in various environments by reducing runoff, boosting soil infiltration, stabilizing soil, and balancing the hydrologic cycle. 

Common Leaf Area Index Data at a Glance

Relevant datasets are from sensors shown in the table below. This is not intended to be an exhaustive list of all sensors or missions producing data relevant to this Data Pathfinder. 

Use the Leaf Area Index Data

Use Cases and Articles

• New, Gridded Level 3 Data Product Facilitates the Use of GEDI Mission Data
• Earth in the Third Dimension: First GEDI Data Available
• Land Surface Modeling and Evaporative Stress Products for Increased Situational Awareness
• Use of satellite images for surface conditions monitoring in the upper Mississippi watershed during the flood event of 2008

Tutorials

• NASA Applied Remote Sensing Training (ARSET): Introduction to Using the VIC Hydrologic Model with NASA Earth Observations
• Data Access and Visualization of Model Data at the NASA GES DISC
• Exploring Earth’s Land Surface with the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS Land Data 
• ORNL DAAC MODIS and VIIRS Data Tools and Services at your Fingertips 
• Using NASA’s Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) to Slice and Dice Big Earth Data 

GIS-Ready Tools and Tutorials

• Spatial Data Access Tool (SDAT) Geospatial Data Visualization/Download based on OGC Standards
• Tutorial: Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox
• HEG: HDF-EOS to GeoTIFF Conversion Tool
• For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
• NASA GIS Tutorials and How-Tos

Data Access Tools

• MISR Level 3 Data Browser

Data Customizing Tools

• AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
• MODIS/VIIRS Subsetting Tools Suite
  • Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
  • Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
  • Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service. Web service clients and libraries are available in multiple programming languages, allowing integration of subsets into a workflow
• For more information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page

Programming Tools

• NASA Earthdata Application Programming Interfaces (APIs)
• Tutorials on GEDI Science Data Products (GEDI L3 and L4)
  • Jupyter Notebooks
  • Getting Started with GEDI L1B, L2A, and L2B Version 2 Data in Python Tutorial Series
• Jupyter Notebook: Getting Started with the AppEEARS API: Submitting and Downloading an Area Request

Earth Observation Data by Sensor

MISR

The Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Land product in netCDF format features LAI. This data product is a global summary of the Level 2 land/surface parameters of interest averaged over a month and reported on a geographic grid.

Global MISR LAI data are available to browse, visualize, and download through the MISR Level 3 Data Browser.

Research quality data are available through Earthdata Search:

• MISR LAI data

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Model Data

MODIS

MODIS LAI data can be visualized and interactively explored using NASA Worldview:

• MODIS LAI Level 4 data

MODIS LAI Level 4 data are available starting in 2000 through Earthdata Search:

• MODIS LAI Level 4 data

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VIIRS

VIIRS LAI Level 4 data are available starting in 2012 through Earthdata Search:

• VIIRS LAI Level 4 data

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Reanalysis Data

MERRA-2

LAI data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through Giovanni:

• MERRA-2 LAI data

MERRA-2 data are available for download in Earthdata Search:

• LAI data

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Surface reflectance is a measure of the fraction of incoming solar radiation reflected from Earth's surface to a satellite-borne or aircraft-borne sensor. Monitoring surface reflectance enables users to detect flood-affected regions, map flood extent, compare areas affected before and after flood events, as well as coordinate emergency response activities.

Common Surface Reflectance Data at a Glance

Relevant datasets are from sensors shown in the table below. This is not intended to be an exhaustive list of all sensors or missions producing data relevant to this Data Pathfinder. An asterisk (*) by an entry indicates that near real-time (NRT) data products are available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE). LANCE data products are available within three hours of a satellite observation. If low-latency is not a primary concern, users are encouraged to use standard science products, which are produced using the best available calibration, ancillary, and ephemeris information. To learn more about the difference between NRT and Standard Science Products, see Near Real Time versus Standard Products.

Use the Surface Reflectance Data

Use Cases and Articles

• Data Chat: Dr. Jeffrey Masek
• A Harmonious New Dataset

Tutorials

• Getting Started with VIIRS Surface Reflectance Data: All about Accessing the Data
• Getting Started with VIIRS Surface Reflectance Data: Using the Data
• Getting Started with NASA MODIS Version 6 Surface Reflectance Data: Accessing the Data
• Getting Started with NASA MODIS Version 6 Surface Reflectance Data: Using the Data
• Getting Started with NASA MODIS Version 6 Surface Reflectance Data: Interpreting Quality Information
• Advancing Science Capabilities with Data Harmonization: NASA's Harmonized Landsat Sentinel-2 Product
• Exploring Earth’s Land Surface with Suomi NPP NASA VIIRS Land Data
• NASA ORNL DAAC MODIS and VIIRS Data Tools and Services at your Fingertips
• Navigating NASA's LP DAAC to Find Answers to your Deepest Land Data Questions

Data Visualizations

• Harmonized Landsat 8 and Sentinel-2 Data
• AGU El Nino Press Conference Release Materials

GIS-Ready Tools and Tutorials

• Tutorial: Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox
• HEG: HDF-EOS to GeoTIFF Conversion Tool
• For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
• NASA GIS Tutorials and How-Tos

Data Access Tools

• Landsat Data from USGS Earth Explorer

Data Customizing Tools

• AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
• MODIS/VIIRS Subsetting Tools Suite
  • Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
  • Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
  • Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service

Programming Tools

• NASA Earthdata Application Programming Interfaces (APIs)
• Jupyter Notebook: Getting Started with the AppEEARS API: Submitting and Downloading an Area Request
• R and Python Data Prep Scripts are available through NASA's Land Processes Distributed Active Archive Center (LP DAAC) that can be used to download data and perform basic data processing functions such as georeferencing, reprojecting, converting, and reformatting. LP DAAC also offers an E-Learning section with resources to learn more about LP DAAC products, how to interact with LP DAAC products in R and Python, and how to use AppEEARS
• Getting Started with Cloud-Native Harmonized Landsat Sentinel-2 (HLS) Data in R
• Jupyter Notebook Tutorial for getting started with HLS data in Python
• Jupyter Notebook: Getting Started with the AppEEARS API: Submitting and Downloading an Area Request
• R you Ready to Python? An Introduction to Working with Land Remote Sensing Data in R and Python

Earth Observation Data by Sensor

Landsat: ETM+, OLI, OLI-2, TIRS-2

The Enhanced Thematic Mapper (ETM+) and the Operational Land Imager (OLI) aboard the NASA/USGS Landsat 7 (ETM+) spacecraft and Landsat 8 (OLI) spacecraft acquire VNIR data at 30 m spatial resolution every 16 days (fewer days as you move away from the equator). Landsat 9 carries two instruments: the OLI-2 (which is a copy of the Landsat 8 OLI) and the Thermal Infrared Sensor-2 (TIRS-2). TIRS-2 measures land surface temperature in two thermal infrared bands.

• Landsat 8 and 9 OLI in NASA Worldview

Research quality surface reflectance data products can be accessed using USGS EarthExplorer:

• Landsat 7 ETM+
• Landsat 8 OLI
• Landsat 9 OLI-2

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MODIS

Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance products provide an estimate of the surface spectral reflectance as it would be measured at ground level in the absence of atmospheric scattering or absorption.

MODIS Surface Reflectance data can be visualized and interactively explored using NASA Worldview:

• MODIS Surface Reflectance data

Multiple Geographic Information Systems (GIS) MODIS Surface Reflectance data layers with different band combinations are available through Esri’s ArcGIS OnLine (AGOL). NASA’s GIS data may be used with open source GIS software such as Quantum GIS or Geographic Resources Analysis Support System (GRASS) GIS at no cost:

• True Color - Surface Reflectance (MODIS/Terra) - Overview (arcgis.com)

Research quality MODIS data products can be accessed directly from Earthdata Search

• MODIS Surface Reflectance
  • Note that a false-color image created by combining bands 7 as red, 2 as green, and 1 as blue is useful for enhancing flood conditions. In a false-color image made with this band combination, liquid water on the ground appears very dark since it absorbs in the red and the shortwave-infrared ranges. To only choose those bands, see the customization option within Earthdata Search in the Tools for Data Access and Visualization section

Near real-time (NRT) MODIS Surface Reflectance data are available through NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) within 60 to 125 minutes after a satellite observation:

• NRT MODIS Surface Reflectance Data

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HLS

A high-resolution land surface reflectance imagery option is Harmonized Landsat and Sentinel-2 (HLS). HLS imagery provide consistent surface reflectance and top of atmosphere brightness data from the Landsat 8 OLI, the Landsat 9 OLI-2, and the Multi-Spectral Instrument (MSI) aboard the ESA (European Space Agency) Sentinel-2A and Sentinel-2B satellites. The harmonized measurement enables global land observations every 2-3 days at 30 m spatial resolution.

• HLS Surface Reflectance imagery in NASA Worldview
• HLS Surface Reflectance imagery from Earthdata Search

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ASTER

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument acquires visible and near-infrared (VNIR) reflectance data at 15 m spatial resolution and short wave infrared (SWIR) reflectance data at 30m spatial resolution. 

ASTER data can be accessed and downloaded using NASA Worldview:

• ASTER LST data

ASTER is a cooperative effort between NASA and Japan's Ministry of Economy Trade and Industry (METI). As a tasked sensor, ASTER acquires data when it is directed to do so over specific targets. This makes its temporal resolution variable depending on the requested target region of interest:

• ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance Version 3.1 (AST_L1T) data products

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VIIRS

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument is aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA-20 satellites. 

VIIRS Surface Reflectance data can be visualized and interactively explored using NASA Worldview:

• Suomi NPP VIIRS Corrected Reflectance
  Note that a false-color image created by combining bands M11 as red, I2 as green, and I1 as blue, or M3-I3-M11, is useful for enhancing flood conditions. In a false-color image made with this band combination, liquid water on the ground appears very dark since it absorbs in the red and the shortwave-infrared ranges
• NOAA-20 VIIRS Corrected Reflectance
  Note that a false-color image created by combining bands M11 as red, I2 as green, and I1 as blue is useful for enhancing flood conditions. In a false-color image made with this band combination, liquid water on the ground appears very dark since it absorbs in the red and the shortwave-infrared ranges

Research quality VIIRS data products can be accessed directly from Earthdata Search:

• Suomi NPP VIIRS Surface Reflectance

Near real-time (NRT) VIIRS Surface Reflectance data are available through LANCE within 60 to 125 minutes after a satellite observation:

• VIIRS/NPP Atmospherically Corrected Surface Reflectance 6-Min L2 Swath IP 375m, 750m NRT
• VIIRS/NPP Surface Reflectance Daily L2G Global 1km and 500m SIN Grid NRT

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Image

NASA has several products that can be used to assess flooding qualitatively: Landsat, MODIS, and VIIRS. However, these sensors are impeded by cloud cover and nighttime conditions. During a storm, this is a significant drawback. Once the storm has passed, with a pre-event image and a post-event image, a more quantitative assessment of flooding extent can be made.

Research-quality (higher-level "standard") data products can be accessed via Earthdata Search or through NASA partner websites:

• MODIS Surface Reflectance from Earthdata Search
  Note that a false-color image created by combining bands 7 as red, 2 as green, and 1 as blue is useful for enhancing flood conditions. In a false-color image made with this band combination, liquid water on the ground appears very dark since it absorbs in the red and the shortwave-infrared ranges. To only choose those bands, see the customization option within Earthdata Search in the Tools for Data Access and Visualization section.
• Suomi NPP VIIRS Surface Reflectance from Earthdata Search
  Note that a false-color image created by combining bands M11 as red, I2 as green, and I1 as blue is useful for enhancing flood conditions. In a false-color image made with this band combination, liquid water on the ground appears very dark since it absorbs in the red and the shortwave-infrared ranges. To only choose those bands, see the customization option within Earthdata Search in the Tools for Data Access and Visualization section.
• Suomi NPP VIIRS Corrected Reflectance in Worldview
  Note that a false-color image created by combining bands M11 as red, I2 as green, and I1 as blue, or M3-I3-M11, is useful for enhancing flood conditions. In a false-color image made with this band combination, liquid water on the ground appears very dark since it absorbs in the red and the shortwave-infrared ranges.
• NOAA-20 VIIRS Corrected Reflectance in Worldview
  Note that a false-color image created by combining bands M11 as red, I2 as green, and I1 as blue is useful for enhancing flood conditions. In a false-color image made with this band combination, liquid water on the ground appears very dark since it absorbs in the red and the shortwave-infrared ranges.
• Landsat Data from USGS Earth Explorer
  Landsat is a joint NASA/USGS program that provides the longest continuous space-based record of Earth's land in existence. On the Earth Explorer site specify your search criteria, then:
  1. Select "Data Sets"
  2. Select Landsat
  3. Select Landsat Collection 1 Level-1
  4. Select Landsat 7 and/or Landsat 8
  These files can be downloaded as Level-1 GeoTIFF Data Products. Note that you will need a USGS login to proceed.

Vegetation indices can be used to measure the amount of green vegetation over a given area, which is used as an assessment of vegetation health. These indices can play a role in assessing areas inundated by floods and used to map flood extent, severity, forecast early warning signs, and more. 

Commonly used vegetation indices are the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and EVI2. The NDVI takes the difference between near-infrared (NIR) and red reflectance divided by their sum: NDVI = (NIR - VIS)/(NIR + VIS). Resulting values range from -1 to 1. Low values of NDVI correspond to low photosynthetic activity (e.g., unhealthy vegetation) or non-vegetated surfaces, such as areas of rock, sand, exposed soils, or snow. Higher NDVI values generally indicate greener, more lush vegetation, including forests, croplands, and wetlands. The EVI minimizes canopy-soil variations and improves sensitivity over dense vegetation. The EVI2 minimizes atmospheric effects.

Common Vegetation Greenness Data at a Glance

Relevant datasets are from sensors shown in the table below. This is not intended to be an exhaustive list of all sensors or missions producing data relevant to this Data Pathfinder. An asterisk (*) by an entry indicates that near real-time (NRT) data products are available through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE). LANCE data products are available within three hours of a satellite observation. If low-latency is not a primary concern, users are encouraged to use standard science products, which are produced using the best available calibration, ancillary, and ephemeris information. To learn more about the difference between NRT and Standard Science Products, see Near Real Time versus Standard Products.

Use the Vegetation Greenness Data

Use Cases and Articles

• In Peril: The Freshwater Ecosystem of the Tonlé Sap Basin
• Western Soils and Plants are Parched
• Data User Profile: Dr. Michael Dietze
• Data User Profile: Dr. Nancy Glenn

Tutorials

• NASA Applied Remote Sensing Training (ARSET): Land Cover Classification with Satellite Imagery
• Data Access and Visualization of Model Data at the NASA GES DISC
• Getting Started with MODIS Version 6 Vegetation Indices Data : Accessing the Data 
• Getting Started with MODIS Version 6 Vegetation Indices Data: Interpreting Quality Information 
• Getting Started with MODIS Version 6 Vegetation Indices Data: Using the Data 
• Links to LP DAAC resources related to vegetation indices

Data Visualizations

• Near Real-Time Global Biosphere
• Global Biosphere March 2017-February 2022
• Brazil and Novo Progresso Land Use Data Over Time
• Why Observe?: Land Cover

GIS-Ready Tools and Tutorials

• Webinar: Creating and Using Normalized Difference Vegetation Index (NDVI) from Satellite Imagery using QGIS
• ARSET: Remote Sensing of Drought
• Tutorial: Accessing ORNL DAAC OGC services in popular GIS tools
• Tutorial: Decoding MODIS and VIIRS Quality Science Datasets using the ArcGIS MODIS-VIIRS Python Toolbox
• HEG: HDF-EOS to GeoTIFF Conversion Tool
• For more information about NASA resources for GIS users, visit the GIS Data Pathfinder
• NASA GIS Tutorials and How-Tos

Data Access Tools

• Earth Observing Dashboard: Clearing the Air
• Users may access observed and modeled data, as well as visualize those data directly through ClimateSERV. Datasets include Evaporative Stress Index, SMAP Soil Moisture, Rainfall, IMERG, and NDVI

Data Customizing Tools

• AppEEARS allows users to subset data by defining specific point(s) or area(s) of interest; output data can be downloaded in CSV (point), GeoTIFF (area), or NetCDF4 (area) formats
• MODIS/VIIRS Subsetting Tools Suite
  • Global Subset Tool: Request a subset for any location as a GeoTiff and in text format, including interactive time-series plots
  • Fixed Sites Subsets Tool: Download pre-processed subsets for more than 3,000 field and flux tower sites for validation of models and remote sensing products
  • Web Service: Retrieve subset data (in real-time) for any location, time period, and area programmatically using a REST web service; web service clients and libraries are available in multiple programming languages, allowing integration of subsets into a workflow
• For more information on a suite of NASA data tools for searching and ordering, data handing, subsetting and filtering, geolocating, reprojecting, and mapping, as well as visualizing and analyzing data, visit the NASA Earthdata Data Tools Page

Programming Tools

• NASA Earthdata Application Programming Interfaces (APIs)
• Accessing Data through ORNL DAAC Web Services on Github 
• R and Python Data Prep Scripts are available through NASA's Land Processes Distributed Active Archive Center (LP DAAC) that can be used to download data and perform basic data processing functions such as georeferencing, reprojecting, converting, and reformatting data. LP DAAC also offers an E-Learning section, with resources to learn more about LP DAAC products, how to interact with LP DAAC products in R and Python, and how to use AppEEARS
• Global Agriculture Monitoring System (GLAM) API Documentation
• Jupyter Notebook: Getting Started with the AppEEARS API: Submitting and Downloading an Area Request

Earth Observations by Sensor

MODIS and VIIRS

Vegetation products created from data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument and the Visible Infrared Imaging Radiometer Suite (VIIRS) can be accessed in several ways. 

Imagery can be interactively explored using NASA Worldview:

• MODIS NDVI data
• MODIS EVI data
  • This dataset is monthly at 1 km spatial resolution

Note: The Terra/MODIS NDVI (rolling 8-day) and EVI (rolling 8-day) are only available in Worldview for the last 20 days; older NDVI or EVI imagery are available using use the Level 3, 16-Day, or Monthly Vegetation Index and EVI layers.

NDVI data products can be visualized as a time-averaged map, an animation, seasonally-averaged maps, scatter plots, or a time series using a NASA online interactive data analysis tool called Giovanni:

• MODIS NDVI
  • Select a map plot, date range, and region and plot the data; data can be downloaded as GeoTIFF

Multiple Geographic Information Systems (GIS) MODIS Surface Reflectance data layers with different band combinations are available through Esri’s ArcGIS OnLine (AGOL). NASA GIS data may be used with open-source GIS software such as Quantum GIS or Geographic Resources Analysis Support System (GRASS) GIS at no cost:

• Vegetation - Surface Reflectance (MODIS/Terra) - Overview (arcgis.com)

Research quality vegetation indices can be accessed directly using Earthdata Search (datasets are available as in HDF format but are, in some cases, customizable to GeoTIFF):

• MODIS Vegetation Indices
• VIIRS Vegetation Indices

Near real-time (NRT) MODIS vegetation data can be accessed and downloaded through NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE):

• Daily Rolling 8-Day Vegetation Indices - 250m and 500m

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Reanalysis Data

MERRA-2

• Model vegetation data are available in Giovanni
  • Greenness Fraction
    • Select a map plot, date range, and region and plot the data. Data can be downloaded as GeoTIFF

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