SDG 11 Data Pathfinder

United Nations graphic for sustainable development goal 11

The world has seen a general increase in urbanization (the amount of built-up area per person) over the past 20 years, according to the United Nations (UN), which notes that "...the share of land allocated to streets and open spaces...averaged only about 16 per cent globally. Of those, streets accounted for about three times as much urban land as open public spaces." Open green spaces are vitally important for maintaining the physical and mental health and well-being of communities.

Through Sustainable Development Goal (SDG) 11, the UN proposes to make cities inclusive, safe, resilient, and sustainable. A critical part of this SDG Goal is monitoring urban sprawl and access to green and public spaces as well as monitoring air quality in urban areas. NASA Earth observations can aid in assessing progress towards meeting these objectives.

The Earth Observations Toolkit for Sustainable Cities and Communities is an online knowledge resource for countries and cities interested in applying Earth observations to support their SDG 11 monitoring and urban policy planning and implementation needs. Key toolkit components include links to data, tools, and various use cases. The toolkit also aims to facilitate engagement among local communities, cities, national agencies, and Earth observation experts, and promote knowledge sharing and collaboration between cities and countries.

Urban sprawl in Las Vegas, NV, from 8 July 1985 (left image) to 1 July 1999 (right image). Both images are Web-Enabled Landsat Data (WELD). These images can be interactively explored in Worldview. Credit: NASA Worldview image

Urban sprawl in Las Vegas, NV, from 8 July 1985 (left image) to 21 March 2021 (right image). The left image is Web-Enabled Landsat Data (WELD), available from 1984-2001, and the right image is Harmonized Landsat Sentinel-2 (HLS), available from 2020 to present. These images can be interactively explored in NASA Worldview. Image: NASA Worldview.

SDG Goals are divided into broad Targets that are further divided into Indicators used to track progress toward accomplishing Targets. NASA provides measurements of air quality, land surface reflectance, land cover, population, and other socioeconomic data that provide metrics for tracking progress toward meeting SDG Targets. The data and resources in this Pathfinder are specifically related to SDG 11 Targets 11.1, 11.3, 11.6, and 11.7 (described below). In addition, the Disasters Data Pathfinder can be used in providing information related to SDG 11 Target 11.5 (reduce the impact from disasters).

SDG Goal 11: Make cities inclusive, safe, resilient, and sustainable

Target 11.1: By 2030, ensure access for all to adequate, safe and affordable housing and basic services and upgrade slums. 
Indicator 11.1.1: Proportion of urban population living in slums, informal settlements, or inadequate housing.
Target 11.3: By 2030, enhance inclusive and sustainable urbanization and capacity for participatory, integrated and sustainable human settlement planning and management in all countries.
Indicator 11.3.1: Ratio of land consumption rate to population growth rate.
Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management.
Indicator 11.6.2: Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted).
Target 11.7: By 2030, provide universal access to safe, inclusive and accessible, green and public spaces, in particular for women and children, older persons and persons with disabilities.
Indicator 11.7.1: Average share of the built-up area of cities that is open space for public use for all, by sex, age, and persons with disabilities.

The NASA datasets listed in the following sections help measure progress toward meeting the above SDG 11 Targets. While not designed to be a complete list of all salient resources available through NASA's Earth science collection, the following information about NASA data, products, and services will help you chart a path to finding the information you need.

Land Surface and Land Use Data

Land Surface and Land Use Data

Land consumption is defined by the UN as uptake of land by urbanized land uses, which often involves conversion of land from non-urban to urban functions. It's important to note, however, that there are different definitions of consumption. For example, aspects of "consumed" or "preserved" or "available for development" are applied to cases such as land occupied by wetlands.

Secondly, there is no one, unequivocal measure of whether land that is being developed is truly "newly-developed" (or vacant) land, or if it is at least partially "redeveloped." As a result, the percentage of current total urban land that is newly developed (consumed) is used as a measure of the land consumption rate. NASA has numerous datasets that can be used to aid in the measurement of these land use.

Land Surface Reflectance

Reflectance imagery layer from the Multispectral Instrument (MSI) aboard the European Space Agency's Sentinel-2A and -2B twin satellites. Image acquired October 17, 2020.

Reflectance imagery layer from the Multispectral Instrument (MSI) aboard the ESA (European Space Agency) Sentinel-2A and -2B twin satellites showing New York City, NY. Image acquired 17 October 2020. Image: NASA Worldview.

Land 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. Surface reflectance is useful for measuring urbanization and land consumption. Satellite-borne moderate resolution instruments that are used for this measurement include the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard NASA's Terra and Aqua satellites and the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) satellite and the NOAA-20 satellite. MODIS acquires measurements in 36 spectral bands and at three native spatial resolutions: 250 m, 500 m, and 1,000 m, depending on the band. VIIRS provides 22 spectral bands at two spatial resolutions (375 m and 750 m), which are resampled to 500 m, 1 km, and 0.05 degrees in the NASA produced data products to promote consistency with the MODIS heritage. MODIS data are acquired every 1-2 days, whereas the wider swath width of VIIRS allows for daily global coverage.

Research quality surface reflectance data products can be accessed directly using NASA Earthdata Search:

NASA's Land Processes Distributed Active Archive Center (LP DAAC) provides a tool called the Application for Extracting and Exploring Analysis Ready Samples (AppEEARS). AppEEARS offers a simple and efficient way to access, transform, and visualize geospatial data, including MODIS and VIIRS surface reflectance data. Two types of sample requests are available: point samples of geographic coordinates or area samples of vector polygons.

NASA's Oak Ridge National Laboratory DAAC (ORNL DAAC) also provides tools for on-demand subsetting of MODIS and VIIRS land data. In particular, the Subsets API allows users to retrieve custom subsets, analytics, and visualizations of MODIS and VIIRS data products.

The Enhanced Thematic Mapper (ETM+) sensor and the Operational Land Imager (OLI) instruments aboard the joint NASA/USGS Landsat 7 (ETM+) spacecraft and Landsat 8 (OLI) spacecraft acquire visible and near-infrared (VNIR) data at 30 m spatial resolution every 16 days (less days as you move away from the equator). Landsat 9 is scheduled for launch in September 2021 and carries two instruments: the OLI-2 (which is a copy of the Landsat 8 OLI) and the Thermal Infrared Sensor-2 (TIRS-2, which measures land surface temperature in two thermal infrared bands). Landsat satellite operations and data archiving are coordinated at the USGS Earth Resources Observation and Science (EROS) Center, which also is the location of LP DAAC. 

Landsat data can be discovered using Earthdata Search. To download Landsat data from the USGS, you will need a USGS Earth Explorer login from the USGS EROS Registration System:

Another higher resolution option is the Harmonized Landsat Sentinel-2 (HLS) dataset, which takes input data from Landsat 8 and the European Space Agency (ESA) Sentinel-2A and -2B satellites to generate a harmonized, analysis-ready surface reflectance data product. The combined measurement enables global land observations every two to three days at 30 m spatial resolution. Note that HLS data are currently provisional.

Explore the Getting Started with Cloud-Native HLS Data in Python Jupyter Notebook for step-by-step instructions about extracting an Enhanced Vegetation Index (EVI) Time Series from HLS. Additional information about HLS is available on the Earthdata HLS Overview page.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) — a cooperative effort between NASA and Japan's Ministry of Economy Trade and Industry — is another high-resolution instrument that acquires visible and near-infrared (VNIR) reflectance data at 15 m resolution, short-wave infrared (SWIR; through 2009) reflectance data at 30 m resolution, and thermal infrared (TIR) data at 90 m resolution. Note that ASTER is a tasked sensor, meaning that it only acquires data when it is directed to do so over specific targets, making its temporal resolution variable depending on the requested target region of interest. 

ASTER surface reflectance products are processed on-demand and can be requested through Earthdata Search (note that there is a limit of 2,000 granules per order):

Land surface reflectance data can be interactively explored using NASA's Worldview data visualization application:

GIS-ready: NASA also provides land surface reflectance data from MODIS and HLS through land-based geospatial services.

Synthetic Aperture Radar

Brooklyn neighborhoods such as Bedford -Stuyvesant with north-south running streets show strong radar return, as the buildings are oriented perpendicular to the imaging radar beam.

Sentinel-1 imagery over New York City acquired August 2020. The image on the left is an ascending pass while the image on the right is a descending pass. Neighborhoods with street grids running parallel to the satellite's direction of travel (perpendicular to the look direction of the radar sensor) have brighter returns than those that are not as well aligned. Image: RTC product processed by ASF DAAC HyP3 2021 using GAMMA software. Contains modified Copernicus Sentinel data 2020, processed by ESA. Street lines from the NYC Department of City Planning.

Synthetic Aperture Radar (SAR) imagery provides a unique perspective for monitoring urbanization. SAR instruments are able to penetrate cloud cover and pollution and work in both day and night conditions. In addition, SAR, operating in the microwave portion of the electromagnetic spectrum, captures different target characteristics than optical sensors and provides unique information that complements standard optical remote sensing methods. For more detailed information about SAR, please see the What is SAR? Earthdata Backgrounder. SAR data in NASA's Earth Observing System Data and Information System (EOSDIS) collection  are archived at and distributed by NASA's Alaska Satellite Facility DAAC (ASF).

SAR data products can be accessed using Earthdata Search or ASF DAAC's Vertex search and discovery tool:

  • Sentinel-1A and Sentinel-1B SAR data from Earthdata Search
    Note: Sentinel-1 operates at C-band and data are available in single or dual polarization. Another option for SAR data is the inclusion of phase information. Level 1 data are produced as single look complex (SLC), in which the phase information is preserved, or as ground-range detected (GRD), in which the phase information is lost. GRD data can be visualized and are appropriate for change detection, for example.
  • Sentinel-1A and -1B SAR data from Vertex
    Vertex allows for the search, preview, and download of SAR data acquired from a variety of space and airborne platforms, including Sentinel-1. Vertex also includes on-demand SAR processing services that allow data identified using the Vertex search tools to be processed automatically. This provides analysis-ready products without requiring a lot of knowledge of SAR processing or personal computing resources.

NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) is an L-band, fully polarimetric airborne SAR instrument. Campaigns over portions of Greenland, Iceland, and North, Central, and South America were conducted from 2008 to present.

From 2006 to 2011, the Phased Array type L-band Synthetic Aperture Radar (PALSAR) aboard the Advanced Land Observing Satellite-1 (ALOS) acquired data using multiple observation modes in single, dual, and full polarization. ALOS was a mission of the Japan Aerospace Exploration Agency (JAXA). ALOS PALSAR imagery is distributed by ASF DAAC through an international agreement with JAXA. In addition to standard L-band SAR products, radiometrically terrain corrected (RTC) analysis-ready products are available in GeoTIFF format through Vertex.

    To learn more about SAR and processing Level 1 data, please see the Earthdata Getting Started with SAR page and NASA's Applied Remote Sensing Training (ARSET) Introduction to SAR training. Other resources include the Earthdata webinars Introduction to SAR Data and Applications of SAR in GIS Environments. ASF DAAC has an extensive collection of SAR Data Recipes that demonstrate many of the applications of SAR for Earth observation.

    Land Cover

    Terra and Aqua Moderate resolution Imaging Spectroradiometer (MODIS) Land Cover Type in Worldview

    Terra and Aqua Moderate resolution Imaging Spectroradiometer (MODIS) Land Cover Type imagery can be interactively explored using NASA Worldview. NASA's Worldview imagery mapping and visualization application provides the capability to browse more than 900 global, full-resolution satellite imagery layers. Interactively explore this image. Image: NASA Worldview.

    The Terra and Aqua MODIS Land Cover Type data product provides global land cover types at yearly intervals and derived from six different classification schemes (the MODIS Land Cover User Guide provides additional information on these schemes). The product is derived using supervised classifications of MODIS Terra and Aqua reflectance data. The supervised classifications then undergo additional post-processing that further refines specific classes.

    LP DAAC's AppEEARS offers the ability to extract subsets, transform, and visualize MODIS and VIIRS land cover-related data products. Two types of sample requests are available: point samples of geographic coordinates or area samples of vector polygons. ORNL DAAC subsetting tools provide a means to simply and efficiently access and visualize MODIS and VIIRS land cover types.

    GIS-ready: NASA also provides land cover data through land-based geospatial services.

    Vegetation Greenness/Phenology

    These images from the NASA/USGS satellite Landsat show the cooling effects of plants on New York City's heat. On the left, areas of the map that are dark green have dense vegetation. Notice how these regions match up with the dark purple regions—those with the coolest temperatures—on the right.

    These Landsat images show the cooling effects of plants on New York City's heat. In the lower image, areas of the map that are dark green have dense vegetation. Notice how these regions match up with the dark purple areas having the coolest temperatures in the upper image. Image: NASA Earth Observatory/Maps by Robert Simmon using Landsat Program data.

    Vegetation indices measure the amount of green vegetation over a given area and can be used to assess vegetation health. A commonly-used vegetation index is the Normalized Difference Vegetation Index (NDVI), which uses the difference between near-infrared (NIR) and red reflectance divided by their sum. NDVI values range from -1 to 1. Low values of NDVI generally correspond to barren areas of rock, sand, exposed soils, or snow, while higher NDVI values indicate greener vegetation, including forests, croplands, and wetlands. EVI is another widely used vegetation index that minimizes canopy-soil variations and improves sensitivity over dense vegetation conditions.

    NDVI and EVI from NASA's MODIS and VIIRS can be accessed in various ways as seen below, and VIIRS provides a global land surface phenology product. A NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) project from the University of Arizona's Vegetation Index and Phenology Lab has generated seamless and consistent sensor-independent quality measures of landscape phenology parameters and vegetation indices by fusing measurements from different satellite missions and sensors. This Vegetation Continuous Fields (VCF) dataset provides global fractional vegetation cover at 0.05 degree (5,600 m) spatial resolution at yearly intervals from 1982 to 2016.

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

    LP DAAC's AppEEARS offers a simple and effective way to extract, transform, visualize, and download MODIS and VIIRS vegetation-related data products. AppEEARS allows users to subset data by defining specific points or areas of interest, and output data can be downloaded in csv (point), GeoTIFF (area), or NetCDF4 (area) format. Explore LP DAAC's Getting Started with Cloud-Native Harmonized Landsat Sentinel (HLS) Data in Python Jupyter Notebook for extracting an EVI Time Series from HLS imagery.

    ORNL DAAC subsetting tools provide a means to simply and efficiently access and visualize MODIS and VIIRS phenology as well as VCF data products. VCF is a global representation of surface vegetation cover as gradations of three ground cover components: percent tree cover, percent non-tree cover, and percent non-vegetated (bare).

    Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool called Giovanni, which was developed by NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC). Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal coverages, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

    Giovanni interface with three steps highlighted: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you'd like to include and then plot the data.

    Giovanni interface with three steps highlighted: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you'd like to include and then plot the data.

    Data can be visualized and interactively explored using NASA Worldview:

    • MODIS NDVI
      This dataset has a spatial resolution of 250 m and a temporal resolution of eight days. 16-day and monthly data are also available within Worldview.
    • MODIS EVI
      This dataset is monthly at 1 km spatial resolution. Rolling 8-day and 16-day data are also available within Worldview.

    For more information on interpreting phenology using remote sensing data, view ARSET's Understanding Phenology with Remote Sensing training.

    Urban-related Socioeconomic Data

    NASA's Socioeconomic Data and Applications Center (SEDAC) provides numerous datasets that can aid in assessing urban environments. These datasets include global urban extent, global human-created impervious surfaces, development threat maps, urban expansion, nightlight changes, and cumulative measures of the human modification of terrestrial lands.

    SEDAC's POPGRID Viewer enables direct comparison of different population datasets based on different data sources and methodologies. The tool incorporates a four-panel display of six different data sets: the Gridded Population of the World (GPWv4.10) 2015 count developed by SEDAC; LandScan 2015 developed by ORNL; WorldPop Estimates 2014 from the WorldPop project; Global Human Settlement Population Grid 2015 (GHS-POP) developed by the European Commission's Joint Research Centre and the Center for International Earth Science Information Network (CIESIN); the Esri World Population Estimate 2016 (WPE); and the High Resolution Settlement Layer (HRSL) developed by the Facebook Connectivity Lab and CIESIN.

    In addition to the datasets archived at SEDAC, several datasets available through ORNL DAAC also can be used to assess human impact on urban environments, including:

    GIS-ready: Datasets at SEDAC are available in several file formats, including GeoTIFF. In addition, many of these datasets are available through human dimensions geospatial services.

    Nightlights via VIIRS Day Night Band (DNB)

    Nighttime lights data over Texas, acquired on February 16, 2021 with the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NOAA–NASA Suomi NPP satellite. Note that the data have been overlain on Landsat imagery so that city structure can still be distinguished. Credit: NASA Earth Observatory

    Nighttime lights data over Houston, TX, acquired 16 February 2021 by the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the NASA/NOAA Suomi NPP satellite. Note that the data have been overlain on Landsat imagery so that the city structure can still be distinguished. Image: NASA Earth Observatory.

    The VIIRS Day/Night Band (DNB) shows Earth's surface and atmosphere using a sensor designed to capture low-light emission sources under varying illumination conditions. DNB imagery is useful for assessing power outages across wide areas.

    NASA also developed the Black Marble. The Black Marble is a daily calibrated, corrected, and validated product suite that enables nighttime data can be used effectively for scientific observations. Black Marble's standard science processing removes cloud-contaminated pixels and corrects for atmospheric, terrain, vegetation, snow, lunar, and stray light effects on the VIIRS DNB radiances. Black Marble data can be accessed at NASA's Level-1 and Atmosphere Archive and Distribution System DAAC (LAADS DAAC). Black Marble imagery in Worldview is an image composite that was assembled from clear, cloud-free images acquired in 2012 and 2016.

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    Air Quality Data

    Air Quality Data

    Air pollution kills an estimated seven million people every year, making it one of the biggest environmental health risks, according to the U.N. Poor air quality is exacerbated in low- to middle-income countries, where 98% of urban centers with a population of more than 100,000 people do not meet World Health Organization guidelines.

    Air pollution is caused by both anthropogenic and natural events, including cookstoves, coal-fired power plants, vehicle emissions, as well as wildfires and dust storms. NASA Earth observing data help air quality managers and public health researchers monitor air pollutants locally, regionally, and globally to further determine the risk for health conditions or diseases that are exacerbated by poor air quality and the locations that might be impacted.

    Aerosol Optical Depth and PM2.5

    Size comparisons for particulate matter as compared to each other, a human hair, and beach sand. Credit: Environmental Protection Agency

    Size comparisons for particulate matter as compared to each other, a human hair, and beach sand. Image: U.S. Environmental Protection Agency.

    Aerosol Optical Depth (AOD) is the measure of aerosols (from dust, haze, smoke, pollution, etc.) distributed within a column of air and is a key measurement for tracking annual mean levels of fine particulate matter (abbreviated PM) in cities. Particulate matter is a mixture of particles suspended in the air and is defined based on the diameter of the particles. Particles that are equal to or less than 2.5 micrometers, which is more than 100 times thinner than a human hair, are designated PM2.5. Particles between 2.5 and 10 micrometers are designated PM10. PM2.5 is significant because these small particles stay suspended in the air longer. AOD is based on the fact that these suspended particles change the way the atmosphere reflects and absorbs visible and infrared light.

    AOD does not equate to PM2.5. However, the two measurements correlate, and there are several different techniques to convert AOD to PM2.5. It is important to note that while there is a relationship between AOD and PM2.5, there are other factors that can affect AOD, like humidity, the vertical distribution of aerosols, and the shape of the particles.

    NASA's ARSET has a Jupyter Notebook that accesses VIIRS AOD data and converts AOD to PM2.5 available through the ARSET GitHub site. For more information on using this notebook please see the MODIS to VIIRS Transition for Air Quality Applications.

    This NASA Worldview image shows atmospheric concentrations of dust using the VIIRS Dark Target Aerosol Optical Depth data product

    This NASA Worldview image shows the new VIIRS Near real-time Dark Target Aerosol Optical Depth imagery layer. It shows high aerosol concentration over the Mediterranean, observed during a Saharan dust event on 6 February 2021. Image: NASA Worldview.

    AOD is a column-integrated value of aerosols in the atmosphere obtained by measuring the scattering and absorption of solar energy from the top of the atmosphere to the surface. The non-aerosol signal of surface reflectance needs to be separated from the aerosol signal to accurately obtain AOD. This is challenging because the satellite instrument cannot penetrate cloud cover and highly reflective surfaces, such as ice or snow. This can lead to misrepresentations of the data. The Dark Target and Deep Blue algorithms for Moderate Resolution Imaging Spectroradiometer (MODIS) data help account for these effects. In the latest dataset collection (MODIS Version 6.1), these two algorithms have been merged, using the highest quality for each. While it does provide the easiest use of global coverage, there are still uncertainties in cloudy, snow-covered or ice-covered scenes, and areas of water with strong sun glint.

    Along with MODIS, VIIRS also collects AOD data at a much finer spatial resolution. VIIRS uses the Deep Blue Algorithm over land and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over water to determine atmospheric aerosol loading for daytime cloud-free, snow-free scenes. With the VIIRS data, each file in NetCDF format includes three relevant AOD data products: AOD estimated at 550 nm over land, over ocean, and over land and ocean.

    The Multi-angle Imaging Spectroradiometer (MISR) instrument aboard the Terra spacecraft collects AOD at 550 nm and provides the AOD fraction due to nonspherical aerosols and the fraction due to small mode aerosols (radius less than 0.35 micrometers), medium mode aerosols (radius 0.25-0.7 micrometers), and large mode aerosols (radius greater than 0.7 micrometers).

    Research quality data products can be accessed using Earthdata Search. Data are in HDF or NetCDF format, and can be opened using a free NASA data viewer called Panoply:

    Visualizing AOD Imagery Using Giovanni

    Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series using the Giovanni web application at GES DISC. Giovanni is a web-based application that provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data without having to download the data (although data downloads are also supported).

    Giovanni interface with three steps highlighted: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you'd like to include and then plot the data.

    Giovanni interface with three steps highlighted: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you'd like to include and then plot the data.

    Follow these steps to plot data in Giovanni: 1) Select a map plot type (for more information on choosing a type of plot, see the Giovanni User Manual). 2) Select a date range. Data are in multiple temporal resolutions, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you'd like to include and then plot the data.

    • OMI AOD in Giovanni
      The Ozone Monitoring Instrument (OMI) aboard NASA's Aura Earth observing satellite (launched in 2004) has a coarser spatial resolution than MODIS and VIIRS, but provides data at individual wavelengths from the ultraviolet (UV) to the visible. Giovanni enables users to plot daily data at these individual wavelengths. This is important because pollutants have different spectral signatures. For example, a wavelength range around 400 nm can be used to detect elevated layers of absorbing aerosols such as biomass burning and desert dust plumes. The two AOD products provided through Giovanni use two different algorithms: OMI Multi-wavelength (OMAERO) and OMI UV (OMAERUV). OMAERO is based on the multi-wavelength algorithm and uses up to 20 wavelength bands between 331 nm and 500 nm. This algorithm uses reflectance for a wide variety of microphysical aerosol models representative of desert dust, biomass burning, volcanic ash, and weakly absorbing aerosol types. OMAERUV uses the near-UV algorithm, which is capable of retrieving aerosol properties over a wider variety of land surfaces than is possible using measurements only in the visible or near-IR since the reflectance of all terrestrial surfaces not covered with snow is small in the UV.
    • MODIS AOD in Giovanni
      Provides data products with both the Dark Target and Deep Blue algorithms as well as the combined Dark Target/Deep Blue algorithm at daily and monthly intervals.
    • MISR AOD in Giovanni
      There are two MISR Level 3 global aerosol data products available in Giovanni: a daily (MIL3DAE) and a monthly (MIL3MAE).

    Near Real-Time AOD Imagery

    Near real-time AOD imagery can be explored and downloaded using NASA Worldview:

    • MODIS Aqua/Terra Combined Algorithm AOD
      The merged Dark Target/Deep Blue AOD layer provides a more global, synoptic view of AOD over land and ocean. It is available from 2000 to present.
    • VIIRS Level 2 Deep Blue Aerosol Product
      This product uses the Deep Blue algorithm over land and the SOAR algorithm over water to determine atmospheric aerosol loading. It is designed to facilitate continuity in the aerosol record. Deep Blue uses measurements from multiple Earth observing satellites to determine the concentration of atmospheric aerosols along with the properties of these aerosols.
    • OMI AOD Multi-wavelength and UV Absorbing and Extinction Layers
      The multi-wavelength layer and the UV absorbing layer display the degree to which aerosols prevent the transmission of light through the process of absorption (attenuation). The UV extinction layer indicates the level at which aerosols prevent light from traveling through the atmosphere. Toggling between these three can provide more distinction on the types of aerosols present.
    • MISR AOD in Worldview
      Imagery layer displays the temporal averages of all aerosol optical depths calculated from radiances acquired from the green band (555 nm) of MISR's cameras over a particular month.

    Aerosol Index

    High Aerosol Index over USA on 13 September 2020 (Suomi NPP/OMPS)

    Image of high aerosol index over USA, as a result of fires in the western US, acquired on 13 September 2020. The Aerosol Index layer from the Ozone Mapping Profiler Suite (OMPS) aboard the joint NASA/NOAA Suomi National Polar orbiting Partnership (Suomi NPP) satellite, is overlaid on top of the true-color image. Image: NASA.

    Aerosol Index (AI) is a measurement related to AOD and indicates the presence of an increased amount of aerosols in the atmosphere. The main aerosol types that cause signals detected in this value are desert dust, significant fire events, biomass burning, and volcanic ash plumes. The lower the AI, the clearer the sky.

    In addition to OMI, both the TROPOspheric Monitoring Instrument (TROPOMI) aboard the ESA Sentinel 5 satellite and the Ozone Mapping and Profiling Suite (OMPS) sensor aboard the Suomi NPP satellite measure AI.

    Research quality data products can be accessed using Earthdata Search:

    • OMI AI
      OMI provides an Ultraviolet Aerosol Index; data are in HDF5 format and can be opened using Panoply. Note that when opening the data in Panoply, there are a number of different data fields from which to choose. Select UVAerosolIndex.
    • TROPOMI AI
      ESA TROPOMI AI provides additional information on this level 2 data product. Data are in NetCDF format, and can be opened using Panoply.

    Visualizing AI Imagery Using Giovanni

    Data products can be visualized as a time-averaged map, an animation, seasonal maps, scatter plots, or a time series through an online interactive tool called Giovanni, which was developed by GES DISC. Follow these steps to plot data in Giovanni: 1) Select a map plot type. 2) Select a date range. Data are in multiple temporal coverages, so be sure to note the start and end date to ensure you access the desired dataset. 3) Check the box of the variable in the left column that you would like to include and then plot the data. For more information on choosing a type of plot, see the Giovanni User Manual.

    Near Real-Time AI Imagery

    NRT data can be accessed and interactively explored using NASA Worldview:

    • OMI AI
    • OMPS AI
      OMPS AI layer indicates the presence of ultraviolet (UV)-absorbing particles in the air.

    Additional Air Quality Resources

    NASA's Atmospheric Science Data Center (ASDC) has several tools for browsing and subsetting data from the MISR instrument aboard the Terra satellite. MISR views Earth with cameras pointed at nine different angles. As the instrument flies overhead, regions of Earth's surface are successively imaged by all nine cameras in each of four wavelengths (blue, green, red, and near-infrared). MISR data can distinguish different types of clouds, aerosol particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in the amount and type of atmospheric aerosol particles, including those formed by natural sources and by human activities; the amount, types, and heights of clouds; and the distribution of land surface cover, including vegetation canopy structure.

    ASDC's MISR Browse Tool allows easy access to images from the MISR instrument. The browse images are ellipsoid color images for each camera and are available at two different resolutions. The MISR Order and Customization Tool provides users with ability to select and order MISR data by date, time, and geolocation.

    GIS-ready: AOD data are available through atmosphere-related geospatial services. ASDC also provides visualization and data access for monthly MISR global AOD within an ArcGIS server.

    Health-related Socioeconomic Data

    Annual global surface of concentrations (micrograms per cubic meter) of mineral dust and sea-salt filtered fine particulate matter of 2.5 micrometers or smaller (PM2.5) as visualized in Worldview. Credit: NASA

    Global map showing annual average PM2.5 concentrations for 2015. This SEDAC data collection combines AOD retrievals from MODIS, MISR, and the Sea-Viewing Wide Field of View Sensor (SeaWiFS). Darker colors indicate higher AOD concentrations. Image: CIESIN Columbia University, March 2018.

    Air quality-related deaths and diseases that are exacerbated by air pollution are preventable, but prevention requires a knowledge of where vulnerable populations exist and the interventions that are needed in these communities. Observations of airborne particulate matter combined with socioeconomic data help achieve this objective.

    SEDAC provides a number of datasets on population exposure and vulnerability:

    GIS-ready: Datasets at SEDAC are available in several file formats, including GeoTIFF. In addition, many of these datasets are available through human dimensions geospatial services.

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    Tools for Data Access and Visualization

    Tools for Data Access and Visualization

    Earthdata Search | Panoply | Giovanni | Worldview | AppEEARS |MODIS/VIIRS Subsetting Tools Suite | Spatial Data Access Tool (SDAT) | Sentinel Toolbox

    Earthdata Search is a tool for data discovery of Earth Observation data collections from NASA's Earth Observing System Data and Information System (EOSDIS), as well as U.S and international agencies across the Earth science disciplines.

    Users (including those without specific knowledge of the data) can search for and read about data collections, search for data files by date and spatial area, preview browse images, and download or submit requests for data files, with customization for select data collections.

    Screenshot of the Search Earthdata site.


    In the project area, for some datasets, users can customize granules. Users can reformat the data and output as HDF, NetCDF, ASCII, KML, or GeoTIFF format, and can choose from a variety of projection options. Data can be subset to obtain only the bands that are needed.

    Earthdata Search customization tools diagram.

    Panoply

    HDF and NetCDF files can be viewed in Panoply, a cross-platform application that plots geo-referenced and other arrays. Panoply offers additional functionality, such as slicing and plotting arrays, combining arrays, and exporting plots and animations.

    Giovanni

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

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

    For more detailed tutorials:

    Worldview

    NASA's Worldview visualization application provides the capability to interactively browse over 950 global, full-resolution satellite imagery layers and then download the underlying data. Many of the available imagery layers are updated within three hours of observation, essentially showing Earth as it looks "right now." This supports time-critical application areas such as wildfire management, air quality measurements, and flood monitoring. Imagery in Worldview is provided by NASA's Global Imagery Browse Services (GIBS). Worldview also includes nine geostationary imagery layers from GOES-East, GOES-West, and Himawari-8 available at ten minute increments for the last 30 days. These layers include Red Visible, which can be used for analyzing daytime clouds, fog, insolation, and winds; Clean Infrared, which provides cloud top temperature and information about precipitation; and Air Mass RGB, which enables visualization of specific air mass types (e.g., dry air, moist air, etc.). These full disk hemispheric views allow for almost real-time viewing of changes occurring around most of the world.

    Worldview data visualization of the nighttime lights in Puerto Rico pre- and post- Hurricane Maria, which made landfall on September 20, 2017. Post-hurricane image shows widespread outages around San Juan, including key hospital and transportation infrastructure.

    Worldview Suomi NPP/VIIRS nighttime lights comparison image showing power outages caused by Hurricane Irma in September 2017. The right image (acquired 1 September 2017) shows the island before Hurricane Irma. The left image (acquired 9 September 2017) shows power outages across island after Hurricane Irma. Interactively explore this image in NASA Worldview. Image: NASA Worldview.

    AppEEARS

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

    Performing Area Extractions

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

    Spatial Subsetting

    Define the region of interest in one of three ways:

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

    Select the date range for the time period of interest.

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

    Adding Data Layers

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

    Extracting an area in AppEEARS

    Selecting Output Options

    Two output file formats are available:

    • GeoTIFF
    • NetCDF4

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

    If GeoTIFF is selected, you must select a projection

    Interacting with Results

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

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

    Interpreting Results in AppEEARS

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

    MODIS/VIIRS Subsetting Tools Suite

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

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

    Directions for subsetting data with the ORNL DAAC MODIS and VIIRS subset tool

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

    Spatial Data Access Tool (SDAT)

    The ORNL DAAC’s SDAT is an Open Geospatial Consortium standards-based Web application to visualize and download spatial data in various user-selected spatial/temporal extents, file formats, and projections. Several data sets including land cover, biophysical properties, elevation, and selected ORNL DAAC archived data are available through SDAT. KMZ files are also provided for data visualization in Google Earth.

    Within SDAT, select a dataset of interest. Upon selection, the map service will open displaying the various measurements, with the associated granule, and a visualization of the selected granule.

    Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool.

    Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool.

    You can then select your spatial extent, projection, and output format for downloading.

    Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool with various output options.

    Canopy Height, Kalimantan Forests, Indonesia, 2014 from the Oak Ridge National Laboratory Distributed Active Archive Center Spatial Data Access Tool with various output options.

    Sentinel Toolbox

    The ESA Sentinel-1 Mission consists of two satellites, Sentinel-1A and -1B, with synthetic aperture radar instruments operating at a C-Band frequency. They orbit 180° apart, together imaging the entire Earth every six days. SAR is an active sensor and so can penetrate cloud cover and vegetation canopy and can observe at night. Therefore, it is ideal for flood inundation mapping. It also provides useful information to detect movement of Earth material after an earthquake, volcanic eruption or landslide. SAR data are very complex to process, but ESA has developed a Sentinel-1 Toolbox to aid with processing and analysis of Sentinel-1 data.

    For more information on active sensors, see What is Remote Sensing.

    Before choosing data, it’s important to determine which band meets your needs, as radar signals penetrate deeper as the sensor wavelength increases. This difference in penetration is due to the dielectric properties of a given medium, which dictate how much of the incoming radiation scatters at the surface, how much signal penetrates into the medium, and how much of the energy gets lost to the medium through absorption.

    SAR signal penetration by sensor wavelength λ

    SAR signal penetration by sensor wavelength λ. Image: NASA SAR Handbook.

    Note that for biomass estimation, L-band and P-band sensors are preferred over higher frequencies and smaller wavelengths for two reasons: 1) at these bands, the radar waves or energy can penetrate the tree canopy and scatter from larger woody components of the forest, and 2) the scattering from larger tree components, unlike leaves, are more stable temporally and remain highly coherent over the acquisition period in the case of repeated measurements for change detection or interferometric applications (adapted from SAR Handbook, 2019).

    The C-band can be used for low-vegetation biomass such as grasslands, shrublands, sparse woodlands, young secondary regeneration, and low-density wetlands.

    Another important parameter to take into consideration when choosing a dataset is the polarization, or the direction in which the signal is transmitted and/or received: horizontally or vertically. Dual polarization, for example, refers to two different signal directions, horizontal/vertical and vertical/horizontal (HV and VH). Knowing the polarization from which a SAR image was acquired is important, as signals at different polarizations interact differently with objects on the ground, affecting the recorded radar brightness in a specific polarization channel.

    Strong scattering in HH indicates a predominance of double-bounce scattering (e.g., stemmy vegetation, manmade structures), while strong VV relates to rough surface scattering (e.g., bare ground, water), and spatial variations in dual polarization indicate the distribution of volume scatterers (e.g., vegetation and high-penetration soil types such as sand or other dry porous soils).

    Strong scattering in HH indicates a predominance of double-bounce scattering (e.g., stemmy vegetation, manmade structures), while strong VV relates to rough surface scattering (e.g., bare ground, water), and spatial variations in dual polarization indicate the distribution of volume scatterers (e.g., vegetation and high-penetration soil types such as sand or other dry porous soils). Image: NASA SAR Handbook.

    SAR data are complex, requiring a certain level of processing skill.

    Once you have downloaded the needed SAR data, it must be calibrated to account for distortion in the data. The objective in performing calibration is to create an image where the value of each pixel is directly related to the backscatter of the surface. So calibration takes into account radiometric distortion, signal loss as the wave propagates, saturation, and speckle. This process is critical for analyzing images quantitatively; it is also important for comparing images from different sensors, modalities, processors, and different acquisition dates.

    Screenshot of the Sentinel-1 toolbox

    Important note: DO NOT unzip the downloaded SAR file. Open the .zip file from within the Sentinel Toolbox. When you expand the Bands folder, you will see an amplitude and an intensity file for each polarization option. (The intensity band is a virtual one and is the square of the amplitude.) Open the amplitude file. Subset the data by zooming in to the area of interest and right-clicking on “Spatial Subset from View.”

    Calibration is done by following these steps:

    1. Radiometric calibration is performed by selecting Radar/Radiometric/Calibration (leave parameters as default).
    2. Geometric correction is done next to fix the main geometric distortions, due to Slant Range, Layover, Shadow, and Foreshortening. Terrain correction can be performed by selecting Radar/Geometric/Terrain Correction/ Range-Doppler Terrain Correction. This requires a digital elevation model (within the processing parameters, SRTM is the default selection). You can also specify a map projection in the processing parameters.

    Sentinel-1 Toolbox Geometric Correction

    Another characteristic of SAR images that must be accounted for is speckle. Speckle is the grey level variation that occurs between adjacent resolution cells, creating a grainy texture. Within the Toolbox, speckle can be removed by selecting “Radar/Speckle Filtering/Single Product Speckle Filter,” and then choosing a type of filter; “Lee” is one of the most common.

    Comparison of speckle in SAR imagery within Sentinel-1 Toolbox

    Change Detection

    One approach for monitoring change detection, caused by forest degradation or deforestation, is the log-ratio scaling method. You will need two images for which you have completed the steps above. The images must be from the same season. This is important for change detection operations as it avoids seasonal changes and focuses on true environmental changes in a change detection analysis.

    Log-ratio image with the ArcMap Imagery basemap

    This log-ratio image over Huntsville, Alabama, was created from a pair of images acquired on 7/17/2009 and 9/04/2010, approximately one year apart. In the log-ratio image, unchanged features have intermediate gray tones (gray value around zero) while change features are either bright white or dark black. Black features indicate areas where radar brightness decreased while in white areas, the brightness has increased. Image: ASF DAAC 2017; Includes Material © JAXA/METI 2009, 2010.

    For further information on SAR change detection, see ASF DAAC's change detection recipe for QGIS or change detection recipe for ArcGIS. The SERVIR SAR Handbook also contains tutorials on change detection, developing time series and making RGB composites; these are provided as Python scripts in Jupyter notebooks.

    Often-used color scheme for multi-dimensional false color SAR composites

    Another option for change detection is to create an RGB composite. When creating RGB composites using SAR data, the example color-scheme is often used. Note that for forest applications in particular, it is always useful to assign cross-polarized (HV/VH) data to the green band as these data are more related to volume scattering of the canopies. Co-polarized data (VV or HH) are suited for the red band, where surface scattering components are more pronounced. When only dual-polarimetric data are available (HH/HV or VV/VH), a color SAR image is often constructed by assigning the ratio of co-polarized to cross-polarized data to the blue channel. For more information on this procedure, read the SAR Handbook Chapter 3.

    Sentinel-1 C-band dual polarimetric VV and VH data: (a) VV, (b) VH, (c) VV/VH ratio, and (d) SAR false color composite with RGB = VV/VH/ratio channel assignment. Image acquired on May 31, 2018.

    Sentinel-1 C-band dual polarimetric VV and VH data: (a) VV, (b) VH, (c) VV/VH ratio, and (d) SAR false color composite with RGB = VV/VH/ratio channel assignment. Image acquired on May 31, 2018. Image: NASA SAR Handbook.

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    Other Resources

    Other Resources

    NASA ARSET Training

    NASA's ARSET, which is part of the NASA Applied Sciences Capacity Building Program Area, trains people to use Earth-observing data for environmental management and decision-making. ARSET training programs relevant to this SDG are:

    External Resources

    Webinar Banner- SEDAC POPGRID (12/3/19)

    The POPGRID Data Collaborative aims to bring together and expand the international community of data providers, users, and sponsors concerned with georeferenced data on population, human settlements, and infrastructure.

    Trends.Earth is a platform from CI for monitoring land change using Earth observations in an innovative desktop and cloud-based system. Trends.Earth allows users to plot time series of key indicators of land change (including degradation and improvement), to produce maps and other graphics that can support monitoring and reporting, and to track the impact of sustainable land management or other projects.

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    Published April 5, 2021

    Page Last Updated: Sep 2, 2021 at 11:51 AM EDT