LP DAAC

NASA’s Earth Science Program is dedicated to the advancement of Earth remote sensing and the scientific use of satellite measurements to expand our understanding of Earth systems. LP DAAC collaborates with the Making Earth System Data Records for Use in Research Environments (MEaSUREs) projects to facilitate the introduction of long-term, consistent, high-quality data records to the land remote sensing community.

GFCC

Principal Investigator: John Townshend, University of Maryland

The Global Forest Cover Change (GFCC) collection is derived from enhanced Landsat Global Land Survey (GLS) datasets and provides global coverage information on surface reflectance, water cover, and forest cover change.

GFSAD

Principal Investigator: Prasad Thenkabail, USGS

The Global Food Security-support Analysis Data (GFSAD) collection provides information on global croplands, including crop density and crop extent. The monitoring of global croplands is critical for policymaking and provides important baseline data that are used in many agricultural studies pertaining to water sustainability and food security.

GLanCE

Principal Investigators: Mark Friedl, Curtis Woodcock, Pontus Olofsson, Thomas Loveland, and Zhe Zhu

The Global Land Cover Mapping and Estimation (GLanCE) data record is an annual product derived from Landsat 5, 7, and 8. This project provides high-quality maps of global land cover, land use, and land cover change at 30 meter spatial resolution annually from 2001 to 2019. These data 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.

LSTE

Principal Investigator: Simon Hook, Jet Propulsion Laboratory
Co-investigator: Kerry Anne Cawse-Nicholson, Jet Propulsion Laboratory

The Land Surface Temperature and Emissivity (LSTE) collection provides global LSTE coverage for common use in surface energy balance studies, land surface modeling, climate change modeling, and urban heat island studies.

NASADEM

Principal Investigator: Sean Buckley, Jet Propulsion Laboratory

NASADEM extends the legacy of the Shuttle Radar Topography Mission (SRTM) by improving the digital elevation model (DEM) height accuracy and data coverage as well as providing several new data products. The improvements were achieved by reprocessing the original SRTM radar signal and telemetry data with updated algorithms and auxiliary data not available at the time of the original SRTM processing.

SRTM

Principal Investigator: Michael Kobrick, Jet Propulsion Laboratory

The NASA Shuttle Radar Topography Mission (SRTM) is a collaborative effort by NASA, the National Geospatial-Intelligence Agency (NGA), and the participation of German and Italian space agencies. This collaboration aims to generate a near-global digital elevation model (DEM) of Earth using radar interferometry. SRTM was the primary (and virtually only) payload on the STS-99 mission of the space shuttle Endeavour, which launched February 11, 2000, and flew for 11 days.

VCF

Principal Investigator: Matthew Hansen, University of Maryland

The Vegetation Continuous Fields (VCF) collection provides global vegetation continuous fields from Advanced Very High Resolution Radiometer (AVHRR) long-term data records version 4 (LTDR v4) from 1982 through 2016. Version 1 of this data product includes a time series of VCF data at 5600 m resolution containing information on percent of tree cover, non-tree vegetation, and bare ground.

VIP

Principal Investigator: Kamel Didan, University of Arizona

The Vegetation Index and Phenology (VIP) collection comprises 34 years of a consistent, global record of vegetation indices and landscape phenology. The VIP collections are based on MODIS, AVHRR, and Satellite Pour l'Observation de la Terre (SPOT) data inputs.

WELD and GWELD

Principal Investigator: David Roy, Michigan State University; Zhang Hankui, South Dakota State University

The NASA-funded Web-enabled Landsat Data (WELD) project generated Landsat Enhanced Thematic Mapper Plus (ETM+) mosaics of the conterminous United States and Alaska from 2002 to 2012. The WELD products were developed specifically to provide consistent data that could be used to derive land cover, geophysical and biophysical products for regional assessment of surface dynamics, and to study Earth system functionality. The WELD data products were decommissioned on December 2, 2019.

Global WELD (GWELD) is an expansion of WELD data products on a global scale using Landsat 4 and 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Collection 1 (Versions 3.0 and 3.1) and Collection 2 (Version 3.2) data. These global products provide monthly and annual data for terrestrial non-Antarctic locations for six 3-year epochs that occur every 5 years from 1985 to 2010. GWELD Version 3.0 data for the 2010 epoch is currently available. GWELD Version 3.1 data for the 2000, 1990, and 1985 epochs and Version 3.2 data for the 2005 epoch are also available.

Airborne Hyperspectral Reflectance Mosaic

Principal Investigators: John Gamon, Ran Wang, Hamed Gholizadeh, Jeannine Cavender-Bares, Christopher J. Helzer

Airborne Hyperspectral Reflectance datasets were acquired over various plots and sites: Cedar Creek Ecosystem Science Reserve (CCESR), Minnesota; Tallgrass Prairie Preserve, Oklahoma; Wood River, Nebraska; and Indian Cave State Park, Nebraska. These fine resolution mosaics can be used to better understand the optical diversity-biodiversity relationship and to investigate the spatial sensitivity of the optical diversity-biodiversity relationship at local scales.

ASTER GED

Principal Investigator: Glynn Hulley, Jet Propulsion Laboratory (JPL)

Using data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on NASA's Terra spacecraft, NASA/JPL derived the most detailed global emissivity map of the Earth, termed the ASTER Global Emissivity Database (GED).

GHISA

Principal Investigator:  Prasad Thenkabail, Itiya P. Aneece, Isabella Mariotto

The Global Hyperspectral Imaging Spectral-library of Agricultural crops (GHISA) is a comprehensive hyperspectral library of the world’s major agricultural crops (e.g., wheat, rice, barley, corn, soybeans, cotton, sugarcane, potatoes, chickpeas, lentils, and pigeon peas). Hyperspectral data for GHISA were acquired from spaceborne, airborne, and ground-based platforms.

G-LiHT

Principal Investigator: Bruce Cook

Goddard’s Light Detection and Ranging (LiDAR), Hyperspectral, and Thermal Imager (G-LiHT) was developed to simultaneously derive information about the composition, structure, and function of terrestrial ecosystems using a combination of airborne LiDAR, imaging spectroscopy, and thermal measurements.

Headwall Hyperspectral Reflectance Mosaic

Principal Investigator: John Gamon, Ran Wang

An imaging spectrometer on an airborne tram system collected images at 1-millimeter spatial resolution for 33 selected plots at the biodiversity (BioDIV) experiment at the CCESR LTER, Minnesota. The hyperspectral range and fine resolution of the data will assist researchers in studying biodiversity in this area. These findings can be used to guide future airborne studies in developing more effective large-scale biodiversity sampling methods.

LGRIP30

Principal Investigator: Prasad Thenkabail

The Global Food Security-support Analysis Data (GFSAD) project provides the highest-known spatial-resolution Landsat-derived Global Rainfed and Irrigated area Product (LGRIP) at 30 meter spatial resolution for the nominal year 2015. The LGRIP product maps agricultural lands, calculates irrigated and rainfed areas, and performs accuracy assessment of the product.

LPJ-EOSIM

Principal Investigator: Thomas Colligan

The Lund-Potsdam-Jena Earth Observation SIMulator (LPJ-EOSIM) model replicates biospheric processes to estimate how plants of different functional types obtain resources through photosynthesis and competition. The model estimates global wetland methane (CH4) emissions using simulated wetland extent and characteristics including soil moisture, temperature, and carbon content. The wetland CH4 flux data will be used to support the United States Greenhouse Gas Center (US GHG Center) and its mission to study natural GHG fluxes. A carbon dioxide (CO2) product is planned for the near future, along with the possibility of more data products containing biospheric variables such as gross primary production and net primary production.

LPJ-PROSAIL

Principal Investigator: Bryce Currey

The LPJ-PROSAIL global simulated imaging spectroscopy products are being developed to provide data analogs for the development of future spaceborne global imaging spectroscopy missions including NASA’s Surface Biology and Geology (SBG). The data products consist of simulated imaging spectroscopy data produced by the LPJ-PROSAIL model. The LPJ-PROSAIL model was developed by coupling LPJ, a dynamic global vegetation model, with PROSAIL, a canopy radiative transfer model. LPJ-PROSAIL will consist of multiple products containing dynamic surface reflectance, dynamic top-of-atmosphere radiance, and vegetation traits. The reflectance and radiance products will have a spectral range of 400 to 2500 nanometers (nm) with a spectral resolution of 10 nm at approximately 50 kilometer spatial resolution. Data will be available for each year from 2000 to present. Data availability within that timeframe will vary by product. Each granule will contain a full year of simulated monthly data. For more information visit the LPJ-PROSAIL website.

MuSLI

Principal Investigator: Mark Friedl

NASA’s Multi-Source Land Imaging (MuSLI) Land Surface Phenology (LSP) (MSLSP) provides a 30 m spatial resolution data product containing phenology timing metrics for North America. These data are useful for a wide range of applications including: ecosystem and agro-ecosystem modeling, monitoring of terrestrial ecosystems and their response to climate change and extreme events, as well as mapping land cover, land use, and land cover change.

FUTURE Community

INCA

Principal Investigator: Josh Gray

NASA’s Indicator of National Climate Assessment (INCA) will provide a 500 meter spatial resolution, yearly data product that spans from January 2001 to December 2016; it will contain global phenology metrics based on Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data. This data product will provide a remote sensing-based land surface phenology climate indicator (LSP-CI) that supports the National Climate Assessment’s need for national scale, long-term monitoring of climate change impacts on ecosystems.