The Enhanced Vegetation Index (L3, Monthly) layer is created from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13A3) data which are provided monthly at 1 kilometer (km) spatial resolution as a gridded Level 3 product in the sinusoidal projection. In generating this monthly product, the algorithm ingests all the Aqua MODIS Vegetation Indices 16-day products that overlap the month and employs a weighted temporal average.

The Enhanced Vegetation Index (EVI) minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin clouds. The MODIS EVI product is computed from surface reflectances corrected for molecular scattering, ozone absorption, and aerosols.

References: MYD13A3 doi:10.5067/MODIS/MYD13A3.061

More information can be found at lpdaac.usgs.gov

This data is provided by the NASA Global Imagery Browse Services for EOSDIS

The Global Imagery Browse Services GIBS system is a core EOSDIS component which provides a scalable, responsive, highly available, and community standards based set of imagery services. These services are designed with the goal of advancing user interactions with EOSDIS’ inter-disciplinary data through enhanced visual representation and discovery.

Contact points:

Resource Contact: EOSDIS NASA GIBS

Online resources:

This dataset consists of two parts:

A) Three data objects needed to ‘fix’ several issues associated with the L2 sea surface temperature (SST) dataset obtained from the MODIS Aqua instrument by the Ocean Biology Processing Group (OBPG) at NASA’s Goddard Space Flight Center. These files are required by the Matlab script, build_and_fix_orbits.m, in the GitHub repo git@github.com:pcornillon/MODIS_L2.git. This script ‘unmasks’ pixels improperly flagged as bad in the vicinity of large SST gradients and those flagged as bad because the difference between the retrieved SST and a reference field exceeds a given threshold. The script also regrids the L2 fields to compensate for the bow-tie effect, which tends to jumble the pixel locations in the along-track direction. The files are:

1) weights_and_locations_from_31191.mat - used to correct for the bow-tie effect.

2) SST_Range_for_Declouding.mat - are monthly climatologies of the minimum and maximum temperatures in 5x5 degree cells determined from the MODIS Aqua L2 SST dataset for 2002-2019. These fields are used to unmask pixels flagged as cloudy based on the reference SST test. The original test used was for a fixed SST range independent of time-of-year and location.

3) Separation_and_Angle_Arrays.n4 - a file containing several fields, one for the along-track separation of pixels at each pixel location, a second for the along-scan separation and a third for the angle the perpendicular to the scan line makes counterclockwise of east. These data are used to determine the eastward and northward components of the SST gradient determined from the 'fixed' SST fields.

B) A set of files used to test build_and_fix_orbits.m. These files are contained in four folders zipped into one file, MODIS_R2019.zip, plus the output file, AQUA_MODIS.20100619T052031.L2.SST.nc4, written by build_and_fix_orbits.m. The .zip file contains the data for one complete MODIS Aqua orbit beginning at 05h20 GMT on 19 June 2010. The folders in this file are:

1) Data_from_OBPG_for_PO-DAAC - metadata files copied from a portion of the OBPG SST granules.

2) Day - daytime granules obtained from the OBPG’s web site.

3) Night - nighttime granules obtained from the OBPG’s web site.

4) Orbits - a files indicating which OBPG granules make up individual orbits for June 2010

Chlorophyll-a concentration (Chl-a) is used as a proxy for phytoplankton biomass to track blooms and monitor the state of the ecosystem. This dataset contains daily satellite Chl-a from July 2002 to July 2023 (further updates will be provided periodically). The data is at a 4.64-km^2 spatial resolution covering an area of the Northwest Atlantic from 39-82°N, 42-95°W. Chl-a was generated by applying the POLY4 algorithm (Clay et al., 2019, see resources) on level-3 binned remote sensing reflectance (Rrs) data retrieved by the Moderate Resolution Imaging Spectroradiometer sensor (MODIS) onboard the Aqua satellite, freely downloaded courtesy of NASA's Ocean Biology Processing Group (OBPG, https://oceancolor.gsfc.nasa.gov/). Regionally-tuned coefficients for POLY4 were generated by matching level-2 MODIS-Aqua pixels (i.e. individual satellite passes) to in situ HPLC (High Performance Liquid Chromatography) Chl-a samples within the region of interest according to the procedure described in Clay et al. POLY4 Chl-a was then optimized by simultaneously forcing it to the 1:1 line against in situ Chl-a to reduce the negative bias observed in higher concentrations. The Chl-a data were then projected onto an Equidistant Cylindrical grid (4.64-km^2) using a weighted mean for overlapping bins. This model and the Northwest Atlantic coefficients are available in the R package oceancolouR (https://github.com/BIO-RSG/oceancolouR).

Chlorophyll-a concentration (Chl-a) is used as a proxy for phytoplankton biomass to track blooms and monitor the state of the ecosystem. This dataset contains daily satellite Chl-a from July 2002 to Dec 2023. The data is at a 4.64-km^2 spatial resolution covering an area of the Northwest Atlantic from 39-82°N, 42-95°W. Chl-a was generated by applying the POLY4 algorithm (Clay et al., 2019, see resources) on level-3 binned remote sensing reflectance (Rrs) data retrieved by the Moderate Resolution Imaging Spectroradiometer sensor (MODIS) onboard the Aqua satellite, freely downloaded courtesy of NASA's Ocean Biology Processing Group (OBPG, https://oceancolor.gsfc.nasa.gov/). Regionally-tuned coefficients for POLY4 were generated by matching level-2 MODIS-Aqua pixels (i.e. individual satellite passes) to in situ HPLC (High Performance Liquid Chromatography) Chl-a samples within the region of interest according to the procedure described in Clay et al. POLY4 Chl-a was then optimized by simultaneously forcing it to the 1:1 line against in situ Chl-a to reduce the negative bias observed in higher concentrations. The Chl-a data were then projected onto an Equidistant Cylindrical grid (4.64-km^2) using a nearest neighbour method. This model and the Northwest Atlantic coefficients are available in the R package oceancolouR (https://github.com/BIO-RSG/oceancolouR).

The SeasFire Cube is a scientific datacube for seasonal fire forecasting around the globe. Apart from seasonal fire forecasting, which is the aim of the SeasFire project, the datacube can be used for several other tasks. For example, it can be used to model teleconnections and memory effects in the earth system. Additionally, it can be used to model emissions from wildfires and the evolution of wildfire regimes.

It has been created in the context of the SeasFire project, which deals with "Earth System Deep Learning for Seasonal Fire Forecasting" and is funded by the European Space Agency (ESA) in the context of ESA Future EO-1 Science for Society Call.

It contains 21 years of data (2001-2021) in an 8-days time resolution and 0.25 degrees grid resolution. It has a diverse range of seasonal fire drivers. It expands from atmospheric and climatological ones to vegetation variables, socioeconomic and the target variables related to wildfires such as burned areas, fire radiative power, and wildfire-related CO2 emissions.

Datacube properties

Feature

Value

Spatial Coverage

Global

Temporal Coverage

2001 to 2021

Spatial Resolution

0.25 deg x 0.25 deg

Temporal Resolution

8 days

Number of Variables

54

Tutorial Link

https://github.com/SeasFire/seasfire-datacube

    Full name
    DataArray name
    Unit
    Contact *




    Dataset: ERA5 Meteo Reanalysis Data





    Mean sea level pressure
    mslp
    Pa
    NOA


    Total precipitation
    tp
    m
    MPI


    Relative humidity
    rel_hum
    %
    MPI


    Vapor Pressure Deficit
    vpd
    hPa
    MPI


    Sea Surface Temperature
    sst
    K
    MPI


    Skin temperature
    skt
    K
    MPI


    Wind speed at 10 meters
    ws10
    m*s-2
    MPI


    Temperature at 2 meters - Mean
    t2m_mean
    K
    MPI


    Temperature at 2 meters - Min
    t2m_min
    K
    MPI


    Temperature at 2 meters - Max
    t2m_max
    K
    MPI


    Surface net solar radiation
    ssr
    MJ m-2
    MPI


    Surface solar radiation downwards
    ssrd
    MJ m-2
    MPI


    Volumetric soil water level 1
    swvl1
    m3/m3
    MPI







          Volumetric soil water level 2




    swvl2
    m3/m3
    MPI


    Volumetric soil water level 3
    swvl3
    m3/m3
    MPI


    Volumetric soil water level 4
    swvl4
    m3/m3
    MPI


    Land-Sea mask
    lsm
    0-1
    NOA


    Dataset: Copernicus

CEMS

    Drought Code Maximum
    drought_code_max
    unitless
    NOA


    Drought Code Average
    drought_code_mean
    unitless
    NOA


    Fire Weather Index Maximum
    fwi_max
    unitless
    NOA


    Fire Weather Index Average
    fwi_mean
    unitless
    NOA


    Dataset: CAMS: Global Fire Assimilation System (GFAS)





    Carbon dioxide emissions from wildfires
    cams_co2fire
    kg/m²
    NOA


    Fire radiative power
    cams_frpfire
    W/m²
    NOA


    Dataset: FireCCI - European Space Agency’s Climate Change Initiative





    Burned Areas from Fire Climate Change Initiative (FCCI)
    fcci_ba
    ha
    NOA


    Valid mask of FCCI burned areas
    fcci_ba_valid_mask
    0-1
    NOA



    Fraction of burnable area
    fcci_fraction_of_burnable_area
    %
    NOA


    Number of patches
    fcci_number_of_patches
    N
    NOA


    Fraction of observed area
    fcci_fraction_of_observed_area
    %
    NOA


    Dataset: Nasa MODIS MOD11C1, MOD13C1, MCD15A2





    Land Surface temperature at day
    lst_day
    K
    MPI


    Leaf Area Index
    lai
    m²/m²
    MPI


    Normalized Difference Vegetation Index
    ndvi
    unitless
    MPI


    Dataset: Nasa SEDAC Gridded Population of the World (GPW), v4





    Population density
    pop_dens
    persons per square kilometers
    NOA


    Dataset: Global Fire Emissions Database (GFED)





    Burned Areas from GFED (large fires only)
    gfed_ba
    hectares (ha)
    MPI


    Valid mask of GFED burned areas
    gfed_ba_valid_mask
    0-1
    NOA


    GFED basis regions
    gfed_region
    N
    NOA


    Dataset: Global Wildfire Information System (GWIS)





    Burned Areas from GWIS
    gwis_ba
    ha
    NOA


    Valid mask of GWIS burned areas
    gwis_ba_valid_mask
    0-1
    NOA


    Dataset: NOAA Climate Indices





    Arctic Oscillation Index
    oci_ao
    unitless
    NOA


    Western Pacific Index
    oci_wp
    unitless
    NOA


    Pacific North American Index
    oci_pna
    unitless
    NOA


    North Atlantic Oscillation
    oci_nao
    unitless
    NOA


    Southern Oscillation Index
    oci_soi
    unitless
    NOA


    Global Mean Land/Ocean Temperature
    oci_gmsst
    unitless
    NOA


    Pacific Decadal Oscillation
    oci_pdo
    unitless
    NOA


    Eastern Asia/Western Russia
    oci_ea
    unitless
    NOA


    East Pacific/North Pacific Oscillation
    oci_epo
    unitless
    NOA


    Nino 3.4 Anomaly
    oci_nino_34_anom
    unitless
    NOA


    Bivariate ENSO Timeseries
    oci_censo
    unitless
    NOA


    Dataset: ESA CCI





    Land Cover Class 0 - No data
    lccs_class_0
    %
    NOA


    Land Cover Class 1 - Agriculture
    lccs_class_1
    %
    NOA


    Land Cover Class 2 - Forest
    lccs_class_2
    %
    NOA


    Land Cover Class 3 - Grassland
    lccs_class_3
    %
    NOA


    Land Cover Class 4 - Wetlands
    lccs_class_4
    %
    NOA


    Land Cover Class 5 - Settlement
    lccs_class_5
    %
    NOA


    Land Cover Class 6 - Shrubland
    lccs_class_6
    %
    NOA


    Land Cover Class 7 - Sparse vegetation, bare areas, permanent snow and ice
    lccs_class_7
    %
    NOA


    Land Cover Class 8 - Water Bodies
    lccs_class_8
    %
    NOA


    Dataset: Biomes





    Dataset: Calculated





    Grid Area in square meters
    area
    m²
    NOA

*The datacube specifications (temporal, spatial resolution, chunk size) have been set up by the Max Planck Institut (MPI) team. For the variables that the contact is MPI, Lazaro Alonso (lalonso bgc-jena.mpg.de) has led the efforts to collect and process them. For the variables that the contact is NOA, Ilektra Karasante (ile.karasante noa.gr) has led the efforts to collect and process them.

Atmospheric correction is a crucial preprocessing step for the analysis of optical satellite imagery like Landsat. Among the radiance-modifying gases, atmospheric water vapor is spatially and temporally variable, and cannot be measured reliably from the Landsat sensors. As such, atmospheric correction of Landsat data requires spatially and temporally explicit auxiliary information about atmospheric water vapor content.

We have compiled a water vapor dataset that can be readily used to perform atmospheric correction of Landsat images. The dataset was generated by a global processing of the MODIS MOD05/MYD05 collection 6.1 products (https://doi.org/10.1029/2002JD003023; MODIS Atmosphere L2 Water Vapor Product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA: doi:10.5067/MODIS/MOD05_L2.006, doi:10.5067/MODIS/MYD05_L2.006). The dataset is comprised of daily global water vapor data for February 2000 to December 2020 for each land-intersecting Worldwide Reference System 2 (WRS-2) scene, as well as a monthly climatology that can be used if no daily value is available.

The dataset was generated by the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE v. 2.0 / 3.6, https://github.com/davidfrantz/force, https://doi.org/10.3390/rs11091124), which is freely available software under the terms of the GNU General Public License v. >= 3. The water vapor dataset can be readily ingested into the FORCE Level 2 Processing System (Frantz et al. 2016, doi:10.1109/TGRS.2016.2530856) to perform atmospheric correction of Landsat imagery. This dataset is an update of https://doi.pangaea.de/10.1594/PANGAEA.893109 and should be used from now on.

MODIS Aqua rolling 8-day composite at 9KM cdm_data_type=Grid Conventions=COARDS, CF-1.6, ACDD-1.3 Easternmost_Easting=179.95835876464844 geospatial_lat_max=89.95833587646484 geospatial_lat_min=-89.95833587646484 geospatial_lat_units=degrees_north geospatial_lon_max=179.95835876464844 geospatial_lon_min=-179.9583282470703 geospatial_lon_units=degrees_east history=Tue May 6 13:39:18 2025: ncks -v sst,chl_ocx /lustre/scratch/orb/aqua/9km/8day/aqua_9km_8day_2010.nc4 2010.extracted.nc4 Thu May 12 07:48:10 2022: ncrcat A2009359_2010001.nc4 A2009360_2010002.nc4 A2009361_2010003.nc4 A2009362_2010004.nc4 A2009363_2010005.nc4 A2009364_2010006.nc4 A2009365_2010007.nc4 A2010001_2010008.nc4 A2010002_2010009.nc4 A2010003_2010010.nc4 A2010004_2010011.nc4 A2010005_2010012.nc4 A2010006_2010013.nc4 A2010007_2010014.nc4 A2010008_2010015.nc4 A2010009_2010016.nc4 A2010010_2010017.nc4 A2010011_2010018.nc4 A2010012_2010019.nc4 A2010013_2010020.nc4 A2010014_2010021.nc4 A2010015_2010022.nc4 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time_coverage_start=2002-07-04T04:00:00Z Westernmost_Easting=-179.9583282470703

This file contains the data set used to develop a random forest model predict background specific conductivity for stream segments in the contiguous United States. This Excel readable file contains 56 columns of parameters evaluated during development. The data dictionary provides the definition of the abbreviations and the measurement units. Each row is a unique sample described as R** which indicates the NHD Hydrologic Unit (underscore), up to a 7-digit COMID, (underscore) sequential sample month. To develop models that make stream-specific predictions across the contiguous United States, we used StreamCat data set and process (Hill et al. 2016; https://github.com/USEPA/StreamCat). The StreamCat data set is based on a network of stream segments from NHD+ (McKay et al. 2012). These stream segments drain an average area of 3.1 km2 and thus define the spatial grain size of this data set. The data set consists of minimally disturbed sites representing the natural variation in environmental conditions that occur in the contiguous 48 United States. More than 2.4 million SC observations were obtained from STORET (USEPA 2016b), state natural resource agencies, the U.S. Geological Survey (USGS) National Water Information System (NWIS) system (USGS 2016), and data used in Olson and Hawkins (2012) (Table S1). Data include observations made between 1 January 2001 and 31 December 2015 thus coincident with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data (https://modis.gsfc.nasa.gov/data/). Each observation was related to the nearest stream segment in the NHD+. Data were limited to one observation per stream segment per month. SC observations with ambiguous locations and repeat measurements along a stream segment in the same month were discarded. Using estimates of anthropogenic stress derived from the StreamCat database (Hill et al. 2016), segments were selected with minimal amounts of human activity (Stoddard et al. 2006) using criteria developed for each Level II Ecoregion (Omernik and Griffith 2014). Segments were considered as potentially minimally stressed where watersheds had 0 - 0.5% impervious surface, 0 – 5% urban, 0 – 10% agriculture, and population densities from 0.8 – 30 people/km2 (Table S3). Watersheds with observations with large residuals in initial models were identified and inspected for evidence of other human activities not represented in StreamCat (e.g., mining, logging, grazing, or oil/gas extraction). Observations were removed from disturbed watersheds, with a tidal influence or unusual geologic conditions such as hot springs. About 5% of SC observations in each National Rivers and Stream Assessment (NRSA) region were then randomly selected as independent validation data. The remaining observations became the large training data set for model calibration. This dataset is associated with the following publication: Olson, J., and S. Cormier. Modeling spatial and temporal variation in natural background specific conductivity. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(8): 4316-4325, (2019).

Biogeographic framework. Space and time classified simultaneously from synoptic time series using hierarchical and topology preserving machine learning acknowledgement=The U.S. MBON projects are funded under the National Ocean Partnership Program (NOPP RFP NOAA-NOS-IOOS-2014-2003803) in partnership between NOAA, BOEM, and NASA cdm_data_type=Grid contact=Joaquin.Trinanes@noaa.gov/mkavanau@ceoas.oregonstate.edu Conventions=COARDS, CF-1.4, Unidata Dataset Discovery v1.0 Easternmost_Easting=179.975 geospatial_lat_max=89.975 geospatial_lat_min=-89.975 geospatial_lat_resolution=0.049999999999999996 geospatial_lat_units=degrees_north geospatial_lon_max=179.975 geospatial_lon_min=-179.975 geospatial_lon_resolution=0.049999999999999996 geospatial_lon_units=degrees_east infoUrl=https://cwcgom.aoml.noaa.gov/thredds/dodsC/SEASCAPE_MONTH/SEASCAPES.nc.html institution=NOAA CoastWatch, OSU, USF, NASA, UAF, IOOS, NMS keywords_vocabulary=GCMD Science Keywords Metadata_Conventions=COARDS, CF-1.4, Unidata Dataset Discovery v1.0 NCO=netCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) Northernmost_Northing=89.975 references=Kavanaugh M. et al. Seascapes as a new vernacular for pelagic ocean monitoring, management and conservation. ICES Journal of Marine Science. DOI: 10.1093/icesjms/fsw086 reprocessing_date=2024-09-29 reprocessing_info=Reprocessed with MODIS Aqua R2022.0 source_data=MODIS, HYCOM, GOES, POES, SSMIS sourceUrl=https://cwcgom.aoml.noaa.gov/thredds/dodsC/SEASCAPE_MONTH/SEASCAPES.nc Southernmost_Northing=-89.975 spatial_resolution=5 km standard_name_vocabulary=CF-12 time_coverage_duration=P0Y1M0DT0H0M0S time_coverage_end=2025-04-15T12:00:00Z time_coverage_start=2002-09-15T12:00:00Z Westernmost_Easting=-179.975