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Using the NASA EOSDIS Common Metadata Repository

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  • 2015 Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods

    https://cmr.earthdata.nasa.gov/search/concepts/C1648035940-SEDAC.xml
    Description:

    The 2015 Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods data set models urban settlements in the Continental United States (CONUS) as of 2015. When applied to the combination of daytime spectral and nighttime lights satellite data, the machine learning methods achieved high accuracy at an intermediate-resolution of 500 meters at large spatial scales. The input data for these models were two types of satellite imagery: Visible Infrared Imaging Radiometer Suite (VIIRS) Nighttime Light (NTL) data from the Day/Night Band (DNB), and Moderate Resolution Imaging Spectroradiometer (MODIS) corrected daytime Normalized Difference Vegetation Index (NDVI). Although several machine learning methods were evaluated, including Random Forest (RF), Gradient Boosting Machine (GBM), Neural Network (NN), and the Ensemble of RF, GBM, and NN (ESB), the highest accuracy results were achieved with NN, and those results were used to delineate the urban extents in this data set.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: -56 -180 84 180

    SEDAC Short Name: CIESIN_SEDAC_USPAT_USUEXT2015 Version ID: 1.00 Unique ID: C1648035940-SEDAC

  • ABoVE: Angular-corrected MODIS MAIAC Reflectance across Alaska and Canada, 2000-2017

    https://cmr.earthdata.nasa.gov/search/concepts/C2192631093-ORNL_CLOUD.xml
    Description:

    This dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances across the ABoVE domain in Alaska and western Canada from 2000 to 2017. Using random forests (RF), a machine-learning approach, the original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) to reduce artifacts and variability due to angular effects. The original MAIAC data's sub-daily temporal resolution and 1 km spatial resolution with seven land bands (bands 1-7) and five ocean bands (bands 8-12) were preserved. The resulting surface reflectance data are suitable for long-term studies on patterns, processes, and dynamics of surface phenomena. The data cover 11 different Terra and Aqua satellite MODIS MAIAC tiles.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 44.12 -180 80.81 180

    ORNL_CLOUD Short Name: ABoVE_MODIS_MAIAC_Reflectance_1858 Version ID: 1 Unique ID: C2192631093-ORNL_CLOUD

  • ABoVE: Burn Severity, Fire Progression, and Field Data, NWT, Canada, 2015-2016

    https://cmr.earthdata.nasa.gov/search/concepts/C2162122286-ORNL_CLOUD.xml
    Description:

    This data set provides a fire progression map for year 2015 and measures of burn severity and vegetation community biophysical data collected from areas that were burned by wildfires in 2014 and 2015 in the Northwest Territories, Canada. Field data collected in 2016 include an estimate of burn severity, woody seedling/sprouting data, soil moisture, peat depth, thaw depth, and vegetation cover for selected sites.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 59.93 -135.54 68.33 -106.76

    ORNL_CLOUD Short Name: Wildfires_NWT_Canada_1548 Version ID: 1 Unique ID: C2162122286-ORNL_CLOUD

  • ABoVE: Burn Severity, Fire Progression, Landcover and Field Data, NWT, Canada, 2014

    https://cmr.earthdata.nasa.gov/search/concepts/C2170968584-ORNL_CLOUD.xml
    Description:

    This data set provides peatland landcover classification maps, fire progression maps, and vegetation community biophysical data collected from areas that were burned by wildfire in 2014 in the Northwest Territories, Canada. The peatland maps include peatland type (bog, fen, marsh, swamp) and level of biomass (open, forested). The fire progression maps enabled an assessment of wildfire progression rates at a daily time scale. Field data, collected in 2015, include an estimate of burn severity, woody seedling/sprouting data, soil moisture, and tree diameter and height of burned sites and similar vegetation characterization at landcover validation sites.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 60.33 -121.6 64.25 -110.68

    ORNL_CLOUD Short Name: Wildfires_2014_NWT_Canada_1307 Version ID: 1 Unique ID: C2170968584-ORNL_CLOUD

  • ABoVE: Corrected MODIS MAIAC Reflectance at Tower Sites, Alaska and Canada, 2000-2016

    https://cmr.earthdata.nasa.gov/search/concepts/C2143403511-ORNL_CLOUD.xml
    Description:

    This dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances by two methods at each of 62 flux tower sites (1 km x 1 km area) across the ABoVE domain in Alaska and western Canada from 2000 to 2015/2016. The original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) using two independent algorithms: the first based on the original BRDF (Bidirectional Reflectance Distribution Function) parameters provided by the MAIAC team, and the second based on a machine learning approach (random forests). The corrected data preserve the original MAIAC data's sub-daily temporal resolution and 1 km spatial resolution with seven land bands (bands 1-7) and five ocean bands (bands 8-12). The resulting tower site sub-daily timeseries of angular corrected surface reflectances are suitable for long-term studies on patterns, processes, and dynamics of surface phenomena.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 42.64 -157.41 71.32 -74.04

    ORNL_CLOUD Short Name: MODIS_MAIAC_Reflectance_1700 Version ID: 1 Unique ID: C2143403511-ORNL_CLOUD

  • ABoVE: Cumulative Annual Burned Area, Circumpolar High Northern Latitudes, 2001-2015

    https://cmr.earthdata.nasa.gov/search/concepts/C2162119000-ORNL_CLOUD.xml
    Description:

    This dataset provides annual cumulative end-of-season burned area in circumpolar high northern latitudes (HNL) above 60 degrees for the years 2001-2015. The data were generated using the Arctic Boreal Burned Area (ABBA) product (a MODIS-based algorithm). The product is delivered in two spatial domains: circumpolar and a North American subset for areas above 60 degree north.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 60 -179.81 72.59 179.15

    ORNL_CLOUD Short Name: Arctic_Boreal_Burned_Area_1526 Version ID: 1 Unique ID: C2162119000-ORNL_CLOUD

  • ABoVE: Dall Sheep Response to Snow and Landscape Covariates, Alaska, 2005-2008

    https://cmr.earthdata.nasa.gov/search/concepts/C2170971503-ORNL_CLOUD.xml
    Description:

    This dataset provides daily estimates of snow depth and snow density for the study area in Lake Clark National Park and Preserve (LCNPP), Alaska. The data were generated using SnowModel and used as snow covariates along with landscape covariates in modeling efforts to study Dall sheep movements in response to dynamic snow conditions. Thirty adult Dall sheep (12 male, 18 female) were captured and outfitted with global positioning system (GPS) collars programmed to acquire locations every seven hours. Given the individual sheep locations, their distances to land cover (e.g., shrub, forest, glacier), landscape characteristics (e.g., elevation, terrain ruggedness index (TRI), vector ruggedness measure (VRM), slope, and aspect), snow depth and density, MODIS normalized difference snow index (NDSI), and other covariates were determined and are provided in the environmental data file. The snow density and depth data are provided at 25-m, 100-m, 500-m, 2000-m, and 10000-m grid resolutions, at 1-day increments, and cover the period September 1, 2005 through August 31, 2008. The sheep, snow, and landscape data cover the years 2006, 2007, and 2008.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 59.98 -154.53 61.05 -153.03

    ORNL_CLOUD Short Name: Dall_Sheep_Snowpack_1602 Version ID: 1 Unique ID: C2170971503-ORNL_CLOUD

  • ABoVE: Environmental Conditions During Fall Moose Hunting Seasons, Alaska, 2000-2016

    https://cmr.earthdata.nasa.gov/search/concepts/C2143402663-ORNL_CLOUD.xml
    Description:

    This dataset provides daily and annual air temperature, river water level, and leaf drop dates coincident with the moose (Alces alces) hunting season (September) for the area surrounding the rural communities of Nulato, Koyukuk, Kaltag, Galena, Ruby, Huslia, and Hughes in interior Alaska, USA, over the period 2000-2016. The main objective of the study was to assess how the environmental conditions impacted the success of hunters who rely on moose as a subsistence resource.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 64.55 -158.53 64.93 -156.66

    ORNL_CLOUD Short Name: Effect_Environment_Moose_1739 Version ID: 1 Unique ID: C2143402663-ORNL_CLOUD

  • ABoVE: Fractional Open Water Cover for Pan-Arctic and ABoVE-Domain Regions, 2002-2015

    https://cmr.earthdata.nasa.gov/search/concepts/C2111722183-ORNL_CLOUD.xml
    Description:

    This data set provides land surface fractional open water cover maps for two overlapping regions: the entire pan-Arctic region (latitude > 45 degrees) and the Arctic-Boreal Vulnerability Experiment (ABoVE) domain across Alaska and Canada. The data are a 10-day averaged time step at 5-km spatial resolution for the period 2002-2015. Data represent the aerial portion of a grid cell covered by open water. The data were produced using high frequency (89 GHz) brightness temperatures from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), with other ancillary inputs from AMSR-E/AMSR2 25-km products and the Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting data record for fractional water is suitable for documenting open water patterns and inundation dynamics in boreal-Arctic ecosystems experiencing rapid climate change.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 39.38 -180 90 180

    ORNL_CLOUD Short Name: ABoVE_Frac_Open_Water_1362 Version ID: 1 Unique ID: C2111722183-ORNL_CLOUD

  • ABoVE: High Resolution Cloud-Free Snow Cover Extent and Snow Depth, Alaska, 2001-2017

    https://cmr.earthdata.nasa.gov/search/concepts/C2143402490-ORNL_CLOUD.xml
    Description:

    This dataset provides estimates of maximum snow cover extent (SCE) and snow depth for each 8-day composite period from 2001 to 2017 at 1 km resolution across Alaska. The study area covers the majority land area of Alaska except for areas covered by perennial ice/snow or open water. A downscaling scheme was used in which Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global reanalysis 0.5 degree snow depth data were interpolated to a finer 1 km spatial grid. In the methods used, the downscaling scheme incorporated MODIS SCE (MOD10A2) to better account for the influence of local topography on the 1km snow distribution patterns. For MODIS cloud-contaminated pixels, persistent and patchy cloud cover conditions were improved by applying an elevation-based spatial filtering algorithm to predict snow occurrence. Cloud-free MODIS SCE data were then used to downscale MERRA-2 snow depth data. For each snow-covered 1 km pixel indicated by the MODIS data, the snow depth was estimated based on the snow depth of the neighboring MERRA-2 0.5 grid cell, with weights predicted using a spatial filter.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 55.57 -179.18 71.42 -132.58

    ORNL_CLOUD Short Name: Snow_Cover_Extent_and_Depth_1757 Version ID: 1 Unique ID: C2143402490-ORNL_CLOUD