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  • ABoVE: Light-Curve Modelling of Gridded GPP Using MODIS MAIAC and Flux Tower Data

    https://cmr.earthdata.nasa.gov/search/concepts/C2445456434-ORNL_CLOUD.xml
    Description:

    This dataset contains gridded estimations of daily ecosystem Gross Primary Production (GPP) in grams of carbon per day at a 1 km2 spatial resolution over Alaska and Canada from 2000-01-01 to 2018-01-01. Daily estimates of GPP were derived from a light-curve model that was fitted and validated over a network of ABoVE domain Ameriflux flux towers then upscaled using MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) data to span the extended ABoVE domain. In general, the methods involved three steps; the first step involved collecting and processing mainly carbon-flux site-level data, the second step involved the analysis and correction of site-level MAIAC data, and the final step developed a framework to produce large-scale estimates of GPP. The light-curve parameter model was generated by upscaling from flux tower sub-daily temporal resolution by deconvolving the GPP variable into 3 components: the absorbed photosynthetically active radiation (aPAR), the maximum GPP or maximum photosynthetic capacity (GPPmax), and the photosynthetic limitation or amount of light needed to reach maximum capacity (PPFDmax). GPPmax and PPFDmax were related to satellite reflectance measurements sampled at the daily scale. GPP over the extended ABoVE domain was estimated at a daily resolution from the light-curve parameter model using MODIS MAIAC daily reflectance as input. This framework allows large-scale estimates of phenology and evaluation of ecosystem sensitivity to climate change.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 50.06 -172.08 79.75 -73.64

    ORNL_CLOUD Short Name: GPP_MODIS_Alaska_Canada_2024 Version ID: 1 Unique ID: C2445456434-ORNL_CLOUD

  • ABoVE: MODIS-derived Maximum NDVI, Northern Alaska and Yukon Territory for 2002-2017

    https://cmr.earthdata.nasa.gov/search/concepts/C2162145492-ORNL_CLOUD.xml
    Description:

    This dataset provides the maximum Normalized Difference Vegetation Index (NDVI) at 1-km resolution over northern Alaska, USA and the Yukon Territory, Canada for each year from 2002-2017, as well as a 16 year maximum NDVI product. MODIS products MOD13Q1 and MYD13Q1 from Collection 6 were acquired at 250-m pixel size from June 1-August 30 of each year. Within each growing season from 2002-2017, the maximum NDVI was determined for each pixel. These maximum NDVI values were then aggregated to 1-km by selecting the maximum NDVI from the sixteen 250-m pixels values nested within each 1-km pixel. A long-term 16-year maximum NDVI was then derived from the time series of annual maximum NDVI values.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 52.17 -175.76 68.97 -97.93

    ORNL_CLOUD Short Name: Alaska_Yukon_NDVI_1614 Version ID: 1 Unique ID: C2162145492-ORNL_CLOUD

  • ABoVE: Passive Microwave-derived Annual Snow Melt Duration Date Maps, 1988-2018

    https://cmr.earthdata.nasa.gov/search/concepts/C2223093928-ORNL_CLOUD.xml
    Description:

    This dataset provides the annual period of snowpack melting (i.e., snow melt duration, SMD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. SMD is the number of days between the main melt onset date (MMOD) and the last day of seasonal snow cover when the melting of snow is complete. These dates were derived from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). This dataset documents variability in SMD across space and the 31-year temporal period. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 51.6 -180 72.41 -107.83

    ORNL_CLOUD Short Name: SnowMeltDuration_PMicrowave_1843 Version ID: 1.1 Unique ID: C2223093928-ORNL_CLOUD

  • ABoVE: Passive Microwave-derived Annual Snowpack Main Melt Onset Date Maps, 1988-2018

    https://cmr.earthdata.nasa.gov/search/concepts/C2143401742-ORNL_CLOUD.xml
    Description:

    This dataset provides the annual date of snowpack seasonal beginning melt (i.e., main melt onset date, MMOD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. MMOD was derived from the daily 19V (K-band) and 37V (Ka-band) GHz bands from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). The PMW MMOD dataset was validated using the transition date from Freeze Degree Days (FDD) to Thaw Degree Days (TDD) from in situ air temperature observations from 31 SNOw TELemetry network (SNOTEL) observations, and compared to the established Freeze-Thaw ESDR (FT-ESDR) spring onset date. The resulting MMOD data record is suitable for documenting the spatial-temporal impacts of MMOD variability in ecosystem services, wildlife movements, and hydrologic processes across the ABoVE domain. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 51.61 -180 72.41 -107.83

    ORNL_CLOUD Short Name: Main_Melt_Onset_Dates_1841 Version ID: 1.1 Unique ID: C2143401742-ORNL_CLOUD

  • ABoVE: Rain-on-Snow Frequency and Distribution during Cold Seasons, Alaska, 2003-2016

    https://cmr.earthdata.nasa.gov/search/concepts/C2162145449-ORNL_CLOUD.xml
    Description:

    This dataset provides maps of rain-on-snow (ROS) events across Alaska for the individual months of November to March 2002-2011 and November to March 2012-2016, and annual water year summary maps for 2003-2011 and 2013-2016. ROS events were defined as changes in passive microwave (PM) detection in surface snow wetness and isothermal states induced by atmospheric processes often associated with winter rainfall. The data are summations of the number of days with ROS events per pixel at 6-km spatial resolution per month or per 5-month water year. The daily ROS record encompassed the months when snowmelt from solar irradiance is minimal and snow cover is widespread and relatively consistent throughout the region. Daily ROS geospatial classification across Alaska was derived by combining snow cover and daily microwave brightness temperature retrievals sensitive to landscape freeze-thaw dynamics from overlapping (1) Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A2 eight-day maximum snow cover extent (SCE) product and (2) Advanced Microwave Scanning Radiometer for EOS (AMSR-E) (2002-2011) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) (2012-to present) Microwave Radiation Imager (MWRI) observations at 19 GHz and 37 GHz.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 48.62 -175.4 73.85 -111.54

    ORNL_CLOUD Short Name: Rain-on-Snow_Data_1611 Version ID: 1 Unique ID: C2162145449-ORNL_CLOUD

  • Aboveground Biomass Change for Amazon Basin, Mexico, and Pantropical Belt, 2003-2016

    https://cmr.earthdata.nasa.gov/search/concepts/C2345897759-ORNL_CLOUD.xml
    Description:

    This dataset provides gridded estimates of aboveground biomass (AGB) for live dry woody vegetation density in the form of both stock for the baseline year 2003 and annual change in stock from 2003 to 2016. Data are at a spatial resolution of approximately 500 m (463.31 m; 21.47 ha) for three geographies: the biogeographical limit of the Amazon Basin, the country of Mexico, and a Pantropical belt from 40 degrees North to 30 degrees South latitudes. Estimates were derived from a multi-step modeling approach that combined field measurements with co-located LiDAR data from NASA ICESat Geoscience Laser Altimeter System (GLAS) to calibrate a machine-learning (ML) algorithm that generated spatially explicit annual estimates of AGB density. ML inputs included a suite of satellite and ancillary spatial predictor variables compiled as wall-to-wall raster mosaics, including MODIS products, WorldClim climate variables reflecting current (1960-1990) climatic conditions, and SoilGrids soil variables. The 14-year time series was analyzed at the grid cell (~500 m) level with a change point-fitting algorithm to quantify annual losses and gains in AGB. Estimates of AGB and change can be used to derive total losses, gains, and the net change in aboveground carbon density over the study period as well as annual estimates of carbon stock.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: -30 -180 40 180

    ORNL_CLOUD Short Name: AGB_Pantropics_Amazon_Mexico_1824 Version ID: 1 Unique ID: C2345897759-ORNL_CLOUD

  • ADAM Surface Reflectance Database v4.0

    https://cmr.earthdata.nasa.gov/search/concepts/C1965336812-ESA.xml
    Description:

    ADAM enables generating typical monthly variations of the global Earth surface reflectance at 0.1° spatial resolution (Plate Carree projection) and over the spectral range 240-4000nm. The ADAM product is made of gridded monthly mean climatologies over land and ocean surfaces, and of a companion API toolkit that enables the calculation of hyperspectral (at 1 nm resolution over the whole 240-4000 nm spectral range) and multidirectional reflectances (i.e. in any illumination/viewing geometry) depending on user choices. The ADAM climatologies that feed the ADAM calculation tools are: For ocean: monthly chlorophyll concentration derived from SeaWiFS-OrbView-2 (1999-2009); it is used to compute the water column reflectance (which shows large spectral variations in the visible, but is insignificant in the near and mid infrared). monthly wind speed derived from SeaWinds-QuikSCAT-(1999-2009); it is used to calculate the ocean glint reflectance. For land: monthly normalized surface reflectances in the 7 MODIS narrow spectral bands derived from FondsdeSol processing chain of MOD09A1 products (derived from Aqua and Terra observations), on which relies the modelling of the hyperspectral/multidirectional surface (soil/vegetation/snow) reflectance. uncertainty variance-covariance matrix for the 7 spectral bands associated to the normalized surface reflectance. For sea-ice: Sea ice pixels (masked in the original MOD09A1 products) have been accounted for by a gap-filling approach relying on the spatial-temporal distribution of sea ice coverage provided by the CryoClim climatology for year 2005.

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

    ESA Short Name: ADAM.Surface.Reflectance.Database Version ID: 3.0 Unique ID: C1965336812-ESA

    This collection does not contain any granules

  • AIRS-AMSU variables-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRSM_CPR_MAT) at GES DISC

    https://cmr.earthdata.nasa.gov/search/concepts/C1236224182-GES_DISC.xml
    Description:

    This is AIRS-CloudSat collocated subset, in NetCDF 4 format. These data contain collocated: AIRS/AMSU retrievals at AMSU footprints, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project. The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each "scene" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time "matchups" between pairs of instruments, and process years of satellite data to produce the climate data records. The short name for this collection is AIRSM_CPR_MAT Parameters contained in the data files include the following: Variable Name|Description|Units CH4_total_column|Retrieved total column CH4| (molecules/cm2) CloudFraction|CloudSat/CALIPSO Cloud Fraction| (None) CloudLayers| Number of hydrometeor layers| (count) clrolr|Clear-sky Outgoing Longwave Radiation|(Watts/m**2) CO_total_column|Retrieved total column CO| (molecules/cm2) CPR_Cloud_mask| CPR Cloud Mask |(None) Data_quality| Data Quality |(None) H2OMMRSat|Water vapor saturation mass mixing ratio|(gm/kg) H2OMMRStd|Water Vapor Mass Mixing Ratio |(gm/kg dry air) MODIS_Cloud_Fraction| MODIS 250m Cloud Fraction| (None) MODIS_scene_var |MODIS scene variability| (None) nSurfStd|1-based index of the first valid level|(None) O3VMRStd|Ozone Volume Mixing Ratio|(vmr) olr|All-sky Outgoing Longwave Radiation|(Watts/m**2) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) Sigma-Zero| Sigma-Zero| (dB*100) TAirMWOnlyStd|Atmospheric Temperature retrieved using only MW|(K) TCldTopStd|Cloud top temperature|(K) totH2OStd|Total precipitable water vapor| (kg/m**2) totO3Std|Total ozone burden| (Dobson) TSurfAir|Atmospheric Temperature at Surface|(K) TSurfStd|Surface skin temperature|(K) End of parameter information

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

    GES_DISC Short Name: AIRSM_CPR_MAT Version ID: 3.2 Unique ID: C1236224182-GES_DISC

  • AIRS-CloudSat cloud mask and radar reflectivities collocation indexes V4.0 (AIRS_CPR_IND) at GES_DISC

    https://cmr.earthdata.nasa.gov/search/concepts/C1236224151-GES_DISC.xml
    Description:

    Version 4.1 is the current version of the data set. Previous versions are no longer available and have been superseded by Version 4.1. This is AIRS-AMSU-CloudSat collocation indexes, in netCDF-4 format. These data map CloudSat profile indexes to the collocated AMSU field of views, and AIRS IR footprints, per AIRS 6-min granule time. Hence it can be considered as Level 1. These data are created within the frames of the MEaSUREs project. The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, & CloudSat), classify each "scene" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time "matchups" between pairs of instruments, and process years of satellite data to produce the climate data records. The short name for this collection is AIRS_CPR_IND

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

    GES_DISC Short Name: AIRS_CPR_IND Version ID: 4.0 Unique ID: C1236224151-GES_DISC

  • AIRS-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRS_CPR_MAT) at GES DISC

    https://cmr.earthdata.nasa.gov/search/concepts/C1236224153-GES_DISC.xml
    Description:

    This is AIRS-CloudSat collocated subset, in NetCDF-4 format. These data contain collocated: AIRS Level 1b radiances spectra, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project. The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each "scene" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time "matchups" between pairs of instruments, and process years of satellite data to produce the climate data records. The short name for this collection is AIRS_CPR_MAT Parameters contained in the data files include the following: Variable Name|Description|Units CldFrcStdErr|Cloud Fraction|(None) CloudLayers| Number of hydrometeor layers| (count) CPR_Cloud_mask| CPR Cloud Mask| (None) DEM_elevation| Digital Elevation Map| (m) dust_flag|Dust Flag|(None) latAIRS|AIRS IR latitude|(deg) Latitude|CloudSat Latitude |(degrees) LayerBase| Height of Layer Base| (m) LayerTop| Height of layer top| (m) lonAIRS|AIRS IR longitude|(deg) Longitude|CloudSat Longitude| (degrees) MODIS_cloud_flag| MOD35_bit_2and3_cloud_flag| (None) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) radiances|Radiances|(milliWatts/m**2/cm**-1/steradian) Sigma-Zero| Sigma-Zero| (dB*100) spectral_clear_indicator|Spectral Clear Indicator|(None) Vertical_binsize|CloudSat vertical binsize| (m) End of parameter information

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

    GES_DISC Short Name: AIRS_CPR_MAT Version ID: 3.2 Unique ID: C1236224153-GES_DISC