As part of the ESA Land Cover Climate Change Initiative (CCI) project a new set of Global Land Cover Maps have been produced. These maps are available at 300m spatial resolution for each year between 1992 and 2015.Each pixel value corresponds to the classification of a land cover class defined based on the UN Land Cover Classification System (LCCS). The reliability of the classifications made are documented by the four quality flags (decribed further in the Product User Guide) that accompany these maps. Data are provided in both NetCDF and GeoTiff format.Further Land Cover CCI products, user tools and a product viewer are available at: http://maps.elie.ucl.ac.be/CCI/viewer/index.php . Maps for the 2016-2020 time period have been produced in the context of the Copernicus Climate Change service, and can be downloaded from the Copernicus Climate Data Store (CDS).

The dataset provides a set of Import Emission Factors (IEF) for USEEIO models developed using the CEDA model (Watershed Technology, Inc.) along with example USEEIO models built with them for year 2022 and supporting information . The dataset accompanies the addendum "Import Greenhouse Gas Emission Factors Derived from CEDA 2024" to EPA report "Estimating embodied environmental flows in international imports for the USEEIO Model" (https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=362470). This dataset is analogous to the "USEEIO Models with Import Emission Factors for Greenhouse Gases for 2017-2022 from EXIOBASE coupled model" dataset (https://doi.org/10.23719/1531676), but its uses CEDA instead of EXIOBASE as the coupled model. See the aforementioned addendum for more information. The factors are provided at the BEA summary and detail levels of sector resolution as reflected in file names. The sector codes for the import factor use the BEA 2017 NAICS based schema used in input-output tables which is the schema used by the associated USEEIO models. US_summary_import_factors_ceda_2022_17sch.csv is the summary level IEFs file and USEEIOv2.4-oriole-22.xlsx is the USEEIO model created with them. US_detail_import_factors_ceda_2022_17sch.csv is the detail level IEFs file and USEEIOv2.4-catbird-22.xlsx is the USEEIO model created with them. Various supporting links and files are provided. Concordance files are provided here that are used to map CEDA commodities and countries to those used in USEEIO (and also available online) to create the import emission factors. The models are named according to an updated USEEIO naming scheme. See the supporting code on the USEEIO github site (link in references) for more details. Model specification files for the detail USEEIO model (USEEIOv2.4-catbird-22.yml) and for the summary model (USEEIOv2.4-oriole-22.yml) that are used to create the USEEIO models in useeior are provided. See the model specification and model data formats on the useeior github site (link in references) for more details. This dataset is associated with the following publication: Ingwersen, W.W., J. Namovich, B. Young, and J. Vendries. Estimating embodied environmental flows in international imports for the USEEIO Model. U.S. Environmental Protection Agency, Washington, DC, USA, 2024.

As part of the ESA Land Cover Climate Change Initiative (CCI) project a set of Global Land Cover Maps have been produced. These are available at 300m spatial resolution for three epochs centred on the year 2010 (2008-2012), 2005 (2003-2007) and 2000 (1998-2002), where each epoch covers a 5-year period.

Each pixel value corresponds to the label of a land cover class defined using UN-LCCS classifiers. For each epoch, the land cover map is delivered along with 4 quality flags which document the reliability of the classification. These are described further in the Product User Guides.

Further Land Cover CCI products, user tools and a product viewer are available at: http://maps.elie.ucl.ac.be/CCI/viewer/index.php

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Mapping of environmental variables often relies on map accuracy assessment through cross-validation with the data used for calibrating the underlying mapping model. When the data points are spatially clustered, conventional cross-validation leads to optimistically biased estimates of map accuracy. Several papers have promoted spatial cross-validation as a means to tackle this over-optimism. Many of these papers blame spatial autocorrelation as the cause of the bias and propagate the widespread misconception that spatial proximity of calibration points to validation points invalidates classical statistical validation of maps. In the paper related to these data, we present and evaluate alternative cross-validation approaches for assessing map accuracy from clustered sample data.

The study area is western Europe, constrained in the north at 52° latitude and at -10° and 24° longitude The projection is IGNF:ETRS89LAEA (Lambert azimuthal equal area projection).

Files:

agb.tif = above ground biomass (AGB) map from version 3 of the 2017 CCI-Biomass product (https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8) AGBstack.tif = covariates used for predicting AGB aggArea.tif = coarse grid used for simulation in the model-based methods ocs.tif = soil organic carbon stock (OCS) map (0-30 cm) from Soilgrids (https://www.isric.org/explore/soilgrids) OCSstack.tif = covariates used for predicting OCS strata.xxx = 100 compact geo-strata (ESRI shape) created with the spcosa package; used for generating clustered samples TOTmask.tif = mask of the area covered by the covariates

Details and data sources of the covariates in AGBstack.tif and OCSstack.tif:

Name

Description

Source

Note

ai

Aridity Index

https://chelsa-climate.org/downloads/

    Version 2.1

bio1

Mean annual air temperature [°C]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio5

Mean daily maximum air temperature of the warmest month [°C]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio7

Annual range of air temperature [°C]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio12

Annual precipitation [kg/m2]

    https://chelsa-climate.org/downloads/
    Version 2.1

bio15

Precipitation seasonality [kg/m2]

    https://chelsa-climate.org/downloads/
    Version 2.1

gdd10

Growing degree days heat sum above 10°C

    https://chelsa-climate.org/downloads/
    Version 2.1

clay

Clay content [g/kg] of the 0-5cm layer

https://soilgrids.org/

Only used for AGB

sand

Sand content [g/kg] of the 0-5cm layer

    https://soilgrids.org/
    as above

pH

Acidity (Ph(water)) of the 0-5cm layer

    https://soilgrids.org/
    as above

glc2017

Landcover 2017

https://land.copernicus.eu/global/products/lc, reclassified to: closed forest, open forest, natural non-forest veg., bare & sparse veg. cropland, built-up, water

Categorical variable

dem

Elevation

https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem

cosasp

Cosine of slope aspect

Computed with the terra package from elevation

    Computed @25m resolution; next aggregated to 0.5km

sinasp

Sine of slope aspect

    Computed with the terra package from elevation
    as above

slope

Slope

    Computed with the terra package from elevation
    as above

TPI

Topographic position index

    Computed with the terra package from elevation
    as above

TRI

Terrain ruggedness index

    Computed with the terra package from elevation
    as above

TWI

Topographic wetness index

Computed with SAGA from 500m resolution (aggregated) dem

gedi

Forest height

https://glad.umd.edu/dataset/gedi

Zone: NAFR

xcoord

X coordinate

Using a mask created from the other covariates

ycoord

Y coordinate

    Using a mask created from the other covariates

Dcoast

Distance from coast

Using a land mask created from the other covariates

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This dataset comprises estimates of forest above-ground biomass (AGB) for the years 2007, 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and 2022. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR (Advanced Synthetic Aperture Radar) instrument and JAXA’s (Japan Aerospace Exploration Agency) Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.

This release of the data is version 6. Compared to version 5, version 6 consists of an update of the maps of AGB for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021 and new AGB maps for 2007 and 2022. AGB change maps have been created for consecutive years (e.g., 2020-2019), for a decadal interval (2020-2010) as well as for the interval 2010-2007. The pool of remote sensing data includes multi-temporal observations at L-band for all biomes and for all years and extended ICESat-2 observations to calibrate retrieval models. A cost function that preserves the temporal features as expressed in the remote sensing data has been refined to limit biases between the 2007-2010 and the 2015+ maps.

The data products consist of two (2) global layers that include estimates of: 1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots per unit area 2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)

Additionally provided in this version release are aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km).

In addition, files describing the AGB change between two consecutive years (i.e., 2016-2015, 2017-2016, 2018-2017, 2019-2018, 2020-2019, 2021-2020, 2022-2021), over a decade (2020-2010) and over 2010-2007 are provided. Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.

Data are provided in both netcdf and geotiff format.

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As part of the ESA Land Cover Climate Change Initiative (CCI) project a static map of open water bodies at 150 m spatial resolution at the equator has been produced.

The CCI WB v4.0 is composed of two layers:

1. A static map of open water bodies at 150 m spatial resolution resulting from a compilation and editions of land/water classifications: the Envisat ASAR water bodies indicator, a sub-dataset from the Global Forest Change 2000 - 2012 and the Global Inland Water product.

This product is delivered at 150 m as a stand-alone product but it is consistent with class "Water Bodies" of the annual MRLC (Medium Resolution Land Cover) Maps. The product was resampled to 300 m using an average algorithm. Legend : 1-Land, 2-Water

1. A static map with the distinction between ocean and inland water is now available at 150 m spatial resolution. It is fully consistent with the CCI WB-Map v4.0. Legend: 0-Ocean, 1-Land.

To cite the CCI WB-Map v4.0, please refer to : Lamarche, C.; Santoro, M.; Bontemps, S.; D’Andrimont, R.; Radoux, J.; Giustarini, L.; Brockmann, C.; Wevers, J.; Defourny, P.; Arino, O. Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community. Remote Sens. 2017, 9, 36. https://doi.org/10.3390/rs9010036

This dataset contains a map of ground movements covering the Afar Rift Zone in Ethiopia, Eritrea, and Djibouti for the time period between October 2014 and August 2019. The Afar region is located where three tectonic plates are pulling apart, creating rift segments which are 50-100 km long. Surface deformation on these segments is not constant in time, with episodes of rifting occurring periodically and magma intrusions causing sudden ground movements. We use frequent Sentinel-1 satellite Interferometric Synthetic Aperture Radar (InSAR) observations to measure surface displacements through time across the whole region. We relate these to ground based Global Navigation Satellite Systems (GNSS) observations and combine data from different satellite tracks to produce maps of the average surface velocity in three directions (perpendicular to the rift zone, parallel to the rift zone, and vertical). The continued observation of these time-varying ground movements is important for understanding how continents break up, with data here providing evidence of how tightly focussed extension is around the rift segments and of the subsurface magma movement at several volcanic centres. These data have been provided in geotiff format instead of the original netcdf format.

This dataset contains observational frequency maps of internal waves (IW) within the UK Continental Shelf (UKCS) region. The maps were generated by automatic processing of the ENVISAT Advanced Synthetic Aperture Radar (ASAR) data archive covering the period from 2006 to 2012. The IW frequency maps were combined with bathymetry and mixed layer depth modelling data to estimate the interaction of IWs with the sea bed. The results are presented in monthly, seasonal, annual and climatology maps.

The Global Ocean Surface Temperature Atlas Plus (GOSTAplus) contains maps of Sea Surface Temperature (SST) climatologies and anomalies, Night Marine Air temperature climatologies and anomalies and Sea Ice coverage spanning the period 1851-1995. Dataset includes gridded, global SSTs from 1951-1990 and Sea Ice coverage from 1903 to 1994. The data are provided by the Met Office. Updated version of some data also available on request.

QUEST projects both used and produced an immense variety of global data sets that needed to be shared efficiently between the project teams. These global synthesis data sets are also a key part of QUEST's legacy, providing a powerful way of communicating the results of QUEST among and beyond the UK Earth System research community. This dataset contains a map of a ecosystem. This map depicts the 825 terrestrial ecoregions of the globe. Ecoregions are relatively large units of land contain ing distinct assemblages of natural communities and species, with boundaries that approximate the original extent of natural communities prior to major land-use change. This comprehensive, global map provides a useful framework for conducting biogeographical or macroecological research, for identifying areas of outstanding biodiversity and conse rvation priority, for assessing the representation and gaps in conservation efforts worldwide, and for communicating the global distribution of natural communities on earth.

What does the data show?

The data shows the annual average of precipitation amount (mm) for the 1991-2020 period from HadUK gridded data. It is provided on a 12km British National Grid (BNG).

Limitations of the data

We recommend the use of multiple grid cells or an average of grid cells around a point of interest to help users get a sense of the variability in the area. This will provide a more robust set of values for informing decisions based on the data.

What are the naming conventions and how do I explore the data?

This data contains a field for the average over the 1991-2020 period. It is named 'pr' (precipitation).

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Data source:

·
Version: HadUK-Grid v1.1.0.0 (downloaded 21/06/2022)

·
Source: https://catalogue.ceda.ac.uk/uuid/652cea3b8b4446f7bff73be0ce99ba0f

·
Filename: rainfall_hadukgrid_uk_12km_ann-30y_199101-202012.nc

Useful links

·
Further information on HadUK-Grid

·
Further information on understanding climate data within the Met Office Climate Data Portal

This dataset contains high resolution (HR) land cover (LC) and land cover change (LCC) maps of a subregion of Siberia, produced by the ESA High Resolution Land Cover (HRLC) Climate Change Initiative (CCI) project. It consists of the following products:

1) HRLC30: High Resolution Land Cover Maps at 30m spatial resolution for years 1990, 1995, 2000, 2005, 2010, 2015, 2019.

2) HRLCC30: High Resolution Land Cover Change Maps at 30m spatial resolution of yearly changes. A map every 5 years (1990-1995, 1995-2000, 2000-2005, 2005-2010, 2010-2015,2015-2019) is provided which reports (high priority) changed pixels and their year within the 5-years temporal interval.

3) Associated uncertainty products

They cover the geographic range (59.4°N – 73.9°N, 64.8°E – 87.4°E).

The data are provided as both GeoTIFF tiles following the Sentinel 2 MGRS tiling scheme and as a GeoTiff format mosaic. These maps are also referred to as historical maps.

The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The Sentinel-3 SYN Fire_cci v1.1 pixel product is distributed as 6 continental tiles and is based upon surface reflectance data from the OLCI and SLSTR instruments (combined as the Synergy (SYN) product) onboard the Sentinel-3 A&B satellites. This information is complemented by VIIRS thermal information. This product, called FireCCIS311 for short, is available for the years 2019 to 2022.The FireCCIS311 Pixel product described here includes maps at 0.002777-degree (approx. 300m) resolution. Burned area (BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Copernicus Climate Change Service (C3S) Land Cover v2.1.1 product. An unpacked version of the data is also available. For further information on the product and its format see the Product User Guide in the linked documentation.

This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2016-2017, derived from Sentinel-1 SAR data acquired from 23/12/2016 to 27/02/2017, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.

In total approximately 1800 S-1A & S-1B scenes are used to derive the surface velocity applying feature tracking techniques. The ice velocity map is provided at 500m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity is provided in true meters per day, towards EASTING(vx) and NORTHING(vy) direction of the grid, and the vertical displacement (vz), derived from a digital elevation model is also provided. The product was generated by ENVEO (Earth Observation Information Technology GmbH).

This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2015-2016, derived from Sentinel-1 SAR data, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.

The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that previous versions of this product provided the horizontal velocities as true East and North velocities.

This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.

Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the masking effect of the forest canopy. The SCFG is given in percentage (%) per pixel.

The global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included.

The SCFG time series provides daily products for the period 2000 – 2022.

The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite.

The retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO (ENVironmental Earth Observation IT GmbH). For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The Snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied.

The main differences of the snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable background reflectance and forest reflectance maps instead of global constant values for snow free land and forest, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the global forest canopy transmissivity based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) to assure in forested areas consistency of the SCFG and the SCFV CRDP v3.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/e955813b0e1a4eb7af971f923010b4a3) using the same retrieval approach.

Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on a manual delineation from MODIS data. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.

Compared to the SCFG CRDP v2.0 (https://catalogue.ceda.ac.uk/uuid/8847a05eeda646a29da58b42bdf2a87c/), the following improvements were applied for the generation of the SCFG CRDP v3.0: 1) the pre-classification module to identify snow free areas has been relaxed to consider more pixels for the SCFG retrieval; 2) the SCFG retrieval has been improved adapting the spectral reflectance value for wet snow; 3) the uncertainty estimation of the SCFG has been updated to account for the changes in the retrieval algorithm; 4) salt lakes retrieved by manual delineation from Terra MODIS data are masked in the SCFG CRDP v3.0 and a new class for salt lakes is added in the coding; 5) the time series, starting in February 2000, was extended from December 2020 to December 2022; 6) two additional layers are provided for each daily product: • the sensor zenith angle in degree per pixel; the image acquisition time per pixel referring to the scanline time of the MODIS granule used for the classification of the pixel.

The SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology. ENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.

There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and ... For full abstract see: https://catalogue.ceda.ac.uk/uuid/80567d38de3f4b038ee6e6e53ed1af8a.

The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Dataset (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from VIIRS VNP14IMGML active fire products.

This dataset is part of v2.0 of the Small Fire Dataset (also known as FireCCISFD11), which covers Sub-Saharan Africa for the year 2019. Data is available here at pixel resolution (0.00017966259 degrees, corresponding to approximately 20m at the Equator). Gridded data products are also available in a separate dataset.