Compendium of Global Gridded Environmental Data Sets

This is an open compendium of global (gridded) environmental datasets (bio-geophysical variables). Here you can find systematic listings of published data sets (a selection of the cutting-edge datasets we plan to upload to OpenLandMap.org) with emphasis on publicly available data sets available under open data license. These reviews are periodically maintained by the www.OpenLandMap.org development team and collaborators. They are only meant to serve as general inventories of what is available, and NOT as an in-depth review article.

Earth Observation programmes such as the NASA’s Terra and Landsat, ESA’s Copernicus, JAXA’s ALOS and similar have been the key drivers of publicly available global environmental data and monitoring systems, mainly thanks to the open data policy introduced since 2010’s (Wulder et al. 2012). Image source: https://terra.nasa.gov/data.

To contribute to this portal, consider submitting new data by editing this Markdown document. Please check also the working version of the OEMC global Land Use Land Cover Taxonomy Tables.

If your work / products are cited here, you are for us a champion of open environmental data and we are enormously thankful for your contributions (please never stop!).

Minimum conditions to include your contribution to this compendium:

  • Please submit / register only global data sets (at least 80% complete; at least 5 continents);
  • The data sets should be peer-reviewed or at least come with fully documented processing steps / metadata and/or an official technical report;
  • Refer to computational notebooks or materials that explain how was the data produced: follow as much as possible the open science principles;
  • The data set should have one of the compatible open data licenses or at least allow for the derivative works to be released as open data;
  • The data set should be current i.e. still in use and the citation / URL should mention the most recent update;
  • Mention of the data should focus on practical things such as “where I can read about how was this data set produced?”, “where can I download this data?”, “what are its technical characteristics such as spatial resolution, temporal coverage, biophysical variables and measurement units?”;

Here are the top 5 technical aspects of the data that should be clear to reader without confusion:

  1. What is the DOI of the data set i.e. what is the permanent URL from which it can be accessed and downloaded?
  2. What is the spatial resolution / effective scale of the data set? We recommend to avoid using abstract metrics such as “3-arcseconds” or similar but use instead round metrics e.g. “90-m spatial resolution”.
  3. Which referent time-period does the data set covers / what is the time-span of the training or input data?
  4. What is the #1 technical documentation for this data set (ideally a peer-review publication DOI or similar)?
  5. For which purpose can/should this data set be used and what is its significance?

Examples of contributions that will not be welcomed:

  • Registering and submitting regional or local data sets i.e. covering only continents or countries;
  • Mentioning, listing and/or self-promoting of proprietary / commercial data sets will not be accepted;
  • Rating or criticizing data sets or expressing personal opinions;
  • Deleting text submitted by others or inserting code that is computational or difficult to maintain;

You can however mention some data sets that are not open if they are without any open data / open source alternative.

Thematic data groups:

All layers are organized around the following themes (based on the UN-GGIM The Global Fundamental Geospatial Data Themes):

  • Buildings and Settlements: includes administrative and socio-economic data, natural hazards and similar,
  • Elevation and Depth: includes Digital Terrain Models and DEM-derived parameters, LiDAR point clouds, canopy heights, hydrological data derived from DTMs and similar,
  • Geology and Soils: includes geological, surface lithology and soil property and class maps,
  • Land Cover and Land Use: includes land cover / land use classes, land cover change and drivers of land use / land cover change,
  • Population Distribution: includes population density and population variables, urbanization and lights at night images,
  • Water: includes surface and underground water resources and similar,
  • Physical Infrastructure: includes road and rail networks, dams, industrial facilities and similar,
  • Climate: includes long-term climatic images, climatic time-series data, future climate predictions and meteorological images,
  • Biodiversity and Nature Conservation: includes wildlife resources, protected areas, natural vegetation and biomes, ecoregions and biodiversity maps,

This list is not universal and might need to be updated and improved. For comparison, NASA’s EarthData portal distinguishes the following global data themes:

  • Atmosphere;
  • Biosphere;
  • Cryosphere;
  • Human Dimensions;
  • Land Surface;
  • Ocean;
  • Solid Earth;
  • Sun-Earth Interactions;
  • Terrestrial Hydrosphere;

Other repositories of global (open) environmental layers

Sorted alphabetically:

LP DAAC data catalog is an example of continuosly updated and complete catalog of global environmental data (primarily based on MODIS and similar EO missions) which are available publicly without restrictions.

Make your data more accessible more usable

If you are making open global data aiming at enabling others to do more advanced analysis & eventually help with better management of natural resources, what are the key steps to improve usability of your data? Within the Open-Earth-Monitor project we specifically looked at hundreds of cases of data sets that have a high potential but are somewhat under-used or not used at all because of poor documentation, being incomplete or requiring specialized knowledge. This is what we believe is especially important to help boost usability and accessibility of your global geospatial data:

  • Aim at making your data 4C-ARCO: complete, consistent, current and correct, then on top of it Analysis-ready and Cloud Optimized (ARCO);
  • Use (open) cloud-native data formats to distribute data (especially for large data) either COG’s, GeoZarr, XArray and/or Geoparquet / Flatgeobuf and similar for tabular / vector data;
  • Optimize compression of the data so that data download is as fast as possible; for example, compression is by default already included in the Cloud-Optimized GeoTIFF data format;
  • Use SpatioTemporal Asset Catalogs (STAC) or similar to register data and the access points (usually also requires that you publish your data via a Simple Storage Service (S3));
  • Best technical documentation for the data is (A) a peer-review publication, and (B) a computational notebook providing a demo of the method used and key results; a peer-review publication increases confidence for non-experts; computational notebooks help increase quality as people then try to extend your work, then discover and report problems and help you make revisions;
  • Always register all versions of your data so that anyone can track fixes and improvements; for each version try to obtain DOI, so that it is also kept for ever;
  • Try to register your data in some higher level aggregator such as STAC index (or this compendium!);
  • Test if your data can be easily used in most-popular GIS software such as QGIS: does it have the right legend? can users quickly find metadata, explanation of units, how was the map produced?
  • Provide support and regularly update your data; like with software no data is bug-free, by updating and improving your data your user community learns to appreciate your efforts and builds higher trust;

Of course, not every version or testing of your data / mapping skills needs to be registered with unique DOI. Also, writing metadata & computational notebooks requires time and resource investments, so you need to plan carefully your time.

Here are also some world-leading providers of tools for making geospatial data more usable and more FAIR (Findable, Accessible, Interoperable, and Reusable) (unsorted):

Buildings and Settlements

Administrative and socio economic data

Administrative data can be used to calculate proximity-based parameters and to orient the users geographically. One publicly accessible global administrative data database is the GADM database of Global Administrative Areas. It comprises borders of countries and lower level subdivisions such as provinces and counties (more than 100,000 areas). Lower level administrative boundaries can be obtained via the FAO’s GeoNetwork server. Even more detailed is the FAO GAUL: Global Administrative Unit Layers which is available for different periods and up to the 3rd admin level, so one can potentially also track changes in political units (the table GAUL_g2015_2014_1_legend.csv contains an example of cca 3500 administrative units with codes). The geoBoundaries is a global Database of Political Administrative Boundaries and contains a snapshot of political and administrative boundaries since 2017.

An important global vector dataset is the World Vector Shoreline data set at scale 1:250,000 (Carlotto, Nebrich, and DeMichele 2017). This can be, for example, used to derive the global distance from the sea coast line map and similar.

The Overture Maps foundation provides up-to-date (current) global vectors on Layers of interest include: admins, base, buildings, places etc. You can simply download the data for are of interest, then convert to PMTiles or similar, then add to your back-end/front-end as a clickable layer.

The World Settlement Footprint (WSF) is a series of datasets that provide comprehensive, high-resolution mapping of human settlements across the globe. WSF is a 10-m resolution binary mask outlining the extent of human settlements globally derived from 2014–2015 multitemporal Landsat-8 and Sentinel-1 imagery (WFS2015) and 2019 multitemporal Sentinel-1 and Sentinel-2 imagery (WFS 2019). WSF Evolution is a 30-m resolution dataset outlining the global settlement extent on a yearly basis from 1985 to 2015. Q. Zhang et al. (2015) produced Normalized Difference Urban Index (NDUI) global at 30-m resolution product which is available for download covering 2000 to 2012 from the AWS open data registry (Singh et al. 2023).

The Socioeconomic Data and Applications Center (SEDAC) a Data Center in NASA’s Earth Observing System Data and Information System (EOSDIS) Hosted by CIESIN at Columbia University has produced a number of open global socio-economic data sets including on themes such as: Agriculture, Climate, Conservation, Governance, Hazards, Health, Infrastructure, Land Use, Marine and Coastal, Population, Poverty, Sustainability, Urban and Water.

Elevation and Depth

Digital Terrain Models

Global Shuttle Radar Topography Mission (SRTM) Digital Elevation Model is on of the most well-known global environmental dataset (Rabus et al. 2003) available with a resolution of approximately 30-m (1 arcsec). The most recent version of the SRTM DEM is the NASADEM. In 2018 two new global elevation models were produced:

Both ALOS AW3D and GLO-30 are the new generation surface models. Both need removal of canopy and buildings before they can be used as terrain models i.e. for surface water runoff and similar. Yamazaki et al. (2019) has produced MERIT DEM and MERIT Hydro, actual DTM post-filtered and hydrologically correct DTM. Both are available for download under open data license but only at 90/100-m resolution. Using the MERIT DEM, Amatulli et al. (2020) have produced Geomorpho90m and Hydrography90m (Amatulli et al. 2022) data sets.

A complete land surface model ETOPO1 Global Relief Model (includes bathymetry data) is available at resolution of 1-km and can be obtained from the NOAA’s National Geophysical Data Center (Amante and Eakins 2009). An updated version of the ETOPO is the Global Land One-km Base Elevation Project (GLOBE) DEM. Global bathymetry data (GEBCO data set) can also obtained from the British Oceanographic Data Centre.

OpenTopography.org maintains a catalog of global elevation data sets including national data sets. A combination (Ensemble-DTM) of GLO-30, AW3D30, MERIT DEM and number of continental / national DTM’s is available via OpenLandMap.org.

Iwahashi and Pike (2007) produced a global landform type map of the world at 250-m. Geomorphological forms (Geomorphon classification system) at 100-m are also available for download via the OpenTopography.org repository and/or Zenodo. Charlie Frye and Richards (2023) produced Named Landforms of the World (NLW2) with most of data at 250-m resolution.

Canopy height data

There are several global reference point data set and products that represent canopy height (often including heights of building / human-built structures). Currently the two global public reference data sets / missions for monitoring canopy height and morphological ecosystem parameters are:

You can visually explore and download the ICESat data from https://www.openaltimetry.org. GEDI and ICESat can be used to derive number of ecosystem parameters including biomass density (above ground), canopy height, terrain height, LAI, canopy structure etc. The complete global gridded canopy height / biomass density products include:

TanDEM-X 30-m DEM Change Maps (DCM) shows significant changes in elevation (usually canopy height) for the period 2016 to 2022. DLR warns that users must be aware that a given elevation change measured in the DEM change maps corresponds to a topographic change with respect to TanDEM-X 30m EDEM, i.e. this is only an estimate of physical height change.

Hydrological / hydrographic data

World river and stream networks (vector files) are available from the Global River Classification (GLORIC) DB. A rasterized version of this data set is available at resolution of 250-m. MERIT Hydro also provides Upstream Drainage Area at 100-m global and should be considered a reference at this scale (Yamazaki et al. 2019). Amatulli et al. (2022) describes Hydrography90m (global hydrographic dataset) although this is not available as open data (authors used the NC-license).

Global reservoirs (GRanD) (polygons) dataset can be obtained from the Global Dam Watch website. Connected to GRanD, Global reservoir bathymetry estimate (ReGeom) dataset is available from zenodo. Global streamflow time series dataset i.e. the GRDC’s (Global Runoff Data Centre) streamflow observations (cca 11,000 stations) can be downloaded from the GRDC data portal.

Lehner and Grill (2013) provide a global river hydrography and river network routing data set (HydroBASINS and HydroSHEDS) at 500-m spatial resolution. Lane et al. (2023) produced a global 90-m resolution map of Non-Floodplain Wetland (Global NFW). Global NFW is based on multiple input data sources including MERIT Hydro, HydroBASINS and similar. Verkaik et al. (2024) produced a global map of steady-state water table depth and water table amplitude at 1-km spatial resolution using the GLOBGM (Groundwater Model at 30 arcsecond spatial resolution).

Several data set exists that quantify global surface water extent and type, wetlands distribution and fresh-water resources in general:

DLR’s (MODIS-based) Global WaterPack (GWP) monthly and yearly product (different water coverage categories) at 250-m spatial resolution is available for the period 2003 to 2022+.

NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) serves data on ocean winds, sea surface temperature, ocean surface topography, sea surface salinity, surface water and similar.

Geology and Soils

Geology and lithology

The current global geological map approved by the Commission for the Geological Map of the World is the 1:25M Geological Map of the World (maps not available publicly). The world geological maps are now being integrated via the OneGeology project which aims at producing a consistent Geological map of the world in approximate scale 1:1M (Jackson 2010); progress can be followed via the interactive OneGeology portal.

USGS has several data portals, e.g. that allow browsing of the International Surface Geology (split into South Asia, South America, Iran, Gulf of Mexico, Former Soviet Union, Europe, Carribean, Bangladesh, Asia Pacific, Artic, Arabian Peninsula, Africa and Afganistan). Hartmann and Moosdorf (2012) have assembled a global, purely lithological database called GLiM (Global Lithological Map). GLiM consists of over 1.25 million digital polygons with classified in three levels (a total of 42 rock-type classes).

USGS, jointly with ESRI, have released in 2014 a Global Ecological Land Units map at 250-m resolution. This also includes a world layer of rock types. The data can be downloaded from the USGS site or via Zenodo.

Fan, Li, and Miguez-Macho (2013) produced global maps of Groundwater Table Depth that has been systematically updated in 2022. The modeled monthly water table depth can be downloaded for each continent. Cuthbert et al. (2019) produced water table ratio and groundwater response times maps at 1-km resolution (available from figshare). The International Groundwater Resources Assessment Centre (IGRAC) maintains the Global Groundwater Information System (GGIS). IGRAC is aiming at producing global groundwater datasets.

Tang and Werner (2023) made a global inventory (polygon map) of mining areas (global mine area coverage): The polygons cover about about 66,000 km-square of features like waste rock dumps, pits, water ponds, tailings dams, heap leach pads and processing/milling infrastructure. The data is available from zenodo. An update of the global mining areas (three layers: mining polygons, mining centroids, mining area per grid cell) is provided by Maus et al. (2022); the data set is available via PANGAEA; also available via https://fineprint.global/viewer.

Earthquakes / natural hazards

Natural hazards include:

  • Earthquakes,
  • Volcanoes,
  • Landslides,
  • Famines & Droughts,
  • Hurricanes, Tornados, and Cyclones,
  • Extreme precipitation and flooding,
  • Extreme Temperature (Heat & Cold),
  • Wildfires,

A number of institutions have jointly produced a Global Seismic Hazard map (Shedlock et al. 2000). This map, although slightly outdated and of limited detail, can be obtained directly from the GSHAP project webite. From the NOAA’s National Geophysical Data Center one can obtain a point map with all major earth quakes (Significant Earthquake Database; cca 5000 quakes), and generate a (kernel density) map for Earthquake magnitude.

University of Hawaii maintains a Global Hazards Information Network, which contains a number of global layers including a map of Global Airports, locations of significant earthquakes and earthquake zones.

Copernicus Global Land Service distributes a global assessment of Burnt Area at 300-m spatial resolution, but available only from 2019 onwards. NASA’s FIRMS (Fire Information for Resource Management System) provides real-time access to occurrence of fires including access to the archive data. The Global Fire Emissions Database provides maps of individual fire size, duration, speed, and direction at relatively coarse spatial resolutions of (10–25 km), but ranging from 1979 to present times (Andela et al. 2019). NASA’s Global Hydrometeorology Resource Center Distributed Active Archive Center (GHRC DAAC) serves global data on lightning, tropical cyclones, and storm-induced hazards through integrated collections of satellite, airborne, and in-situ data sets.

NOAA’s Global Drought Information System (GDIS) serves global estimates of drought conditions. Maps are usually only available at coarse resolution of 25, 10 or 5-km or similar.

Soils

Soil maps (physical, chemical, biological soil properties and soil classes) are especially important for spatial prediction of distribution of vegetation and (plant) species distribution. FAO, IIASA, ISRIC, ISSCAS, JRC have produced in 2007 a 1-km resolution gridded soil-class map, produced by merging various national soil maps. This product is also known as the Harmonized World Soil Database. Global predicted WRB soil type maps at 1-km can be download as geotifs from here. USGS / USDA have produced a map of Global Soil Regions map at resolution of 60 arcsec, and which is based on the FAO-UNESCO soil map. Melton et al. (2022) produced a global map of peatland (Peat-ML) i.e. organic soils at 10-km spatial resolution.

A list of seven gridded soil property maps (at resolution of 5 arc-minutes i.e. about 10-km) — soil-carbon density, total nitrogen density, field capacity, wilting point, profile available water capacity, thermal capacity, and bulk density — is available via the International Geosphere-Biosphere Program Data and Information System (IGBP-DIS) data set. Some additional soil property maps such as pH and soil moisture, can be also obtained from the Atlas of Biosphere project. Global Soil Dataset for Earth System Model (GSDE) contains the largest number of soil properties (gridded) at 1-km spatial resolution (Shangguan et al. 2014).

FAO’s Global Soil Partnership has produced a Global Soil Organic Carbon Map (GSOCmap) at 1-km resolution. ISRIC — World Soil Information maintains a global soil profile database (WoSIS) with over 100,000 profiles and over 50 analytical and descriptive parameters (Batjes, Ribeiro, and Van Oostrum 2020). Predictions of soil properties (GeoTIFFs) at spatial resolution of 250-m or better are available from multiple sources including:

Crowther’s lab distributes a number of soil-biology global data sets (1-km) including maps of soil nematodes density, bacterial biomass, fungal biomass and similar.

Some soil properties are often directly estimated from the EO data. For example the soil moisture GLASS SM data set and the gap-free global daily surface soil moisture at 1-km grid resolution. Bare soil surface fraction and bare soil spectral bands can be also derived directly from EO data e.g.  global composites produced Demattê et al. (2020); data available via GEE.

Lembrechts et al. (2022) produced maps of Global Soil Bioclimatic variables (including soil temperatures) which are available at 1-km spatial resolution for depth intervals 0–5 and 5–15 cm (training data used to produce these maps is also available). Pelletier et al. (2016) produced a Gridded Thickness of Soil, Regolith, and Sedimentary Deposit Layers. Xu et al. (2024) produced estimates of global NH3 emissions (atmospheric ammonia) from croplands for 2018; data is available at 5-arcmins (about 10-km) from Zenodo.

An in-depth review of the global soil data sets is available in Dai et al. (2019). Status of the soil information in the world can be also followed via David G. Rossiter’s compendium of On-Line Soil Survey Information.

Land Cover and Land Use

Land cover

Land cover maps show distribution of above-surface cover in general categories and are used primarily for spatial planning and modeling. Ground truth observations of land cover can be obtained from multiple sources e.g. the geo-wiki.org project (Fritz et al. 2017), FROM-GLC (Gong et al. 2019); the most comprehensive recent global training data sets for land cover mapping to date (cca 2M training points covering from years 1984 to 2020) is provided by the Global Land Cover Estimation (GLanCE) project project (Stanimirova et al. 2023). Shi et al. (2023) have produced Globe230k: a benchmark dataset for Global Land Cover Mapping which includes cca 230,000 annotated images with a size of 512 × 512 and a spatial resolution of 1-m.

Land cover maps are commonly derived using semi-automated methods and remote sensing images as the main inputs. Global land cover / land cover change maps are today primarily derived from the Sentinel-2 and Landsat imagery (Wulder et al. 2022). For example, the GlobalForestWatch.org imagery showing deforestation/reforestation were derived from the 30 m resolution Landsat images (Hansen et al. 2013). The global Analysis-Ready Landsat mosaics (Landsat-ARD) are available for download as harmonized scenes from the University of Maryland GLAD (Global Land and Discovery group) (Potapov et al. 2020), but these datasets are significant in size and hence require significant processing facilities.

Current publicly available global land cover and/or land use maps include (L. Liu et al. 2021):

An in-depth review of time-series of global land cover / land use maps is also provided by Mora et al. (2014) and Garcı́a-Álvarez et al. (2022). Garcı́a-Álvarez et al. (2022) also provides all legends for all global land cover products.

HYDE (History database of the Global Environment) contains historic (estimated) maps (10-km resolution) of main land use categories up to pre-historic times 10,000 BCE to CE 2020 (Klein Goldewijk et al. 2017). HYDE includes irrigated areas, rice, intensive pasture, extensive rangelands and similar. Data can be downloaded from the University of Utrecht Copernicus Land Change Lab.

A detailed Water mask of the world is available also from the Global Surface Water Explorer hosted by European Commission JRC (Pekel et al. 2016). A Landsat-based water dynamics assessment (annual maps) at 30-m is also provided by Pickens et al. (2020). Global WaterPack (GWP) monthly and yearly product comprises different water coverage categories at 250-m spatial resolution.

Due to the availability of the Sentinel-2 10-m resolution data, several land cover products are now available at very high resolution (but then only covering recent 3–5 years). These include:

Several initiatives aim at integrating multiple land cover products (Herold et al. 2016) and/or running land cover classification by fusing multisource EO data (Xiao-Peng Song and Townshend 2017; L. Liu et al. 2021).

ESA’s WorldCereal provides access to high resolution predictions (10-m) of winter and spring cereals, maize, active cropland, irrigation and similar for year 2021 (Van Tricht et al. 2023).

Biophysical indices

There are number of biophysical indices including vegetation indices that can be derivd from optical EO images such as MODIS, Landsat and/or Sentinel. This includes (note: formulas for biophysical indices for MODIS, Landsat etc might differ):

8-day, monthly and annual biophysical indiced such as EVI and Gross and Net Primary Production (GPP and NPP) from MODIS (250-m) are available via the MOD13Q1 and MOD17A2H products. These, however, often come with many missing pixels due to clouds and artifacts and require significant post-processing to reach a complete consistent structured product. Global GPP at 300-m spatial resolution is also available based on the Sentinel-3/OLCI, but covering only recent years (>2023).

H. Ma et al. (2022) produced global consistent 8-day Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product at 250-m spatial resolution (available for download from Zenodo). Xiong et al. (2023) produced an improved global consistent and complete 8-day 250-m NDVI and EVI products covering 2000 to 2021 (data is available for download from Figshare). Global LAnd Surface Satellite (GLASS) serves a number of post-processed products including FAPAR and Leaf Area Index (LAI) at 250-m and also covering period 2000 to 2021. Based on the H. Ma et al. (2022), Hackländer et al. (2024) produced an aggregated complete & consistent monthly FAPAR product which also includes P25, P50 and P75 quantiles.

Global cloud-free Landsat composites (Red, NIR, SWIR1, SWIR2) for the world for multiple periods (2000, 2014, 2018, 2022) can be obtained from GLAD Global Forest Change.

Forest resources

FAO periodically (every 5 years) organizes the so called Forest Resources Assessment (FRA) — an international compilation of forest resource assessment (forest maps, health and vitality status, forest functions and policies connected with forest management). This assessment typically results in a comprehensive report that includes both graphical and tabular data; gridded global FRA maps are typically not available.

The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data product at 30-m resolution. Japan Aerospace Exploration Agency (JAXA) has also released in 2014 a global 25-m resolution PALSAR mosaic and forest/non-forest map (2007–2010 and 2015–2021). This data is freely available for download via the JAXA pages. Copernicus Global Land Cover and Tropical Forest Mapping and Monitoring service (LCFM) serves 10-m resolution sub-annual maps of tropical forest status.

Detail maps of mangrove forests cover, habitat type and properties (Bunting et al. 2022), the key products of the https://www.globalmangrovewatch.org/ project can be obtained from zenodo. Vancutsem et al. (2021) produced long-term (1990–2019) maps of forest cover changes in the humid tropics (Tropical moist forests TMF) at 30-m spatial resolution. The data is available for download via the project homepage.

Forest resources and canopy height have been mapped by the GLAD group from the Maryland university (Potapov et al. 2021) The data is publicly available under open data license from: https://glad.umd.edu/dataset. The WRI’s Global Forest Watch service can be used to access annual tree cover maps for 2000–2022+, forest cover change and canopy height change (Hansen et al. 2013). These global forest change data is openly available and can be downloaded including the Landsat cloud-free mosaics for the whole world at 30-m resolution. Lesiv et al. (2022) produced a Global forest management data for 2015 at 100-m spatial resolution which is available from Zenodo.

Note that tree cover loss can be permanent (e.g. change in land use, permanent deforestation, or natural transition i.e. ecological succession) or can be temporary i.e. effect of fires, floods, pests and similar), therefore it is important to consider any forest inventory data within time-frame, previous state and considering succession rates.

Land use change

Land use change often comes with a special legend and should not be confused with land cover mapping. Some typical legend entries for land use change includes:

  • Deforestation,
  • Reforestation,
  • Urbanization,
  • Loss of wetlands,
  • Wetland degradation,
  • Water reduction (surface water change),
  • Water expansion (surface water change),
  • Desertification,
  • Crop expansion,
  • Pastureland expansion,
  • Land abandonment,

Annual dynamics of global land cover and its long-term changes from 1982 to 2015 at 5-km resolution is part of the Global Land Surface Characterization Parameters (GLASS) product (H. Liu et al. 2020); data is available for download from Pangea. GLAD Global Land Cover and Land Use Change 2000–2020 (GLCLUC2020) provides estimate of the land cover for the last 20+ years at 30-m resolution quantifies changes in forest extent and height, cropland, built-up lands, surface water, and perennial snow and ice extent (Potapov et al. 2022). This data set can be downloaded (per tile) via the GLAD’s GEE repository. WRI’s Global Forest Watch provides high resolution
forest cover change at 30-m resolution. TanDEM-X 30-m DEM Change Maps (DCM) shows significant changes in elevation (usually canopy height) for the period 2016 to 2022.

Population Distribution

Population density maps

For global modeling one of the most important global socio-economic data layers are the population density maps and attached socio-economic variables. The Socioeconomic Data and Applications Center (SEDAC) distributes the Global Population Density Grid Time Series Estimates (1970–2000), Global Rural-Urban Mapping Project data set, Version 1 (GRUMPv1) and Gridded Population of the World, Version 4 (GPWv4) all at resolution of up to 1-km. These are the currently most detailed gridded time-series dataset with consistent population density and structure; for modeling future, a Global 1-km Downscaled Population Base Year and Projection Grids Based on the SSPs (1990–2100) (J. Gao 2020) is also available. It consists of global urban, rural, and total population for the base year 2000, population projections at ten-year intervals for 2010–2100 at a resolution of 1-km, and was developed for the purpose of the IPCC climate modeling framework.

Joint Research Centre (JRC) of the European Commission has produced a global 100-m spatial resolution product called Global Human Settlement (GHS) population grid multitemporal (1975-2030) layer (Schiavina et al. 2022). This can be considered the most detailed global population density product to date and is also available via OpenLandMap.org.

Facebook (Meta) through its not-for-profit project “Data for Good” is building a series of cutting-edge population data sets including the population density maps at 30-m (also includes age structure and gender). These are usually based on a combination of legacy population data from censuses and EO products / inventory of buildings etc that are then used to downscale the values to high spatial resolution (Tiecke et al. 2017). The 30-m resolution population density maps for 160+ countries and territories around the world are available for download from https://portal.mwater.co/.

Lights at night images

The lights at night map contains the lights from cities, towns, and other sites with persistent lighting, including gas flares. Images of lights at night have shown to be highly correlated with industrial activity and Gross Domestic Product (Doll, Muller, and Morley 2006). A time-series of (1-km resolution) annual global night light images (1992–2013) is available via the NOAA’s National Geophysical Data Center the Version 4 DMSP-OLS Nighttime Lights Time Series. The harmonized nighttime light (NTL) time-series composites for 1992–2020 produced by Zhao et al. (2022) are available at 1-km resolution.

Currently the most detailed global Lights at night images are the global VIIRS nighttime lights (Annual VNL V2) covering 2012–2020 at 500-m spatial resolution (Elvidge et al. 2021). The Annual VNL V2 contains average, average-masked, mean, minimum and maximum values of nighttime lights. The lights at night images on OpenLandMap.org are based on time-extrapolated values so that they cover 2000–2022 and are hence compatible with other global land products.

Water resources

Global Surface Water dynamics

Global surface water dynamics can be followed from three global data sets:

A MODIS-based water mask (MOD44WV) product at somewhat coarser resolution (250-m) can be also used to represent water dynamics for the last 25 years.

World oceans

World Ocean Database (world’s largest collection of uniformly formatted, quality controlled, publicly available ocean profile data) is distributed by NOAA. The data set covers 1990 to 2021 period. MODIS distributes a number of global products for global oceans including sea surface temperature (SST) at 4-km resolution (daily, 8-day, monthly or annual) and similar (SST images are available for download from https://opendata.oraclecloud.com/).

The Global Fishing Watch (founded by Oceana, SkyTruth and Google) hosts a Global data set of industrial activities at sea. This includes 53 billion vessel GPS positions and results of classification of over 67 million image tiles (SAR, Sentinel-1, Sentinel-2) (Paolo et al. 2024). The GFW is currently maintaining the Open Ocean Project that aims at providing a total inventory of Industrial fishing vessels, non-fishing vessels, fixed infrastructure and small-scale fishing boats all as open data.

Haigh et al. (2023) describes the GESLA (Global Extreme Sea Level Analysis) that contains high-frequency sea-level information from tide gauge stations distributed worldwide. Global maps of the sea level change are also available for download from the University of Hawaii Sea level center.

Physical Infrastructure

Urbanization / human-induced changes

Quantification of urbanization and human impact is usually modeled by using population density maps, lights at night images and/or data on buildings. Partners in the GLOBIO consortium created a World Map of Human Impacts on the Biosphere for various time periods. This is basically a map showing a current status of the roads, railways and settlement density. Human impact maps can be also browsed via the UNEP Grid Arendal. Daniel J. Weiss et al. (2018) and D. J. Weiss et al. (2020) have produced a global map of accessibility and optimal travel time to healthcare.

The International Biosphere-Geosphere Programme, the Stockholm Environment Institute, the Stockholm Resilience Center, the CSIRO in Australia and the International Human Dimensions Programme on Global Environmental Change are producing an outreach project on the Anthropocene and planetary boundaries named “Globaia”.

Theobald et al. (2020) provides a global time-series data set representing Global human modification for 1990, 2000, 2010, 2015, and 2017 and at 300-m spatial resolution. The Wildlife Conservation Society is hosting Human Impact Index (HII) and Human Footprint maps that can be directly downloaded (Sanderson et al. 2022).

The Copernicus Sentinel-5P has produced number of monthly emission products at 2-km that can be used to quantify and monitor (industrial, traffic) emissions of NOx, CO/methane and SO2. The gap-filled monthly values of NOx and methane emissions are available in OpenLandMap.org.

A comprehensive global assessment of the human impacts to marine ecosystems can be followed via the work of the National Center for Ecological Analysis and Synthesis in Santa Barbara. This group have produced a Global Map of Human Impacts to Marine Ecosystems by using a number of connected input GIS layers (Halpern et al. 2008), which are available for download. Distribution of global airports and flight routes can be freely accessed from the openflights.org. Chris Harrison produced the world map of internet connectivity and traffic.

Climate TRACE provides an inventory of major source of greenhouse gas (GHG) emissions around the world and provides independently produced estimates of how much each emits. These data is available publicly, but requires pre-processing as it is a combination of point and polygon sources.

Intact forest landscapes

An important global forest landscape coverage / wildlife datasets is the world map of intact forest landscapes (IFL) (areas hardly touched by mankind) at scale 1:1,000,000 (includes four classes of intact forests: 1. intact closed forests; 2. intact open forests, 3. woodlands and savannas, closed forests; and 4. open forests, woodlands and savannas). IFL is maintained by the Greenpeace organization (Potapov et al. 2008) and is available for years 2000, 2013, 2016, and 2020. Another similar data set is the World Wilderness Areas and which is distributed via the UNEP GEO Data Portal (McCloskey and Spalding 1989).

The Global Generalized Original and Current Forest Cover dataset (V 3.0; polygon map) produced by UNEP-WCMC shows where were forest in past (assumed) and can be used to quantify deforestation at global scale.

Climate

Climatic datasets

Copernicus makes a difference between three groups of climatic data sets:

  • Station / point data sets;
  • Gridded data sets derived from modeling / reanalysis (current and past climate);
  • Projected gridded predictions / seasonal forecasting of future climate;

Key climatic variables of interest are usually:

  • minimum temperature (°C),
  • maximum temperature (°C),
  • average temperature (°C),
  • precipitation (mm),
  • solar radiation (kJ m-2 day-1),
  • wind speed (m s-1),
  • water vapor (cm liquid water),
  • water vapor pressure (kPa),
  • probability of occurrence of snow,
  • snow thickness (cm),
  • soil moisture (cm liquid water),

The key public gridded global climatic data sets (current and past climate) include:

  • CHELSA Climate with multiple long term, monthly and even daily global products at 1-km (Karger and Zimmermann 2019);
  • WorldClim with monthly historic and projected climatic data at 1-km spatial resolution (Fick and Hijmans 2017);
  • TerraClimate gridded monthly temperature, precipitation, and other water balance variables at 4-km spatial resolution (Abatzoglou et al. 2018);
  • NASA’s NEO global climatic monthly images at 10-km covering 2000–2022+ or finer;
  • Copernicus ERA5 available from 1940 providing hourly data on many atmospheric, land-surface and sea-state parameters together with estimates of uncertainty but at a coarse spatial resolution of cca 25-km;

In addition to basic climatic variables, WorldClim and CHELSA Climate also provide BioClimatic variables at 1-km spatial resolution e.g. frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), growing season characteristics and similar (Brun et al. 2022). From the sources listed above, CHELSA Climate is possibly the most comprehensive most up-to-date repository with global 1-km data and even includes daily global 1km products (CHELSA-W5E5), e.g. daily precipitation images (Karger et al. 2021). T. Zhang et al. (2022) produced a global seamless 1-km resolution daily land surface temperature dataset 2003–2020 (primarily based on MODIS 8-day LST products); data is available for download via Iowa State University’s DataShare. Yao et al. (2023) produced Global seamless and high-resolution temperature dataset (GSHTD), 2001–2020 at 1-km spatial resolution and which is available for download. 5-km resolution daily temperature and long-term GLASS Land Surface Temperature product data set for 1981–2000 based on NOAA-AVHRR has been produced by J. Ma et al. (2020) and is available for download from http://glass.umd.edu/LST/; monthly averages (still with significant gaps) are also available via Zenodo.

More detailed climatic images can be obtained via the Global Energy and Water Cycle Experiment project and the British Atmospheric Data Centre (BADC). The National Center for Atmospheric Research maintains a ClimateDataGuide portal.

The National Geophysical Data Centre (NGDC) provides free access to numerous remote sensing based global maps — from solar parameters to cloud imagery, energetic particle measurements and similar (collectively called “Climate monitoring” products). The National Snow and Ice Data Center maintains a number of global data sets important for monitoring global snow and ice — frozen ground maps, monthly satellite-derived snow water equivalent (SWE) climatologies and similar. These data can be obtained for northern and southern hemisphere at resolution of 25-km. Gruber (2012) produced a Global Permafrost Zonation Index Map (excluding Antartica) which is available at resolution of 1-km. NASA’s VIIRS (Visible Infrared Imaging Radiometer Suite) Land products include also VIIRS-land snow cover daily (gap-filled) at 375-m resolution. These can be downloaded from the NSIDC data portal. ESA CCI (Climate Change Initiative) also maintains and distributes Daily Snow Cover Fraction Viewable (SCFV), Daily Snow Cover Fraction on Ground (SCFG) i.e. a canopy corrected snow fraction to adjust for snow on ground also in forested areas (both at 1-km resolution and covering 2000–2020), and Snow Water Equivalent (SWE) at 10-km resolution covering 1979–2020.

DLR (German Space Agency) Geoportal distributes the Global SnowPack monthly and annual products at 500-m spatial resolution covering 2000 to 2023 (current) (Dietz, Kuenzer, and Dech 2015). NASA’s National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) provides data and information for snow and ice processes, interactions among snow, ice, atmosphere, and ocean.

Cui et al. (2021) have produced 1-km resolution maps of present-time and future Koppen-Geiger climate classification zones and bioclimatic variables, available for download from http://glass.umd.edu/KGClim. The Koppen-Geiger climate classification is is one of the most widely used climate classification systems with five main classes: A (tropical), B (arid), C (temperate), D (continental), and E (polar).

There are multiple daily and monthly global soil moisture products either purely based on EO images or a combination of downscaling and modeling. Fang et al. (2022) produced 1-km soil moisture data set covering years 2015 to 2020 by downscaling the SMAP product. National Snow and Ice Data Center has also produced SMAP-Derived 1-km Downscaled Surface Soil Moisture Product. This data set is available for download from NSIDC. Han et al. (2023) produced a global daily soil moisture data set at 1-km resolution called GSSM1-km covering period 2000–2020. The GSSM1-km dataset can be downloaded from Figshare. Y. Zhang et al. (2023) used ensemble learning to produce a daily 1-km spatiotemporally continuous soil moisture product for 2000–2020 called GLASS SM. The GLASS SM is freely available at http://glass.umd.edu/soil_moisture/; the annual average global soil moisture dataset at 1-km resolution was also generated, which can be downloaded from zenodo. Zheng, Jia, and Zhao (2023) produced a gap-free global daily surface soil moisture at 1-km grid resolution for 2000–2020, which is available for download from the National Tibetan Plateau/Third Pole Environment Data Center (almost 1TB of data).
Coarser resolution (10-km or coarser) soil moisture global images are also available such as the ESA’s CCI soil moisture product, SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture and the ASCAT Soil Wetness Profile Index and Root Zone Soil Moisture Profile Index.

Global soil temperatures i.e. Global Soil Bioclimatic variables are available at 1-km spatial resolution thanks to Lembrechts et al. (2022).

OpenLandMap.org provides access to number of original climatic data sets produced at OpenGeoHub including:

Meteorological images

A number of MODIS atmospheric / terrestrial products are available with daily updates (usually at 1-km spatial resolution) including:

MODIS produces daily estimates of the global Land Surface Temperature, which are supposedly highly accurate with an average accuracy of ±1 degree Kelvin (Wan et al. 2004).

The Meteosat Second Generation (MSG) satellites (from Meteosat-8 onwards) produce SEVIRI 15–minutes images at resolution of 1-km. The most interesting SEVIRI data set for environmental applications is the High Rate SEVIRI, which consists of 12 spectral channels including: visible and near infrared light, water vapor band, carbon dioxide and ozone bands. Meteosat LST products are available from the Copernicus Global Land Service.

LSTM (Land Surface Temperature Monitoring) is an ESA mission developed by Airbus Defense and Space, set to start in 2028. The satellite will have Thermal Infrared (TIR) observation capabilities at high spatial resolution (30–50-m) over land and coastal regions in support of agriculture management services, and possibly a range of additional services.

Biodiversity and Nature Conservation

World ecosystems / biomes / ecoregions

Numerous global data products have been produced to show either boundaries of natural ecosystems also referred to as ecoregions or biomes. Ecoregions are terrestrial, freshwater and/or marine areas with characteristic combinations of soil and landform that characterize that region. Leading providers of the global maps on biodiversity and nature conservation include:

The World Wildlife Fund has produced the global (polygon) map of Terrestrial ecoregions, which shows some 867 distinct eco-units, including the relative richness of terrestrial species by ecoregion (Olson et al. 2001). Another similar data set is the Freshwater Ecoregions of the World (Abell et al. 2008).

USGS distributes a “World Ecological Land Units” with 3,923 terrestrial ecological land units (ELUs) at a base resolution of 250 meters and available for download from the USGS Global Ecosystems Data repository. Sayre et al. (2019) also produced a 30-m resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units.

FAO and IIASA maintain also a global map of Global Agro-Ecological Zones (GAEZ). GAEZ classification, soil suitability, terrain slopes and land cover are provided at 1-km spatial resolution. The GAEZ data viewer provides easy access to all data layers.

Jung et al. (2020) have produced a global map of terrestrial habitat types at 1-km spatial resolution. This is based on the International Union for Conservation of Nature (IUCN) habitat classification scheme.

The ESA’s CCI Global Plant Functional Types (PFT) data provides yearly data for the time period from 1992 to 2020. PTF global dataset has 14 layers, each describing the percentage cover (0–100%) of a plant functional type at a spatial resolution of 300-m: broadleaved evergreen trees, broadleaved deciduous trees, needleleaved evergreen trees, needleleaved deciduous trees, broadleaved evergreen shrubs, broadleaved deciduous shrubs, needleleaved evergreen shrubs, needleleaved deciduous shrubs, natural grasses, herbaceous cropland (i.e., managed grasses), built, water, bare areas, and snow and ice.

Potential Natural Vegetation maps

Potential Natural Vegetation (PNV) is the hypothetical vegetation cover that would be present if the vegetation were in equilibrium with environmental controls, including climatic factors and disturbance, and not subject to human management. When considering PNV, one needs to distinguish between potential natural and potential managed vegetation, and actual natural and actual managed vegetation (Hengl et al. 2018).

Schematic explanation of differences between (A) potential and actual natural/managed vegetation, and (B) current and historic vegetation in the context of global land area. Image source: Hengl et al. (2018).

Griscom et al. (2017) produced the Global Reforestation Potential Map at 1-km resolution. Bastin et al. (2019) produced Potential forest cover map at 1-km resolution assuming no humans present. Potential distribution of LCCS land cover classes at 250-m spatial resolution based on the UN FAO Land Cover Classification System (LCCS) is part of the NatureMap project. Bonannella et al. (2023) provides an updated predictions of the potential distribution of global biomes at 1-km including for the future climate scenarios (2040, 2060 and beyond).

Species distribution and biodiversity maps

Global Biodiversity Information Facility (GBIF) provides access to global data showing the distribution of all flora and fauna species. The density maps are available only at resolution of 1 arcdegree (about 100-km) and often without any bias-correction. Global maps of biodiversity measures for various groups of taxa (e.g. vascular plants, birds and mammals) can be browsed using the Global Ecosystem Atlas. Similar type of maps can be browsed via the UNEP’s World Conservation Monitoring Centre.

A shape file showing location of hotspot regions is distributed by the Conservation International. Kreft and Jetz (2007) produced a global map of plant species diversity (number of plant species) by using field records from 1,032 locations (maps are available only in coarse resolution of 120-km). BirdLife International publishes a number of global maps indicating Endemic and Important Bird Areas (EBAs and IBAs).

Pimm et al. (2014) produced maps of species richness for mammals, birds, amphibians and other taxa at 10-km spatial resolution. EarthMap project produced a species richness (number of species per grid cell) maps for amphibians, birds, mammals, reptiles and a representative set of plant taxa whose distribution overlaps in each 10-km cell, and a global map of Areas of global significance for conservation (Brooks et al. 2019).

Sabatini et al. (2022) produced a number of global biodiversity maps including the map of plant species richness at 1-km spatial resolutions. These maps are based using sPlot database (whichs is available upon request by submitting a project proposal to sPlot’s Steering Committee). Auamaps serves standardized distribution maps (at a coarse spatial resolution) for over 33,500 species of fishes, marine mammals and invertebrates, based on the FishBase and SeaLifeBase data (Hodapp et al. 2023). Species distribution maps can be also accessed from the Ocean Biodiversity Information System (Moudrỳ and Devillers 2020).

Protected areas

National and international protected sites and attributes can be downloaded (ESRI Shapefiles; after registering on the website) via the World Database On Protected Areas. International Union for Conservation of Nature and Natural Resources (IUCN) has released a Red List of Threatened Species that contains assessments for +130,000 species. Distribution maps (presence) for these species (with limited coverage) can be downloaded from the IUCN website.

Neugarten et al. (2023) produced maps of planet’s critical areas for biodiversity and people at 2-km resolution. This finding supports recent commitments by national governments under the Global Biodiversity Framework to conserve at least 30% of global lands and waters, and proposals to conserve “half Earth”.

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