Supplemental materials and tables for the Guidelines on Biophysical Modelling

ARIES (Villa et al., 2014)

ARIES (Artificial Intelligence for Ecosystem Services). Provides easy access to data and models through a web-based explorer and using Artificial Intelligence to simplify model selection, promoting transparent reuse of data and models in accordance with the FAIR principles.

Yes

Yes

Yes

Yes

Extent, Condition, Ecosystem Services

Data4Nature[2]

Data4Nature (formerly known as EnSym -  Environmental Systems Modelling Platform) is a decision support tool that is designed to answer questions about where organizations should invest in their natural resources. Data4Nature is specifically designed with SEEA EA in mind.

Yes

Yes

Yes

No

Extent, Ecosystem Services

ESTIMAP (Zulian et al., 2014)

ESTIMAP (Ecosystem Services Mapping tool) is a collection of models for mapping ecosystem services in a multi scale perspective (it can be applied at different scales) (Zulian et. al., 2018).

Yes

Yes

Yes

No

Ecosystem Services

InVEST (Sharp et al., 2018)

A compilation of open-source models for mapping and valuing ecosystem services. InVEST is the flagship tool of the Natural Capital Project and has been the most widely used ecosystem service modelling tool globally.

Yes

Yes

Yes

Ecosystem Services, Condition

i-Tree[3]

i-Tree is a tool developed by the USDA Forest Service with capabilities of modelling ecosystem services related to trees, particularly in urban settings (i.e. air filtration, carbon storage urban heat island mitigation, and rainfall interception and infiltration).

Yes

Yes

Yes

Ecosystem Services (forest related)

Nature Braid (Jackson et al., 2013)

The Nature Braid (formerly LUCI/Polyscape) provides a suite of high spatial resolution ecosystem services models designed to improve decision-making around restoration and land management. The Nature Braid is particularly well suited for mapping soil, water and chemical transport processes at high resolution.

Yes

Yes

No

Extent, Condition, Ecosystem Services (hydrological, soil)

Climate Change Initiative (CCI) Land cover v2

300m

European Space Agency

Data from the MERIS 2003 to 2012 archive, SPOT-Vegetation

70 to 74%

Global

Annually from 1992 to 2019

https://www.esa-landcover-cci.org/, https://www.esa-landcover-cci.org/?q=node/158 

Official documentation: http://cci.esa.int/sites/default/files/CCI_Data_Policy_v1.1.pdf

MODIS-based Global Land Cover Climatology

500m

USGS Land Cover Institute

MODIS images from 2001 to 2010

73.6% overall

Global

Annually 2001 to 2018

https://lpdaac.usgs.gov/products/mcd12q1v006/

Official documentation: https://www.sciencedirect.com/science/article/pii/S0034425718305686

Copernicus Global Land Service Land Cover

100m

Copernicus

Proba-V

80%

Global

2015 - 2019

https://lcviewer.vito.be/download

Buchhorn et al. (2019)

GlobeLand

30m

National Geomatics Center of China (NGCC)

Landsat primarily,

MODIS NDVI, global geographic information, global DEM, thematic data (global mangrove forest, wetland and glacier, etc.) and online resources (Google Earth, Bing Map, OpenStreetMap and Map World)

80.33%

Global between 80N and 80S

2000 and 2010

http://www.globallandcover.com

Chen et al. (2014)

FROM-GLC

10m

Tsinghua University, Beijing

Landsat, Sentinel 2

72.8%

Global

2017

http://data.ess.tsinghua.edu.cn

Gong et al. (2019)

Water availability

Hydrological flow

Volume of water discharged by a watershed or river over a timeframe

Volume

Process-based models

- InVEST

- LUCI / Nature Braid

- WaterWorld

- VIC (semi-distributed macroscale model)

- SWAT

- WaterWorld

Global surface water explorer: https://global-surface-water.appspot.com/#features

Hydrosheds: https://www.hydrosheds.org/page/overview

Groundwater table

Upper surface of the zone of saturation

Depth

- Spatial interpolation

- Numerical models

-

GGIS provides maps of aquifers across the globe, as well as other ground water data, typically at the country level: https://www.un-igrac.org/global-groundwater-information-system-ggis

Global Ground Water Monitoring Network (https://ggmn.un-igrac.org/) allows for interpolation between ground stations

The GRACE model detects changes in gravity which are used to assess changes in water stocks: http://www2.csr.utexas.edu/grace/gravity/

Soil

Impervious surface (soil sealing)

Paved surface areas (e.g. buildings, roads)

Area (percentage)

Earth Observation data

-

The GMIS data set available from CIESIN consists of two components: 1) global percent of impervious cover; and 2) per-pixel associated uncertainty for the global impervious cover. These layers are co-registered to the same spatial extent at a common 30m spatial resolution, see: https://sedac.ciesin.columbia.edu/data/set/ulandsat-gmis-v1

Global impervious surface area (GISA) dataset (30m) from 1972 to 2019.) based on Landsat images (Xin Huang, et al. 2021).

Soil Organic Carbon Content

The amount of carbon stored in soil

- Stock (tC/ha) or

- Concentration (g/kg)

- Look-up tables

- Spatial interpolation

- Geostatistical models

S-world model has maps and accounts detailing soil organic carbon concentration in (%, 0-30cm) and (%, 30-100)

GSDE Global soil data set for Earth Systems Modelling

http://globalchange.bnu.edu.cn/research/soilw

ISRIC SoilGrids: https://soilgrids.org/

FAO Global Soil Organic Map GSOC: http://54.229.242.119/GSOCmap/

Air quality

Pollutant concentrations

The amount of pollutants (e.g. micrograms per cubic meter (µg/m3) parts per million (ppm)), such as particulate matter and nitrogen dioxide, that cause damage to human health or the environment.

Typically modelled with an air pollution dispersion model, photochemical modelling, and receptor modelling. These models estimate concentrations based on meteorological data, pollution sources and chemical reactions.

The EPA provides access to several air pollutant models: https://www.epa.gov/scram/modelling-applications-and-tools

World air pollution: https://waqi.info/

PM 2.5 grids: https://sedac.ciesin.columbia.edu/data/set/sdei-global-annual-gwr-pm2-5-modis-misr-seawifs-aod

Water quality (inland freshwater including lakes, rivers, and streams)

Pollutant concentrations

The amount of pollutants, such as nitrogen or other chemicals that cause damage to human health or the environment.

Process-based models are common including export coefficient approaches

InVEST, LUCI/Nature Braid, ARIES, SWAT all provide approaches for water quality modelling. Different pollutants may require different models and approaches

SDG 6.3.2 core parameters: total phosphorus, total nitrogen, pH and dissolved oxygen in rivers, lakes and reservoirs or aggregated for a particular country or catchment: https://gemstat.org/data/maps/

Dissolved oxygen

The amount of oxygen dissolved in water, which is available for biota and enters via diffusion from the atmosphere. Rapidly moving water will typically have more dissolved oxygen than stagnant water, as will water with lower amounts of biomass.

Spatialization of point data

Spatial extrapolation;

Machine learning

Ibid

Water quality (inland freshwater including lakes, rivers, and streams)

Chlorophyll-a

A photosynthetic pigment used as an indicate algal levels in water

For large water bodies, multispectral imagery, such as MERIS have been used to map chlorophyll-a concentrations in lakes

Approaches for oceans have been modified for use in large lakes, e.g., ocean data available: https://oceancolor.gsfc.nasa.gov/atbd/chlor_a/

Turbidity

SWAT

Soil quality

Heavy metal content

The concentration of heavy metal, such as lead, which are detrimental to human health Especially relevant in urban areas

Spatial interpolation, geostatistical models

Indicator category

Indicator example

Definition

Unit

Modelling approach

Available models

Global data sources

Species

Species diversity

The varied species on Earth

Total number of species or number of species within different taxonomic groups (e.g., birds, fishes) or guilds (e.g., soil biota)

Functional Diversity

Species Area Relationships

Species Distribution Models (SDMs)

Macroecological models

www.iucnredlist.org (See Section 5.4.1 for more details)

Global Biodiversity Information Facility (GBIF) database: https://www.gbif.org/occurrence/search

Species abundance

The number of individuals of a single species

The number of individuals belonging to the same species

Abundance (number of individuals)

Species Abundance Models

Maxent, R

www.iucnredlist.org

Global Biodiversity Information Facility (GBIF) database: https://www.gbif.org/occurrence/search

Red-list indices/conservation status

Species-level risk of extinction

Risk category

https://sis.iucnsis.org/apps/org.iucn.sis.server/SIS/index.html

https://www.ramas.com/

www.iucnredlist.org

Global Biodiversity Information Facility (GBIF) database: https://www.gbif.org/occurrence/search

Biodiversity Intactness Index; Mean species abundance

Indices to measure how much of local biodiversity remains intact

GLOBIO measures MSA by combining the impact of land use change, climate change, atmospheric N deposition, biotic exchange, atmospheric CO2 concentration, fragmentation, infrastructure, harvesting, human population density, and energy use on biodiversity loss (Alkemade et al., 2009)

GLOBIO (also available within the InVEST modelling framework)

Indicator category

Indicator example

Definition

Unit

Modelling approaches

Available models

Global data sources

Biomass

Density

Biomass density

Growing stock volume (m3/ha)

Above ground biomass (AGB) (ton/ha)

http://globbiomass.org/wp-content/uploads/GB_Maps/
Globbiomass_global_dataset.html

Indicator category

Indicator example

Definition

Unit

Modelling approaches

Available models

Global data sources

Processes

Net Primary Productivity (NPP);
Dry matter Productivity (DMP)

The rate at which an ecosystem accumulates biomass

cg/cm³/year

DMP uses units customized for agro-statistical purposes (kg/ha/day).

Dynamic Vegetation models 

General Ecosystem Models

Leaf Area Index (LAI)

Models include variables such as solar radiation, nitrogen, CO₂, water, temperature, fraction of phosynthetically active radiation.

MODIS satellite imagery provides estimates, LPJ DGVM (Lund–Potsdam–Jena Dynamic Global Vegetation model)

NPP: https://neo.sci.gsfc.nasa.gov/view.php?
datasetId=MOD17A2_M_PSN

DMP: https://land.copernicus.eu/global/products/dmp

LAI: https://land.copernicus.eu/global/products/lai

Indicator category

Indicator example

Definition

Unit

Modelling approaches

Available models

Global data sources

Composition

Diversity

The abundance and evenness of different soil types within a BSU. This indicator may also be aggregated for EAs, ETs, or EAAs.

Richness, Shannon’s Index, or Simpson’s Index

Metric calculated based on thematic maps

Vegan package in R

LUCI / Nature Braid

Ecosystem extent accounts likely form the basis for these indicators

Global Biodiversity Information Facility (GBIF) database, can also be used as input: https://www.gbif.org/occurrence/search

Connectivity/ fragmentation

Barrier density

The number of barriers, such as roads or dams, which may prevent the migration of species.

Number per area or length per area

Metric calculate based on point or line data

ArcGIS, QGIS

Dams for freshwater barriers: http://globaldamwatch.org/data/

Road maps:

https://sedac.ciesin.columbia.edu/data/set/groads-global-roads-open-access-v1

https://www.openstreetmap.org/

Patch size

Mean patch size (MPS) is the average size of all patches of all habitats over a landscape.

Area (ha, m, or km)

Calculated metric based on thematic maps

Frag stats
(R stats version is available)

Ecosystem extent accounts likely form the basis for these indicators.

Shape

Several shape indices are available, typically based on edge to area ratios.

Ratio of perimeter to edge

Calculated metric based on thematic maps

Frag stats
(R stats version is available

Ecosystem extent accounts likely form the basis for these indicators.

Land cover

Global Land Cover Share Database

Based on contributions from various institutions by a combination of “best available” high resolution national, regional and/or sub-national land cover databases. Provides 11 major thematic.

30 arc-seconds

Global

Latham et al. (2014)

1998-2012

http://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036355/

See Section 4.4 for an overview of land cover data sources

Forest cover

See Table 6 for an overview of Hansen forest cover data

Soil

S-world

Combines Harmonized World Soil database and ISRIC‐WISE 3.1 soil profile database along with auxiliary data summaries at 30-arc second spatial scale, such as temperature, precipitation, and topography.

30-arc second spatial scale

Global

Stoorvogel et al. (2017)

2016-static

http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/

ISRIC

Provides global prediction of soil properties including, organic carbon, bulk density, Cation Exchange Capacity, pH, soil texture fractions and coarse fractions. These predictions are based on remote sensing-based soil covariates primarily derived from MODIS.

250 m grid

Global

Hengl et al. (2017)

2016-static

https://soilgrids.org/#!/?layer=ORCDRC_M_sl2_250m&vector=1

or

ftp://ftp.soilgrids.org/data/

Harmonised World Soil Database (HSWD) v1.2

The HWSD used the spatial information provided by the FAO-UNESCO Digital Soil Map of the World (DSMW) and national/regional maps, along with soil profile information to create a global-scale map of soils. The database uses FAO classifications at the soil unit level (FAO-74, FAO-85, FAO-90).  Aside from mapping information (classification, ID, etc.), a further range of characteristics are included at the topsoil (0 to 30cm) and the subsoil (30 to 100cm) level.

30 arc-second

Global

FAO (2009)

2009

http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/

Global Soil Data set for use in Earth System Models (GSDE)

The GSDE is based on the Digital Soil Map of the World similar to the HWSD but uses additional databases to help improve the accuracy of the updated map. Uses mainly the FAO classification, but includes local soil classification.The data set gives information on a suite of soil properties at eight depths up to 2.3 m.

1km and 10km

Global

Shangguan et al. (2014)

2014-static

http://globalchange.bnu.edu.cn/research/soilw

Global Soil Salinity Map

This data set contains maps for multiple years of  soil salinity, with a validation accuracy in the range of 67–70%.

250m

Global

Ivushkin et al. (2019)

1986, 1992, 2000, 2002, 2005, 2009 and 2016

https://data.isric.org/geonetwork/srv/api/records/c59d0162-a258-4210-af80-777d7929c512

Soil water properties

Global High-Resolution Soil-Water Balance

This data set used input variables from the WorldClim and Global-PET gridded data sets to calculate the soil water balance at the monthly and annual scales. Available data include Mean annual AET, Monthly AET, Monthly Soil Water Stress, Priestley-Taylor Alpha coefficient.

30 arc-seconds (1km at equator)

Global

Trabucco and Zomer (2019)

2010

https://cgiarcsi.community/data/global-high-resolution-soil-water-balance/

HiHydroSoil

Provides information about soil hydraulic properties is important for hydrological modelling and crop yield modelling. HiHydroSoil is a global data set with information about hydraulic properties.

250m

Global

De Boer (2016)

2015

https://www.futurewater.eu/2015/07/soil-hydraulic-properties/

Digital Elevation Models

Shuttle Radar Topography Mission (SRTM)

Consistently created global digital elevation model produced using. STRM does not cover latitudes north of 60°. EarthEnv has STRM-like DEM to 83°N.

30 m 1 arc-second by 1 arc-second ~30m

~80% of the globe

Farr et al. (2007)

Available since 2002-static

https://www2.jpl.nasa.gov/srtm/dataprod.htm

ASTER Global Digital Elevation Model

Generated using stereo-pair images collected by the ASTER instrument onboard Terra

90 meters with a resolution of 30 meters in the United States

~99% of the globe

NASA/METI/AIST/Japan Spacesystems, and U.S./Japan ASTER Science Team (2001)

released in June 2009-static

https://ssl.jspacesystems.or.jp/ersdac/GDEM/E/

EarthEnv

Generated by fusing ASTER GDEM2 and CGIAR-CSI v4.1

90 meters

~91% global coverage

Robinson et al. (2014)

2014-static

https://www.earthenv.org/DEM.html

MERIT 10m DEM

High accuracy global DEM at 3 arcsecond resolution (~90 m at the equator), which eliminates major error components from existing DEMs (NASA SRTM3 DEM, JAXA AW3D DEM, Viewfinder Panoramas' DEM), several other data sets were also used as supplementary data including - NASA-NSIDC ICESat/GLAS GLA14, U-Maryland Landsat forest cover, NASA Global Forest Height, JAMSTEC/U-Tokyo G3WBM water body.

3″ resolution (~90 meters at equator)

Global coverage

Yamazaki et al. (2017)

2017

http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/

Rivers and watersheds and water

Hydrosheds

Includes a suite of information on river networks, watershed boundaries, drainage directions, and flow accumulations. Hydrosheds are a derivative of STRM data.

~3 arc-seconds (~90 m at equator) best available for some data sets, otherwise, 15 arc-second, and 30 arc-second resolutions-Derived from SRTM data

Near global coverage

Lehner et al. (2008)

Available since 2008-static

https://www.hydrosheds.org/

Hydro 1k

Produced using the USGS’s 30-arc second DEM, and includes hydrologically corrected DEM’s stream basins.

GRACE satellite data

The twin GRACE-FO satellites follow each other in orbit around the Earth, separated by about 137 miles (220 km). From distance measurements between the two satellites, GRACE data can be used to estimate Earth's gravity field. These data are then used to monitor changes in underground water storage, the amount of water in large lakes and rivers, soil moisture, ice sheets and glaciers, and sea level caused by the addition of water to the ocean.   

Base products are 1 degree, updated monthly. Approaches to disaggregate to finer spatial resolution exist.

Global

NASA

https://grace.jpl.nasa.gov/about/how-to-cite/

Ongoing monthly updates  

https://grace.jpl.nasa.gov/data/get-data/

Aquastat and Aquamaps

The AQUASTAT core database provides the platform for organizing and presenting over 180 variables and indicators on water resources and their use which include water withdrawal, wastewater, pressure on water resources, irrigation and drainage, and a few components on environment and health. They can be searched and extracted, along with their metadata, for 200+ countries and for different regions over an extensive time period (from 1960 to 2017). AquaMaps is complementary to AQUASTAT, FAO's Information System on Water and Agriculture. While AQUASTAT focuses on collecting mainly statistical data and qualitative information on (sub)country level, AquaMaps concentrates on geographical information.

Variable

Global

[5]

Geography and population: Every year water resources: these are long-term average annual values and therefore remain the same over the years. Updates of data for some specific sub-categories are done in collaboration, when data become available.

http://www.fao.org/nr/water/aquamaps/ 

Google’s / JRC Global surface water

See Table 6 for an overview of Global surface water data set

Precipitation

There are 30 globally available precipitation data sets collected at different spatial and temporal scales with some focusing on ground-based measurements and others using satellite observations (Sun et al., 2018). Examples are: TRMM (http://www.ambiotek.com/1kmrainfall/ and https://gpm.nasa.gov/) and CHIRPS (http://chg.geog.ucsb.edu/data/chirps/index.html).

Climate

WorldClim v1 and v2: global climate data

The “current/observational” layers were created through spatially interpolating climate data from a large database of climate stations, while the future climate change conditions under the Representative Concentration Pathways were created through downscaled GCM data.

30 arc-sections to 10 minutes

Global

Fick and Hijmans (2017); Hijmans et al. (2005)

2005, 2017

https://www.worldclim.org/

CHELSA

A high-resolution climate data set for land surface areas. It includes temperature and precipitation patterns for various time periods.

CHELSA is based on a quasi-mechanistical statistical downscaling global reanalysis and global circulation model output.

30 arc-seconds

Global

Karger et al. (2017)

Multiple time series; V1.2 released 2019

https://chelsa-climate.org

Global Potential Evapotranspiration (Global-PET) and Global Aridity Index (Global-Aridity)

The Global-PET and Global-Aridity data sets were modelled from the WorldClim data set using the Hargreaves method for PET and the Aridity Index.

30 arc-seconds

Global

Zomer et al. (2008)

V2 released 2019

https://cgiarcsi.community/data/global-aridity-and-pet-database/

GloREDa:  Global Rainfall Erosivity Database & R-factor map

This data set used information from a large database of rainfall data and covariates from the WorldClim data set to create a spatially interpolated global map of rainfall erosivity. This map can be used as input to global studies of soil erosion using the Revised Universal Soil Loss Equation (RUSLE).

30 arc-seconds

Global

Panagos et al. (2017)

2017

https://esdac.jrc.ec.europa.eu/content/global-rainfall-erosivity

Biodiversity

IUCN Red List of threatened species

Compiled polygon data for red listed species considered to be from comprehensively assessed taxonomic groups and selected freshwater groups. Freshwater species are mapped to pre-defined river/lake catchment units.  Contains spatial data for about two-thirds of the 96,500 species that they have assessed. The maps are developed as part of a comprehensive assessment of global biodiversity in order to highlight taxa threatened with extinction, and thereby promote their conservation.

30 arc-seconds

Global

http://www.iucnredlist.org

2019

https://www.iucnredlist.org/resources/spatial-data-download

Terrestrial Biodiversity Indicators

Biodiversity indicator values (scores) for grid cells at 1-kilometer resolution, based on several pieces of information including total counts (presence) of mammals, birds, amphibians and reptiles from IUCN and Birdlife International, total counts or presence, of critically endangered and endangered mammals, birds, amphibians and reptiles, the presence of endemic species/ species unique to the region, extinction risks for species over 50, 100 and 500 years, and biome vulnerability, identified from the WWF ecoregions.

1 km

Global

IUCN (2016)

2019

https://datacatalog.worldbank.org/data set/terrestrial-biodiversity-indicators

Protected areas

WCMC & IUCN World Database on Protected Areas (WDPA)

Not specified

Global

UNEP-WCMC and IUCN

Updated regularly

https://www.protectedplanet.net/

Global Biodiversity Information Facility

GBIF is an international network and data infrastructure funded by the world's governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth

Varies with data set

Global

Updated regularly

https://www.gbif.org/occurrence/search

Socio-economic

Global Roads Open Access Data Set (gROADS)

A global compilation of road maps with positional accuracy of 50 m (NASA Socioeconomic Data and Applications Center (SEDAC), 2009).

Not specified

Global

CIESIN and ITOS (2013)

Ranges from 1980s to 2010 on the country (most countries have no confirmed date)

https://sedac.ciesin.columbia.edu/data/set/groads-global-roads-open-access-v1

GDP

Gap-filled multiannual data sets in gridded form for Gross Domestic Product and Human Development Index. Sub-national data were only used indirectly, scaling the reported national value and thus, remaining representative of the official statistics.

5 arc-min resolution

Global

Kummu et al. (2020)

1990-2015

https://datadryad.org/stash/dataset/doi:10.5061/dryad.dk1j0

Population

(Leyk et al. 2019)

Urban TEP

Web-based platform that uses Earth Observations and auxiliary information to assess the urban environment and monitor and predict settlement development. Includes global urban footprint data set.

Varies with product. Highest resolution is 12 m

Global

Leyk et al. (2019)

1985-2015

https://urban-tep.eu/#!

WorldPop

Global population data are available through WorldPop, which uses a combination of census, survey, satellite and cell phone data to produce gridded outputs (Tatem 2017).

1 km for the globe and 100 m for individual country data

Global

Tatem (2017)

2000-2020 for global data

https://www.worldpop.org/

High Resolution Settlement Layer (HRSL)

Based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. Population grids are available for both urban and rural areas

1 arc-sec

140 countries

Facebook Connectivity Lab and CIESIN

2015

https://ciesin.columbia.edu/data/hrsl/#data

Pollution

Socioeconomic Data and Applications Center (sedac)- Air polution

Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, v1 (1998 – 2016).

0.01 degrees

70 degrees north to 55 degrees south

Van Donkelaar et al. (2018)

1998-2016

https://sedac.ciesin.columbia.edu/data/set/sdei-global-annual-gwr-pm2-5-modis-misr-seawifs-aod

World's Air Pollution: Real-time Air Quality Index

Global data on air quality. Only stations with particulate matter (PM2.5/PM10) are published.

Point data from ~12,000 stations

1000 major cities from 100 countries

Real time with variable length of availability

https://aqicn.org/sources

Instream pollution

Provides a global overview of the water quality of ground and surface waters of water bodies and the trends at global, regional and local levels. ~250 variables are available including instream pollution.

Point data from approximately 4000 stations. Million entries for rivers, lakes, reservoirs, wetlands and groundwater systems

75 countries

1965 to 2019

https://gemstat.org/about/

Stream flow calibration

Stream flow

Provides a global overview of the water quality of ground and surface waters of water bodies and the trends at global, regional and local levels. ~250 variables are available including streamflow.

Point data from approximately 4000 stations. Million entries for rivers, lakes, reservoirs, wetlands and groundwater systems

75 countries

1965 to 2019

https://www.bafg.de/GRDC/EN/01_GRDC/13_dtbse/database_node.html

Crops

Global croplands (GFSAD30 project)

Provides cropland products (e.g. croplands with rainfed agriculture) across the world at a 30 m resolution.

30 m

Global

Thenkabail et al. (2012); Teleguntla et al.  (2015)

2015

https://croplands.org/home

Earthstat

A wide range of data on the global food system, including crop and pastureland fraction from 2000, and harvested area and yield for 175 crops.

Resolution varies with data set

Global

Citation varies with data set

variable

http://www.earthstat.org/

FAOSTAT

Food and agricultural data for 245 countries

Tabular data

Global tabular

1961 to present

http://www.fao.org/faostat/en/#home

Other

Global Visible Infrared Imaging Radiometer Suite (VIIRS) Night-Time Lights produced by The Earth Observations Group (EOG).

15 arc second

Global

NASA

2012-YTD, Daily

https://blackmarble.gsfc.nasa.gov/