Global environment data

Disaster risk reduction progress score is an average of self-assessment scores, ranging from 1 to 5, submitted by countries under Priority 1 of the Hyogo Framework National Progress Reports. The Hyogo Framework is a global blueprint for disaster risk reduction efforts that was adopted by 168 countries in 2005. Assessments of "Priority 1" include four indicators that reflect the degree to which countries have prioritized disaster risk reduction and the strengthening of relevant institutions.

Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 3 (IT-3) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 15 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed.

Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 1 (IT-1) is defined as the portion of a country’s population living in places where mean annual concentrations of PM2.5 are greater than 35 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed.

Population-weighted exposure to ambient PM2.5 pollution is defined as the average level of exposure of a nation's population to concentrations of suspended particles measuring less than 2.5 microns in aerodynamic diameter, which are capable of penetrating deep into the respiratory tract and causing severe health damage. Exposure is calculated by weighting mean annual concentrations of PM2.5 by population in both urban and rural areas.

Electricity production from renewable sources in kilowatt-hour (kWh), excluding hydroelectric, includes geothermal, solar, tides, wind, biomass, and biofuels.

Bulk density (fine earth) in cg/cm³ at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. This map is the result of resampling the mean SoilGrids 250 m predictions (Poggio et al. 2021) for each 5000 m cell.

Contains data from the National Flood Hazard Layer, a GIS database of flood risks and regulatory flood determination data.

A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).

A measure of annual emissions of fluorinated gases (hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphur hexafluoride (SF6)), from industrial processes including IPCC 2006 codes 2.B Chemical Industry, 2.C Metal Industry, 2.E Electronics Industry, 2.F Product Uses as Substitutes for Ozone Depleting Substances, 2.G Other Product Manufacture and Use The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).

National Flood Hazard Layer data from FEMA, published as a map service.

A map service from FEMA providing the National Flood Hazard data, covering floodplain information. It is published by FEMA as a REST map service.

The National Flood Hazard Layer (NFHL) is a compilation of GIS data that comprises a nationwide digital Flood Insurance Rate Map. The GIS data and services are designed to provide the user with the ability to determine the flood zone, base flood elevation, and floodway status for a particular location. It also has information about the NFIP communities, map panels, cross sections, hydraulic structures, Coastal Barrier Resource System, and base maps such as road, stream, and public land survey data. Through flood studies, FEMA produces Flood Insurance Study Reports, FIRM Panels, and FIRM Databases. FIRM Databases that become effective are incorporated into the NFHL. Updates to the NFHL are issued through Letters of Map Revision (LOMRs) and Letters of Map Amendment (LOMAs). Continuously updated, the NFHL serves as a Digital Flood Insurance Rate Map representing the current effective flood data for those communities where maps have been digitized. NFHL data can be viewed with widely available GIS software, including freely available programs that work with GIS shapefiles. For more information on the NFHL, see the online resources referenced herein. Using base maps: The minimum horizontal positional accuracy for base map hydrographic and transportation features used with the NFHL is the NSSDA radial accuracy of 38 feet. Letter of Map Amendment (LOMA) point locations are approximate. The location of the LOMA is referenced in the legal description of the letter itself. LOMA points can be viewed in the NFHL Interactive Map on the FEMA GeoPlatform.

The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector). This criterion assesses the extent to which environmental policies and institutions foster the protection and sustainable use of natural resources and the management of pollution.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. The ECOSTRESS Tiled Water Use Efficiency Instantaneous Level 4 Global 70 m (ECO_L4T_WUE) Version 3 data product provides Water Use Efficiency (WUE) data generated by dividing the Breathing Earth System Simulator (BESS) Gross Primary Production (GPP) by the Priestley-Taylor Jet Propulsion Laboratory Soil Moisture (PT-JPL-SM) transpiration to estimate WUE; the ratio of grams of carbon that plants absorb to kilograms of water that plants release. The product provides a BESS GPP estimate that represents the amount of carbon surrounding the plants. This tiled data product uses a modified version of the Military Grid Reference System (MGRS), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 meter (m) spatial resolution. The ECO_L4T_WUE Version 3 data product is provided in Cloud Optimized GeoTIFF (COG) format, and each variable is distributed as a separate COG. This product contains four variables including Water Use Efficiency (WUE), Gross Primary Production (GPP), cloud mask, and water mask. Known Issues * Solar Array Obstruction: Some ECOSTRESS scenes may be affected by solar array obstructions from the ISS, potentially impacting data quality in obstructed pixels. The 'FieldOfViewObstruction' metadata field is reliably populated and accurate for all products generated on or after October 2024, following fixes to the Product Generation Executive (PGE) docker configuration and updates to the L1A_RAW_PIX software. Prior to October 2024, the field was not reliably populated; a separate list of confirmed affected scenes was maintained and is recommended for verifying historical data. * Historical acquisition gaps and anomalies (2018–2023): Irrecoverable data loss occurred due to Mass Storage Unit (MSU) failures, resulting in permanent gaps in the observational record where no ECOSTRESS data was acquired during the following periods: - September 29, 2018 – December 4, 2018 (primary MSU failure) - March 14, 2019 – May 14, 2019 (secondary MSU failure) - February 17, 2020 (instrument in SAFEHOLD mode) - Other early issues, including band 4 striping (February 8–16, 2020), 3-band mode limitations (May 15, 2019–April 28, 2023), and noisy data periods (e.g., May–July 2025), have been mitigated through reprocessing, firmware updates, and instrument recovery. Version 3 products incorporate these improvements where data exists and are unaffected by ongoing issues, but the permanent historical gaps remain, and caution is advised for time-series analyses spanning affected periods. * Geolocation considerations: While geolocation is improved in Version 3 tiled products (fixed MGRS grid), gridded products may reference legacy "GeolocationAccuracyQA" flags. Use "best" and "good" scenes for applications sensitive to precise positioning.

Soil moisture content by volume, at tension 500 kPa (pF 3.7) (cm³/100cm³). WoSIS_latest is a 'dynamic dataset' that contains the most recent complement of quality-assessed and standardised soil data served from WoSIS (ISRIC World Soil Information Service). The source data were shared by a wide range of data providers (see: https://www.isric.org/explore/wosis/wosis-contributing-institutions-and-experts). Being dynamic, the contents of 'wosis_latest' will change once new point data are acquired, cleansed and standardised, additional soil properties are considered, and/or when possible amendments are required. Static snapshots of 'wosis_latest' are released at irregular intervals for consistent citation purposes and to discuss methodological changes; the last snapshot is available at https://doi.org/10.5194/essd-16-4735-2024. For general information about WoSIS please see the FAQ-page at https://www.isric.org/explore/wosis/faq-wosis.

This file provides an overview of the site locations where profiles (or point data) in WoSIS are located. Depending on the type of survey, one or more profiles can occur within a site in accordance with ISO 28528 soil domain conventions. WoSIS_latest is a 'dynamic dataset' that contains the most recent complement of quality-assessed and standardised soil data served from WoSIS (ISRIC World Soil Information Service). Being dynamic, this dataset will grow/change once new point data are acquired and standardised, additional soil properties are considered, and/or when possible amendments are required. Static snapshots of wosis_latest are released at irregular intervals for consistent citation purposes, see https://doi.org/10.5194/essd-16-4735-2024 and WoSIS FAQ-page (https://www.isric.org/explore/wosis/faq-wosis).

The World Soil Information Service (WoSIS) provides quality-assessed and standardised soil profile data to support digital soil mapping and environmental applications at broad scale levels. Since the release of the first ‘WoSIS snapshot’, in July 2016, many new soil data were shared with us, registered in the ISRIC data repository, and subsequently standardised in accordance with the licences specified by the data providers. Soil profile data managed in WoSIS were contributed by a wide range of data providers, therefore special attention was paid to measures for soil data quality and the standardisation of soil property definitions, soil property values (and units of measurement), and soil analytical method descriptions. We presently consider the following soil chemical properties (organic carbon, total carbon, total carbonate equivalent, total Nitrogen, Phosphorus (extractable-P, total-P, and P-retention), soil pH, cation exchange capacity, and electrical conductivity) and physical properties (soil texture (sand, silt, and clay), bulk density, coarse fragments, and water retention), grouped according to analytical procedures (aggregates) that are operationally comparable. Further, for each profile, we provide the original soil classification (FAO, WRB, USDA, and version) and horizon designations insofar as these have been specified in the source databases. Measures for geographical accuracy (i.e. location) of the point data as well as a first approximation for the uncertainty associated with the operationally defined analytical methods are presented, for possible consideration in digital soil mapping and subsequent earth system modelling. The present snapshot, referred to as ‘WoSIS snapshot - September 2019’, comprises 196,498 geo-referenced profiles originating from 173 countries. They represent over 832 thousand soil layers (or horizons), and over 6 million records. The actual number of observations for each property varies (greatly) between profiles and with depth, this generally depending on the objectives of the initial soil sampling programmes. The downloadable ZIP file has the data in TSV (tab separated values) and GeoPackage format. It contains the following files: - ReadmeFirst_WoSIS_2019dec04.pdf (546.7 KB) - wosis_201909.gpkg (2.2 GB, same data as in the tsv) - wosis_201909_attributes.tsv (8.7 KB) - wosis_201909_layers_chemical.tsv (893.5 MB) - wosis_201909_layers_physical.tsv (890.7 MB) - wosis_201909_profiles.tsv (18.8 MB) To read the data in R, please, uncompress the ZIP file and specify the uncompressed folder. Then use read_tsv to read the TSV files, specifying the data types for each column (c = character, i = integer, n = number, d = double, l = logical, f = factor, D = date, T = date time, t = time). setwd("/YourFolder/WoSIS_2019_September/") attributes = readr::read_tsv('wosis_201909_attributes.tsv', col_types='cccciicd') profiles = readr::read_tsv('wosis_201909_profiles.tsv', col_types='icccdddiicccciccccicccc') chemical = readr::read_tsv('wosis_201909_layers_chemical.tsv', col_types='iiddclcdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccc') physical = readr::read_tsv('wosis_201909_layers_physical.tsv', col_types='iiddclcdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccc') For more detailed instructions on how to read the data with R, please visit https://www.isric.org/accessing-wosis-using-r. Citation: Batjes N.H, Ribeiro E, and van Oostrum A.J.M, 2019. Standardised soil profile data for the world (WoSIS snapshot - September 2019), https://doi.org/10.17027/isric-wdcsoils.20190901. The dataset accompanies the following data paper: Batjes N.H., Ribeiro E., and van Oostrum A.J.M., 2019. Standardised soil profile data to support global mapping and modelling (WoSIS snapshot - 2019). Earth System Science Data, https://doi.org/10.5194/essd-12-299-2020.

Contains data from the National Flood Hazard Layer, a GIS database of flood risks and regulatory flood determination data.

The ICRAF-ISRIC Soil VNIR Spectral Library contains visible near infrared spectra of 4,438 soils selected from the Soil Information System (ISIS) of the International Soil Reference and Information Centre (ISRIC). The samples consist of all physically archived samples at ISRIC in 2004 for which soil attribute data was available. The spectra were measured at the World Agroforestry Center's (ICRAF) Soil and Plant Spectral Diagnostic Laboratory. The samples are from 58 countries spanning Africa, Asia, Europe, North America, and South America. Associated attribute data, such as geographical coordinates, horizon (depth), and physical and chemical properties, are provided in a single relational database. The purpose of the library is to provide a resource for research and applications for sensing soil quality both in the laboratory and from space.

The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station. EMIT uses imaging spectroscopy to take measurements of sunlit regions of interest between 52° N latitude and 52° S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal. In addition to its primary objective described above, EMIT has demonstrated the capacity to characterize methane (CH4) and carbon dioxide (CO2) point-source emissions by measuring gas absorption features in the shortwave infrared bands. The EMIT Level 2B Methane Enhancement Data (EMITL2BCH4ENH) Version 2 data product is a total vertical column enhancement estimate of methane in parts per million meter (ppm m) based on an adaptive matched filter approach. EMITL2BCH4ENH provides per-pixel methane enhancement data used to identify methane plume complexes, per-pixel methane uncertainty due to sensor noise, and per-pixel methane sensitivity that can be used to remove bias from the enhancement data. The EMITL2BCH4ENH Version 2 data product includes methane enhancement granules for all captured scenes, regardless of methane plume complex identification. Each granule contains three Cloud Optimized GeoTIFF (COG) files at a spatial resolution of 60 meters (m): Methane Enhancement (EMIT_L2B_CH4ENH), Methane Uncertainty (EMIT_L2B_CH4UNCERT), and Methane Sensitivity (EMIT_L2B_CH4SENS). The EMITL2BCH4ENH COG files contain methane enhancement data based primarily on EMITL1BRAD radiance values. Each granule is approximately 75 kilometers (km) by 75 km, nominal at the equator, with some granules at the end of an orbit segment reaching 150 km in length. Known Issues * Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe.

National Flood Hazard Layer (Fema Flood Maps) - Hosted by FEMA

Contains data from the National Flood Hazard Layer, a GIS database of flood risks and regulatory flood determination data.

Sodic soil grade based on WRB soil types and soil pH predicted using the global compilation of soil ground observations. Accuracy assessement of the maps is availble in Hengl et at. (2017) DOI: 10.1371/journal.pone.0169748. Data provided as GeoTIFFs with internal compression (co='COMPRESS=DEFLATE'). Measurement units: grade.

Total annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF, standardized to carbon dioxide equivalent values divided by the economy's population. This measure excludes GHG fluxes caused by Land Use, Land-Use Change and Forestry (LULUCF), as these fluxes have larger uncertainties.

Contains data from the National Flood Hazard Layer, a GIS database of flood risks and regulatory flood determination data.

A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from industrial combustion (subsector of the energy sector) including IPCC 2006 code 1.A.2 Manufacturing Industries and Construction. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).

A FEMA National Flood Hazard Layer map service relating to floodplain and hazard information, with keywords referencing Oklahoma and the OWRB. It is published by FEMA.

Contains data from the National Flood Hazard Layer, a GIS database of flood risks and regulatory flood determination data.

Contains data from the National Flood Hazard Layer, a GIS database of flood risks and regulatory flood determination data.

Change of emissions (as %) of current year with respect to emissions in baseline year 1990 emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5). Negative values indicate that the emission level for that year is lower than the emissions level in 1990.

Cummulative probability of organic soil based on the TAXOUSDA and TAXNWRB predicted using the global compilation of soil ground observations. Accuracy assessement of the maps is availble in Hengl et at. (2017) DOI: 10.1371/journal.pone.0169748. Data provided as GeoTIFFs with internal compression (co='COMPRESS=DEFLATE'). Measurement units: probability.

Distribution of peatland that covers the tropics and sub tropics, excluding small islands. It was mapped in 231 meters spatial resolution. Peat is here defined as any soil having at least 30cm of decomposed or semi-decomposed organic material with at least 50% of organic matter. This corresponds to 29% of carbon content using 1.72 as the transformation factor. The peatland map is produced by adding the peat forming wetlands: mangrove (20), swamp/bog (30), Fen (40), riverine (50), and floodswamps (60) (note: the number in parentheses refer to pixel code of each class in Wetlands dataset). Our map of peatlands was contrasted against n=275 geo-positioned soil profiles containing peat, with 65% of agreement. Further fieldwork is however needed to validate our map. Mangroves are here considered to host the thresholds of depth and organic matter content needed for peat definition, although mineral soil may prevail. Mangroves contribute with ca. 180,000 km2 to the 1.7 million km2 of peatlands (11%), which would need further ground validation (i.e. in areas like Indonesian Papua have large extents of mangrove that contribute to peat, which would need ground-truthing to validate if they contain peat as defined here).

Clay content (0-2 micro meter) mass fraction in ‰ at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. This map is the result of resampling the mean SoilGrids 250 m predictions (Poggio et al. 2021) for each 5000 m cell.

A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the building sector (subsector of the energy sector) including IPCC 2006 codes 1.A.4 Residential and other sectors, 1.A.5 Non-Specified. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).

Predicted probability in percent per class predicted using the global compilation of soil ground observations. Accuracy assessement of the maps is availble in Hengl et at. (2017) DOI: 10.1371/journal.pone.0169748. Data provided as GeoTIFFs with internal compression (co='COMPRESS=DEFLATE'). Measurement units: probability.

Annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF divided by the GDP in constant 2021 US$.