Côte d'Ivoire and Ghana are the main largest producers of cocoa in the world. However, the cultivation of this crop has led to the loss of vast tracts of forest areas in both countries. Efficient and accurate methods for remotely identifying cocoa farms are essential for the implementation of sustainable cocoa practices and the periodic and effective monitoring of forests. This map, generated using Random Forest image classification, shows the 2019 distribution of cocoa farms in both countries. The estimated area for cocoa is 4.8Mha for Cote d'Ivoire and 2.3Mha for Ghana.

This layer presents the Effective Leaf Area Index (LAIe) Anomalies, representing the deviation of current vegetation density from the historical average. Monitoring the change of LAI is essential for assessing the evolution of the vegetation over Africa.

LAI anomalies are calculated relative to the average values between 2003 and 2010. The dataset captures LAI anomalies every 10 days, reflecting the high variability and rapid changes in African vegetation cover. Increases in temperature and precipitation deficits are the primary drivers for negative anomalies (reduced foliage density). Beyond climatic factors, human activities or animal grazing may also locally impact the state and density of leaves.

This layer is part of the a href="https://fapar.jrc.ec.europa.eu/_www/index.php">Earth Land Information System (ELIS).

The Fraction of Photosynthetically Active Radiation Absorbed (FAPAR) is used to track the overall primary productivity associated with atmospheric CO2 fixation. FAPAR anomalies relative to the average between 2003 and 2010 show large surface variations, in terms of values and coverage, of vegetation productivity conditions over Africa. Temperature and precipitation deficits are the main drivers for the negative anomalies. Each location with a negative anomaly (FAPAR value lower than the long-term mean for that location – shades of red) indicates relative vegetation stress during that 10-day interval. Each location with a positive anomaly (FAPAR value higher than long-term mean for that location – shades of green) indicates relative favourable vegetation growth conditions during that 10-day interval. FAPAR values and their anomalies provide useful information for water and agricultural management purposes.

This layer is part of the Earth Land Information System (ELIS).

Food crisis response planning can save lives if put in place in a timely manner. To do this, decision makers must be warned of climate extreme events impacting agricultural production. The Anomaly hotSpot of Agricultural Production tool (ASAP) is an online decision support system for early warning about hotspots of agricultural production anomaly (crop and rangeland), developed by the JRC for food security crises prevention and response planning anticipation. This map shows the frequency at which countries were classified as hotspots for agricultural production problems between 2004 and 2018. Hotspots are identified on a monthly basis.

Food crisis response planning can save lives if put in place in a timely manner. To do this, decision makers must be warned of climate extreme events impacting agricultural production. The Anomaly hotSpot of Agricultural Production tool (ASAP) is an online decision support system for early warning about hotspots of agricultural production anomaly (crop and rangeland), developed by the JRC for food security crises prevention and response planning anticipation. This map shows the frequency of ASAP anomaly warnings for crop growth for 2004-2018. It highlights the high sensitivity of the main agricultural areas in Northern Africa, the Horn of Africa and the Southern African Development Community to drought conditions.

Food crisis response planning can save lives if put in place in a timely manner. To do this, decision makers must be warned of climate extreme events impacting agricultural production. The Anomaly hotSpot of Agricultural Production tool (ASAP) is an online decision support system for early warning about hotspots of agricultural production anomaly (crop and rangeland), developed by the JRC for food security crises prevention and response planning anticipation. This map shows the frequency of ASAP anomaly warnings for rangeland growth for 2004-2018. It highlights the high sensitivity of the main agricultural areas in Northern Africa, the Horn of Africa and the Southern African Development Community to drought conditions.

The EC JRC global map of forest cover provides a spatially explicit representation of forest presence and absence for the year 2020 at 10m spatial resolution.

The year 2020 corresponds to the cut-off date of the Regulation from the European Union "on the making available on the Union market and the export from the Union of certain commodities and products associated with deforestation and forest degradation" (EUDR, Regulation (EU) 2023/1115). In the context of the EUDR, the global forest cover map can be used as a non-mandatory, non-exclusive, and not legally binding source of information. Further information about the map and its use can be found on the EU Observatory on Deforestation and Forest Degradation (EUFO) in the section on Frequently Asked Questions.

Forest means land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10%, or trees able to reach those thresholds in situ, excluding land that is predominantly under agricultural or urban land use. Agricultural use means the use of land for the purpose of agriculture, including for agricultural plantations (i.e. tree stands in agricultural production systems such as fruit tree plantations, oil palm plantations, olive orchards and agroforestry systems) and set- aside agricultural areas, and for rearing livestock. All plantations of relevant commodities other than wood, that is cattle, cocoa, coffee, oil palm, rubber, soya, are excluded from the forest definition.

The global map of forest cover was created by combining available global datasets (wall-to-wall or global in their scope) on tree cover, tree height, land cover and land use into a single harmonized globally-consistent representation of where forests existed in 2020.

The workflow consisted in first mapping the global maximum extent of tree cover circa the year 2020 from the combination of ESA World Cover 2020 and 2021, WRI Tropical Tree Cover 2020, UMD Global land cover and land use 2019, Global Mangrove Watch 2020, and JRC Tropical Moist Forest 2020 datasets. In the second step, a series of overlays and decision rules were applied to reduce this maximum extent of tree cover and align it with the Forest definition using datasets covering cropland and commodity expansion (ESA World Cereal, UMD Global land cover and land use 2019, UMD Global Cropland Expansion, High-resolution global map of smallholder and industrial oil palm plantations, and WRI Spatial Database of Planted Trees), land use change (UMD global forest cover loss, JRC Tropical Moist Forest, IIASA Global Forest Management), built-up (JRC Global Human Settlement), and water (JRC Global Surface Water).

The detailed mapping approach will be described in a separate technical report expected to be released by March 2024. The accuracy of this map has not been yet assessed but will be reported as soon as available.

Please also refer to the list of known issues and to the JRC Data Catalogue entry.

The map is based on Copernicus Global Land Cover data which shows actual land cover (what physically covers land across the globe—forests, grasslands, croplands, lakes, wetlands, built-up area, etc) at 100m x 100m resolution. The land cover data for Africa was overlaid on (high resolution) satellite imagery of Africa, which is then used to determine the land use (by visual interpretation) associated with the various land cover patterns on the Copernicus Global Land Cover map.

Copernicus land cover data offers several advantages: it is of high quality, has a high resolution (100m x 100m), and offers time series satellite imagery. More importantly, the Copernicus Global Land Service is a continuous process and its datasets are updated annually. This means that the Soils4Africa map of agricultural land can be updated every time the land cover data for the new year becomes available (the map is currently based on 2019 data).

The Copernicus dataset already includes the category ‘cropland,’ which by definition is part of agricultural land. The Soils4Africa map broadens the scope of that dataset by infering information on agricultural use and including other kinds of agricultural land use such as grazing pastures and plantations.

When land is mapped as other than ‘cropland'-- like ‘shrubland’ for example-- it is more difficult to interpret and determine whether it is under agricultural use. The Copernicus dataset includes information about ‘fractional cover’-- or the percentage of a particular pixel under a particular kind of land cover (for example, 30% of a 100m x 100m pixel could be forest and 20% could be shrubland). The Soils4Africa map takes into account how fractional cover varies over an area to establish rules for interpreting its land cover data to determine whether it is under agricultural use and for what purpose. These rules were validated by comparing it with ground level information on land use (or land use pattern) for specific areas drawn from the interpretation of satellite imagery from Google Earth.

For example, ground-level observation shows that forest cover upwards of 30% in a given area when matched by shrubland cover of over 30%, is characterized by woody vegetation with a smooth canopy. Therefore, such area is more likely to be under plantations rather than natural forest. Thus, such an area should be counted as agricultural land, even if less than 15% of it is under crops.

Monitoring of sea surface temperature (SST) provides fundamental information on the global climate system and for the study of marine ecosystems. This layer compares the SST value of the last full month with the long-term mean SST. A positive anomaly (warm colours) means the monthly SST is warmer than the long-term average for that month; a negative anomaly (cool colours) means it is cooler than the average.

The United Nations Educational, Scientific and Cultural Organization (UNESCO) seeks to encourage the identification, protection and preservation of natural heritage around the world considered to be of outstanding value to humanity. This is embodied in an international treaty called the Convention concerning the Protection of the World Cultural and Natural Heritage, adopted by UNESCO in 1972. What makes the concept of World Heritage exceptional is its universal application. World Heritage sites belong to all the peoples of the world, irrespective of the territory on which they are located.

Monitoring of sea surface temperature (SST) provides fundamental information on the global climate system and for the study of marine ecosystems. For example, it helps estimating heat stress conducive to coral bleaching, the process by which they expel the symbiotic algae living in their tissues and become white (bleached) and vulnerable. The NOAA Coral Reef Watch's daily global 5km satellite SST Anomaly (SSTA) compares the daily SST value with the long-term mean SST. A positive anomaly (+1.0 °C or more, warm colours) means the daily SST is warmer than the long-term average for that day; a negative anomaly (-1.0 °C or less, cold colours) means it is cooler than the average.

In most people's mind, soil would not figure highly in a list of the natural resources of Africa. However, healthy and fertile soils are the cornerstones of food security, key environmental services, social cohesion and the economies of most African countries. Unfortunately, soil in Africa tends to reach public awareness only when it fails – often with catastrophic consequences as seen by the famine episodes of the Sahel in the 1980s and more recently in Niger and the Horn of Africa. In the context of major global environmental challenges such as food security, climate change, fresh water scarcity and biodiversity loss, the protection and the sustainable management of soil resources in Africa are of paramount importance. This layer presents the diversity of soil types across Africa. This map was produced by the Joint Research Centre of the European Commission for the Soil Atlas of Africa.

Agricultural drought events can affect large regions across the world. Soil moisture (or soil water content) is an important variable for plant growth, and - together with precipitation and evapotranspiration - is a basic component of the hydrological cycle. The Soil Moisture Anomaly (SMA) indicator is used to detect and monitor agricultural drought, that is when there is reduced crop production due to insufficient soil moisture. It is computed as a deviation from the climatological reference period, and is updated 3 times a month (after the 10th, the 20th and the last day of the month). This layer displays the map for the last full decade of the current month. Negative anomalies (shades of brown) represent dry conditions.

Tropical moist forests have a huge environmental value. They play an important role in biodiversity conservation, terrestrial carbon cycle, hydrological regimes, indigenous population subsistence and human health (1-5). They are increasingly recognized as an essential element of any strategy to mitigate climate change. Deforestation, and degradation compromise the functioning of tropical forests as an ecosystem, lead to biodiversity loss and reduced carbon storage capacity. Deforestation and fragmentation are increasing the risk of virus disease outbreaks. This map shows where deforestation occurred in the last three decades (between 1982 and 2020) and the year when the forest cover has been deforested for the first time (followed or not by a regrowth).

Tropical moist forests have a huge environmental value. They play an important role in biodiversity conservation, terrestrial carbon cycle, hydrological regimes, indigenous population subsistence and human health (1-5). They are increasingly recognized as an essential element of any strategy to mitigate climate change. Deforestation, and degradation compromise the functioning of tropical forests as an ecosystem, lead to biodiversity loss and reduced carbon storage capacity. Deforestation and fragmentation are increasing the risk of virus disease outbreaks. This map shows where degradation occurred in the last three decades (between 1982 and 2020) and the year when the forest has been degraded for the first time (and remained degraded up to 2020).

Tropical moist forests have a huge environmental value. They play an important role in biodiversity conservation, terrestrial carbon cycle, hydrological regimes, indigenous population subsistence and human health (1-5). They are increasingly recognized as an essential element of any strategy to mitigate climate change. Accurate characterization of the tropical moist forests changes is needed to support conservation policies and to better quantify their contribution to global carbon fluxes. The transition map captures the dynamics of changes in tropical moist forests between the initial observation period (1990) and the end of the year 2020.

This dataset represents a geographic clip to Africa of the World Database on Protected Areas (WDPA), which is the authoritative and most complete global dataset on terrestrial and marine protected areas. The parent database is a joint initiative between the UN Environment Programme (UNEP) and the International Union for Conservation of Nature (IUCN), managed by the UNEP World Conservation Monitoring Centre (UNEP-WCMC). Information is submitted and verified by international secretariats, national and regional governments, NGOs, communities, and landowners.

This spatial dataset has been specifically filtered and clipped to include only protected areas located within the terrestrial and marine boundaries of the African continent and its associated island nations.

The dataset comprises both spatial data (GIS boundaries) and attribute data (descriptive information) for protected sites across Africa. Key attributes include site name, designation type (national, regional, or international), governance model, IUCN management category, marine/terrestrial status, and legal establishment date.

The WDPA is the primary global mechanism used to track progress toward international area-based conservation targets. This African subset is widely used across various sectors for:

• Conservation & Science: Ecological research, protected area monitoring, and biodiversity assessments specific to African biomes.
• Corporate & Financial Risk: Environmental impact assessments and supply chain risk analyses for infrastructure and commercial projects operating in Africa.
• Policy & Reporting: Informing national and international decision-making and tracking regional progress toward the UN Sustainable Development Goals and the Kunming-Montreal Global Biodiversity Framework.

The global WDPA dataset is updated and released on a monthly basis through the Protected Planet platform. This clip was extracted on May 2026.

Vegetation fires have become a major concern in Africa because of their negative impacts on the environment and on human welfare. Uncontrolled (and un-prescribed) wildfires cause forest and vegetation degradation and related biodiversity loss, resulting in immediate and long-term impacts on the livelihoods of local communities and upstream impacts on national and regional economies. Fires in the tropical environment are a major contributor to tropical forest degradation and, if too frequent, can lead to savannisation of these areas. Vegetation fires are also a significant source of trace gases and aerosols in the atmosphere and contribute to the anticipated climate change, particularly with emissions of CO2. This layer shows the deviation of dekadal fire occurrences from the long-term average of the same 10-day period. A positive anomaly means more fire events than average for the last full 10-day period (red). A negative anomaly means less fire events than average for the last full 10-day period (green).

Crop conditions monitoring is highly relevant for food security early warning and response planning in food-insecure areas of the world. GEOGLAM (the Group on Earth Observations' Global Agricultural Monitoring Initiative) aims to reinforce the international community's capacity to produce and disseminate relevant, timely, and accurate forecasts of agricultural production at national, regional, and global scales using Earth Observation data.

Copernicus4GEOGLAM, one of the Copernicus Land Monitoring Services managed by the EC Joint Research Centre, aims to produce baseline information that allows countries in Africa to improve their agricultural monitoring systems.

This dataset aggregates crop maps requested by three East African nations, showing the agricultural situation at the end of the long rain season of 2021. The results are made fully and freely accessible, covering the following areas:

• Kenya: 98,687 km²
• Tanzania: 116,190 km²
• Uganda: 89,296 km²

This dataset provides the global geographic distribution of key livestock species—cattle, sheep, and goats—for the year 2010, sourced from the Gridded Livestock of the World (GLW 3) database. Expressed as the total number of animals per pixel at a spatial resolution of 5 minutes of arc, these layers are essential for diverse applications in agricultural socio-economics, food security, environmental impact assessments, and epidemiology.

Chlorophyll-a concentrations (Chla) are an indicator of phytoplankton abundance and biomass in open waters. They can be an effective measure of trophic status and are commonly used to measure water quality. This layer compares the Chla value from the last full month with the long-term mean Chla. A positive anomaly (warm colours) means the monthly Chla is higher than the long-term average for that month; a negative anomaly (cool colours) means it is lower than the average.

Increasing water scarcity and water quality issues are serious constraints in Africa and worldwide. Measuring precipitation anomalies is important for detecting and characterizing meteorological droughts, and, in the agricultural sector especially, for effectively managing climate related uncertainties. This layer shows the deviation of the precipitations of the last full month from the long-term average of the same month. A positive anomaly (shades of blue) means there was more rainfall than average during that month. A negative anomaly (yellow to red) means there was less rainfall than average during that month.