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.

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.

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.

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.

Soil organic carbon (SOC) is the carbon that remains in the soil after partial decomposition of any material produced by living organisms. It constitutes a key element of the global carbon cycle through atmosphere, vegetation, soil, rivers and the ocean. It is a crucial contributor to food production, mitigation and adaption to climate change. Soils represent the largest terrestrial organic carbon reservoir. Depending on local geology, climatic conditions and land use and management (amongst other environmental factors), soils hold different amounts of SOC. This map shows the amount of carbon stored in the soil (from 0 to 30 cm depth), expressed in Mg (megagrams or tonnes) per km2.

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.

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