The Anomaly hotSpots of Agricultural Production (ASAP) Decision Support System was launched operationally in 2017 for providing timely early warning information on agricultural production based on Earth Observation and agro-climatic data in an open and easy to use online platform. Over the last three years, the system has seen several methodological improvements related to the input indicators and to system functionalities. These include: an improved dataset of rainfall estimates for Africa; a new satellite indicator of biomass optimised for near-real-time monitoring; an indicator of crop and rangeland water stress derived from a water balance accounting scheme; the inclusion of seasonal precipitation forecasts; national and sub-national crop calendars adapted to ASAP phenology; and a new interface for the visualisation and analysis of high spatial resolution Sentinel and Landsat data. In parallel to these technical improvements, stakeholders and users uptake was consolidated through the set up of regionally adapted versions of the ASAP system for Eastern Africa in partnership with the Intergovernmental Authority on Development (IGAD) Climate Prediction and Applications Centre (ICPAC), for North Africa with the Observatoire du Sahara et du Sahel (OSS), and through the collaboration with the Angolan National Institute of Meteorology and Geophysics (INAMET), that used the ASAP system to inform about agricultural drought. Finally, ASAP indicators have been used as inputs for quantitative crop yield forecasting with machine learning at the province level for Algeria’s 2021 and 2022 winter crop seasons that were affected by drought.

This study investigates the feasibility of implementing a 3D hydrogeological model in a vast data-scarce area in northwestern Niger where groundwater resources are strongly impacted by climate changes and anthropogenic activities (uranium mining) in northwestern Niger, West Africa. A large scale fully integrated hydrological model was built and calibrated using HydroGeoSphere. The model conceptualization and parametrization were done using various sources of datasets. The model was calibrated using a stepwise approach by increasing the temporal resolution of the model inputs: steady state, dynamic equilibrium, and transient state. The calibration results showed that the model could reproduce the measured groundwater heads with a correlation coefficient of up to 0.79. The model was then validated by comparing total water storage computed from GRACE data over the TMB and the outputs of the model. The results showed the model reproduced quite well the amplitudes and direction of the variations measured by GRACE. Therefore, GRACE data were useful in the qualitative validation of the model. The outputs of the model allowed a quantitative analysis of the groundwater resources of the Tim Mersoï Basin (TMB). The global recharge was estimated at 15.74 mm/year, i.e., an interannual average of 13.58% of the rainfall. Most importantly, this model can be used in a hierarchical model set up to provide initial and boundary conditions for local scales (mining areas for example) studies.

The United Nation 2023 Water Conference offers a critical opportunity to catalyse actions and innovations that bring increased water security to vulnerable communities across the globe. Researchers have an important role in supporting the delivery of needed on-the-ground impact, but their work must be informed by the priorities and necessities of Global South implementors.

Due to the semi-arid nature of the Sahelian countries in Africa, irrigation infrastructures are essential in supporting the improvement of agricultural production. Their proper operation is, therefore, a key indicator for the sustainable development of agriculture in this region. However, there is a lack of critical assessment on the operating state of these hydro-agricultural facilities in Burkina Faso. In this study, we applied a multiple correspondence analysis (MCA) to 4070 hydro-agricultural facilities from 1950 to 2020 and classified them according to the Permanent Interstate Committee for Drought Control in the Sahel’s (CILSS) typology classification system (Type 1 to Type 5). The MCA made it possible to see the relationships between a development typology and variables such as “functionality”, “condition of the development”, or “year of construction”. The results indicate that the irrigated lands with surface areas of less than 100 ha, which were funded by the government or organizations (associations, NGOs) and managed by local communities, are the least functional ones and in bad condition. Their dysfunction indeed conceals deep-seated causes that have not yet been resolved as the infrastructures keep on deteriorating. Therefore, establishing a sustainable and efficient management system for these agricultural infrastructures is imperative. The findings of this study can be used as a practical decision-making tool for implementing agricultural policies in the Sahel region.

African rangelands are threatened by anthropogenic land-use activities, adverse climate phenomena such as droughts, and poor land management. These undermine their capacity to support various fauna and flora, provide ecosystem services, and sustain livestock agriculture, i.e., a key economic activity in Africa. Therefore, preserving the integrity of African rangelands is critical for addressing African food security challenges. Using multi-sensor Earth observation data and Random Forest classifier, this study characterized the spatial distribution of African rangelands, to support grazing and browsing capacity modelling, assessment of rangeland changes, and rangeland management policy development and decision making. The results show that rangelands could be characterised with good accuracies exceeding 70% in most AfriCultuReS pilot countries using the high-resolution land cover map and MCD12Q1 products as training and validation data. The spatial distribution maps can be used as masks that would aid accurate monitoring of rangeland health, productivity, phenology and changes.

The late Middle Pleistocene, starting at around 300 ka, witnessed large-scale biological and cultural dynamics in hominin evolution across Africa including the onset of the Middle Stone Age that is closely associated with the evolution of our species—Homo sapiens. However, archaeological and geochronological data of its earliest appearance are scarce. Here we report on the late Middle Pleistocene sequence of Wadi Lazalim, in the Sahara of Southern Tunisia, which has yielded evidence for human occupations bracketed between ca. 300–130 ka. Wadi Lazalim contributes valuable information on the spread of early MSA technocomplexes across North Africa, that likely were an expression of large-scale diffusion processes.

Earth observation of waterbodies through time is a powerful tool in understanding both the location of waterbodies and their temporal dynamics. Water Observations from Space (WOfS), developed and well-tested in Australia, is a service providing historical surface water observations derived from Landsat satellite imagery from 1987 to present day. WOfS provides better understanding of where water is usually present; where it is seldom observed; and where inundation of the surface has been occasionally observed by satellite. We applied the WOfS algorithm to Africa and validated its accuracy through image interpretation of satellite and aerial imagery using an online tool created by the NASA Servir program, Collect Earth Online. The Digital Earth Africa Product Development Task Team, composed of four regional geospatial organisations RCMRD, AfriGIST, AGRHYMET and OSS, conducted the validation campaign and provided both the regional expertise and experience required for a continental-scale validation effort. In order to understand the accuracy and bias of the WOfS algorithm in Africa at both the continental-scale and regional zones, we generated 2900 sample points covering the continent including the main islands and distributed them into 7 Agro-ecological zones. We assessed whether the point was flooded, dry, or cloud covered, for 12 months in 2018, resulting in 34,800 assessed observations. As water information is available through WOfS in near real-time, it can be used for environmental monitoring, flood mapping, monitoring planned water releases, and management of water resources in highly regulated systems. WOfS is expected to be used by ministries and state departments of agriculture and water management in countries, international organizations, academia and the private sector.

An increasing number of products and services based on satellite Earth Observation (EO) data are being developed for use by decision-makers in African agricultural contexts, providing information such as weather and climate forecasts, crop yields and water availability. Capacity development to support impactful use of EO data is a key component of many EO-for-development initiatives, but there is little consensus over where or how capacity should be developed. Our goal in this piece is to provide a critical perspective on the capacity development required to support the creation of more impactful EO data services. Drawing on a capacity needs assessment carried out as part of the AfriCultuReS project (a major EO-for-development initiative), we identify proximate factors which inhibit the success of EO data services such as flawed communication strategies, low relevance in African agricultural contexts, duplication of existing products, and lack of financial sustainability. We link these proximate challenges to deeper issues such as unequal access to funding and resources, fragmentation in the EO field, and relational asymmetries of power, all of which combine to exclude important forms of knowledge from decision-making. Based on this needs assessment, we argue that capacity development requires broader systems-based approaches which develop the capacities of all actors (including those in the Global North) to respect different forms of knowledge, use and participate in co-design approaches, and recognise and challenge the asymmetries of power which currently limit the involvement of certain groups in processes of EO data service design.

A central focus for governing bodies in Africa is the need to secure the necessary food sources to support their populations. It has been estimated that the current production of crops will need to double by 2050 to meet future needs for food production. Higher level crop-based products that can assist with managing food insecurity, such as cropping watering intensities, crop types, or crop productivity, require as a starting point precise and accurate cropland extent maps indicating where cropland occurs. Current continental cropland extent maps of Africa are either inaccurate, have too coarse spatial resolutions, or are not updated regularly. An accurate, high-resolution, and regularly updated cropland extent map for the African continent is therefore recognized as a gap in the current crop monitoring services. Using Digital Earth Africa’s Open Data Cube platform, and working in conjunction with multiple regional African geospatial institutions, we co-develop a 10 metre resolution cropland extent map over the African continent using a Random Forest machine learning classifier and an annual time-series of Sentinel-2 satellite images. Members of the regional African geospatial institutions (RCMRD, OSS, Afrigist, AGRHYMET, and NADMO) were instrumental in defining the specifications of the product, in developing and implementing a continental scale reference data collection strategy, and assisted with iterative model building. The cropland extent map comes packaged with three layers: a pixel-based classification, a pixel-based cropland probability layer, and an object-based segmentation filtered classification. All the components of Digital Earth Africa’s cropland extent map: models, reference data, code, and results are open source and freely available online through Digital Earth Africa’s mapping and analysis platforms. A fuller description of the dataset, including methods, the validation results, and how to access the different datasets can be seen on the DE Africa user guide: https://docs.digitalearthafrica.org/en/latest/data_specs/Cropland_extent_specs.html