Irrigated agriculture is right in the centre of the nexus of water resource management and food security in Africa. As a part of the EO Africa Explorers initiative, the EOMAJI project has the aim of inventorying existing irrigation perimeters and improving their performance, enhancing the management of water licensing and permits, and laying ground for sustainable development of irrigated agriculture in Africa. To achieve this purpose, EOMAJI is making use of inputs from the scientific ECOSTRESS and PRISMA missions together with Sentinel-2 and -3 imagery, in order to produce daily ET estimates at a farm scale. Built upon previous experience on ET modelling with Sentinel imagery (ESA Sen-ET and ET4FAO projects), this project investigates i) the potential of PRISMA hyperspectral observations to improve biophysical trait estimations, with special focus on quantification of photosynthetic and non-photosynthetic vegetation fractions, which are key inputs in order to better quantify the photosynthetic and heat exchange capacity of the canopy; and ii) sharpening, data fusion, and gap filling techniques to produce continuous cloud-free daily ET maps. These high spatial and temporal resolution ET products will serve as an exploration of the future operational satellite mission capabilities in deriving high-level products of crop yield and irrigation accounting. These products will allow a better understanding of water use efficiency of cultivated landscapes. Both intermediate (ET, biophysical traits) and final products (crop yield, irrigation delimitation and accounting) will be evaluated against available in situ measurements collected outside Africa, in already running long-term experimental sites (Majadas de Tiétar in Spain and other ICOS sites), as well as in African sites in collaboration with African end-users and scientists: the Ministry of Agriculture of Burkina Faso, the Ministry Lands and Water Affairs of Botswana, and the University of Pretoria/CSIR in South Africa.

Smallholder agriculture is responsible for more than half of the global food yield, and predominantlythrives in low- to middle-income regions of Africa, Asia, and Latin America. However, owing to thedynamic nature and diversity inherent in smallholder agriculture, accurate agricultural estimates pose aformidable challenge. Developing countries are prominent for smallholder and intercropping practices,but the impacts of these practices on yields are also not well captured in research. Additionally,experimental fields are used to calibrate agro-models for these regions which do not reasonablyrepresent the complex diversity of the smallholder system. The aforementioned gaps have motivatedthis research and necessitate a deeper understanding of crop yields along the gradient of mono andintercropped cropping systems using field data from local smallholder farmers. First, we integratedremote sensing data (Sentinel 2) to generate Leaf Area Index (LAI) phenology to calibrate our cropsimulations. Then we simulated yields across mono and intercropped fields in the Model for Nitrogenand Carbon in Agro-ecosystems (MONICA). The results of the spatial simulations revealed theimpacts of annual weather conditions and their interaction with local conditions on annual attainableyields. Potato yields between 4200 – 7500 kg ha–1 are attainable across different spatiotemporal windows and under all sowing windows. While our simulated grain maize yields are between 2000 –4500 kg ha–1 and attainable across different spatiotemporal windows and under all sowing windows.Our results further revealed higher yields for potatoes under mid and late-sowing options; unfortunately,the current practice on the Jos plateau is early sowing to avoid the severe impacts of diseases, andpotatoes are harvested at the onset of diseases to avoid tuber decay. This implies that maximum yieldsmight not be attainable under the current climate change status. On the other hand, shifting sowingwindows did not show significant variations for maize yields, especially from 2015 to 2010. We,therefore, suggest early warning systems to inform farmers about the best windows for sowingannually. The impacts of pests, diseases, and smallholder farming complexities cannot be captured inthe simulations without the LAI phenological calibrations. Through this integrated approach, valuableinsights were provided into optimizing crop simulation. This highlights the significance of remotesensing data especially the Sentinel 10m bands in improving phenological data for smallholder regionswhere such data are lacking or very tedious to collect.

Improved agricultural water management can significantly reduce water demand by enhancing soil water management. A cost-effective method based on remote sensing for the estimation of the crops evapotranspiration (ETa) can help to optimize the irrigation scheduling and to enhance water use efficiency. This project, funded by ESA, started in November 2022, within the “EO Africa Explorers” framework. The team is composed by Planetek Italia, the International Center for Advanced Mediterranean Agronomic Studies, Planetek Hellas with the participation of local stakeholders playing the role of Early adopters, particularly the Egyptian National Authority for Remote Sensing & Space Sciences and the “October sixth for agricultural projects” company. The aim is to demonstrate an open-source innovative model, assessing ETa by a combination of ET0, crop coefficient (Kc) and water stress coefficient (Ks) over a pilot area in northern Egypt. The output will be integrated into a web platform as a Decision Support System (DSS) to improve irrigation water management. As second project's aim, the capabilities of hyperspectral sensors in measuring ETa are evaluated. Data acquired from PRISMA and EnMap sensors in the VNIR part of the spectrum, as well as Landsat and Ecostress for the thermal, are utilized for this purpose. Additionally, Sentinel-2 data are used as performance benchmark for hyperspectral data. Machine learning techniques is also used to better exploit the potential of the hyperspectral content and evaluate possible improvements in the performance of the ETa prediction models. The results are validated using in-situ ground data collected twice a month during the growing seasons of two different crops. The model's development and validation has followed an iterative process involving development, testing, validation, and refinement cycles. Initial results from the first season demonstrate the feasibility of the methodology and indicate good agreement between EO-derived ETa and the in-situ data.

The design of science-based policies to improve the sustainability of agriculture in Sub-Saharan Africa is challenged by a limited understanding of fundamental system properties, such as the spatial distribution of active cropland, crop types, and field sizes across larger regions. High-resolution field delineations are a prerequisite for a detailed understanding of the properties of agriculture in heterogeneous landscapes. We integrated very high spatial resolution Earth observation data and state-of-the-art deep learning methods to derive detailed crop field delineations across large spatial extents. We based our approach on a pre-trained Residual U-Net and leveraged self-training based on quality-filtered pseudo-labels to minimize the requirements for costly, human-annotated reference data. We deployed the fine-tuned model on image mosaics of pansharpened SPOT6/7 data (1.5 m spatial resolution) from the Airbus OneAtlas Living Library for the target year 2017. We conducted quality filtering of fields based on prediction confidence, size, and shape attributes. The result of our work is the first national-level dataset of individual fields for Mozambique (covering ~800,000 km²) for the target year 2017. For validation, we compared predictions with a set of human-labeled fields. We obtained mean field-level intersection over union (IoU) scores of around 0.7, representing an increase of more than 0.1 compared to the pre-trained model. Up to 60% of the fields reached IoU scores above 0.8, which is advancing the state-of-the-art in field delineation in Sub-Saharan Africa. Key challenges in these smallholder-dominated systems were fragmented landscapes with very small (< 0.001 ha) and very large (> 100 ha) fields, a pronounced heterogeneity in land management, as well as steep gradients in climate, topography, and land use intensity. The associated field-level uncertainty estimates permit users to balance omission and commission errors matching the accuracy requirements of the intended downstream applications. The ongoing generation of a second field inventory for the target year 2022 will enable assessments of land consolidation and fragmentation, offering valuable insights for future land use planning and decision-making processes. Our work has substantial relevance for assessments of the socio-economic and environmental properties linked to agriculture, including livelihoods, productivity, or biodiversity, as well as their trade-offs.

Maize production potential mapping is essential for optimally targeting agronomic strategies and optimizing agricultural inputs, particularly in low-yield environments. Existing studies often use single-source information to assess production potential, failing to account for other factors affecting production. Within the EO AFRICA R&D Research Project CropClim (Remote Sensing and modelling to assess crop-specific response to climate stressors). We integrate meteorological weather forecasts, cropping systems characterization and conditions from Sentinel-1 and Sentinel-2 time series, and crop modelling simulations to assess maize production potential in Busia County in Kenya. For this Random Forest classifier was trained on input features from the Sentinel-1 data and Sentinel-2 data to characterize crop types and crop conditions in the growing season. As input for classification, Sentinel-1 backscatter was integrated with Sentinel-2 spectral bands and derived vegetation indices. The crop condition was based on a multi-year model based on optical data. The goal is to uncover production performance potential, which contributes to exploring site-specific strategies for enhancing maize production. The study integrated these factors within a fuzzy expert system model to generate low, moderate, and high production potential zones. The results show that 79% and 67% of maize-cultivated zones are characterized by low production potential in the 2019 and 2021 growing seasons, respectively. Also, 16% and 24% are characterized by moderate maize production potential in the two seasons, whereas 4% and 8% are characterized by high production potential. The study concludes that integrating information on croplands with process-based model output and in-season meteorological forecasts offers opportunities for identifying areas for enhancing maize production. This also has implications for applying agricultural inputs and tailoring measures for production improvement at spatial explicit scales. Keywords: Remote Sensing; Crop conditions; Meteorological forecasts; Fuzzy expert systems

Flooding is one of the most destructive natural catastrophes claiming lives and causing property damage across the world. In West Africa, frequent catastrophic flood events have occurred in recent years in the Comoe Basin, Côte d’Ivoire. The aim of this research is to identify flood-vulnerable zones in the catchment area using fuzzy-analytical hierarchy process (F-AHP) models and help improve the resilience of populations within the basin. This multi-criteria analysis method combines fuzzy logic and the AHP approach and involves determining and analysing the weights of various factors including slope land use and drainage densities and establishing a comprehensive calculation model. Fuzzy logic is used to objectively establish the classes of the different factors identified and AHP method to weigh the factors retained for the evaluation of flood zones. This multi-criteria analysis approach allows the integration of multi-source earth observation (EO) data and a total of 11 parameters were considered in this study. Flood susceptibility maps created using EO data and geographic information systems were divided into five classes. F-AHP flood susceptibility models identified 33 %, 16% and 7% of the catchment area as moderate, high and very high flood risk zones, respectively. Results generated maps identifying existing and futures areas vulnerable to flooding which may support decision-makers and land-use planners to implement flood mitigation plans. Key words: Flooding, Fuzzy-AHP, remote sensing, GIS, susceptibility, Comoe Catchment, Cote d’Ivoire.

This study explores the integration of Earth Observation (EO) data with community insights to enhance water resource management in Lake Victoria, Kenya. Utilizing data from EO satellites such as SWOT and Sentinel alongside insights from the Dunga and Sangorota communities along Lake Victoria Kenya, the research assesses how water variability impacts local livelihoods, health, and resilience. Findings indicate significant challenges from water scarcity, flooding, and pollution, which severely affect daily life and increase vulnerability to climate variability. Notably, the communities have observed fluctuations in lake water levels, attributed to varied precipitation patterns and anthropogenic factors, disrupting traditional livelihoods and exacerbating health risks. The study highlights the critical role of women, who are primarily responsible for water management, emphasizing the need for gender-sensitive approaches in governance and infrastructure development. By merging EO data with socio-economic ground analysis, this research offers a comprehensive view of the water-related challenges facing the Lake Victoria communities. It advocates for the development of integrated water resource management strategies that blend local and scientific knowledge to enhance policy making, community preparedness, and sustainable development. This integrated approach not only improves the accuracy and applicability of EO data for local water management but also fosters community engagement and empowerment through participatory science. The study underscores the importance of infrastructure improvements, community education, and the establishment of early warning systems to mitigate water-related challenges in the Lake Victoria region. Keywords: Earth Observation, community resilience, water resource management, Lake Victoria - Kenya, gender dynamics, policy development.

A profound amount of global food production is contributed by smallholder farmers. Hence, information concerning crop health, crop type, and crop yield in smallholder farming areas would be essential for planning and making informed decisions about food security, agricultural insurance, and crop market stability. Even though current advances in image processing and the availability of multi modal satellite imagery provide an opportunity for monitoring agricultural landscapes with fine temporal and spatial granularity, the generation of crop type information and annual and seasonal crop statistics in smallholder farming areas is still a challenging task. This is mainly attributed to complex farm patterns characterized by fragmentation, intercropping and spatial occupancy with complex terrain. More importantly, in tropical smallholder farming areas the extensive prevalence of thick clouds in the growing season poses a formidable challenge to utilize available optical sensors which call for the proper fusion of sparse multi-source multi-temporal earth observation data. The study site is situated in the northwestern part of Ethiopia, where the smallholder farming system is the mainstay of livelihood production. In this study, we have fused cloud-free sparse time series optical and radar images from Sentinel-2, PlanetScope, and TerraSAR-X dual polarimetry radar observations in the peak growing season. For training and validation of deep learning models, spatially distributed crop-type samples were collected from the field and further visual inspection and quality check of samples is done using mono-time SkySat high-resolution optical images. For modeling crop yield, field based measurement of leaf area index and crop yield data are also collected from selected farm plots. After proper cloud masking and spatial co-registration of all datasets, time series temporal profiles were generated for model training and testing. Sparse data fusion is done on spectral, feature, and decision level fusion using state-of-the-art deep learning models for time series earth observation, viz, Transformers, Inception time, Long-short Term Memory Networks (LSTM), temporal Convolutional Neural Networks (tempCNN) and Gated Recurrent Units (GRUs). Using the same data and procedure, a performance comparison is also done with known machine learning models – random forests and support vector machines using both mono-source and fused inputs. Results show that in the mono source scenario, though time series-based deep learning models surpassed the performance of random forest and support vector machines the performance difference is not by a big margin. During multi-temporal sparse time series fusion, in all experimental setups decision-level fusion performs better than feature-level fusion. Beyond performance variations among models and specific crop types, multi-temporal sparse earth observation data fusion has yielded promising results to map crop types in complex smallholder farming areas where an overall F-1 score of ~80% is achieved. Results of modeling yields from dominant crops indicate that ~90% of crop yield variance can be explained by spectral and vegetation indices from time series earth observation datasets. Beyond indicated results, the study identifies challenges and further gaps to be addressed in crop type mapping and yield estimation in tropical smallholder farming areas.

African pastoral communities are profoundly vulnerable to climate change and drought, which pose severe risks to their livelihoods dependent on livestock and nomadic herding practices. Index-based livestock insurance (IBLI) initiatives aim to mitigate the financial impact of these risks by compensating pastoralists for livestock losses based on predetermined indices. In this study, we explore and demonstrate opportunities to leverage Earth Observation data for advancing IBLI implementation in African pastoral communities. Existing IBLI risk assessment models primarily utilize simple greenness indices (such as the Normalized Difference Vegetation Index or NDVI), which may inadequately represent the variegated forage types present in natural grazing lands. We propose a refined approach by partitioning satellite leaf area index data for 20+ years into herbaceous and woody leaf components, to mirror the distinct grazing and browsing habits of different livestock. We also derive and showcase more accurate historical (multi-epochal) woody vegetation maps for African drylands, derived using combined SAR/Optical satellite data, which is a precursor to our methods for partitioning leaf area index into different forage types. Finally, through a comparative analysis using historical livestock mortality data from Northern Kenya, we showcase how partitioned leaf area index outperforms traditional NDVI-based assessments in predicting livestock mortality, demonstrated by lower root mean squared error and higher R² values. This research underscores the potential of utilizing detailed satellite-derived forage data to reduce basis risk and enhance the precision of IBLI assessment, fostering more resilient pastoral communities.

Food insecurity remains a pressing challenge across Africa, exacerbated by factors like population growth, climate change impacts, and limitations in agricultural monitoring systems. Timely and accurate information on crop conditions is crucial for early warning, food supply planning, and enhancing resilience. However, many African nations lack the capacity for consistent, reliable crop monitoring to support food security decision-making. We explore how leveraging Earth observation (EO) data through regional and national crop monitoring initiatives can help bridge this gap. We highlight lessons from successful programs like SERVIR, NASA Harvest Crop Monitor, and country-level implementations to demonstrate the value of EO-based crop monitoring for enhancing food security preparedness in Africa. The case of Zambia exemplifies these opportunities. With over 20 million people to feed and 32.1% of land under agriculture, Zambia has a critical need for crop monitoring to increase food security. Previously hindered by lack of timely reporting, Zambia has implemented a National Crop Monitor integrating EO, existing data, and field observations into rapid crop condition assessments. Developed through the Alliance of Bioversity, CIAT, University of Maryland, NASA Harvest, and national partners, this system enables proactive planning aligned with seasonal outcomes. Capacity building has been key, with training provided to agriculture, meteorology and statistics personnel on using EO for crop assessments. Applicable across Southern Africa and based on the GEOGLAM model, this initiative responds to needs identified through SERVIR assessments and stakeholder engagements. The bulletins complement other agriculture programs like crop modeling while directly informing food security policies. Through case studies like Zambia, we highlight the successes, challenges, and best practices in leveraging EO data and collaborative partnerships for effective crop monitoring in Africa. Lessons learned from initiatives like SERVIR and NASA Harvest offer valuable insights for enhancing food security through timely crop information systems tailored to country needs.

Food security became a key policy of the Gabonese transitional government on August 30, 2023, to help the country achieve self-sufficiency within the next ten years. It reinforces the Sustainable Development Goals, which focus on the fight against hunger on a national scale. It is in this same context that the agro-industry, in which the agricultural giant OLAM holds a large stake, has invested in its New Agricultural Strategy, doubling production in line with its vision for 2024. In line with this agricultural strategy, the use of Earth observation data is an added value that helps to boost sales. Thanks to the availability of satellite images from the Copernicus program in the dense and humid Equatorial context,dense and humid cloud cover does not favor optimal use of S2A and S2B images. The methodology employed consists in processing the Sentinel image time series during the dry season using artificial intelligence techniques based on object-oriented classification for the recognition of oil palms in order to estimate them per unit area in a GIS to create visibility of the harvest at the location of the giant OLAM. This approach, based on the use of Earth observation data, is helping to strengthen control over production, which has been subject to variations during previous harvesting campaigns due to difficulties encountered with Elephant pests.

Abstract The burgeoning population in Uganda and Kenya, anticipated to reach 100 million and 73 million respectively by 2050, necessitates a substantial increase in food production. Both countries, already grappling with land and water scarcity, face amplified challenges due to climate change, which significantly impacts pest and disease prevalence, thereby affecting food security and ecosystem health. In Kenya, the rise in average annual temperature and unpredictable weather patterns have led to increased incidence of pests and diseases, affecting livelihoods and crop production. Similarly, Uganda is experiencing shifts in disease spread, soil degradation, and increased food insecurity due to climate variability. To address these pressing concerns, AFRI4CAst project develops a modeling platform, utilizing satellite remote sensing, to provide real-time agricultural disease monitoring. The platform fuse diverse Earth Observation data into disease models and indicators, focusing on cereals such as wheat, maize, and rice. AFRI4CAst employs most of the available data and products, and investigate the use of both ECOSTRESS and PRISMA missions for advanced disease detection, targeting rust disease and aflatoxins production in in Kenya and Uganda. PRISMA hyperspectral data capabilities are central to the early and precise detection of rust diseases in maize and wheat crops. By calculating vegetation indices, from the onset of the crop, the system aspires to identify disease outbreaks at different infection stages, enabling rapid response to mitigate spread and damage. This approach promises enhanced accuracy in detecting internal leaf changes even before external symptoms manifest, offering a significant advancement in early disease detection. ECOSTRESS is used for monitoring aflatoxin contamination risks. High resolution land surface temperature, humidity, and precipitation data feed models for the simulation of the biological cycle of Aspergillus flavus, and Fusarium spp. to reliably and effectively predict fungi contamination and daily aflatoxin production in maize and rice. AFRI4CAst is involving African stakeholders in the calibration and validation of the modeling framework, addressing data collection challenges, and ensuring the model’s scalability for additional crops and diseases. In conclusion, AFRI4CAst is poised to play a pivotal role in providing scientific and technical support to Food Security and Safety policies in Africa, with a particular focus on disease management. The project’s multifaceted approach, aims to address the urgent need for timely and accurate information on disease risks, thereby contributing to the development of resilient and sustainable agricultural systems in the face of climate change.

Climate change is already disrupting weather patterns across Africa, increasing the likelihood of droughts and shifting growing seasons in many parts of the continent. This is having a negative impact on domestic crop production. One way to offset these crop losses and improve food security is to expand efficiently managed irrigation where water is available. Efficient irrigation is best achieved when farmers have accurate and up-to-date knowledge of the status of their crops. One way to gain this kind of knowledge, especially over large areas, is through the use of Earth Observation based information products. Within ARIES, we are developing high-resolution plant water and drought indicator products based on hyperspectral (PRISMA and EnMap) and thermal (Sentinel-3 and ECOSTRESS) data, exploring the future potential of these emerging data types. The product design process is being carried out in close collaboration with several African partners from southern, western and eastern Africa to ensure usefulness and applicability as well as knowledge and capacity transfer. Plant Water, Leaf Area Index and Canopy Water have been selected as key information products to be derived from hyperspectral data. Soil moisture, evaporative stress, and scaled drought condition index are computed based on high resolution ECOSTRESS data. In addition, a 20m resolution crop water stress index has been derived based on the fusion of Sentinel-2, Sentinel-3 and ECOSTRESS data. During the symposium we would like to present our approach and results from our test sites in Zambia (managed by AKTC) and Mali (monitored by ACF), where our partners are providing information and knowledge on irrigated and rainfed fields. We will discuss the relevance not only of the results, but also of capacity building and the provision of open-source methods to allow anyone to independently calculate the above products for their area of interest. The project ARIES is one of the EOAfrica Explorers and is funded by ESA under ESA Contract No: 4000139191/22/I-DT

Drought is a recurring phenomenon in the Borena region located in southern Ethiopia. The imbalance between potential evapotranspiration and precipitation or due to a prolonged period of significantly below-average precipitation during the growing season often results in drought conditions, posing significant threats to the biodiversity, agriculture, and economic activities of the region. This area has recently endured severe drought events due to consecutive years of minimal rainfall, severely impacting the region’s ecosystem services, livestock production and, most importantly, jeopardizing the livelihoods of the agro-pastoralist communities. The use of Earth Observation (EO) data has a large potential to be used as an early warning system for drought occurrences and to support rapid decision-making with timely spatio-temporal information. This work proposed the Composite Drought Index (CDI), which jointly ingests hydro-meteorological indicators along with vegetation indices to provide a more holistic depiction of both drought occurrences and impact. Specifically, the CDI incorporates anomalies in precipitation, temperature, and vegetation proxies (e.g., normalized difference vegetation index (NDVI)). In this case, we developed a CDI for the Borena region using open source data from ESA’s Copernicus program (Sentinel, ECWMF) and analyzed the spatial-temporal patterns of drought episodes between 2000 and 2023 (using MODIS NDVI to fill the gap before Sentinel missions). Additionally, the Mann–Kendall trend test and Sen’s slope were employed to understand the trends of these input variables and determine their magnitude of change. The study successfully identified the occurrence of extreme drought events in 2007, 2011, 2014, 2016, 2017, and 2021. The findings also showed a decreasing trend in rainfall, an increase in temperature, and a diminishing trend in vegetation conditions during the study period, with about 58% of the region showing a significant decreasing trend of NDVI during the main growing season. Future work will additionally incorporate actual evapotranspiration (ET) estimates based on thermal infrared (TIR) imagery within the CDI, as this has the potential to more rapidly detect water stress in vegetation compared to spectral indices such as NDVI. These findings can guide the development of climate policies, disaster risk reduction, and strategies in Ethiopia, contributing to the mitigation of future drought impacts and the promotion of sustainable dryland natural resources practices.

Droughts and water insecurity have been highlighted as a significant global challenge with substantial societal and ecological impact, and the occurrence and severity of these is projected to increase with anthropogenic climate change (Caretta et al., 2022). This highlights the need for reliable and decision-relevant climate information and Earth Observations (EO) to monitor such events, on all weather and climate time scales. The ESA funded project Water Stress and Climate Indices for Africa (WaSCIA), aims to provide easy to access EO and climate data, that can help detect early onsets of water stress related to drought conditions, severity, and spatial extent over the pilot region - Senegal. This should improve the understanding of water-crop productivity in the long term, and support efforts towards improved food security. The WaSCIA project is led by Telespazio UK with the partnership of RSS-Hydro, Telespazio France, AGRHYMET (Centre Regional de Formation et d'Application en Agrométéorologie et Hydrologie Opérationnelle), LPAOSF (Laboratoire Physique de l'Atmosphère et de l'Océan Simeon Fongang) and DGPRE (Direction de la Gestion et de la Planification des Ressources en Eau Sénégal). The WaSCIA project has developed two applications to deliver the project objectives, WaSCIA HydroSENS-SWS, provided by RSS-Hydro, and WaSCIA Climate Indices Application, the latter of which will be the focus of this paper. These applications are deployed on the Web Advanced Space Developer Interface (WASDI), allowing EO experts to develop and deploy EO online applications, without the need for any specific IT/ Cloud skills. The WaSCIA Climate Indices Application provides users with easy access to 20 climate extreme indices calculated using hourly ERA5-Land. Additionally, for Standardised Precipitation Evapotranspiration Index (SPEI), a key drought indicator, we incorporate potential evapotranspiration values from the University of Bristol (Singer et al., 2020). Using Jupyter Notebooks, we provide access to two tools for exploring this information through a simple plotting script and a threshold warning tool. These tools enable users to easily interact with the data and extract valuable insights. The threshold warning tool utilises the climatology, along with near real-time (NRT) observations to analyse the climate indices and provide a user-friendly output. In doing so, this tool offers valuable output for enhancing decision-making and is designed to assist farmers, agronomists, and agricultural stakeholders in making informed decisions related to crop management and protection decisions.

Aquatic weeds continue to threaten the quality of surface water resources and, broadly, the provision of services for economic development and human livelihoods. A variety of aquatic weeds are invading surface water resources such as rivers, dams, and lakes. The spatial distribution of these weeds varies strongly over time. Within the WHYmapping project, started during the first EO AFRICA phase, we developed an algorithm to derive daily maps of water hyacinth from Sentinel-2 and -3. During the second phase, our project EXPLO-AQUA took this a step further: first, by applying the WHYmapping algorithm to archived data, creating long time series. These were used to systematically investigate the effects of meteorology and herbicide spraying on the prolification of aquatic weeds. Second, by expanding the Sentinel-2 algorithm to discriminate between different types of aquatic vegetation. The satellite data were validated using field observations from dedicated campaigns. At Hartbeespoortdam reservoir, stakeholders recently started annually spraying the dam with a selective herbicide, which leads to removal of water hyacinth, but subsequent proliferation of Salvinia molesta. Salvinia gradually disappears as water hyacinth re-emerges. This research also seeks to examine effects of meteorology and herbicide spraying on the co-existence balance between algae and macrophytes in Hartbeespoort dam.

Marine pollution, particularly from plastic waste, poses a significant threat to the coastal areas of West Africa, affecting biodiversity, food security, and public health. This abstract presents insights from a Use Case developed within the GDA Marine Environment and Blue Economy project led by Planetek Italia, and the University of Palermo, supporting the World Bank's efforts in the West Africa Coastal Areas (WACA) region. This study uses advanced Earth Observation (EO) based services and plastic drift modelling techniques to focus on Liberia's major rivers as significant sources of plastic pollution. The adapted TrackMPD Lagrangian model, applied using the Copernicus marine services data, allowed to track the position of plastic particles over time. Innovative outputs, including density and beaching maps, have been performed. The former maps provide critical insights into the distribution and accumulation of plastic debris on the sea surface; the latters identified the coastal area most impacted by beaching phenomena along the West African coast. Simulation results reveal the widespread challenge of plastic pollution, indicating its impact beyond the originating coastal areas of Liberia, reaching countries within the Gulf of Guinea. These findings underscore the urgent need for collaborative action and informed decision-making to mitigate the adverse effects of marine pollution. The collaboration between ESA's GDA program and the World Bank facilitates the application of EO technologies to address marine plastic pollution. By supporting initiatives like the West Africa Coastal Areas Resilience Investment Project, this partnership enables informed policy development and proactive interventions to safeguard marine ecosystems and promote sustainable blue economy practices. Furthermore, the evidences reported in this study, highlight the potential for scalability and replicability of the modelling approach, emphasizing its relevance for assessing and mitigating water pollution in coastal regions worldwide. By leveraging EO satellites and global numerical models, similar methodologies can be applied to address marine pollution challenges in diverse geographic contexts. In conclusion, this Use Case demonstrates the pivotal role of EO technologies in understanding, monitoring, and combatting marine pollution, thereby contributing to the long-term resilience and sustainability of coastal communities in West Africa and beyond.

In the next few decades a large increase in population is expected to occur on the African continent, leading to a doubling of the current population, which will reach 2.5 billion by 2050. At the same time, Africa is experiencing substantial economic growth. As a result, air pollution and greenhouse gas emissions will increase considerably with significant health impacts to people in Africa. In the decades ahead, Africa’s contribution to climate change and air pollution will become increasingly important. The time has come to determine the evolving role of Africa in global environmental change. We are building an Atmospheric Composition Virtual Constellation, as envisioned by the Committee on Earth Observation Satellites (CEOS), by adding to our polar satellites, geostationary satellites in the Northern Hemisphere : GEMS over Asia (launch 2022); TEMPO over the USA (launch 2023) and Sentinel 4 over Europe to be launched in the 2024 timeframe. However, there are currently no geostationary satellites envisioned over Africa and South-America, where we expect the largest increase in emissions in the decades to come. In this paper the scientific need for geostationary satellite measurements over Africa will be described, partly based on several recent research achievements related to Africa using space observations and modeling approaches, as well as first assessments using the GEMS data over Asia, and TEMPO over the USA. Our ambition is to develop an integrated community effort to better characterize air quality and climate-related processes on the African continent.

Abstract Nitrogen Dioxide (NO2) is associated with various cardio-respiratory illnesses such as asthma in exposed populations. It enters the atmosphere through emissions from natural processes and anthropogenic activities. This study investigated the capability of social and environmental variables in explaining tropospheric NO2 column density in South Africa. Spatially-explicit NO2 density data, used here as a response variable, was acquired from Sentinel-5P sensor. The explanatory variables consisted of environmental variables (i.e., Enhanced Vegetation Index, Land Surface Temperature and Aerosol Optical Depth [AOD]), and 32 social variables covering energy usage, demography, dwelling type and age distribution. The Multiscale Geographically Weighted Regression was applied to predict NO2 from these socio-environmental variables at the municipality scale. The results showed an R2 of 92% between the observed and predicted NO2, indicating the significance of the socio-environmental variables considered in the model. Moreover, it was found that provinces that host most of energy generation plants and associated high AOD levels, i.e., Gauteng, Mpumalanga, and Limpopo, have high NO2 density. Among the energy variables, electricity and wood for cooking and heating had the biggest contributions to NO2 density and residence in flats/apartments and clusters in complexes significantly reduced NO2 while informal dwellers had an opposite effect. Female proportion was the most important demographic variable and was inversely related to NO2, while Age groups had mixed effect on NO2 pollution. These findings provide evidence that socio-environmental variables can be used to predict NO2 pollution and also the importance of localized modelling in associating the two sets of variables. Keywords: NO2 pollution; Social variables; Environmental variables; Multiscale Geographically Weighted Regression; South Africa

The CROSMOD project aims at developing a procedure for crop leaf area index estimates together with evapotranspiration and at analyzing the impacts of extreme weather events on crops growth, by integrating multiple satellite data and water-energy-crop modelling, able to support farmers precision agriculture. The use of hydrological models with remote sensing data helps to fill the gap when it comes to some missing or hard-getting in-situ parameters, especially in regions with poor monitoring networks as it is the case for the Doukkala region. In this study, a distributed hydrological energy water balance model (FEST-EWB-SAFY) was implemented and calibrated for different crops fields. Leaf-Area Index (LAI) data from Sentinel2 were used to calibrate the model in its crop development part, as well as Land surface temperature (LST) data from the LANDSAT 8 and 9 was used to calibrate the energy budget component. Due to the missing information of irrigation, a standard FAO irrigation approach has been implemented. The model showed good performance regarding the simulation of the LAI (including values and spatial variability), while lower performances were obtained for Soil Moisture (SM), Evapotranspiration (ET) and LST due to the missing data on water allocation. The use of more recent/accurate soil data and some scientific instruments like Eddy-Covariance and SM sensors can highly improve the results of the model. To overcome these limitations, a data assimilation approach has been implemented based on LAI data, showing a substantial improvement in contrast to the calibration approach, with the objective of detecting and monitoring crop exposure to shocks which are not reproducible by the model alone, which alter the canopy morphology and physiology.

Agriculture is the main stain of the Rwandan economy, covering more than 62% of the country’s employment. However, pest infections pose a serious threat to Rwandan agriculture as well as food security with severe negative impact on smallholder farmers. Fall armyworm (FAW) is one of the most destructive pests of maize and other cereal crops in Rwanda. However, the risks and the damage of FAW outbreak are not the same across Rwanda because of differences in crop extent and management as well as climatic conditions. Estimating risk of FAW infestation in maize and other cereal crops is essential to support FAW management and the Rwandan Integrated Pest Management (IPM) Programme. In this study, using a combination of field surveys, sentinel 2 data and ancillary data, we conducted a two-stage analysis: (1) Using vegetation indices derived from sentinel-2, we monitored maize stress due to FAW infestation in a 24 ha masharland in Eastern province of Rwanda. (2) We developed a country wide risk assessment of FAW outbreak specifically for maize crops. Overall, the results show that FAW is indeed a threat to maize crop especially in large scale monoculture maize crop. During the agricultural season A (September-December, 2022), we found a significant decrease in maize biomass in the investigated marshland due to the FAW outbreak. At the country level, we found that FAW follows predictable patterns related to crop management, climatic conditions and soil fertility conditions. Furthermore, the results suggest that the risk of FAW infestation is higher in the Eastern agro-ecological (~ 70%) and lower in the Central and Western zones (~40%) likely because of difference in the extent of maize crops production system. The results suggest that longterm monoculture practised in the consolidated agriculture has significantly increased the risk of FAW across Rwanda.

Arid semi-arid lands (ASALs) support most smallholder farming and pastoralism in Africa and are particularly vulnerable to climate shocks, such as droughts. The droughts often lead to humanitarian crises because farmers and pastoralists in ASALs do not have sufficient coping mechanisms. Decision makers use Earth Observation to monitor land surface conditions of drought. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and related data products represent the leading-edge in Earth Observation and analytical techniques for drought monitoring. The data products include evapotranspiration (ET) and evaporative stress index (ESI), which are driven by thermal infrared emissions detected by ECOSTRESS. Thermal infrared is sensitive to atmospheric water vapor and other sources of noise, which lead to uncertainties in ET and ESI estimates. The recently launched Hyperspectral Precursor and Application Mission (PRISMA) and the Environmental Mapping and Analysis Program (ENMAP) afford an opportunity to improve ET and ESI estimates via ECOSTRESS data integration. PRISMA and ENMAP collect spectral information over hundreds of narrow bands that are sensitive to biochemical processes and biophysical properties related to ET and ESI. The purpose of the ESA HyRelief project is two-fold: (i) select a modeling approach that improves estimates of ECOSTRESS ET and ESI with PRISMA and ENMAP hyperspectral narrowbands and (ii) co-produce a new minimal viable ET and ESI product that early adopters in Kenya (National Drought Management Authority—NDMA, International Livestock Research Institute—ILRI, Regional Centre for Mapping of Resources for Development—RCMRD) can use for drought monitoring. Here, we present the results of the ET and ESI model assessment in Kapiti Ranch of Machakos County Kenya, demonstrate the preliminary MVP, and discuss scaling out for continental scale drought monitoring with upcoming ESA LSTM and CHIME missions.

Crop Simulation Models (CSM) are commonly used to estimate crop yield at a local scale. Meanwhile, Remote Sensing (RS) data provides valuable information on crop parameters like soil moisture and leaf area index (LAI) across different spatial scales. Data Assimilation (DA) is a powerful technique that combines CSM and RS data from satellite imagery to enhance simulated crop state variables and model outputs, such as total biomass and yield. In this study, we aimed to implement a joint assimilation strategy for LAI and soil moisture data in the WOFOST model. The goal was to simulate rainfed grain maize yield at the field scale and evaluate its performance at both the field and administrative zone levels. The Ensemble Kalman Filter (EnKF) algorithm was applied to achieve this integration. The LAI and soil moisture data were sourced from Sentinel 3 and Soil Moisture Active Passive (SMAP) L3 Radiometer Global Daily 9 km Soil Moisture, respectively. The study tested various assimilation scenarios, including deterministic modeling, independent assimilation of LAI from Sentinel 3, independent assimilation of soil moisture from SMAP, and joint assimilation of both LAI and soil moisture data. Ongoing validation involves comparing the simulated grain maize yield with field observations and independent grain maize statistics data in the major maize-growing administrative zones of western and southwestern Ethiopia. The expected outcomes include improved accuracy in grain maize yield predictions at the field scale and enhanced crop monitoring and forecasting at local and national levels.

GEO Blue Planet, the Group on Earth Observations’ blue arm, supports the sustained development and use of ocean and coastal observations for societal benefit. As part of its coastal activities, the initiative has been working closely with stakeholders in the African marine community to explore the essential role of and identify gaps in ocean observation and prediction in addressing coastal sustainability in Africa, aligned with the AU Blue Economy Strategy and the UN Ocean Decade roadmap for Africa. The presentation aims to showcase this collaborative Africa-EU activity, which kicked-off during the GEO Blue Planet 5th Symposium held in Accra in 2022, and present achievements and expected outcomes. The most recent being a 4-day workshop held in Nairobi in March 2024 in collaboration with UNEP, IOC Africa, GMES and Africa, among others. The workshop discussed in-depth EO intelligence for driving solutions on coastal sustainability and resilience with input from different stakeholders across Africa. 58 participants from 17 countries attended, representing international and UN bodies, regional organisations, national agencies, research institutions, public and private marine data providers and civil society organisations. After two days of presentations and discussions to set the stage, the workshop culminated in hands-on sessions to brainstorm EO-driven solutions around 4 themes i) shoreline changes and seabed mapping ii) coastal flooding and inundation iii) ecosystem mapping and iv) water quality. For each theme, participants identified a specific challenge and came up with a concept for an EO-based service/tool addressing, working top down, from decisions to be made, to information needed to provide supporting evidence, to EO-derived data products, monitoring and prediction services required, to the EO platforms. GEO Blue Planet functions as a network of ocean and coastal-observers, social scientists and end-user representatives from a variety of stakeholder groups. The coastal activities are led by its European Office, hosted by Mercator Ocean International, with funding through the Horizon Europe project EU4OceanObs. https://geoblueplanet.org/ocean-observation-and-prediction-for-coastal-sustainability-in-africa/

Mangroves are a vital natural heritage and our ability to ensure their preservation is a key issue in the fight against global warming, the preservation of biodiversity and the livelihoods of the growing human population concentrated along the coasts. Over the last half century, however, mangroves have suffered an alarming loss of global cover. In order to improve the understanding of mangrove dynamics and functioning, up-to-date and recurring mangrove mapping and monitoring tools are essential. MangMap is a new online monitoring platform dedicated to the production and dissemination of specific products and services useful for mangrove mapping and monitoring at local scales. MangMap relies on generic processing services using Sentinel-2 satellite image time series, updated every 5 days from 2018. MangMap provides a complementary approach to existing tools. The platform provides an interactive dashboard dedicated to the production of enduser driven products useful for mangrove monitoring at local scales. Each image transformed into reflectance levels (2A) is available for download along with 11 automatically processed spectral indexes characterising the mangrove environments. An automated vector contour of mangrove spatial distribution is calculatedquarterly. Temporal composites of all indexes are processed monthly, quarterly, semi-annually and annually. Endusers can browse and import all data sets with simple tools. They can perform specific on-demand analyses within their own areas of interest, uploaded to the platform or drawn on the products on the screen, for a chosen period of time: indicator of mangrove spatial evolution; calculation of index differences; statistics on index evolutions. In its demo mode, the first version of the platform focuses on 5 pilot sites, which will be complemented by 11 other study sites spread over the regions of South America, Africa, Asia and Oceania. Further upgrades, based on feedback from early users, will aim to improve the enduser experience and provide new Earth Observation products and services based on very high spatial resolution imagery and radar data time series. Overall, MangMap, with its interactive website and technical platform, is designed to support studies and institutional diagnosis with simple tools, services and products to help map mangroves with up-to-date pre-processed spaceborne data. Ultimately, MangMap’s aims is to contribute to policy-making for the preservation and restoration of coastal ecosystems.

The world is increasingly faced with the multifaceted challenges of climate change, and its impacts are reverberating across diverse ecosystems and societies. Shoreline retreat resulting from the rising sea levels is among the most threatening impacts of climate change in coastal areas. Human activities contribute significantly to coastal erosion through sand mining and haphazard settlement along the coast. This study aims to calculate the dynamic of the coastline over The Gambia from 1984 to 2043. Using the GIS and remote sensing techniques, the coastline dynamics were depicted. Landsat historical images spanning from 1984 to 2043 are employed using the End Point Rate (EPR), a statistical technique in the Digital Shoreline Analysis System (DSAS) software which determines the shoreline velocity between two dates. The results reveal various trends in coastline dynamics between 1984 and 1994. At Kombo South, Kombo Saint Mary, Kanifing, Banjul (all located in the southern coast of the country), and Lower Niumi (situated as the northern coast of the country), the estimated annual changes were -35.6, -16, 20.8, 34.2, and -34.1 metres respectively. Notably, the following decade (1994-2004) saw a reversal of these trends, with Kombo South, Kombo Saint Mary, Kanifing, Banjul, and Lower Niumi experiencing changes of 34.3, 13, -22.5, -30.4, and 32.3 metres, respectively, compared to the previous decade (1984-1994). Between 2004 and 2014, coastline dynamics changed further, with estimated annual changes of -0.54, -1.11, -0.3, 3.6, and -0.10 metres at Kombo South, Kombo Saint Mary, Kanifing, Banjul, and Lower Niumi, respectively. The following decade (2014-2023) showed a reversal of the trends observed in the previous decade (2004-2014), reflecting estimated annual changes of 0.27, 0.84, 1.14, -2, and -1.29 metres at Kombo South, Kombo Saint Mary, Kanifing, Banjul, and Lower Niumi, respectively. The forecasts indicate that the region will enter a decade of accelerated growth, with dynamic rates of around 2.7, 8.4, 14.1, -20, and -12.9 metres per year by 2033 in Kombo South, Kombo Saint Mary, Kanifing, Banjul and Lower Niumi respectively. In 2043, the projection of 5.4, 16.8, 28.2, -40, and -25.8 metres per year doubles the rates forecast for 2033 in the respective regions. This study would be a useful guide for monitoring the coastline along the southern and northern coast of The Gambia.

The results of a retrospective study of the last three decade concluded on a net increasing trend and or aggravation of the impacts of coastal erosion along the Cameroon’s Coastline. In general, the increasing significance of these phenomenon highlights the unpreparedness of the exposed population and the increasing vulnerability of the physical and human milieus of the coastal zone. The main objective of this work was to show how the advance radar processing technique can provide important information for coastal erosion characterization by identifying areas of recent active erosive zones: sediment accumulation as well as ablation. The application of interferometric technique on a couple of Sentinel 1 images with six year (2018 to 2024) enable the generation of two relevant products for coastal erosion risk monitoring: the Interferometric Land Use and the Multi-Temporal Coherence image. This all-weather capability of radar is one of the main advantages of radars images compared to optical sensors which are always covered by cloud in this area. The main findings show that the coastline is very dynamic. There are areas where the retreat has been close to one hundred metres in six years after field validation. This clearly demonstrates the capability of this technique for quick assessment of the coastline. Appropriate measures should be taken to reverse the trend, notably by reducing the exposure of the population or strengthening their resilience.

South Africa (SA) is among several African nations susceptible to droughts. Particularly noteworthy are the severe droughts that occurred in the Western Cape (WC) province of SA between 2015 and 2018, reportedly the most extreme in over a century. Consequently, the WC provincial government enforced increased water usage restrictions. Projections on climate change indicate a global trend towards more frequent and intense droughts, underscoring the urgency for enhanced drought monitoring and early warning systems in SA. Classification of drought into various phases (meteorological, agricultural, hydrological, and socio-economic) has led to the development of distinct indices for monitoring the different phases of droughts according to their onset, duration, magnitude, frequency, and spatial coverage. Recent research advocates for an integrated approach to drought monitoring by combining the different indices using multi-index methodologies. Furthermore, due to constraints associated with limited temporal and spatial resolutions of in-situ data, there is a growing reliance on Earth Observation (EO) data for integrated drought monitoring in SA. Consequently, drought monitoring systems reliant on EO data, capable of combining multiple indices to generate integrated drought information, are essential for preparing, planning, and mitigating the adverse impacts of droughts in SA. In this context, the ANIN Drought System for South Africa, a National Incubator project under the EO-Africa programme of the European Space Agency (ESA), has developed a drought monitoring and early warning system. This system combines various drought indices, computed using EO data, to generate integrated and timely drought information. The primary focus of this study is to assess the effectiveness of the ANIN Drought System in monitoring droughts in the Breede-Olifants Water Management Area (BOWMA) of SA. To achieve this, a comparative analysis was conducted between drought information obtained using the ANIN Drought System and that derived from in-situ based indices such as the Standardized Precipitation Index (SPI), Standardised Streamflow Index (SSI), and the Standardised Groundwater Index (SGI) during the 2000-2023 period in BOWMA. The findings indicated satisfactory correlation and highlighted that, generally, groundwater in BOWMA exhibits greater resilience to climate-induced droughts compared to surface water resources. Consequently, human activities such as abstractions significantly impact groundwater resources. These outcomes not only validate the ANIN Drought System but also underscore its capability to provide critical drought information essential for the formulation or enhancement of drought risk management policies and practices in SA.

Drought is one of the most destructive natural disasters on the planet. Droughts can have severe environmental and economic consequences across large parts of Africa. Understanding and forecasting the mechanism that causes drought is critical for improving early warning and disaster risk management. Using multi-source data and statistical analysis approaches such as the Joint Probability Density Function (JPDF), this study examined the meteorological drought and its return years in North and West Africa between 1982 and 2018. At 1–12-month timescales, the Standardized Precipitation Index (SPI) was used to assess large-scale spatio-temporal drought characteristics. SPI-12 was used to determine drought intensity, severity, and duration in the research area. The results showed that the drought magnitude (DM) was highest in 2008–2010, 2000–2003, and 1984–1987, with 5.361, 2.792, and 2.187, respectively. The lowest DM values were found in 1997–1998, 1993–1994, and 1991–1992, with DM values of 0.113, 0.658, and 0.727, respectively. It was assured that the probability of drought return years was higher when the drought duration was shorter, with short droughts occurring more regularly, but not all severe droughts hit after longer time intervals. We also found a direct link between drought and the North Atlantic Oscillation Index (NAOI) over Algeria, Morocco, and the sub-Saharan countries, as well as some hints that drought is linked to the Southern Oscillation Index (SOI) over Ghana, Guinea, Mali, Sierra Leone, Burkina Faso, Cote d'Ivoire, Nigeria, and Niger.

Drought significantly impacts agricultural systems, affecting crop yields, food security, and socio-economic stability. Earth Observation (EO) data enhances drought monitoring, providing insights into crop conditions in near-real time. Yet, current monitoring primarily identifies drought hazards, not their impacts or risks. A comprehensive understanding of drought risk and impact requires context-specific information, such as irrigation and cropping systems. Within the EO Africa National incubator project ADM-Kenya we co-developed solutions with several actors and stakeholders to create EO-based products assessing drought risk and impacts. We selected Sentinel-2 and Sentinel-3 data as the primary sources for drought impact and risk assessment and for deriving agriculturally relevant information, including crop condition, evapotranspiration as well as information on farming systems, i.e., irrigated/ rainfed and mono/mixed cropping. Additionally, yield statistics, meteorological data, and information on phenology were used. Sentinel-2 time series and derived vegetation indices were used to monitor the impact of drought on agricultural systems by tracking intra-seasonal changes in croplands and classifying drought affected and unaffected areas using random forest. To categorize severity classes instead of the binary output, specific thresholds were derived based on the baseline conditions. Furthermore, national scale irrigated/rainfed maps were generated based on Sentinel-2 time series and harmonics. Sentinel-2 data were used for mapping mixed cropping systems for selected regions. Crop yield data and biophysical predictors from satellite remote sensing (SPI, NDVI, NDII, LST, albedo) were used for the drought hazard model. Calibration of MODIS and Sentinel-3 data was done for the LST, NDVI and NDII to provide a longer time-series. The drought hazard and impact information is linked to spatially explicit information on farming systems and additional socioeconomic and environmental data were used for drought risk assessment. Lastly, Sentinel-2, and -3 data were used for derivation of daily 20-m evapotranspiration using machine learning and two-source energy balance model. The project linked drought hazard and impact data with information on farming systems, incorporating socio-economic and environmental data for a comprehensive risk assessment. The crop condition accuracy ranged from 75-90%, and farming systems classification accuracy was 97.87%. A static drought vulnerability map, combined with hazard/exposure data, visualized monthly drought risk at a 1 km resolution. The developed products showed high agreement with existing datasets, confirming their reliability in drought risk and impact assessment.

Managing water is crucial for balancing supply and demand while keeping our environment healthy. Agriculture, which uses 70% of freshwater globally, faces serious challenges in areas where water is scarce, especially in dry regions. To tackle these issues, we need integrated approaches that involve all sectors and consider both large-scale drivers of water availability and their impacts on local areas like irrigation districts and cities. Having timely information on water resources is key for governments and agencies to make smart decisions about water management. This presentation showcases the outcomes of the AFRI-SMART project “EO-Africa multi-scale smart agricultural water management” (ESA National Incubators EXPRO+ call), where a multi-scale and multi-models approach has been implemented for improving the integrated and sustainable water management and irrigation practices in Morocco, combining remote sensing and hydrological modeling together with the different users' perspectives. Nowadays, water availability in Moroccan reservoirs is currently low after 5 years of drought conditions, with ensuing conflicts between public water supply, agricultural and industrial activities. At national scale, ground observations, satellite data and hydroclimatic models are integrated to provide the best estimate of water availability indices that characterize large-scale variability in water supply. These feed into the basin-scale HydroBlocks model of the Oum Er-Rbia basin, which combines a 1-D land surface model with a cluster-based landscape representation. HydroBlocks is coupled to the RAPID streamflow routing scheme to provide high resolution streamflow estimates and reservoir levels. Withdrawals from the reservoirs are supplied to the Doukkala irrigation district, where daily, high-resolution, actual and optimized irrigation amounts are predicted using the energy-water balance model FEST-EWB and satellite LST (Landsat, downscaled Sentinel3) and vegetation indices (Sentinel2). The system is used to reconstruct historic water availability to identify times of supply risks, monitor and forecast water supply and plan potential interventions to reduce risks. An open online decision support tool has been developed to provide intuitive near real-time visualization of information from the satellites/models and explore forecasts and future scenarios. We also discuss the collaboration with end user groups in helping to define the management problem and identification of critical decisions in water management across scales.

Droughts are defined as complex climatological events characterised by water deficits due to below-average precipitation leading to imbalances in the hydrological cycle. Increased temperature and shifts in the precipitation patterns due to climate change led to a surge of drought events observed in Africa.Mozambique, located in Eastern-Austral Africa is especially vulnerable to agricultural droughts as the country's population predominantly lives in rural areas. According to the Food and Agriculture Organisation (FAO), the smallholder farms account for the vast majority of the country's production and water shortages have devastating environmental, agricultural, and economic impacts on the country.Hence, accurate satellite-based drought monitoring and early warning system development will be key to mitigate drought impact on rural communities and vegetation ecosystems.As part of the Drysat project funded by the Austrian Development Agency, we propose a novel approach to retrieve a drought index at a kilometer-scale resolution derived from surface soil moisture (SSM) products from Sentinel-1 (S1) and ASCAT-HSAF at 500m and 6.25km sampling respectively. Both S1 and ASCAT SSM products are validated over Mozambique against state-of-the-art land surface model (ERA5-Land at 9km resolution) and in-situ sensors installed at the start of the project. The high resolution S1 SSM product is also compared to the 300m sampling root zone soil moisture product from FAO's Water Productivity Open-access portal (WAPOR) produced by combining Normalized difference vegetation index (NDVI) and land surface temperature.By comparing the high-resolution S1 SSM dynamics against the climatology computed from ASCAT's long data record we generate a S1-ASCAT monthly kilometer-scale drought index for the period 2016 to 2023 over six districts located the Mozambican regions of Sofala (Buzi, Muanza), Inhambane (Mabote, Govuro, Massinga) and Gaza (Chokwé). The S1-ASCAT drought index is then evaluated against state-of-the-art indices derived from normalized anomalies calculated from the Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS) dataset and the NDVI dataset from the Copernicus Global Land Service.This study explores the potential of high-resolution SSM based on active microwave remote sensing to monitor agricultural droughts. Our results show that first, Sentinel-1 is able to monitor surface soil moisture over Mozambique, and second, drought indicator based on Sentinel-1 and ASCAT can temporally and spatially capture sub-regional drought patterns over Chokwé, Mabote, Massinga, Buzi, Muanza and Govuro.

The rapid urbanization of Africa, driven by natural population growth and rural-to-urban migration, significantly impacts the environment and presents challenges for urban management. Monitoring and understanding these dynamics requires data that are accurate, up-to-date, and analysis-ready. Currently, this information is only provided by global products that are not tailored to the African context with documented high uncertainties in classification accuracies. The DIY-BU-Mapping tool is an open, cloud-based mapping tool designed to generate local analysis-ready data on urban expansion in African cities. It is intended for use by decision-makers, stakeholders, and the scientific community. The tool addresses the growing need for objective, accurate, frequent, and timely data in developing urban environments. It utilizes Sentinel-1 and Sentinel-2 imagery along with various data sources. The tool is designed to assist users with varying skill levels in the classification process, from data collection and preparation to final map production. It consists of two parts: The first one creates a sampling dataset using reference data from 2021. For built-up areas, 'open buildings' from Google or 'building footprints' from Microsoft are used as reference datasets, while the WorldCover v200 from ESA is used for other land covers. The second part performs the classification, starting with dividing the sample data into training and validation (70/30). For each year starting from 2016, Sentinel images are collected, clouds are masked, and several spectral, textural, and statistical indices are calculated. These, along with a slope map, are used as features in a random forest classifier that is trained for 2021 and applied to each year. To ensure that built-up pixels are consistent over time, a pixel change trajectory analysis is applied. The accuracy of the method was compared with reference data as well as with globally available datasets, such as Dynamic World, WorldCover, ESRI land cover, WSF2019, and GHSL2023. The findings indicate that the locally fine-tuned maps produced by our tool outperform existing global multi-temporal products. Furthermore, we discuss both the limitations and strengths of the tool and the resulting maps. We release both the tool and its code freely and openly to encourage the scientific community to use the code, fostering the advancement of new Earth observation applications.

Earth observation and data visualization are essential for understanding spatial relationships between human settlements and their surroundings. In an increasingly urbanised world, understanding the dynamics of human settlements and their interaction with the surrounding environment is paramount for advancing the transition to more sustainable economies. Earth observation and data visualisation emerge as indispensable tools in this pursuit, providing insights into the spatial relationships that shape cities. Through satellite imagery and advanced data analysis tools, we can uncover patterns of urban growth, assess environmental impacts, and identify vulnerabilities, laying the foundation for informed decision-making in urban planning and investments. The contribution discusses and provide evidence from a case study in Ghana on how the JRC’s tools such as the Degree of Urbanisation methods and the Global Human Settlements Layer, and other EO data can harness project identification and planning activities at lower costs, gaining inspiration especially from secondary cities, who are the most rapidly expanding yet the most resource scarce urban areas. These include risk assessment of natural hazards such as flooding and droughts, visualisation, and projections of urban sprawl, and suggested spatial optimisation, and demographics, densities and built-up assessment that would facilitate planning and urban activities in EU projects. The case study in northern Ghana jointly developed by DG INTPA and JRC identifies urbanisation and land use change trends at the regional level, examines urban-environment dynamics (settlement expansion, land use/land cover changes and flood hazard/exposure analysis), and applies a spatial analysis approach to the Water and Sanitation (WASH) sector. The pilot study focusing on Ghana and its secondary cities in the northern region (Tamale, Yendi, Bolgatanga and Wa) provides an overview of the earth observations tools and models developed by the JRC Disaster Risk Management Unit and available for improving urban development and assisting EU Delegations in better city profiling by understanding past, current, and projected patterns of urbanisation, with the long-term goal of identifying projects of first necessity at the city level and optimising future investments.

In recent years, there has been a growing reliance on new technologies to address the multifaceted challenges faced by cities in economic, social, environmental, and governance spheres. Remote sensing, with its advanced capabilities, is poised to emerge as a key tool for local governments striving for smart and sustainable development. However, the utilization of Earth observation (EO) in urban planning remains an area ripe for research, offering significant potential value, particularly in bridging the gap between theoretical frameworks and practical applications. Urban development often suffers from a lack of evidence-based decision-making frameworks, especially in emerging economies, resulting in declining living standards within urban areas. To confront this challenge, the Urban Development Explorations using Natural Experiments (UDENE) project has been initiated. Drawing upon extensive EO data from Copernicus satellites and local sources, the project aims to support evidence-based decision-making in urban development, particularly in partner countries in Europe and Africa. The overarching goal is to foster the creation of safe, resilient, and sustainable cities. A central component of the project involves establishing a virtual laboratory where urban planners and innovators can experiment with development concepts and strategies. By utilizing multidimensional models of urban areas across different temporal and geographical scales, the project facilitates the identification and analysis of natural occurrences akin to development initiatives, termed "natural experiments". Leveraging the abundance of EO data available in accessible Data Cube format, the project seeks to enhance causal analysis and explore a vast array of natural experiments. The project outcomes will manifest in the form of an exploration tool for natural experiments and a matchmaking tool connecting existing EO products, processes, or services. To validate the feasibility of the model and assess its potential impact, three specific use cases have been identified: the effect of a linked park system on heat load in Tunisia, the effect of a new ring road on air quality in Serbia, and the effect of a high-rise district on earthquake preparedness in Turkey. These use cases offer insights into various urban development scenarios, leveraging EO data to inform decision-making and bolster urban planning strategies.

Villages where agricultural activities predominate depend on the availability of land resources for production. Moreover, as incomes vary greatly according to a village's land endowment, boundaries are essential for linking agricultural activities to space and natural resources. In Benin, there is no official information on the boundaries and size of village terroirs. The aim of this work is to develop an automatic method for delimiting village territories in northern and central Benin, covering five departments: Alibori, Borgou, Atacora, Donga and Collines. For this research, we used the geographical database of localities in Benin produced in 2018 by the National Geographical Institute of Benin, and the raster product "Global Human Settlement Layer" for 2018 at 10 m spatial resolution and showing the distribution of built-up areas. In addition, a field survey conducted in selected villages in the north and center of the country enabled participatory delineation of territories with the local population. Four automatic village boundaries delimitation methods were tested including circular buffers and Voronoi polygons, both in non-weighted and weighted versions (weight being proportional to the settlements densities). The results were evaluated using the field survey, and the impact of the choice of the method on village-level NDVI and landscape composition metrics was quantified. Ultimately, a virtual village terroir map was produced for northern and central Benin, to facilitate further village-level remote sensing studies.

Climate change is profoundly affecting the global water, energy and carbon cycles and increasing the likelihood and severity of extreme events. Better decision support systems are essential to accurately predict and monitor environmental disasters and to optimally manage water and environmental resources. An effective combination of state-of-the-art remote sensing products, in-situ observations and advanced hydrological modelling is needed to improve our knowledge of the water cycle and to enhance early warning and monitoring systems for floods and droughts in Africa. In the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) project (https://hsaf.meteoam.it/), operational satellite products for estimating precipitation and soil moisture are available and distributed in near real time for the entire African continent. Two main precipitation products are provided: (1) the precipitation rate product (called H60), generated by combining geostationary infrared imagery and passive microwave radiometer observations (15 min, 4.8 km), delivered with a very short latency (15 min) suitable for near real-time applications, e.g. flash floods, landslides; and (2) the daily accumulated rainfall product (H64), obtained by merging soil moisture derived rainfall with passive microwave rainfall estimates from radiometer (1 day, 25 km), delivered with 1 day latency and suitable for long-term applications such as rainfall-runoff modelling and climate monitoring. For soil moisture, two main products are provided: (1) the surface soil moisture product (H122) obtained from the Advanced SCATterometer (ASCAT) (daily, 6.25 km); and (2) the root zone soil moisture product (H26) obtained by assimilation of the surface product into the ECMWF modelling system (daily, 10 km), available for a layer depth of 0 to 289 cm. Long-term data record (>15 years) are also available for the different products. The presentation will give a brief overview of the precipitation and soil moisture products and in particular their use for operational applications in Africa through case studies (e.g. Nigeria flood in October 2022 and Libya flood in September 2023) and long-term implementation for river discharge estimation in more than 400 basins in Africa.

Human-induced climate change is the greatest threat the world has ever faced, according to the United Nations (UN). The impacts of our changing climate are already having devastating consequences for people and the planet, particularly in Africa, with threats to health, food and water security, livelihoods, biodiversity, etc. Therefore, it is imperative to take action for mitigation and adaptation, and climate data are key to make informed decisions. We now have a wealth of climate models and Earth Observation (EO) data from an ever-growing network of in situ and remote sensing instruments. However, these sources of data often do not translate directly into information that can easily be used by stakeholders, and they require high computational power and expert knowledge. Digital Twins (DTs) are tools that combine models and observations to provide actionable information for stakeholders. DTs allow users to easily explore the available data and test potential interventions to help decision making. In this work, we present a novel approach for the technical component underpinning DTs. Our proposed solution is an innovative model-data fusion that consists of developing machine-learning emulators for specific processes within Earth system models and driving them using EO data. These emulators are much faster and lightweight than the models they replicate, and can be easily run by non-experts without dedicated high-performance computing facilities, thus providing a way to democratise access to climate data. We have successfully developed such emulators from land surface model JULES for several applications such as Gross Primary Productivity (GPP) over Europe. We are now developing emulators for hydrological applications in Africa, including soil moisture and wetland methane emissions, and a framework to automate and streamline emulator development. In this contribution, we will present results from these emulators over Africa and discuss their potential to help agriculture, food security, and climate adaptation.

This abstract presents a comprehensive framework aimed at bolstering climate adaptation strategies for maize cultivation through the development and operation of the FAO PLAN-T platform. The platform is being implemented by SISTEMA GmbH for the Food and Agriculture Organization of the United Nations (FAO). Plan-T will become a simplified tool to assess the optimal variety of maize and the optimal planting date for a given location In Zambia. The proposed methodology integrates a diverse array of data sources, including meteorological, agronomic, and satellite data, to enhance seasonality computation and identify climate stressors affecting maize varieties. Additionally, the framework aims to assess the potential use of ECOSTRESS to evaluate improvements in ground resolution for better results. Improving crop productivity estimation constitutes a significant aspect of the framework, involving the refinement of the AQUACROP agronomic model through the incorporation of new input data and the calibration of model parameters. This process entails refining varietal parametrization, modeling yield response, and validating outputs with field data, ensuring the reliability and accuracy of yield estimations in different contexts. Variety selection and optimal planting date assessment rely on detailed analyses of physiological responses to stressors and soil water balance, utilizing climate and soil moisture data to inform decision-making processes. By considering factors such as crop water requirements, soil moisture levels, and climatic conditions, the framework aims to optimize planting strategies and maximize crop yields in maize cultivation. The platform's frontend prototype already allows selecting a location, retrieving soil chemical parameters, ranking maize varieties based on they productivity, and verifying the most suitable planting date. Its evolution will improve data visualization and exploitation capabilities and will be integrated within the FAO's Digital Services portfolio. In summary, FAO Plan-T serves as a valuable tool for informed decision-making in climate adaptation strategies for maize cultivation. By integrating advanced methodologies and tools, the platform empowers stakeholders with actionable insights into variety suitability, planting dates, and potential crop productivity, thereby facilitating sustainable agricultural practices amidst changing climatic conditions.

For assessing grassland fire danger, accurate estimations of grassland curing are critical. Curing refers to the evolution of live fuel into dead fuel component of fuel. This study evaluated the spatio-temporal variation of degree of curing for the Golden Gate Highlands National Park (GGHNP) for the period from 2016 to 2020. The estimation of degree of grass curing was carried out with the high spatial resolution fused remote sensed data of MODIS and Sentinel 2 employing Index-then-Blend (IB) data fusion methods on Google Earth Engine (GEE) to compute Grassland Curing Index (GCI) using MapVictoria Algorithm. Four commonly used soil and vegetation moisture content indices (GCI_GVMI, NDVI & GVMI; GCI_NDMI, NDVI & NDMI; GCI_SIWSI, NDVI & SIWSI; GCI_SWCI, NDVI & SWCI) were computed, converted into Grassland Curing Maps (GCMs) which were then synthesized into fire danger maps. The GCMs were used for correlation analysis with precipitation and soil moisture. Derived fire danger maps were overlaid with active fire points from VIIRS (FIRM) for accuracy assessments. The results showed September as the month of highest degree of curing, February the month of lowest degree of curing and May as an onset month of increase of degree of curing. Almost 90% of the park vegetation is susceptible to fire. There was negative correlation between GCIs and precipitation, and soil moisture. All GCIs exhibited the capability estimation of degree of curing as maximum number of active fire points fell under high to extreme fire danger zone with GCI_NDMI outperforming other indices. These results indicate that GCI_NDMI derived from fused remotely sensed data is a promising GCI for accurate assessment of degree of curing over mountainous grassland environment. The study paved a way for the increasing the spatial resolution for estimation of degree of curing for fire and fuel management of park and other fire agencies within mountainous grassland environment. The study can also be useful for the livestock agricultural practitioners and the games for pasture management.

Desertification is a major environmental issue that threatens many parts of the globe. Between the 1980s and 2000s, deserts expanded to over 9% of drylands, impacting the lives of over 500 million people in 2015. Algeria is one of the countries most affected as the desert already occupies nearly 2 million km² (80% of the total land area). In the context of the MAPSPADES project, we developed an analytical workflow to map the spatiotemporal evolution of desertification from 2002 to 2022 in the steppic region of Algeria and to understand its driving factors*. The study area covers the entire Algerian steppe, which spans over 270,000 km2 in the northern part of the country (11% of the territory) and is characterised by severe rainfall deficit and soils with poor organic matter. First, we constructed pixel-level annual vegetation maps for the steppe region of Algeria from Landsat multispectral imagery. These maps show a composite index, the Vegetation Density Index (VDI), as a locally adjusted proxy, derived by linear regression between two spectral indices from NDVI, MSAVI, TGSI, and Albedo over a large number of points. The highest correlation was obtained with the MSAVI-TGSI pair. We then mapped these continuous VDI values to 5 classes indicating different levels of vegetation sparsity (extremely severe, severe, moderate, light, no). The method was validated by manual labelling of 877 high-resolution ground truth images with an accuracy of 84%. Our second task was to capture main desertification. To this end, we applied the intensity and the gravity centre analysis, which revealed a cyclical behaviour of transitions between vegetation classes, in particular between 'extremely severe' and 'severe' during the period 2002-2014. This is consistent with the climatic profile of the region characterised by alternating dry and wet years. With the exception of the period from 2008 to 2010, most time intervals show a steady increase in areas of extremely scarce vegetation and a decrease in areas of moderate vegetation. The main trends are a northward expansion of desert areas in the western and central parts of the steppe from 2010, while greener areas appear to be increasingly grouped in the east from 2014. Finally, we applied a detailed analysis using residual trends and machine learning classification to identify areas of significant land degradation independent of rainfall and soil moisture, and the most prominent driving factors, respectively. Analysis from 2008 onwards shows that most of the areas with significant residual negative trends are located in the central region, covering an area of more than 37,000 km² (11% of the total steppe). Using mean VDI and VDI change trend as clustering features, we applied regionalisation to divide the steppe into 5 distinct regions. We then built per-region machine learning models to predict annual VDI from several driving factors (e.g. rainfall, soil moisture, wind, population density) and to derive feature importance. Based on this analysis, we determined a characteristic profile for each region based on vegetation dynamics and the most influential drivers. (*): The developed code and datasets are publicly available on https://github.com/mejdik/mapspades, https://doi.org/10.17026/PT/POJGN2 and https://doi.org/10.17026/PT/0DNFS0.

Starting in 2025, under the European Deforestation Regulation (EUDR), producers of commodities such as coffee and cocoa must certify that their products are free from deforestation. This poses a significant challenge for smallholders, who must provide precise spatial data verifying the origin of their goods. This research outlines innovative workflows and collaborations with stakeholders aimed at reducing the financial and technological burdens on smallholders, ensuring they can meet EUDR standards without compromising their competitiveness. We introduce a cost-effective GNSS device equipped with a Galileo-enabled receiver and connectivity options like SIM and Wi-Fi, designed to assist in the precise demarcation of land parcels. This tool enhances data collection and improves commodity traceability throughout the supply chain, ensuring compliance with deforestation monitoring requirements. Further, we advocate the use of open geospatial datasets and platforms, which provide essential, user-friendly tools for capturing and analyzing geospatial data. These resources are crucial for identifying deforestation hotspots and generating the required reports without necessitating large financial investments or advanced technical skills. Partnerships with governmental bodies, NGOs, and private sector entities play a pivotal role in this initiative. For instance, our GNSS device was tested in the mapping of cashew-cocoa agroforestry fields in collaboration with a smallholder cooperative union, the Union Inter régionale des Coopératives Agricoles de CI (UNICOPACI) in Mankono, Côte d'Ivoire. Capacity building efforts including workshops, educational materials, and mentoring enhance stakeholders' abilities to use geospatial tools effectively and interpret satellite imagery for sustainable land management. Additionally, we emphasize the need for policy reforms that facilitate the inclusion of smallholders in geospatial initiatives, urging the creation of local geospatial datasets and promoting data sharing to ease compliance with EUDR requirements. In summary, leveraging technological innovation and open geospatial resources, along with fostering strong partnerships and advocating for supportive policies, can empower smallholders to navigate the complexities of EUDR compliance. This approach not only aids in sustainable land management but also contributes to broader global conservation efforts, promoting a more equitable and environmentally responsible agricultural supply chain.

Over the years, changes in Landcover resources in Nigeria are occurring at an accelerated rate, magnitude, and spatial extent; culminating in disruption in ecosystem services, climate change, and food insecurity. Human activities remains the major driver of changes in Landcover in the country. Monitoring these changes is essential for appropriate policy prescription. Given the large area coverage of Nigeria, earth observation satellite remains the optimum approach to monitor Landcover dynamics in Nigeria. As a result, the integration of a Nigerian Satellite (NigeriaSat-2) and a European Satellite (Airbus Satellite Pour l’Observation de la Terre -SPOT) was used to monitor Landcover dynamics in Nigeria. This report presents Earth Observation (EO) change detection work undertaken to ascertain the extent of broad land cover changes in Nigeria since 2012 and demonstrate the utility of NigeriaSat-2 (N2) and SPOT for rapid change identification. The work took a sample of 50 Areas Of interest (AOI) representative of Nigeria. Each AOI compared N2 imagery (2012 – 2016) to recent SPOT imagery from circa 2022. Four main work packages were implemented to provide insights into the scale of change across Nigeria and demonstrate the utility of satellite imagery and change detection to help manage a range of applications: (i) Data selection involving search for N2 and SPOT archives to pick 50 representative sample AOIs across Nigeria; (ii) Data processing carried out to process the image data ready for change detection (Ortho-rectification, spatial harmonisation, and radiometric co-registration); (iii) Landcover change detection involving Forest, Vegetation, Urban, Water and Bare land, landcover maps from N2 and SPOT, comparing them to ascertain the extent of change; (iv) Image change detection through comparison of the N2 and SPOT imagery directly, creating a change magnitude image to rapidly screen images for interesting changes. The main results from the work are summarised as follows: (i) 50 AOIs and high quality N2 - SPOT images pairs were successfully identified and used to provide a representative sample of Nigeria with a time difference of approximately 9 years; (ii) Land cover accuracies of 86% and 87% for N2 and SPOT respectively were achieved; (iii) Land cover change results show urban land today occupies 3.94% more of Nigeria compared to the N2 baseline, with a reduction of -0.76% and -5.39% respectively for forest and vegetation; (iv) The Image change detection has highlighted significant man-made change and the technology can aid image analysts to rapidly identify change for given areas.

The context In the Sahel, where wood accounts for around 90% of energy consumption (source: World Bank, 2020), unsustainable resource use is the main driver of deforestation. Such practices endanger forests and increases household energy costs. This project aims at using remote sensing techniques to support forest monitoring in order to reduce wood harvesting in over-exploited forests. Masae used available open source EO tools and datasets provided by the Copernicus program to classify forest areas into classes describing their density / health and assess their recent evolution. This work helped monitor the area to support local authorities in monitoring deforestation activities. The value from EO data and Open-Source tools The Sahel is not well covered by existing forest monitoring datasets. For example, Global Forest Watch, a reference in the field, doesn't cover the Sahel. Hence the need to develop a specific dataset. Masae used Sentinel-2 images on the entire year of 2020 and elevation data to train a machine learning model at the pixel level. Image processing was performed using the eo-learn library, which slices the images into tiles and performs batch processing (called EO-Patch). After applying a cloud removal mask using the SLC layer, a linear interpolation was used on each pixel to obtain one image per month. Vegetation indicators were then calculated from the Sentinel-2 bands such as the NDVI, NDWI, etc. The final dataset consists of Sentinel-2 bands, vegetation indices and elevation data from the GLO-30 dataset. The training dataset was collected during a ground truth campaign, which determined land cover at 180 GPS coordinates for 6 land use classes: 5 different forest types and 1 for water. The pixels within a given radius around each of these GPS coordinates make up the training dataset, with a total of over 200,000 pixels. Masae trained a Gradient Boosting algorithm on the training set (90% of the observations) to achieve precision and recall scores above 90% for each class on the test set (10% of the observations). Masae also predicted these classes for the 2019 year. A rules-based decision model on vegetation indices was then used to detect areas where deforestation and degradation had occurred. Lessons learnt and way forward Masae used EO open-source tools and datasets provided by the Copernicus program to identify different types of forest areas. Our model helped map the areas affected by deforestation and forest degradation which was a valuable resources to organize the work on the ground. We could take this approach one step further by using a physical model to estimate the forests productivity and better monitor farmer-managed natural regeneration plots.

Earth observation (EO) data is a pivotal element in the achievement of the Sustainable Development Goals (SDGs) worldwide. This paper presents the findings of a regional study conducted by CRTEAN, a consortium member of OSS, within the scope of Phase 1 of the GMES & Africa program. This study aims to assess and monitor the EO use in land and water resources management (LWM) for SDG reporting and decision-making in Algeria, Libya, Mauritania, and Tunisia. The study's foundational elements were established firstly through an initial investigation spanning national studies conducted by OSS institution members. Subsequently, the selection of interviewees was methodically guided by the identification of institutions that were considered to be paramount within sectors associated with agriculture, forestry, and environmental management. Then, interviews and a questionnaire were conducted with key stakeholders to assess their level of awareness and use of EO data for different projects that relate to SDGs. The findings of the survey indicate a growing awareness of the significance of EO data, technologies, and services in NA countries, particularly within key sectors such as agriculture, research, and higher education. Notably, there is a high awareness of the EU's Copernicus program, with notable use of its EO data and thematic services over recent years. By contrast, the EO use demonstrates an imbalance between the private and public sectors. The latter is a principal user and customer of geo-information data, yet its level of access and upgrading to a geospatial information system remains relatively low. Moreover, there is a lack of continued training to enhance competencies and skills and training sessions related to geoinformatics and EO informatics, particularly for local authorities. Based on the survey results, the study discussed the key technical and operational considerations to integrate the EO dimension in decision support and policy making. Then, it explored some opportunities for using EO in LWM management, assessed the suitability of EO in each legal and institutional framework, and recommended a way forward for implementing the EO in a new era through a roadmap for water and natural resources managers, policymakers, and stakeholders in NA countries.

Land degradation and desertification are among of the world’s greatest environmental challenges. Africa is particularly vulnerable to land degradation and desertification, and it is the most severely affected region. These phenomena are having a negative impact on biodiversity, food security and community well-being. To support decision-making and better orient and guide sustainable land and water management actions, it is essential to get reliable, timely and accurate geospatial information on degraded lands and impacts of actions to restore these. This is what led the “GMES North Africa Consortium” during GMES Phase 1 (2018-2021), with the support of African private sector, the African Union Commission (AUC) and the European Commission Joint Research Centre (JRC), to develop MISLAND North-Africa, an operational land degradation monitoring and decision support system which delivers updated freely Earth Observation (EO) derived information and data. The system was developed in response to end-users needs, making it possible to get insights on land degradation hotspots and bright spots through an intuitive webservice for large audiences and a QGIS plugin for experts. High spatial resolution datasets were used to refine some of the indicators such as Landsat-derived vegetation indices for land productivity assessment and Sentinel-2 data for assessing burnt areas. In the GMES Phase 2 (2022-2025), the idea of upscaling MISLAND North-Africa (6 Million Km2) to MISLAND-Africa (30 Million Km2) - which is an African-wide land degradation monitoring service (including the islands) - was approved during the first continental workshop on land degradation in Abidjan (Oct, 2022) by key African institutions (CILSS, ICPAC, RCMRD, CSE, SASSCAL, etc.) with the contribution of regional - international organizations - initiatives (AUC, EU, UNCCD, FAO, AfriGEO and JRC). Furthermore, a collaborative framework was developed, involving the key actors in the development and operationalization of this service. A network of experts for joint monitoring of land degradation in Africa has been set up to support its development. To date, MISLAND-Africa is a reference system responding to African end-users needs. To them, accessing timely and accurate information/indicators/data on land degradation (SGD 15.3.1 and LDN theme) and its spatiotemporal extent and severity will no longer be an obstacle to monitoring the “hotspots” where priority action should be conducted or awareness-raising campaign should be planned to ensure the long-term sustainability of African landscapes.

Africa’s high vulnerability to climate change, while contributing only minimally, is not supported well by current knowledge. Research on lakes on the African continent is concentrated in the African Great Lakes and is sparser than that on lentic ecosystems in other regions of the world. Not only are African lakes poorly studied, but water quality and meteorological records are also scarce. A relevant source of data for studying lakes and assessing how climate change is affecting these essential freshwater resources is satellite imagery. The most comprehensive collection of reliable satellite measurements of water level, extent, lake surface temperature, ice cover, water surface reflectance and associated turbidity and chlorophyll-a concentration is provided by the Lakes Climate Change Initiative (CCI) project. The products cover the period from 1992 to 2020 and quantify over 2000 relatively large lakes distributed globally, of which 161 are in Africa. To illustrate the scientific contribution of the project, this study exploits satellite remote sensing data to investigate the pattern of lakes and meteo-climatic variables and the effects of weather events on spatial-temporal water quality variables for a subset of Sub-Sahelian African lakes, which have significantly different trophic and morphological characteristics. The results show significant correlations between precipitation and turbidity as well as precipitation and chlorophyll-a concentrations, a proxy for primary production. In addition, correlations between chlorophyll-a and air and lake surface water temperatures were detected. The observed impacts of extreme events, even if limited in number of lakes, suggest a rapid degradation of water quality, consistent with recent scientific literature. The findings suggest the importance of monitoring the effects of extreme weather events on water quality variables in sub-Sahelian African lakes and the potential role of remote sensing to fill the existing knowledge gap and support a more sustainable management of lakes’ resources and climate risk mitigation actions.

In the Senegal River Valley where smallholder farmers rely on controlled flooding to crop rice, predicting paddy production hinges on the accurate monitoring and prediction of paddy flooding patterns. Underlying critical factors for timely flooding include the early availability of credit, without which land preparation, input purchases, and other production implements cannot be secured. This study under the Hyperlocal Elicitation and Understanding of Risks to Stability in Complex Systems (HEURISTICS) Project, presents a novel approach where a Gompertz function is primed with Sentinel-2 satellite data to forecast the time and space patterns of the onset of plot-level flooding, and the end-of-season harvestable area. After each satellite data pulse, best-fitting models are used to predict the flooding progression for the remainder of the season. By mid-February, with only nine observations, predictions exhibit remarkable accuracy against reported official data, with a root mean squared error (RMSE) of around 4% and a mean absolute error (MAE) of approximately 2%, despite high variance in flooding across the grid cells and years. We thus achieve over 1 month improvement of the lead time of the forecast over government data, while demonstrating that satellites can actually sense smallholder farmers’ tactical response to delays in the confirmation and disbursement of agricultural credit – a key impediment to agricultural productivity which is not currently measurable through best, ground-based current practice. The output, combined with machine reading techniques to automate the parameterization of mechanistic crop models such as DSSAT, also allows for the skillful prediction of rice production levels at the district (ADM3) level. By integrating near-real-time satellite data, this research contributes to the SERVIR West Africa phase 2 development of Earth observation-based support services for smallholder agricultural financing in Senegal, helping de-risk investment in local value chains, abating the increasing dependency on expensive food imports, and strengthening food sovereignty.

Floods represent a major challenge for populations and infrastructures in Côte d'Ivoire and Senegal, causing significant economic losses and endangering human lives. In this context, the use of artificial intelligence (AI) offers promising prospects for improving early detection and management of floods. This methodological note aims to present a detailed approach for using AI in the automated detection of flooded areas in these two countries. The coastal regions of Côte d'Ivoire and Senegal are regularly hit by floods, causing considerable damage to local populations, agriculture and infrastructure. Despite the progress and initiatives implemented in natural disaster monitoring, early detection and accurate mapping of flooded areas remain major challenges in tackling the problem of flooding. The difficulty of quickly detecting flooded areas due to the complexity of weather conditions and topography, and the lack of automated systems and reliable data for monitoring and forecasting large-scale flooding, are key issues for better flood management. This also implies the need to improve the coordination of multi-actor emergency response and flood risk management. The aim of this study is to develop an artificial intelligence system for the automated detection of flooded areas in Côte d'Ivoire and Senegal, enabling real-time monitoring and rapid intervention in the event of an emergency. For automated detection of flooded areas, we plan to use machine learning techniques, in particular convolutional neural networks (CNNs) for satellite image analysis, due to their ability to extract complex features from satellite images. CNNs are capable of recognizing complex patterns in data and are well suited to detecting objects in images. We will also consider the use of signal processing algorithms for the analysis of meteorological data to predict flood risks. By exploiting deep learning methods and Sentinel-1 and MNT satellite data, this project aims to provide a robust and efficient system for automated detection of flooded areas in Côte d'Ivoire and Senegal, thus contributing to the reduction of risks associated with natural disasters.

Effective monitoring of river height and discharge is essential for water resource assessment, planning and management. However, limited, and inconsistent data, both spatially and temporally, and inadequate data sharing cooperation among member states poses a great challenge in the sustainable planning and management of shared Nile River Basin. This research explores the potential of leveraging modern Earth observation missions and technologies such as Sentinel-3 and Sentinel-6, alongside low-cost GNSS reflectometry as open data platform, to address the challenges of data availability and access in the region. The overall goal of this project has been to lower the barrier for (African) stakeholders to make use of satellite products for river altimetry and enable a low-cost and scalable GNSS-reflectometry solutions for cross-validating and complimenting ground observations. To bridge this gap, our approach comprises two components. Firstly, to develop an open-source Python module capable of ingesting and selecting satellite radar altimetry data, hence lowering the barrier to using altimetry products. Secondly, build and deploy low-cost GNSS-reflectometers (using Raspberry Pi’s and the Actinius Icarus platform) at strategic locations along Lake Victoria shores with existing monitoring infrastructure. These devices served as demonstrators for validating and complimenting altimetry river stage observations. We discuss cross-validation results of 2 pilot stations in Entebbe and Jinja, at the shores of Lake Victoria. The GNSS reflectometer locations were intentionally chosen close to ground hydromet stations, enabling us to use them as control sites for comparison. This integrated approach, aimed to enhance the accuracy and accessibility of river flow data in the Nile Basin hence enhancing access and cooperation in the basin. This paper highlights the methodology, challenges, opportunities, and outcomes of the project, demonstrating the potential of combining inland radar altimetry and low-cost GNSS reflectometry for effective river monitoring and open data sharing within the Nile Basin.

The Kossou and Buyo lakes in Côte d’Ivoire are essential water reservoirs, fed by the Bandama and Sassandra rivers, and play a crucial role in various sectors including water supply, fishing, and energy. Over the past decade, these lakes have experienced significant variations in their surface area and water level, impacting water availability and electricity supply in Ivory Coast, leading to power shortages in 2010 and 2021. Accurate and regular monitoring of these lakes is therefore necessary for sustainable water resource management. This study utilizes altimetric data, radar and optical images, as well as ground measurements from 1995 to 2022 (approximately 25 years). The methodology consists of three main steps: obtaining water level data from satellites such as ERS-2, ENVISAT, SARAL, Sentinel 3A, and Sentinel 3B using Altis software; extracting lake surfaces from satellite image processing of Sentinel 1 and 2; and establishing the correlation between variations in water levels and lake surfaces. The results show that on Lake Buyo, the variation in water level is nearly linear over a period of 26 years, while on Lake Kossou, two trends are observed with a decrease until 2016 followed by an increase until 2021. Regarding lake surfaces, significant variations have been observed on both lakes during the study period. The findings of this study clearly identify the need for continuous monitoring of water resources, highlighting significant variations in the water levels of Lakes Kossou and Buyo. They also demonstrate successful collaboration between African researchers (Virtual University of Cote d’Ivoire, CURAT) and European researchers (IRD, France) in the field of Earth Observation. By utilizing satellite data and radar altimetry techniques, it illustrates how international partnerships can be leveraged to effectively monitor and manage natural resources in Africa.

The Rangeland Monitoring using Earth Observation (RAMONA), funded as an ESA Continental Demonstrator project, aims to provide high resolution monitoring of key rangeland variables using Sentinel 1-3 data. The goals are to 1) define the extent of African rangelands, 2) classify African rangelands into functional types to facilitate detection of social-ecological change 3) describe and quantify the vegetation phenology of African rangeland types and 4) provide temporal estimates of herbaceous biomass, a key variable in rangeland functioning that affects livestock grazing, biodiversity patterns, fire regimes and carbon dynamics and, ultimately, livelihoods. The project is in its final stages and here we will present an overview of the four core variables including their technical specifications, interrelations and dependencies, accuracy assessment and explore some of the most significant continental-scale patterns that these novel data sets reveal and how they can be used in real-world use cases. RAMONA data is freely available via a web portal (app.ramona.earth) and we will demonstrate how RAMONA can be accessed for both viewing and data download. We conclude that the use of Sentinel data allows timely, high-resolution and accurate monitoring of African rangelands. Based on feedback from various African and international stakeholders, there is substantial scope for uptake of RAMONA variables among a variety of stakeholders if ongoing production and regular updates can be guaranteed.

Recent advancements in hyperspectral Earth observation (EO) usher in a new era in global ecosystem monitoring. Precursor satellite missions like EnMAP and PRISMA are pioneering globally sampled hyperspectral time series collections, paving the way for upcoming operational missions like ESA's CHIME and NASA's SBG. These missions promise unparalleled precision for retrieving indicators relevant for ecosystem monitoring, revealing critical ecological functions and invaluable insights for biodiversity assessments. Therefore, hyperspectral EO holds great potential for supporting targeted actions in line with international policy directives, such as the UN's Sustainable Development Goals and the Kunming-Montreal Global Biodiversity Framework. In this study, we demonstrate how hyperspectral satellite time series advance Southern African rangeland monitoring. We utilize EnMAP data acquired nearly monthly in 2023 and 2024, covering a transect from central Namibia to Angola. The rangelands along this transect provide habitat for wildlife and grazing areas for livestock. Our focus is on retrieving time series of non-photosynthetic vegetation (NPV), a vital indicator for rangeland applications. NPV is an integral component of biomass and plays a significant role in regulating carbon, water, and nutrient uptake. Additionally, NPV indicates fuel conditions or drought stress, highlighting its ecological and economic importance. Initially, we develop a rangeland spectral library consisting of NPV, green vegetation, and non-vegetation signatures. Utilizing a feature space defined by the Normalized Difference Vegetation Index (NDVI) and the Cellulose Absorption Index (CAI), we propose a universal strategy for library development from image data. Next, we establish a linear regression model between the CAI and NPV fractions obtained from artificially mixing library signatures. Finally, we apply model coefficients to derive NPV fraction time series from the EnMAP data. Our results reveal spatial-temporal NPV cover patterns, enabling a detailed assessment of rangeland vegetation condition across Namibia. Reference information obtained from field data and VHR images confirms the overall performance of the model in accurately estimating NPV fractional cover. While many hyperspectral retrieval algorithms rely on complex modeling setups, we deliberately focus on a simple workflow, emphasizing high reproducibility and automation. In this way, we aim to enable user-friendly re-implementation and operationalization. The EnMAP time series utilized in this study represents a unique, high-quality hyperspectral data source, serving for algorithm development and demonstrating the advantages of future spaceborne hyperspectral satellite missions for NPV product development. Such developments illustrate the translation of science into application and policymaking by providing essential tools and information for targeted actions.

Rangelands encompass a significant portion of land in Ethiopia and play a crucial role in supporting the livelihoods of many people; however, the traditional livestock farming practices have yielded low production and productivity. To attain this development goal, utilising machine-learning algorithms complemented with Earth observation (EO) data is one solution to characterize and monitor the rangeland conditions as well as to enhance livestock production and productivity. However, these innovative approaches never fully exploited and applied in Ethiopia so far. Therefore, the objective of this study was to develop models that could estimate, monitor, track, and visualize in a web map platform about the spatial patterns of net primary production (NPP), aboveground biomass (AGB) and rangelands carrying capacity (RCC) in the Awash Valley. The Sentinel-2 multispectral datasets, Sentinel-1 synthetic aperture radar backscatters and Digital Elevation Model were major datasets used in this study. The random forest machine-learning model was employed on the Google Earth Engine platform with combining data layers from Sentinel-1, Sentinel-2 and topographic measurements to create a model for AGB estimation and web enabled visualization. Besides, we used ArcGIS 10.8 to determine NPP and RCC based on the AGB data. The accuracy of the model was validated using AGB from a 1m x 1m 57-sample plots ground truth measured data. The estimated forage production with a resolution of 30 m agreed well with ground truth measured data having the R2 = 0.81 and a RMSE of 523.7 kg/ha. Besides, the estimated AGB was between 465.9 and 2947.7 Kg/ha and the NPP estimates were between 221.3 and 1400.2 Kg/ ha. Furthermore, the RCC was between 2.7 and 16.9 ha/AU/Y. We examined that VH, VV, Green leaf index; Normalized Difference Vegetation Index and Specific Leaf Area Vegetation Index are the five most important predictors for rangeland AGB estimation. Accordingly, the result indicated that AGB is accurately estimated and visualized in a web-enabled map based on the synergy of topographic, optical and radar satellite image products. Our study also showed that the synergy of topographic, optical and radar satellite image products are valuable tools for managing rangelands in a way that can adapt to changing climates, by providing spatially explicit and near-real-time forage production estimates. Lastly, the study's findings will help the livestock sector both nationally and regionally by demonstrating the various applications of the Random Forest Algorithm and EO data for the development of the livestock sector. Keywords: Earth Observation; Random Forest Model; Above Ground Biomass; Net Primary Production; Rangeland Carrying Capacity.

Introduction Savannah ecosystems are crucial in supporting biodiversity and regulating the global carbon cycle. In Africa, savannah and woodlands store a significant portion (52%) of the total above-ground carbon. However, obtaining detailed forest structural data, such as canopy height and cover, is essential for effective forest management. Despite the success of earth observation data, optical-based systems have been demonstrated to be of limited value in the direct measurement of forest structure. The Global Ecosystem Dynamics Investigation (GEDI) is a space-based LiDAR system designed to accurately capture vertical vegetation structures globally, using a waveform LiDAR with a 25m footprint. Method This study investigated the suitability of GEDI data in characterizing vegetation structures within and around the Okavango Delta. Our dataset consisted of 491,686 GEDI points spanning from 2019 to 2022, which were pre-processed in Python, and sentinel-2 data radiometrically pre-processed using FORCE framework. Employing a random forest regression algorithm, we estimated vegetation structure parameters, allocating 70% of the data for training and 30% for validation. Integration of these datasets enabled upscaling satellite-based estimates of forest structure across continuous spatial extents. Two fusion models were assessed: one combining spectral-temporal and phenology metrics (STM-phenology), and another utilizing only spectral-temporal metrics (STM). Result STM-phenology showed superior predictive performance for canopy height, canopy cover, and plant area index, with modest R2 values. However, foliage height diversity had the lowest predictive performance. Spatial patterns of the modelled attributes highlighted the overall sparse level of vegetation with structures clearly represented. Wetlands, agricultural fields, and settlement areas could be easily detected due to abrupt changes in vegetation structure - lower canopy heights and sparser canopy cover in contrast to surrounding woodlands, closed/open bushlands, and shrubs. We also employed Recursive Feature Elimination – Random Forest Regression (RFE-RFR) to identify the most contributing remote sensing predictors. Spectral temporal metrics had more influence particularly the upper percentiles (50th – 90th) except for canopy cover where phenological metrics were the most influential variables. Conclusion This study highlights the potential of integrating open-source data for large-scale vegetation structure mapping in data-scarce regions. Despite the moderate model performance, our research reveals the effectiveness of GEDI in characterizing lower stature and discontinuous savannah vegetation, extending its applicability beyond its original focus on continuous vegetation typical of tropical and temperate forests.

Abstract: The FATIMA project, "Fodder quality AssessmenT in Senegalese rangelands based on Sentinel-2 IMAges," aimed to create an effective method for estimating forage quality in Senegalese rangelands during the dry season. With this project, ressources have been concentrated on developing a functional notebook as a tool for estimating forage quality at the national scale, with a resolution of 10-m during this critical time of the year. This study presents an integrated methodology for estimating several parameters of forage quality for herbaceous and woody biomass in Senegalese rangelands using Sentinel-2 images. Near-infrared spectroscopy (NIRS) analyses were conducted to predict 9 quality parameters from samples of herbaceous and woody forage biomass collected in 2021 from 11 sites in the silvopastoral zone of Senegal. Multilinear regression models (MML) were calibrated with Sentinel-2 data using 17 vegetation indices. Model performance was assessed through cross-validation. Results showed better performance for estimating 2 quality parameters (fiber and nitrogenous content) respectively for herbaceous biomass (ADF: R2=0.65; RMSE=2.4 / MAT: R2=0.53; RMSE=3.04) and woody biomass (ADF: R2=0.63; RMSE=5.17 / MAT: R2=0.60; RMSE=1.77). Spatiotemporal analysis of forage quality revealed an overall decrease during the dry season, reflected by decreased nitrogenous content and increased fiber content. Across the study area, forage quality was higher in the North than in the South. This study demonstrates the usefulness of Sentinel-2 data for assessing forage quality during the dry season in Sahelian rangelands. Furthermore, its high spatial (10m) and temporal (10days) resolution make it a promising data source for rapid and accurate monitoring of fodder quality. This innovative approach, combining Sentinel-2 imagery and NIRS data, offers new perspectives for identifying specific areas requiring particular attention in terms of forage resource management. It allows targeting regions where strategies forimproving forage quality and animal production would be most beneficial, thereby enhancing pastoralist resilience to environmental challenges in the Sahelian region. Keywords: forage quality, near infrared spectroscopy, Sentinel-2, vegetation indices, multilinear model, dry season, Senegalese rangeland.

In response to the pressing need for enhanced disaster risk reduction (DRR) in Africa, the Africa Multi-Hazard Early Warning and Action System for Disaster Risk Reduction (AMHEWAS for DRR) has emerged as a collaborative initiative led by the African Union Commission (AUC). Working closely with Regional Economic Communities and Member States, and benefiting from technical and scientific support from UNDRR and CIMA Foundation, AMHEWAS aims to strenghten Africa's resilience against natural hazards through a multi-tiered approach.Operating across continental, regional, and national scales, AMHEWAS establishes a comprehensive system to strengthen early warning capabilities and promote effective disaster risk management strategies. At its core, AMHEWAS employs a network of Situation Rooms as central hubs for real-time information exchange, response coordination, and advisory dissemination to national institutions. This network is underpinned by unified standard operating procedures, ensuring consistent application protocols continent-wide.Key to AMHEWAS' effectiveness are the Continental Watch (CW) and the Drought Watch (DW), two impact-based forecast bulletin addressing rain, wind, flood and drought hazards. Drawing insights from automated forecast systems, CW and DW deliver timely, actionable information to decision-makers, facilitating proactive measures to mitigate potential disaster impacts. Additionally, ongoing disasters prompt the production of Disaster Situation Reports (DSRs), fostering collaborative efforts between the AUC, affected Regional Economic Communities (RECs), and national stakeholders to inform DRR initiatives and ensure timely responses.AMHEWAS integrates data and forecasting products from global and regional sources, issuing advisories that consider hazards, exposure, vulnerability, and national coping capacity. These advisories, employing a threshold-based mechanism, help categorize potential impacts and guide appropriate response levels. Innovative automated approaches estimate impact potential, leveraging forecast hazards and coping capacity data.Continuously evolving, AMHEWAS seeks to further integrate regional forecasting products, augmenting risk knowledge and enhancing information product quality. Through its collaborative framework and innovative strategies, AMHEWAS endeavors to bolster Africa's resilience against natural hazards and foster a safer, more secure future for its inhabitants.

Unsustainable urban and infrastructure development, inadequate management of natural habitats and resources, as well as air pollution are threatening the development of coastal areas in West Africa. These areas are the source of 56 % of West Africa’s GDP (Gross Domestic Product) and home to 31 percent of West Africa’s population. Population is typically located in areas highly vulnerable to disasters. Disasters such as land subsidence, storm surge, and coastal flooding in combination with climate change and related effects such as sea sea-level rise are exacerbating the vulnerability of the economy and population in this region. The Earth Observation is proven to be extremely useful in the management of climate change & disaster resilience improvement. ESA-Disaster Risk Reduction (DRR) is a project within the Earth Observation for Sustainable Development (EO4SD) program aiming to demonstrate on large scale in developing countries the usefulness of Earth Observation to reduce disaster risks and to support the management of the natural hazard assessment at large scale. The general objective of this initiative in The Gambia is the definition and implementation of a demonstration of EO-derived information in support to of the analysis and monitoring of disasters and their impact in on its coasts and urban areas. The specific purpose of the collaboration is to inform future policy and investment decisions in the country to strengthen the resilience of the country’s short, but vulnerable coastline, and highly exposed urban population and assets. The Area of Interest (AOI) comprises 412,4 Km2 of the Great Banjul area. Among the activities of the project, the ESA-DRR team, supported the assessment of the landslide and subsidence hazard assessment in Greater Banjul area (Gambia), through cooperation with the World Bank (ADB). In particular, the historical ground motion of the Greater Banjul area, through the Rheticus® Displacement service that implements the Persistent Scatterers Interferometry (PSI) technique was provided together with others information layers like bathymetry, storm surge/coastal flood mapping, and exposure mapping. The provided EO services are presented.

From May 01 to 03, Rwanda was interested by continuous torrential rainfalls, that caused severe damages in several regions of the country, particularly in the Western, Northern and Southern provinces: according to the governor, 14 people died in Karongi, 26 in Rutsiro, 18 in Rubavu, 19 in Nyabihu and 18 in Ngororero. A heavy rainfall event triggered flash floodings and landslides in the Sebeya river catchment. According to National Meteorological Agency, this event reached cumulative rainfall amount of 110-130 mm in a time span of about 60h, leading to the dire situation experienced that have killed 130 people in Rwanda and destroyed more than 5000 homes, as announced by government spokesperson. This event confirms the susceptibility of Rwanda to slope failures occurrence, and places landslides monitoring and inventories as a crucial point. For these reasons, the focus of this study was to perform a landslide rapid mapping after a strong or prolonged rainfall event, by using multiband and multitemporal satellite images. This study presents a landslide inventory map of Sebeya catchment, derived from a semi-automatic mapping procedure, exploiting both Sentinel-2 and Planetscope datasets. This procedure is based on pre-event and post-event NDVI (Normalized Difference Vegetation Index) variations, performed trough the RdNDVI estimation. Different parameters, such as NDWI (Normalized Difference Water Index), Slope and Land Use map were employed to mask and filter the RdNDVI. A visual validation of landslide inventory was finally carried out to achieve the reliability of the procedure in landslides detection. Despite the high presence of false positives, this landslides inventory detected all landslides occurred in the study area, confirming the reliability of this procedure for rapid detection and mapping of landslide phenomena, after rainfall events. This approach can be particularly helpful in the areas where there is no monitoring systems and/or landslides inventories, as well as a powerful support for susceptibility mapping and for early warning systems.

Conventionally, landslides are chaotic systems that can lead to significant impacts, particularly in tropical environments where a combination of intense rainfall, active tectonics, steep topography, and high population density can be found. However, the processes controlling landslide initiation and their evolution over time remain poorly understood. With the explosive growth of spatiotemporal landslide observation data in the past decade, deep learning (DL) and advanced differential synthetic aperture radar interferometry (A-DinSAR) coupling models are demonstrating impressive potential for landslide system forecasting tasks. The space-time transformer for chaotic system forecasting, an emerging DL architecture, has shown broad success in other domains but has had limited adoption in this area. In this paper, we couple the space-time transformer with the most powerful A-DinSAR technique, PS-inSAR, to forecast a signature in landslide dynamic susceptibility mapping (LDSM) using Sentinel-1 data. We conduct experiments on two real-world generated datasets from tropical regions in Cameroon and the Democratic Republic of Congo. The results reveal that the coupling method improves model accuracy and efficiency in terms of both memoryconsumption and speed compared to state-of-the-art methods, indicating that historical deformations significantly impact LDSM in tropical environments. This study serves as a reference for future research and provides a foundation for space-time forecasting in landslide risk management

On September 8th, 2023, an earthquake of around 7.2 on the Richter scale struck the El Haouz region, located southwest of Marrakech. This led to an unprecedented quake, causing the loss of nearly 3000 lives, widespread destruction of numerous towns and villages, and the obstruction of major roadways leading to these affected areas. The event's epicenter was located at coordinates 30.99° N, 8.41° W, and it had a focal depth of 10.7 kilometers. Unlike previous earthquakes in Morocco, this particular earthquake was unanticipated and devastating as it occurred within the intraplate context and reached a maximum strength of VI-VII on the Modified Mercalli intensity scale. Using differential synthetic aperture radar interferometry (DInSAR), the study's main objective was to estimate and map the co-seismic deformation caused by the earthquake. To estimate the co-seismic deformation caused by the earthquake and obtain insights into the underlying fault mechanism and seismic activity in the region, two SAR pairs of the Sentinel-1A mission with an ascending pass were used to generate an interferogram and measure the phase difference between an acquisition on September 3 and one after the earthquake on September 15. The co-seismic deformation pattern of the earthquake, as revealed by the data analysis, shows a thrust fault mechanism, with the rupture occurring on an ENE reverse fault. The maximum estimated co-seismic deformation was observed near the epicenter, with an upward movement of up to 23.4 cm in the line-of-sight direction. Furthermore, there was an upward shift of up to 5 cm on the southwestern side of the Tizi-N-Test corridor and a subsidence of around 5 cm on the northeastern side of the corridor. The results of this study will offer valuable knowledge for seismologists, geologists, and disaster management experts to enhance their understanding of earthquake dynamics and seismic activity in this particular area. Moreover, there are plans to conduct a more in-depth investigation by employing Multi-Temporal SAR analysis to investigate the relationship between ground deformation and other geophysical parameters, including faults mechanism and slip, crustal stress changes, and aftershock activity.

Land use land cover changes (LULCC) are an important part of environmental studies on a global scale. Many of the issues relating to the environment in recent times are largely caused by changes in land use land cover. For example, issues such as the greenhouse effect, food scarcity, vegetation loss, and soil degradation are frequently caused by the frequent LULCC. In recent times, mining has destroying most of the arable lands as farmers rent their farmlands for mining activities, which has resulted in the decline of crop production. To manage and monitor our natural resources, it is crucial to model changes in the land cover and its effect on crop production. This study was carried out to investigate the drivers and extent of the decline in crop production in the Western Region, of Ghana. Household surveys, focus group discussions, and field observations were used to identify socio-economic factors that influence LULCC. The socio-economic data were analyzed using rankings and binary logistic regression techniques. The logistic regression model was used to establish the relationship between socio-economic drivers and land cover change (Crop production). Remote sensing and GIS techniques were used to analyze LULCC over 8 years, employing Sentinel images of 2016, 2018, and 2022. We performed a supervised classification based on the Random Forest Algorithm to derive vegetation maps. The results revealed significant LULCC from one class to another. From 2016 to 2018, mining and built-up increased by 109.19% and 45.67%, respectively, while Dense Forests, agricultural lands, and water bodies decreased by 17.66%, 20.9%, and 31.09% respectively. From 2018 to 2023, Dense forests decreased by 16.01%, and agricultural lands by 17.15%, while mining increased by 125.43%, built up by 59.28%, and waterbodies by 7.94%. Thus, crop loss occurred at a higher rate in mining areas compared to the core agricultural area. Mining and development in built-up were the main direct causes of crop loss. Extensive land utilization for mining and development is the major threat to crop loss in the study area. The outcome of this research demonstrated the soundness of remote sensing applications to access changes in vegetation and crop loss in the study area.

Reservoir management is essential and complex as most dams are operated for multipurpose including hydropower, irrigation, and drinking water supply. Proper management is hindered as a result of lack of data, instrumentation, or information management systems. These may lead to dam breaches and water spills which may lead to floods downstream and loss of revenue by dam operators. Recent experiences from Ghana and Zambia show that these losses are large and have huge socio-economic impacts. TEMBO Africa - a Horizon Europe Framework project aims to use transformative new methods to measure five essential hydrological variables for multipurpose dam management (rainfall, soil moisture, open water, river flow and bathymetry) at less than 10% of current costs. These reduced costs are essential to have realistic business models and a sustainable scheme for services such as dam and reservoir management and sediment management. The TEMBO Africa project addresses some of these challenges by using a combination of satellite data, open source models, in situ data from innovative sensors, and agile scalable mobile platforms for better reservoir management. In our presentation, we will highlight some first results from using GNSS for water level and structural stability measurements in dams, cameras for streamflow measurements, drones and low-cost sensors for bathymetric measurements in small and large reservoirs, time series of reservoir volumes from remote sensing and their integration into real-time water balance estimates of both small and large reservoirs in Africa. The TEMBO Africa project intends to build a self-sustaining network of innovative sensors through bankable business models in Kenya, Zambia and Ghana, which will be extended to other parts of Sub-Saharan Africa. The service is under development in collaboration with dam reservoir management authorities in Ghana and Zambia.

Soil organic carbon (SOC) is a critical component of productive soils, and it is very important to allow for an efficient allocation of resources, optimal agricultural management, and the maintenance of fertile soils for best crop growth. The fast and accurate monitoring of SOC is among the most important activities to be included in the strategies to mitigate global warming because soils contain about 75% of the total carbon pool in land-based ecosystems. The proper estimation of SOC using satellite data is a challenging topic for the scientific community. Hyperspectral data acquired by new-generation spaceborne imagers like PRISMA (ASI, Italy) and EnMAP (German Mission) offers new opportunities to accurately map and quantify soil properties. Current SOC estimation from remote sensing is mainly targeted at the local scale, e.g., by using local databases to calibrate machine learning (ML) retrieval models. In this study, to avoid local parametrization of the retrieval algorithms, a wide number of PRISMA images (i.e., 32 clear sky images acquired during 2019–2024), encompassing a wide/regional topsoil variability and acquired over the southern African countries, were used. Samples of the free Open Soil Spectral Library (OSSL) dataset (https://soilspectroscopy.org/) have been used for CAL/VAL. The average of Sentinel-2 NDVI (up to 0.15) for ±12 days of each cloud-free PRISMA image present in the archive has been calculated in Google Earth Engine (GEE) and used as the primary criteria for PRISMA image selection. Among the primarily gathered dataset, a total of 104 samples were labeled as bare soils in the applied PRISMA images by using masks embedded in the EnMAP-Box tool (https://www.enmap.org/). The minimum, maximum, and standard deviation of these samples were 0.12%, 1.03%, 5.83%, and 1.23, respectively. The Level 2D PRISMA images were co-registered by the AROSICS algorithm using the Sentinel-2 image acquired at the closest date with PRISMA acquisitions to assure the co-registration (of about 0.5 pixels of RMS). Low signal-to-noise ratio (up to 100) and atmospheric water absorption bands were excluded, and a total of 173 spectral bands were used. Subsequently, smoothing of the spectra using a Savitzky-Golay filter with a third-order polynomial and a filter length of 7 was carried out. A k-fold (k = 10) was used for cross-validation. The primary results show that the first derivative of PRISMA absorbance spectra pre-treatments coupled with the Partial Least Squares Regression (PLSR) algorithm provides acceptable accuracy for SOC estimation (R2 = 0.68, RMSE = 0.684%). The capability of different ML retrieval algorithms, as well as different parametrizations of the MLs and different spectral pre-treatments, will be evaluated to find the best settings for SOC mapping. Increasing the number of PRISMA images in the archive over time will provide the opportunity to improve the accuracy of SOC estimation by covering more diverse soil samples in this region, including a wider range of SOC. The ongoing work, like the temporal transferability of spectral features and applying the button-up approach, is devoted to the up-scaling of this methodology to the African agricultural fields within the Gabon, Mozambique, and South Africa regions, selected within the context of the ongoing PRISMA4AFRICA project funded by the ESA.

Soil organic matter (SOM) consists of decaying plant and animal material, substances released by plant roots, and soil organisms. It supports physical, chemical, and biological functions in the soil. These factors often lead to physicochemical changes that alter the way soil reflects light, making SOM easier to assess using remote sensing than costly and spatially limited field surveys, which are also expensive. The spatial and spectral properties of Sentinel-2 have proven useful for estimating and predicting SOM content. This work aimed to quantify and model SOM content in eastern KwaZulu-Natal, South Africa, using Sentinel-2 spectral data, multiple linear regression, and principal component analysis. The results showed nine spectral indices consisting of four soil-based (Modified Soil Adjusted Vegetation Index; MSAVI, MSAVI2, Soil-adjusted Vegetation Index; SAVI) and five vegetative indices (Renormalized Difference Vegetation Index; RDVI, DVI, Enhanced Vegetation Index; EVI, Transformed Vegetation Index; TVI, Normalised Difference Vegetation Index; NDVI, V) with strong factor loadings (>0.9) for estimating SOM in PC1, with MSAVI being the best (0.97). Colour indices and GNDVI had strong loadings ranging from 0.30 to 0.85 for PC2, while brightness indices had strong but negative factor loadings with PC3. Overall, PC1 is characterized by soil and vegetation indices, while PC2 represents soil coloration and PC3 represents soil brightness indices. The MLR model prediction for SOM using spectral indices showed a higher R2 value (0.60) than the MLR model prediction for SOM using PCs (0.34), as well as lower error values for spectral indices than for PCs. In summary, Sentinel-2 derived spectral indices for soil, vegetation, soil colour, and brightness from response to SOM conditions using different principal components. Further, SOM research is encouraged using finer-resolution products over broader geographic landscapes.