Geospatial Applications Specialist

Organizational Context

The Agrifood Economics and Policy Division (ESA) supports sustainable agrifood systems through economic research and policy analysis. ESA provides evidence-based support for national and global policy processes, focusing on agricultural policies, agribusiness, rural transformation, food security, and climate-smart agriculture. The division produces flagship publications like The State of Food and Agriculture (SOFA). Through the ESTELLA Programme, ESA utilizes Earth Observation (EO) data for agricultural monitoring, crop mapping, and yield forecasting, collaborating with international partners and operating in various countries. ESTELLA employs advanced EO processing platforms and AI to produce validated monitoring products, linking them to national farmer registries for targeted policy services.

Job Purpose

The Geospatial Applications Specialist will provide expert technical support in Earth Observation (EO)-based agricultural monitoring. This role involves processing satellite imagery, applying machine learning and geospatial AI, and validating data to deliver scalable products such as crop type maps, yield forecasts, and drought/flood indicators. The specialist will utilize Sen4Stat and Sen4CAP frameworks, alongside emerging GeoAI approaches. A key focus is integrating EO outputs with national farmer registries to enable operational services for smallholder farmers and inform policy decisions. The position requires close collaboration with national agencies to enhance their capacity in producing and utilizing EO-derived agricultural statistics.

Responsibilities

Process and analyze multi-temporal satellite image time series (Sentinel-1, -2, Landsat) for crop monitoring and land cover mapping. Operate and customize Sen4Stat for crop type mapping and yield data production, ensuring consistency with national statistical frameworks. Adapt Sen4CAP workflows for crop compliance monitoring in smallholder farming systems. Support the national transfer and integration of Sen4Stat/Sen4CAP products. Develop and apply EO-derived models for biomass and crop yield forecasting, integrating biophysical indicators with agronomic and climatic variables. Calibrate and validate these models against national survey data and field observations. Apply geospatial foundation models and deep learning for crop classification and land cover mapping. Develop embedding-based approaches for EO time series analysis. Integrate GeoAI workflows into cloud-based processing environments. Refine drought monitoring algorithms and integrate flood monitoring products into early warning systems. Produce crop-specific impact assessments linking EO indicators with farmer registry data. Support the design of workflows linking EO products to national farmer registries for service targeting. Develop EO-based farm-level risk profiles for insurance and credit schemes. Translate geospatial findings into technical notes and policy recommendations. Support field data collection protocols and conduct validation of EO-derived products. Apply and adapt global EO datasets like WorldCereal. Collaborate with national technical counterparts to integrate EO products into statistical cycles. Develop training materials and facilitate workshops on EO tools.

Work Experience

Minimum of 1 year for Category C, 5 years for Category B, or 10 years for Category A of relevant experience in applying Earth Observation (EO) data to agricultural monitoring, food security, or policy analysis, with a focus on developing country contexts.

Skills

Proficiency in operating Sen4Stat and Sen4CAP processing chains for agricultural monitoring. Expertise in EO-based crop yield forecasting and biomass estimation using satellite-derived indicators. Proven ability in satellite image time series analysis (Sentinel-1/2, Landsat). Experience with geospatial deep learning methods, foundation models, and embedding-based approaches for EO data. Experience in flood and drought monitoring using EO data. Familiarity with linking EO products to national farmer registries. Experience applying global EO datasets (e.g., WorldCereal). Proficiency in cloud-based EO platforms like Google Earth Engine. Demonstrated experience in agricultural monitoring in developing countries. Experience in ground truth data collection and validation of EO products. Proficiency in Python or R for geospatial analysis and machine learning. Ability to collaborate with national institutions and develop training materials.

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