Overview
Provide specialized technical expertise in Earth Observation (EO)-based agricultural monitoring, combining satellite image processing, machine learning, geospatial AI, and ground truth validation. The focus is on delivering scalable, institutionally embedded products.
Key Responsibilities
- Preprocess and analyze multi-temporal satellite image time series for crop monitoring, land cover mapping, and phenological analysis.
- Operate and customize the Sen4Stat processing chain for crop type maps and yield data.
- Operate and adapt Sen4CAP workflows for crop compliance monitoring.
- Develop and apply EO-derived biomass and crop yield forecasting models.
- Apply geospatial foundation models for crop type classification, land cover mapping, and change detection.
- Develop and evaluate embedding-based approaches for EO time series representation.
- Develop and refine drought monitoring algorithms using EO-derived indicators.
- Support the integration and operational testing of flood monitoring products.
- Support the design and implementation of workflows linking EO-derived crop and yield products to national farmer registries.
- Support the design and implementation of field data collection protocols.
- Conduct validation and accuracy assessment of EO-derived products.
- Apply and adapt WorldCereal global cropland and crop type baselines.
- Collaborate directly with national technical counterparts to integrate EO-derived products.
- Develop training materials and facilitate technical workshops.
Required Experience
- Minimum of 1 year for category C, 5 years for category B, 10 years for category A, of relevant experience in applying EO data to agricultural monitoring, food security, or policy analysis, with a demonstrable focus on developing country contexts.
- Demonstrated hands-on experience operating Sen4Stat and/or Sen4CAP processing chains.
- Proven expertise in EO-based crop yield forecasting and biomass estimation.
- Proven expertise in processing and analyzing multi-temporal Sentinel-1/2 and/or Landsat data.
- Experience applying geospatial deep learning methods.
- Experience implementing drought monitoring algorithms and/or integrating flood monitoring tools.
- Experience designing and implementing workflows to link EO-derived crop and yield products with national farmer registries.
- Experience applying WorldCereal, WorldCover, or comparable global EO products.
- Proficiency in Google Earth Engine or equivalent cloud-based EO processing environments.
- Demonstrable experience applying EO to crop area estimation, yield forecasting, or drought monitoring in sub-Saharan Africa, Central Asia, or comparable contexts.
- Experience designing field data collection protocols, managing ground truth datasets, and conducting rigorous accuracy assessments.
- Proficiency in Python (and/or R) for EO data processing, machine learning, and geospatial analysis.
- Proven ability to work with national counterparts.
- Record of preparing technical documentation, training materials, and facilitating workshops.
Qualifications
(For Consultants:) Advanced university degree in Environmental Science, Data Science, Geography, Agronomy, Statistics or related fields / (For PSA.SBS:) University degree or specific technical specialization in Environmental Science, Data Science, Geography, Agronomy, Statistics or related fields; Consultants with bachelor`s degree need two additional years of relevant professional experience.
