MSc thesis topic: Using Sentinel-1 for crop monitoring in a complex smallholder agricultural landscape
Farming in sub-Sahara Africa (SSA) is, for most practitioners being smallholder farmers, a marginal activity characterized by low productivity levels. On the other hand, the smallholder sector in SSA generally accounts for the largest share in agricultural produce's total volume.
Therefore, increasing the productivity of these producers will impact agricultural production levels significantly. To increase productivity, income generation, and livelihoods of farmers in SSA, it is essential to support farmers' agronomic practices by enhancing their common-sense decision making with access to high-quality personalized advice.
Satellite data services have been developed for large-scale agriculture and focused purely on information service delivery to improve management decisions. However, the potential to leverage the agronomic information retrieved from Remotes Sensing (R.S.) to provide valuable services to smallholder farmers remains mostly unexplored, notably in Sub-Saharan Africa (SSA).
We aim to integrate Remote Sensing-based information with digital-extension service delivery to smallholder farmers, addressing a gap in the sugarcane and maize value chain on knowledge delivery to farmers.
Smallholder farmers in SSA typically farm in
complex landscapes, where their fields are small, mixed crop (including weeds),
irregular field boundaries, and different agronomic practices (such as planting date, irrigation cycles, etc). This makes it very challenging to map and monitor crops. To add to the challenge, the tropics are known for their cloud cover, which means that clouds may cover the landscape in crucial monitoring time frames.
Optical imagery such as Sentinel-2 is widely used, however radar is used less in the smallholder farming context. We would like to explore how radar can be used to monitor crop phenology (what stages are the crops in, e.g., flowering) but also field homogeneity (are all crops in the same stage or are some parts lagging). Finally, we want to explore how radar and optical imagery can be combined for other uses, such as biomass estimation. The added value of using radar is that cloud cover is less of an issue when monitoring.
- Assess how well radar can be used for crop monitoring in complex landscapes.
- Explore what crop-relevant information can be derived from radar imagery (phenology for example).
- Assess and evaluate how optical and radar remote sensing can be used/combined for estimating field homogeneity.
- Potentially: explore how radar can be used for biomass estimation (combined with optical).
- Remote Sensing and Cropping Practices: a review; Bégué et. al 2018
- Crop type classification using a combination of optical and radar remote sensing data: a review; Aiym Orynbaikyzy et. al. 2019
- R or Python (jupyter notebooks)
- Some agronomic knowledge/interest
- Optional: travel to Mozambique for field data collection
Theme(s): Sensing & measuring; Modelling & visualisation