Monitoring croplands using data assimilation (DA) techniques
The Python notebooks included in this repository were developed for a training workshop that aimed to introduce a heterogeneous community of weather scientists, meteorologists, remote sensing experts, agronomists, etc. to current developments in using Earth Observation (EO) data for crop monitoring. Tho this end, several Python codes and self-directed Python notebooks were developed.
The notebooks review some concepts that are important in crop monitoring using Earth Observation. These include:
- Accessing meteorological data from reanalysis services.
- Showcasing contemporary EO data from e.g. Sentinel 2.
- Illustrating high level, agronomically-relevant, EO-derived parameters over large areas.
- Intuitively build-up towards dynamic crop models, considering how to model the effect of e.g. weather.
- A simple familiarisation with a well-known and widely used crop model (WOFOST).
- An example of assimilating observational streams (leaf area index, soil moisture) into the WOFOST model using an Ensemble Kalman Filter.