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Earth Observation, Crop Modelling & Data Assimilation workhop

National Centre for Earth Observation (NCEO, UK) & GSSTI (Ghana)

NCEO logo GSSTI logo H2020MULTIPLY logo

J Gomez-Dans (NCEO & UCL) [email protected]

DOI

This repository contains a number of Jupyter Python notebooks that demonstrate accessing datasets, including meteo and EO data, developing and running crop models, as well as deploying data assimilation systems to monitor crop growth.

Running the notebooks on the browser

  • Binder A brief exploration of meteorological data from an agroclimatology perspective.
  • Binder Exploring MODIS LAI data products over Ghana.
  • Binder A brief illustration of Sentinel-2 data over northern Ghana.
  • Binder This notebook develops the intuition of a very simple production efficiency model (PEM).
  • Binder A notebook demonstrating the use of the WOFOST crop model, applied to maize in northern Ghana.
  • Binder Using data assimilation (DA) with crop growth models (WOFOST), an example using the Ensemble Kalman Filter (EnKF)

Installing on your own computer

If you want to install this on your own computer, you can either close or download the repository, install the Miniconda (or Anaconda) python distribution, and you can install all the required packages using

conda env create -f environment.yml

This will install your own environment.