Python implementation to produce the composite drought indicator (CDI) based on openly available STAC imagery

The CDI incorporates three main components that impact drought severity: 1) precipitation deficit, 2) excess temperature and 3) vegetation response, using the following indices:

• Precipiation Drought Index (PDI)
• Temperature Drought Index (TDI)
• Vegetation Drought Index (VDI)

A jupyter notebook is provided to explain how each of the drought indicators are processed and eventually merged to produce CDI

This repository was developed within the framework of the EO-DBE project funded by the EO-Africa R&D Reseach facility and the European Space Agency (ESA).

The following libraries will be needed to properly use the Jupyter Notebook:

• numpy
• pandas
• geopandas
• rasterio
• xarray
• rioxarray
• cubo[ee] # this install cubo and also all GEE dependencies
• leafmap
• localtileserver
• notebook

Functions were tested with python=3.9

Vicente Burchard-Levine vburchard@ica.csic.es

Hector Nieto

Tinebeb Yohannes

Elias Cherenet Weldemariam

Getachew Mehabie Mulualem

Ana Andreu

pyCDI: a Python implementation of the composite drought index (CDI)

Copyright 2024 Vicente Burchard-Levine and contributors.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.