## 2 Materials and Methods
### Data  
We applied and reprocessed different raster and vector datasets to assess the vegetation dynamics and ecosystem resilience in the Horn of Africa. Time series images of NDVI Terra 16-day of MODIS13Q1 version 6 at 250 m spatial resolution and Land Surface Temperature (LST) of MODIS Terra 8-day at 1 km from MODIS dataset of LPDAAC NASA Products, and CHIRPS daily gridded precipitation at 0.05\({}^{\circ}\) from CHIRPS dataset were all accessed and retrieved from Climate Engine ([[https://app.climateengine.org/climateEngine](https://app.climateengine.org/climateEngine)]([https://app.climateengine.org/climateEngine](https://app.climateengine.org/climateEngine))) for the last 20 years. The Climate Engine provides both climate, hydrology, and remote sensing related products in an easily accessible spatial format, spatially filtered datasets, and it minimized computational barriers through Google Earth's cloud computing methods [32; 33].  
We utilized existing time series Global GPP and ET images of high spatial resolutions (0.0083\({}^{\rm o}\)) as annual MOD17A3 and MOD16A3 products prepared by the Numerical Terra-dynamic Simulation Group from the NASA MODIS outputs at the University of Montana ([[http://files.ntsg.umt.edu/data/NTSG_Products/](http://files.ntsg.umt.edu/data/NTSG_Products/)]([http://files.ntsg.umt.edu/data/NTSG_Products/](http://files.ntsg.umt.edu/data/NTSG_Products/))). These satellite images cover 15 years since 2000 are used to estimate the ecosystem water use efficiency and compute the ecosystem resilience in the Horn of Africa. The global daily GPP and annual ET of the MODIS products were applied in previous research works [19; 34; 35]. The MOD17 images for the global GPP exclude areas of no or limited vegetation cover, and the images were initially validated using class C 0.5\({}^{\circ}\) of the Ecosystem Model-Data Intercomparison as a base dataset [35]. High-resolution SPEI-12 dataset (0.05\({}^{\rm o}\)) from CEDA Archive of the Natural Environment Research Council's Data Repository ([[https://catalogue.ceda.ac.uk/](https://catalogue.ceda.ac.uk/)]([https://catalogue.ceda.ac.uk/](https://catalogue.ceda.ac.uk/))) was further processed and used to detect the trend and correlation analysis of the drought in recent years, and used to identify the most severe drought year to be considered for the ecosystem resilience analysis.  
Other ancillary data used in this study include Digital Elevation Model (DEM), Land Cover, and Agroecological Zones. The DEM was extracted from the USGS SRTM data center prepared by CGIAR-CSI ([[http://srtm.csi.cgiar](http://srtm.csi.cgiar)]([http://srtm.csi.cgiar](http://srtm.csi.cgiar))), and it has close to 30 m spatial resolution near the Equator. The European Space Agency (ESA) land cover product of 300 m for the year 2015 was extracted from CCI-LC (Climate Change Initiative-Land Cover) project which prepares reliable Global LC maps from 1992 to 2015 with 31 land cover classes ([[ftp://geo10.elie.ucl.ac.be/v207/](ftp://geo10.elie.ucl.ac.be/v207/)]([ftp://geo10.elie.ucl.ac.be/v207/](ftp://geo10.elie.ucl.ac.be/v207/))). We reclassified the land cover product into nine major classes using the land cover CCI Product user guide, version 2. The latest CCI-LC products show a higher potential for cropland monitoring and can provide a good base for general classification [36]. The agroecological zones shapefile for Africa was accessed from RCMRD  
Figure 2: Land cover types (**a**) and agroecological zones (**b**) in the Horn of Africa. Data source: ESA land cover 2015, and RCMRD GeoPortal for the Agroecological zones.  
GeoPortal ([[http://geoportal.rcmrd.org/](http://geoportal.rcmrd.org/)]([http://geoportal.rcmrd.org/](http://geoportal.rcmrd.org/))) using the Food and Agricultural Organization of the United Nations (FAO) methodology (2009), RCMRD provides geospatial datasets especially for the Eastern and Southern Africa region. The agroecological zones shapefile was masked to the area of Interest.