## 5 Conclusions  
In this study, the pixel-based results of the annual and seasonal trends of MODIS NDVI showed there were areas of increasing and decreasing vegetation change in the last two decades across the Horn of Africa. There was an overall positive NDVI trend in the region, while 49.3% (1,149,713 km\({}^{2}\)) of the area experienced a decreasing NDVI trend. The effects of climate variability could be evidenced with NDVI as an indicator of the inter-annual variability in the region, and the weakest NDVI trends were observed in the grassland cover and the topic-cool arid zones. The NDVI was negatively correlated with LST, and it was positively correlated with the precipitation variability in the Horn of Africa. In the study area, 39.52% (683,197 km\({}^{2}\)) had a significant negative correlation between NDVI and LST, whereas 28.85% (533,385 km\({}^{2}\)) was significantly correlated with precipitation. The response of vegetation to precipitation and LST was specifically poor in the Tropic cool-humid zone which reflected the saturation impact of the biomes in the region. We further investigated the water use efficiency and ecosystem resilience in the region using MODIS GPP and ET to drought and climate drivers during 2000-2014. The trend of eWUE showed an overall increasing trend with a large impact on the grassland cover mainly due to its higher efficiency under water stress and drought conditions. Based on multi-annual pixel-based correlation analysis, the precipitation had the most significant effect on the eWUE variations in the Horn of Africa in comparison to the influences of LST and SPEI-12 drivers. The final ecosystem resilience to drought showed 54.9% of the region was resilient to drought disturbance, while 32.6% was completely non-resilient. The ecosystems in the humid agro-ecological zones specifically the cropland and wetland were the slightly non-resilient to severe drought conditions which need special attention in the regional ecosystem planning and management. Future research works should work more on the impacts of multiple drivers and integrate climate change scenarios. Conceptualization: B.C. and S.M. ; data curation: S.M., D.C., and L.G. ; formal analysis: S.M., B.C., P.P., and L.G. ; funding acquisition: B.C. ; investigation: S.M. and P.P. ; methodology: S.M., B.C., X.S., and D.C; project administration: B.C. ; resources: B.C. and S.M. ; software: S.M., H.Z., and L.G. ; supervision: B.C., and P.P. ; validation: B.C., P.P., S.S., A.K., and X.S. ; visualization: S.M., H.Z., and D.C.; writing--original draft: S.M., P.P., B.C., and M.G. ; writing--review and editing: B.C., P.P., M.G., A.K., S.S., H.Z., and X.S. All authors have read and agreed to the published version of the manuscript. This research work was supported by National Key R&D Program of China (grant nos. 2018 YFA0606001, 2017 YFA0604301, and 2017 YFA0604302), the research grants 41771114 and 41977404 funded by the National Natural Science Foundation of China, and the research grant O88 RA901 YAfunded by the State Key Laboratory of Resources and Environment Information System. The first author was sponsored by the Chinese Government Scholarship for his PhD study. In this study, publicly available datasets were accessed and analyzed. Time series images of MODIS NDVI Terra 16-day, Land Surface Temperature of MODIS Terra 8-day, and CHIRPS daily gridded precipitation datasets can be accessed here (accessed on 26 October 2020): [[[https://app.climateengine.org/climateEngine](https://app.climateengine.org/climateEngine)]([https://app.climateengine.org/climateEngine](https://app.climateengine.org/climateEngine))]. The Global GPP and ET images as annual MOD17A3 and MOD16A3 products are available: [[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/)) (accessed on 21 December).