{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T21:41:14Z","timestamp":1774474874537,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,2]],"date-time":"2022-02-02T00:00:00Z","timestamp":1643760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000270","name":"Natural Environment Research Council","doi-asserted-by":"publisher","award":["NE\/L002612\/1"],"award-info":[{"award-number":["NE\/L002612\/1"]}],"id":[{"id":"10.13039\/501100000270","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>East Africa has experienced a number of devastating droughts in recent decades, including the 2010\/2011 drought. The National Drought Management Authority in Kenya relies on real-time information from MODIS satellites to monitor and respond to emerging drought conditions in the arid and semi-arid lands of Kenya. Providing accurate and timely information on vegetation conditions and health\u2014and its probable near-term future evolution\u2014is essential for minimising the risk of drought conditions evolving into disasters as the country\u2019s herders directly rely on the conditions of grasslands. Methods from the field of machine learning are increasingly being used in hydrology, meteorology, and climatology. One particular method that has shown promise for rainfall-runoff modelling is the Long Short Term Memory (LSTM) network. In this study, we seek to test two LSTM architectures for vegetation health forecasting. We find that these models provide sufficiently accurate forecasts to be useful for drought monitoring and forecasting purposes, showing competitive performances with lower resolution ensemble methods and improved performances over a shallow neural network and a persistence baseline.<\/jats:p>","DOI":"10.3390\/rs14030698","type":"journal-article","created":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T20:38:40Z","timestamp":1644179920000},"page":"698","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3294-794X","authenticated-orcid":false,"given":"Thomas","family":"Lees","sequence":"first","affiliation":[{"name":"School of Geography and the Environment University of Oxford, South Parks Road, Oxford OX1 3QY, UK"}]},{"given":"Gabriel","family":"Tseng","sequence":"additional","affiliation":[{"name":"Mila Quebec AI Institute, Montreal, QC H2S 3H1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2169-8009","authenticated-orcid":false,"given":"Clement","family":"Atzberger","sequence":"additional","affiliation":[{"name":"Institute of Geomatics, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter-Jordan-Stra\u00dfe 82, 1190 Vienna, Austria"}]},{"given":"Steven","family":"Reece","sequence":"additional","affiliation":[{"name":"School of Geography and the Environment University of Oxford, South Parks Road, Oxford OX1 3QY, UK"}]},{"given":"Simon","family":"Dadson","sequence":"additional","affiliation":[{"name":"School of Geography and the Environment University of Oxford, South Parks Road, Oxford OX1 3QY, UK"},{"name":"UK Centre for Ecology and Hydrology, Maclean Building, Crowmarsh Gifford, Wallingford OX10 8BB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/s11269-006-9076-5","article-title":"Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness","volume":"21","author":"Wilhite","year":"2007","journal-title":"Water Resour. 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