{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-machine-learning-approach-to-forecasting","title":"A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health","arxiv_id":"1602.06335","date":"2017-05-26","proceeding":null,"authors":[],"abstract":"Drought threatens food and water security around the world, and this threat\nis likely to become more severe under climate change. High resolution\npredictive information can help farmers, water managers, and others to manage\nthe effects of drought. We have created an open source tool to produce\nshort-term forecasts of vegetation health at high spatial resolution, using\ndata that are global in coverage. The tool automates downloading and processing\nModerate Resolution Imaging Spectroradiometer (MODIS) datasets, and training\ngradient-boosted machine models on hundreds of millions of observations to\npredict future values of the Enhanced Vegetation Index. We compared the\npredictive power of different sets of variables (raw spectral MODIS data and\nLevel-3 MODIS products) in two regions with distinct agro-ecological systems,\nclimates, and cloud coverage: Sri Lanka and California. Our tool provides\nconsiderably greater predictive power on held-out datasets than simpler\nbaseline models.","url_abs":"http://arxiv.org/abs/1602.06335v4","url_pdf":"http://arxiv.org/pdf/1602.06335v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-machine-learning-approach-to-forecasting","repo_url":"https://github.com/JohnNay/forecastVeg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}