{"id":1078,"date":"2025-01-12T08:04:47","date_gmt":"2025-01-12T15:04:47","guid":{"rendered":"https:\/\/richardson-lab.nau.edu\/?p=1078"},"modified":"2025-01-12T08:04:50","modified_gmt":"2025-01-12T15:04:50","slug":"new-machine-learning-paper-using-neon-data","status":"publish","type":"post","link":"https:\/\/richardson-lab.nau.edu\/?p=1078","title":{"rendered":"New machine learning paper using NEON data"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Darby, Yujie, and Andrew are coauthors on a <a href=\"https:\/\/www.mdpi.com\/2073-445X\/14\/1\/124\">paper led by Jeffrey Uyekawa and Ben Lucas<\/a> of  NAU&#8217;s Department of Mathematics and Statistics in a special Landscape Ecology section of the journal <em>Land<\/em>.  The study uses the Extreme Gradient Boosting machine learning model to model land-atmosphere CO<sub>2<\/sub> fluxes, at 30 minute temporal resolution, across 44 <a href=\"https:\/\/www.neonscience.org\">NEON<\/a> sites. In addition to standard k-fold cross-validation techniques, the study applies a &#8220;leave-one-site-out&#8221; (L1SO) approach to test predictions at a site which had not been used, in any way, to train the model. The results show strong potential for machine learning-based models to make more skillful predictions than state-of-the-art process-based models, being able to estimate the multi-year mean carbon balance to within an error \u00b150 gCm<sup>\u22122<\/sup>y<sup>\u22121<\/sup>\u00a0for 29 of 44 test sites. L1SO model performance was better when ecologically similar sites were included in the training data, and worse when there were no similar sites in the training data (e.g., more unique ecosystems in the Pacific Northwest, Florida, and Puerto Rico).  Results also point to the enormous potential of machine learning to predict not only the long-term carbon balance of an unknown site, but even the inter-annual variation in that carbon balance.\u00a0 These results have significant implications for being able to accurately predict the carbon flux or gap-fill an extended outage at any AmeriFlux site, and for being able to make skillful predictions of ecosystem-scale carbon balance in support of natural climate solutions.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A companion paper on water fluxes is in preparation.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"379\" src=\"https:\/\/richardson-lab.nau.edu\/wp-content\/uploads\/2025\/01\/2016-09_NIWO_Tower_1620x600-1024x379.jpg\" alt=\"\" class=\"wp-image-1079\" srcset=\"https:\/\/richardson-lab.nau.edu\/wp-content\/uploads\/2025\/01\/2016-09_NIWO_Tower_1620x600-1024x379.jpg 1024w, https:\/\/richardson-lab.nau.edu\/wp-content\/uploads\/2025\/01\/2016-09_NIWO_Tower_1620x600-300x111.jpg 300w, https:\/\/richardson-lab.nau.edu\/wp-content\/uploads\/2025\/01\/2016-09_NIWO_Tower_1620x600-768x284.jpg 768w, https:\/\/richardson-lab.nau.edu\/wp-content\/uploads\/2025\/01\/2016-09_NIWO_Tower_1620x600-1536x569.jpg 1536w, https:\/\/richardson-lab.nau.edu\/wp-content\/uploads\/2025\/01\/2016-09_NIWO_Tower_1620x600.jpg 1620w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">NEON tower at Niwot Ridge, Colorado (courtesy of <a href=\"https:\/\/www.neonscience.org\/sites\/default\/files\/styles\/he\/public\/2020-06\/2016-09_NIWO_Tower_1620x600.jpg?h=cc094f85&amp;itok=nC16578a\">NEON<\/a>). <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Darby, Yujie, and Andrew are coauthors on a paper led by Jeffrey Uyekawa and Ben Lucas of NAU&#8217;s Department of Mathematics and Statistics in a special Landscape Ecology section of the journal Land. The study uses the Extreme Gradient Boosting machine learning model to model land-atmosphere CO2 fluxes, at 30 minute temporal resolution, across 44 &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/richardson-lab.nau.edu\/?p=1078\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;New machine learning paper using NEON data&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1078","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>New machine learning paper using NEON data - The Richardson Lab<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/richardson-lab.nau.edu\/?p=1078\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"New machine learning paper using NEON data - The Richardson Lab\" \/>\n<meta property=\"og:description\" content=\"Darby, Yujie, and Andrew are coauthors on a paper led by Jeffrey Uyekawa and Ben Lucas of NAU&#8217;s Department of Mathematics and Statistics in a special Landscape Ecology section of the journal Land. 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