Articles | Volume 17, issue 11
https://doi.org/10.5194/essd-17-6531-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-6531-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
26 Nov 2025
Data description paper |
| 26 Nov 2025
| 26 Nov 2025
Tracking vegetation phenology across diverse biomes using Version 3.0 of the PhenoCam Dataset
Adam M. Young
CORRESPONDING AUTHOR
National Ecological Observatory Network, Battelle, Boulder, CO, USA
Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA
Thomas Milliman
Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA
Earth Systems Research Center, University of New Hampshire, Durham, NH, USA
Koen Hufkens
BlueGreen Labs (BV), Melsele, Belgium
Keith L. Ballou
Information Technology Services, Northern Arizona University, Flagstaff, AZ, USA
Christopher Coffey
Information Technology Services, Northern Arizona University, Flagstaff, AZ, USA
Kai Begay
Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA
Michael Fell
Information Technology Services, Northern Arizona University, Flagstaff, AZ, USA
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Mostafa Javadian
Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Alison K. Post
Cooperative Institute for Research in Environmental Sciences, Earth Lab, University of Colorado Boulder, Boulder, CO, USA
Christina Schädel
Woodwell Climate Research Center, Falmouth, MA, USA
Zakary Vladich
Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Oscar Zimmerman
Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA
Dawn M. Browning
USDA ARS, Jornada Experimental Range, Las Cruces, NM, USA
Christopher R. Florian
National Ecological Observatory Network, Battelle, Boulder, CO, USA
Minkyu Moon
Department of Environmental Science, Kangwon National University, Chuncheon, South Korea
Michael D. SanClements
National Ecological Observatory Network, Battelle, Boulder, CO, USA
Bijan Seyednasrollah
Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
Mark A. Friedl
Department of Earth and Environment, Boston University, Boston, MA, USA
Andrew D. Richardson
Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
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Zia Mehrabi, Kaitai Tong, Julie Fortin, Radost Stanimirova, Mark Friedl, and Navin Ramankutty
Earth Syst. Sci. Data, 17, 3473–3496, https://doi.org/10.5194/essd-17-3473-2025, https://doi.org/10.5194/essd-17-3473-2025, 2025
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We present a global geospatial database of cropland and pastures for the year 2015. We built these data by fusing satellite-based land cover data with agricultural census data using machine learning. This database is an update to an earlier version representing the year 2000. It can be used to study issues such as land use, food security, climate change, and biodiversity loss. We provide a reproducible code base that can be used to easily update the product for future years.
Elchin E. Jafarov, Hélène Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev., 18, 3857–3875, https://doi.org/10.5194/gmd-18-3857-2025, https://doi.org/10.5194/gmd-18-3857-2025, 2025
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This study improves how we tune ecosystem models to reflect carbon and nitrogen storage in Arctic soils. By comparing model outputs with data from a black spruce forest in Alaska, we developed a clearer, more efficient method of matching observations. This is a key step towards understanding how Arctic ecosystems may respond to warming and release carbon, helping make future climate predictions more reliable.
Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
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The Tipping Points Modelling Intercomparison Project (TIPMIP) is an international collaborative effort to systematically assess tipping point risks in the Earth system using state-of-the-art coupled and stand-alone domain models. TIPMIP will provide a first global atlas of potential tipping dynamics, respective critical thresholds and key uncertainties, generating an important building block towards a comprehensive scientific basis for policy- and decision-making.
Derrick Muheki, Bas Vercruysse, Krishna Kumar Thirukokaranam Chandrasekar, Christophe Verbruggen, Julie M. Birkholz, Koen Hufkens, Hans Verbeeck, Pascal Boeckx, Seppe Lampe, Ed Hawkins, Peter Thorne, Dominique Kankonde Ntumba, Olivier Kapalay Moulasa, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2024-3779, https://doi.org/10.5194/egusphere-2024-3779, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Archives worldwide host vast records of observed weather data crucial for understanding climate variability. However, most of these records are still in paper form, limiting their use. To address this, we developed MeteoSaver, an open-source tool, to transcribe these records to machine-readable format. Applied to ten handwritten temperature sheets, it achieved a median accuracy of 74%. This tool offers a promising solution to preserve records from archives and unlock historical weather insights.
Josefa Arán Paredes, Koen Hufkens, Mayeul Marcadella, Fabian Bernhard, and Benjamin D. Stocker
EGUsphere, https://doi.org/10.1101/2023.11.24.568574, https://doi.org/10.1101/2023.11.24.568574, 2025
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Mechanistic vegetation models serve to estimate terrestrial carbon fluxes and climate impacts on ecosystems across diverse conditions. Here we present the {rsofun} R package, providing an implementation of a model for site-scale ecosystem photosynthesis including functions for Bayesian model-data integration. The package {rsofun} lowers the bar of entry to ecosystem modelling and model-data integration and serves as an open-access resource for model development and dissemination.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Danica L. Lombardozzi, William R. Wieder, Negin Sobhani, Gordon B. Bonan, David Durden, Dawn Lenz, Michael SanClements, Samantha Weintraub-Leff, Edward Ayres, Christopher R. Florian, Kyla Dahlin, Sanjiv Kumar, Abigail L. S. Swann, Claire M. Zarakas, Charles Vardeman, and Valerio Pascucci
Geosci. Model Dev., 16, 5979–6000, https://doi.org/10.5194/gmd-16-5979-2023, https://doi.org/10.5194/gmd-16-5979-2023, 2023
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We present a novel cyberinfrastructure system that uses National Ecological Observatory Network measurements to run Community Terrestrial System Model point simulations in a containerized system. The simple interface and tutorials expand access to data and models used in Earth system research by removing technical barriers and facilitating research, educational opportunities, and community engagement. The NCAR–NEON system enables convergence of climate and ecological sciences.
David Harning, Thor Thordarson, Áslaug Geirsdóttir, Gifford Miller, and Christopher Florian
Geochronology Discuss., https://doi.org/10.5194/gchron-2022-26, https://doi.org/10.5194/gchron-2022-26, 2022
Preprint withdrawn
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Volcanic ash layers are a common tool to synchronize records of past climate, and their estimated age relies on external dating methods. Here, we show that the chemical composition of the well-known, 12000 year-old Vedde Ash is indistinguishable with several other ash layers in Iceland that are ~1000 years younger. Therefore, chemical composition alone cannot be used to identify the Vedde Ash in sedimentary records.
Stefan Metzger, David Durden, Sreenath Paleri, Matthias Sühring, Brian J. Butterworth, Christopher Florian, Matthias Mauder, David M. Plummer, Luise Wanner, Ke Xu, and Ankur R. Desai
Atmos. Meas. Tech., 14, 6929–6954, https://doi.org/10.5194/amt-14-6929-2021, https://doi.org/10.5194/amt-14-6929-2021, 2021
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The key points are the following. (i) Integrative observing system design can multiply the information gain of surface–atmosphere field measurements. (ii) Catalyzing numerical simulations and first-principles machine learning open up observing system simulation experiments to novel applications. (iii) Use cases include natural climate solutions, emission inventory validation, urban air quality, and industry leak detection.
Xin Huang, Dan Lu, Daniel M. Ricciuto, Paul J. Hanson, Andrew D. Richardson, Xuehe Lu, Ensheng Weng, Sheng Nie, Lifen Jiang, Enqing Hou, Igor F. Steinmacher, and Yiqi Luo
Geosci. Model Dev., 14, 5217–5238, https://doi.org/10.5194/gmd-14-5217-2021, https://doi.org/10.5194/gmd-14-5217-2021, 2021
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In the data-rich era, data assimilation is widely used to integrate abundant observations into models to reduce uncertainty in ecological forecasting. However, applications of data assimilation are restricted by highly technical requirements. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module which is friendly to ecologists with limited programming skills. MIDA also supports a flexible switch of different models or observations in DA analysis.
Kyle B. Delwiche, Sara Helen Knox, Avni Malhotra, Etienne Fluet-Chouinard, Gavin McNicol, Sarah Feron, Zutao Ouyang, Dario Papale, Carlo Trotta, Eleonora Canfora, You-Wei Cheah, Danielle Christianson, Ma. Carmelita R. Alberto, Pavel Alekseychik, Mika Aurela, Dennis Baldocchi, Sheel Bansal, David P. Billesbach, Gil Bohrer, Rosvel Bracho, Nina Buchmann, David I. Campbell, Gerardo Celis, Jiquan Chen, Weinan Chen, Housen Chu, Higo J. Dalmagro, Sigrid Dengel, Ankur R. Desai, Matteo Detto, Han Dolman, Elke Eichelmann, Eugenie Euskirchen, Daniela Famulari, Kathrin Fuchs, Mathias Goeckede, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Scott L. Graham, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, David Hollinger, Lukas Hörtnagl, Hiroki Iwata, Adrien Jacotot, Gerald Jurasinski, Minseok Kang, Kuno Kasak, John King, Janina Klatt, Franziska Koebsch, Ken W. Krauss, Derrick Y. F. Lai, Annalea Lohila, Ivan Mammarella, Luca Belelli Marchesini, Giovanni Manca, Jaclyn Hatala Matthes, Trofim Maximov, Lutz Merbold, Bhaskar Mitra, Timothy H. Morin, Eiko Nemitz, Mats B. Nilsson, Shuli Niu, Walter C. Oechel, Patricia Y. Oikawa, Keisuke Ono, Matthias Peichl, Olli Peltola, Michele L. Reba, Andrew D. Richardson, William Riley, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Camilo Rey Sanchez, Edward A. Schuur, Karina V. R. Schäfer, Oliver Sonnentag, Jed P. Sparks, Ellen Stuart-Haëntjens, Cove Sturtevant, Ryan C. Sullivan, Daphne J. Szutu, Jonathan E. Thom, Margaret S. Torn, Eeva-Stiina Tuittila, Jessica Turner, Masahito Ueyama, Alex C. Valach, Rodrigo Vargas, Andrej Varlagin, Alma Vazquez-Lule, Joseph G. Verfaillie, Timo Vesala, George L. Vourlitis, Eric J. Ward, Christian Wille, Georg Wohlfahrt, Guan Xhuan Wong, Zhen Zhang, Donatella Zona, Lisamarie Windham-Myers, Benjamin Poulter, and Robert B. Jackson
Earth Syst. Sci. Data, 13, 3607–3689, https://doi.org/10.5194/essd-13-3607-2021, https://doi.org/10.5194/essd-13-3607-2021, 2021
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Methane is an important greenhouse gas, yet we lack knowledge about its global emissions and drivers. We present FLUXNET-CH4, a new global collection of methane measurements and a critical resource for the research community. We use FLUXNET-CH4 data to quantify the seasonality of methane emissions from freshwater wetlands, finding that methane seasonality varies strongly with latitude. Our new database and analysis will improve wetland model accuracy and inform greenhouse gas budgets.
Shawn D. Taylor and Dawn M. Browning
Biogeosciences, 18, 2213–2220, https://doi.org/10.5194/bg-18-2213-2021, https://doi.org/10.5194/bg-18-2213-2021, 2021
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Grasslands in North America provide multiple ecosystem services and drive the production of a lot of grain, beef, and other staples. We evaluated a grassland productivity model using nearly 500 years of grassland camera data and found the areas where the model worked well and locations where it did not. Long-term productivity projections for the suitable locations can be made immediately with the current model, while other areas, such as the southwest, will need further model development.
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NEON (National Ecological Observatory Network): AmeriFlux BASE PR-xLA NEON Lajas Experimental Station (LAJA), Ver. 6-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1773394, 2023d.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xAB NEON Abby Road (ABBY), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617726, 2023e.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xAE NEON Klemme Range Research Station (OAES), Ver. 7-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671891, 2023f.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xBA NEON Barrow Environmental Observatory (BARR), Ver. 7-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671892, 2023g.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xBL NEON Blandy Experimental Farm (BLAN), Ver. 7-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671893, 2023h.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xBN NEON Caribou Creek – Poker Flats Watershed (BONA), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617727, 2023i.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xBR NEON Bartlett Experimental Forest (BART), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1579542, 2023j.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xCL NEON LBJ National Grassland (CLBJ), Ver. 7-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671894, 2023k.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xCP NEON Central Plains Experimental Range (CPER), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1579720, 2023l.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xDC NEON Dakota Coteau Field School (DCFS), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617728, 2023m.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xDJ NEON Delta Junction (DEJU), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1634884, 2023n.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xDL NEON Dead Lake (DELA), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1579721, 2023o.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xDS NEON Disney Wilderness Preserve (DSNY), Ver. 7-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671895, 2023p.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xGR NEON Great Smoky Mountains National Park, Twin Creeks (GRSM), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1634885, 2023q.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xHA NEON Harvard Forest (HARV), Ver. 9-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1562391, 2023r.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xHE NEON Healy (HEAL), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617729, 2023s.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xJE NEON Jones Ecological Research Center (JERC), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617730, 2023t.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xJR NEON Jornada LTER (JORN), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617731, 2023u.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xKA NEON Konza Prairie Biological Station – Relocatable (KONA), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1579722, 2023v.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xKZ NEON Konza Prairie Biological Station (KONZ), Ver. 9-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1562392, 2023w.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xLE NEON Lenoir Landing (LENO), Ver. 6-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1773398, 2023x.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xMB NEON Moab (MOAB), Ver. 7-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671896, 2023y.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xML NEON Mountain Lake Biological Station (MLBS), Ver. 7-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671897, 2023z.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xNG NEON Northern Great Plains Research Laboratory (NOGP), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617732, 2023aa.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xNQ NEON Onaqui-Ault (ONAQ), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617733, 2023ab.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xNW NEON Niwot Ridge Mountain Research Station (NIWO), Ver. 7-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671898, 2023ac.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xPU NEON Pu'u Maka'ala Natural Area Reserve (PUUM), Ver. 6-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1773399, 2023ad.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xRM NEON Rocky Mountain National Park, CASTNET (RMNP), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1579723, 2023ae.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xRN NEON Oak Ridge National Lab (ORNL), Ver. 6-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1773400, 2023af.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xSB NEON Ordway-Swisher Biological Station (OSBS), Ver. 7-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671899, 2023ag.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xSC NEON Smithsonian Conservation Biology Institute (SCBI), Ver. 7-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671900, 2023ah.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xSE NEON Smithsonian Environmental Research Center (SERC), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617734, 2023ai.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xSJ NEON San Joaquin Experimental Range (SJER), Ver. 7-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671901, 2023aj.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xSL NEON North Sterling, CO (STER), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617735, 2023ak.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xSP NEON Soaproot Saddle (SOAP), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617736, 2023al.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xSR NEON Santa Rita Experimental Range (SRER), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1579543, 2023am.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xST NEON Steigerwaldt Land Services (STEI), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617737, 2023an.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xTA NEON Talladega National Forest (TALL), Ver. 7-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671902, 2023ao.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xTE NEON Lower Teakettle (TEAK), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617738, 2023ap.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xTL NEON Toolik (TOOL), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617739, 2023aq.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xTR NEON Treehaven (TREE), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1634886, 2023ar.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xUK NEON The University of Kansas Field Station (UKFS), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617740, 2023as.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xUN NEON University of Notre Dame Environmental Research Center (UNDE), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617741, 2023at.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xWD NEON Woodworth (WOOD), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1579724, 2023au.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xWR NEON Wind River Experimental Forest (WREF), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617742, 2023av.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xYE NEON Yellowstone Northern Range (Frog Rock) (YELL), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617743, 2023aw.
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Short summary
Here, we describe the PhenoCam V3.0 public data release, which characterizes vegetation phenology in ecosystems across the US and globally using repeat digital photography. This V3.0 release includes new data records (a camera-derived normalized difference vegetation index and simplified data sets) and provides >4800 site years of phenological time series and transition dates, a 170 % increase relative to the previous release (V2.0). Over 450 of the time series are 5 years or longer in length.
Here, we describe the PhenoCam V3.0 public data release, which characterizes vegetation...
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