Articles | Volume 30, issue 15
https://doi.org/10.5194/hess-30-4927-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-4927-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Aug 2026
Research article |
| 05 Aug 2026
| 05 Aug 2026
Near real-time estimation of daytime and nighttime evapotranspiration using GOES-R observations and machine learning models
Sadegh Ranjbar
CORRESPONDING AUTHOR
Department of Biological Systems Engineering, University of Wisconsin – Madison, Madison, WI, USA
Department of Earth and Planetary Sciences, Yale University, New Haven, CT, USA
Danielle Losos
Department of Geography, University of Colorado Boulder, Boulder, CO, USA
Sophie Hoffman
Department of Biological Systems Engineering, University of Wisconsin – Madison, Madison, WI, USA
Yafang Zhong
Department of Atmospheric and Oceanic Sciences, University of Wisconsin – Madison, Madison, WI, USA
Jason A. Otkin
Space Sciences and Engineering Center, University of Wisconsin – Madison, Madison, WI, USA
Ankur R. Desai
Department of Atmospheric and Oceanic Sciences, University of Wisconsin – Madison, Madison, WI, USA
Martha C. Anderson
USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA
Christopher R. Hain
NASA Marshall Space Flight Center, Earth Science Branch, Huntsville, AL, USA
Paul C. Stoy
Department of Biological Systems Engineering, University of Wisconsin – Madison, Madison, WI, USA
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-413, https://doi.org/10.5194/essd-2026-413, 2026
Preprint under review for ESSD
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We created the first continent-wide dataset to test the gradient flux method, which estimates how gases move between ecosystems and the atmosphere. This approach can provide reliable results when sensors are placed under favorable conditions, especially for shorter, less complex vegetation. The dataset will help improve future monitoring and support a better understanding of ecosystem responses to environmental change.
Yun Yang, Martha Anderson, Charles Morton, Yanghui Kang, Feng Gao, Weina Duan, Hui Liu, John Volk, and Christopher Hain
Geosci. Model Dev., 19, 6967–6989, https://doi.org/10.5194/gmd-19-6967-2026, https://doi.org/10.5194/gmd-19-6967-2026, 2026
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Evapotranspiration (ET) describes the transfer of water from land to the atmosphere and is fundamental to understanding agriculture, ecosystems, and drought. We implemented the established DisALEXI model on Google Earth Engine (GEE-DisALEXI), enabling scalable, high-resolution ET mapping over large regions. This paper evaluates this version's accuracy, highlights example applications, discusses limitations, and outlines opportunities for future improvements.
Tiia Määttä, Ankur R. Desai, Masahito Ueyama, Rodrigo Vargas, Eric J. Ward, Zhen Zhang, Gil Bohrer, Kyle Delwiche, Etienne Fluet-Chouinard, Järvi Järveoja, Sara H. Knox, Lulie Melling, Mats B. Nilsson, Matthias Peichl, Angela Che Ing Tang, Eeva-Stiina Tuittila, Jinsong Wang, Sheel Bansal, Sarah Feron, Manuel Helbig, Aino Korrensalo, Ken W. Krauss, Gavin McNicol, Shuli Niu, Zutao Ouyang, Kathleen Savage, Oliver Sonnentag, Robert Jackson, and Avni Malhotra
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Anam M. Khan, Olivia E. Clifton, Jesse O. Bash, Sam Bland, Nathan Booth, Philip Cheung, Lisa Emberson, Johannes Flemming, Erick Fredj, Stefano Galmarini, Laurens Ganzeveld, Orestis Gazetas, Ignacio Goded, Christian Hogrefe, Christopher D. Holmes, László Horváth, Vincent Huijnen, Qian Li, Paul A. Makar, Ivan Mammarella, Giovanni Manca, J. William Munger, Juan L. Pérez-Camanyo, Jonathan Pleim, Limei Ran, Roberto San Jose, Donna Schwede, Sam J. Silva, Ralf Staebler, Shihan Sun, Amos P. K. Tai, Eran Tas, Timo Vesala, Tamás Weidinger, Zhiyong Wu, Leiming Zhang, and Paul C. Stoy
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Geosci. Model Dev., 18, 4293–4316, https://doi.org/10.5194/gmd-18-4293-2025, https://doi.org/10.5194/gmd-18-4293-2025, 2025
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This study introduces a new 3D lake–ice–atmosphere coupled model that significantly improves winter climate simulations for the Great Lakes compared to traditional 1D lake model coupling. The key contribution is the identification of critical hydrodynamic processes – ice transport, heat advection, and shear-driven turbulence production – that influence lake thermal structure and ice cover and explain the superior performance of 3D lake models to their 1D counterparts.
Qing Ying, Benjamin Poulter, Jennifer D. Watts, Kyle A. Arndt, Anna-Maria Virkkala, Lori Bruhwiler, Youmi Oh, Brendan M. Rogers, Susan M. Natali, Hilary Sullivan, Amanda Armstrong, Eric J. Ward, Luke D. Schiferl, Clayton D. Elder, Olli Peltola, Annett Bartsch, Ankur R. Desai, Eugénie Euskirchen, Mathias Göckede, Bernhard Lehner, Mats B. Nilsson, Matthias Peichl, Oliver Sonnentag, Eeva-Stiina Tuittila, Torsten Sachs, Aram Kalhori, Masahito Ueyama, and Zhen Zhang
Earth Syst. Sci. Data, 17, 2507–2534, https://doi.org/10.5194/essd-17-2507-2025, https://doi.org/10.5194/essd-17-2507-2025, 2025
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We present daily methane (CH4) fluxes of northern wetlands at 10 km resolution during 2016–2022 (WetCH4) derived from a novel machine learning framework. We estimated an average annual CH4 emission of 22.8 ± 2.4 Tg CH4 yr−1 (15.7–51.6 Tg CH4 yr−1). Emissions were intensified in 2016, 2020, and 2022, with the largest interannual variation coming from Western Siberia. Continued, all-season tower observations and improved soil moisture products are needed for future improvement of CH4 upscaling.
Jacob A. Nelson, Sophia Walther, Fabian Gans, Basil Kraft, Ulrich Weber, Kimberly Novick, Nina Buchmann, Mirco Migliavacca, Georg Wohlfahrt, Ladislav Šigut, Andreas Ibrom, Dario Papale, Mathias Göckede, Gregory Duveiller, Alexander Knohl, Lukas Hörtnagl, Russell L. Scott, Jiří Dušek, Weijie Zhang, Zayd Mahmoud Hamdi, Markus Reichstein, Sergio Aranda-Barranco, Jonas Ardö, Maarten Op de Beeck, Dave Billesbach, David Bowling, Rosvel Bracho, Christian Brümmer, Gustau Camps-Valls, Shiping Chen, Jamie Rose Cleverly, Ankur Desai, Gang Dong, Tarek S. El-Madany, Eugenie Susanne Euskirchen, Iris Feigenwinter, Marta Galvagno, Giacomo A. Gerosa, Bert Gielen, Ignacio Goded, Sarah Goslee, Christopher Michael Gough, Bernard Heinesch, Kazuhito Ichii, Marcin Antoni Jackowicz-Korczynski, Anne Klosterhalfen, Sara Knox, Hideki Kobayashi, Kukka-Maaria Kohonen, Mika Korkiakoski, Ivan Mammarella, Mana Gharun, Riccardo Marzuoli, Roser Matamala, Stefan Metzger, Leonardo Montagnani, Giacomo Nicolini, Thomas O'Halloran, Jean-Marc Ourcival, Matthias Peichl, Elise Pendall, Borja Ruiz Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, Marius Schmidt, Christopher R. Schwalm, Ankit Shekhar, Richard Silberstein, Maria Lucia Silveira, Donatella Spano, Torbern Tagesson, Gianluca Tramontana, Carlo Trotta, Fabio Turco, Timo Vesala, Caroline Vincke, Domenico Vitale, Enrique R. Vivoni, Yi Wang, William Woodgate, Enrico A. Yepez, Junhui Zhang, Donatella Zona, and Martin Jung
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Nicholas K. Corak, Jason A. Otkin, Trent W. Ford, and Lauren E. L. Lowman
Hydrol. Earth Syst. Sci., 28, 1827–1851, https://doi.org/10.5194/hess-28-1827-2024, https://doi.org/10.5194/hess-28-1827-2024, 2024
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Gifford H. Miller, Simon L. Pendleton, Alexandra Jahn, Yafang Zhong, John T. Andrews, Scott J. Lehman, Jason P. Briner, Jonathan H. Raberg, Helga Bueltmann, Martha Raynolds, Áslaug Geirsdóttir, and John R. Southon
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Sreenath Paleri, Luise Wanner, Matthias Sühring, Ankur Desai, and Matthias Mauder
EGUsphere, https://doi.org/10.5194/egusphere-2023-1721, https://doi.org/10.5194/egusphere-2023-1721, 2023
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R. Bradley Pierce, Monica Harkey, Allen Lenzen, Lee M. Cronce, Jason A. Otkin, Jonathan L. Case, David S. Henderson, Zac Adelman, Tsengel Nergui, and Christopher R. Hain
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Jason A. Otkin, Lee M. Cronce, Jonathan L. Case, R. Bradley Pierce, Monica Harkey, Allen Lenzen, David S. Henderson, Zac Adelman, Tsengel Nergui, and Christopher R. Hain
Atmos. Chem. Phys., 23, 7935–7954, https://doi.org/10.5194/acp-23-7935-2023, https://doi.org/10.5194/acp-23-7935-2023, 2023
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Michael P. Vermeuel, Gordon A. Novak, Delaney B. Kilgour, Megan S. Claflin, Brian M. Lerner, Amy M. Trowbridge, Jonathan Thom, Patricia A. Cleary, Ankur R. Desai, and Timothy H. Bertram
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Reactive carbon species emitted from natural sources such as forests play an important role in the chemistry of the atmosphere. Predictions of these emissions are based on plant responses during the growing season and do not consider potential effects from seasonal changes. To address this, we made measurements of reactive carbon over a forest during the summer to autumn transition. We learned that observed concentrations and emissions for some key species are larger than model predictions.
Xuanli Li, Jason B. Roberts, Jayanthi Srikishen, Jonathan L. Case, Walter A. Petersen, Gyuwon Lee, and Christopher R. Hain
Geosci. Model Dev., 15, 5287–5308, https://doi.org/10.5194/gmd-15-5287-2022, https://doi.org/10.5194/gmd-15-5287-2022, 2022
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This research assimilated the Global Precipitation Measurement (GPM) satellite-retrieved ocean surface meteorology data into the Weather Research and Forecasting (WRF) model with the Gridpoint Statistical Interpolation (GSI) system. This was for two snowstorms during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games' (ICE-POP 2018) field experiments. The results indicated a positive impact of the data for short-term forecasts for heavy snowfall.
Sangchul Lee, Dongho Kim, Gregory W. McCarty, Martha Anderson, Feng Gao, Fangni Lei, Glenn E. Moglen, Xuesong Zhang, Haw Yen, Junyu Qi, Wade Crow, In-Young Yeo, and Liang Sun
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-187, https://doi.org/10.5194/hess-2022-187, 2022
Manuscript not accepted for further review
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Watershed modeling is important to protect water resources. However, errors are involved in watershed modeling. To reduce errors, remotely sensed evapotranspiration data are widely used. However, the use of remotely sensed evapotranspiration data still includes errors. This study applied two remotely sensed data (evapotranspiration and leaf area index) into watershed modeling to reduce errors. The results showed advancement of watershed modeling by two remotely sensed data.
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.
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NEON (National Ecological Observatory Network): AmeriFlux BASE US-xAB NEON Abby Road (ABBY), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617726, 2025a.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xAE NEON Klemme Range Research Station (OAES), Ver. 9-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671891, 2025b.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xBL NEON Blandy Experimental Farm (BLAN), Ver. 9-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671893, 2025c.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xBR NEON Bartlett Experimental Forest (BART), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1579542, 2025d.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xCL NEON LBJ National Grassland (CLBJ), Ver. 9-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671894, 2025e.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xCP NEON Central Plains Experimental Range (CPER), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1579720, 2025f.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xDC NEON Dakota Coteau Field School (DCFS), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617728, 2025g.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xDL NEON Dead Lake (DELA), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1579721, 2025h.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xDS NEON Disney Wilderness Preserve (DSNY), Ver. 9-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671895, 2025i.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xGR NEON Great Smoky Mountains National Park, Twin Creeks (GRSM), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1634885, 2025j.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xHA NEON Harvard Forest (HARV), Ver. 11-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1562391, 2025k.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xJE NEON Jones Ecological Research Center (JERC), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617730, 2025l.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xJR NEON Jornada LTER (JORN), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617731, 2025m.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xKA NEON Konza Prairie Biological Station – Relocatable (KONA), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1579722, 2025n.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xKZ NEON Konza Prairie Biological Station (KONZ), Ver. 11-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1562392, 2025o.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xLE NEON Lenoir Landing (LENO), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1773398, 2025p.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xMB NEON Moab (MOAB), Ver. 9-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671896, 2025q.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xML NEON Mountain Lake Biological Station (MLBS), Ver. 9-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671897, 2025r.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xNG NEON Northern Great Plains Research Laboratory (NOGP), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617732, 2025s.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xNQ NEON Onaqui-Ault (ONAQ), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617733, 2025t.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xNW NEON Niwot Ridge Mountain Research Station (NIWO), Ver. 9-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671898, 2025u.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xRM NEON Rocky Mountain National Park, CASTNET (RMNP), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1579723, 2025v.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xRN NEON Oak Ridge National Lab (ORNL), Ver. 8-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1773400, 2025w.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xSB NEON Ordway-Swisher Biological Station (OSBS), Ver. 9-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671899, 2025x.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xSC NEON Smithsonian Conservation Biology Institute (SCBI), Ver. 9-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671900, 2025y.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xSE NEON Smithsonian Environmental Research Center (SERC), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617734, 2025z.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xSJ NEON San Joaquin Experimental Range (SJER), Ver. 9-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671901, 2025aa.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xSL NEON North Sterling, CO (STER), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617735, 2025bb.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xSP NEON Soaproot Saddle (SOAP), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617736, 2025cc.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xSR NEON Santa Rita Experimental Range (SRER), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1579543, 2025dd.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xST NEON Steigerwaldt Land Services (STEI), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617737, 2025ee.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xTA NEON Talladega National Forest (TALL), Ver. 9-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1671902, 2025ff.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xTE NEON Lower Teakettle (TEAK), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617738, 2025gg.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xTR NEON Treehaven (TREE), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1634886, 2025hh.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xUK NEON The University of Kansas Field Station (UKFS), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617740, 2025ii.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xUN NEON University of Notre Dame Environmental Research Center (UNDE), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617741, 2025jj.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xWD NEON Woodworth (WOOD), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1579724, 2025kk.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xWR NEON Wind River Experimental Forest (WREF), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617742, 2025ll.
NEON (National Ecological Observatory Network): AmeriFlux BASE US-xYE NEON Yellowstone Northern Range (Frog Rock) (YELL), Ver. 10-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1617743, 2025mm.
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Short summary
Water moves from land to air in a process called evapotranspiration, which affects weather, crops, and water supply. Using satellites and AI, we created a system that tracks this water movement every five minutes, day and night, even through clouds. This provides continuous insights that can help manage water, predict weather, and better understand the water cycle.
Water moves from land to air in a process called evapotranspiration, which affects weather,...
