Articles | Volume 13, issue 2
https://doi.org/10.5194/essd-13-281-2021
© Author(s) 2021. 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-13-281-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
09 Feb 2021
Data description paper |
| 09 Feb 2021
| 09 Feb 2021
A daily, 250 m and real-time gross primary productivity product (2000–present) covering the contiguous United States
Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
College of Agricultural, Consumer & Environmental Sciences,
University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
DOE Center for Advanced Bioenergy and Bioproducts Innovation,
University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Kaiyu Guan
CORRESPONDING AUTHOR
Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
College of Agricultural, Consumer & Environmental Sciences,
University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
DOE Center for Advanced Bioenergy and Bioproducts Innovation,
University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
National Center for Supercomputing Applications, University of Illinois
at Urbana-Champaign, Urbana, IL 61801, USA
Genghong Wu
Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
College of Agricultural, Consumer & Environmental Sciences,
University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
DOE Center for Advanced Bioenergy and Bioproducts Innovation,
University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Bin Peng
Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
DOE Center for Advanced Bioenergy and Bioproducts Innovation,
University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
National Center for Supercomputing Applications, University of Illinois
at Urbana-Champaign, Urbana, IL 61801, USA
Sheng Wang
Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
College of Agricultural, Consumer & Environmental Sciences,
University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
National Center for Supercomputing Applications, University of Illinois
at Urbana-Champaign, Urbana, IL 61801, USA
Related authors
Chongya Jiang, Kaiyu Guan, Ming Pan, Youngryel Ryu, Bin Peng, and Sibo Wang
Hydrol. Earth Syst. Sci., 24, 1251–1273, https://doi.org/10.5194/hess-24-1251-2020, https://doi.org/10.5194/hess-24-1251-2020, 2020
Short summary
Short summary
Quantifying crop water use at each field every day is challenging because of the complexity of the evapotranspiration (ET) process and the unavailability of data at high spatiotemporal resolutions. We fuse multi-satellite data and employ a sophisticated model to estimate ET at 30 m resolution and a daily interval. With validation against 86 site years of ground truth in the US Corn Belt, we are confident that our ET estimation is accurate and a reliable tool for water resource management.
Julien Lamour, Shawn P. Serbin, Alistair Rogers, Kelvin T. Acebron, Elizabeth Ainsworth, Loren P. Albert, Michael Alonzo, Jeremiah Anderson, Owen K. Atkin, Nicolas Barbier, Mallory L. Barnes, Carl J. Bernacchi, Ninon Besson, Angela C. Burnett, Joshua S. Caplan, Jérôme Chave, Alexander W. Cheesman, Ilona Clocher, Onoriode Coast, Sabrina Coste, Holly Croft, Boya Cui, Clément Dauvissat, Kenneth J. Davidson, Christopher Doughty, Kim S. Ely, Jean-Baptiste Féret, Iolanda Filella, Claire Fortunel, Peng Fu, Maquelle Garcia, Bruno O. Gimenez, Kaiyu Guan, Zhengfei Guo, David Heckmann, Patrick Heuret, Marney Isaac, Shan Kothari, Etsushi Kumagai, Thu Ya Kyaw, Liangyun Liu, Lingli Liu, Shuwen Liu, Joan Llusià, Troy Magney, Isabelle Maréchaux, Adam R. Martin, Katherine Meacham-Hensold, Christopher M. Montes, Romà Ogaya, Joy Ojo, Regison Oliveira, Alain Paquette, Josep Peñuelas, Antonia Debora Placido, Juan M. Posada, Xiaojin Qian, Heidi J. Renninger, Milagros Rodriguez-Caton, Andrés Rojas-González, Urte Schlüter, Giacomo Sellan, Courtney M. Siegert, Guangqin Song, Charles D. Southwick, Daisy C. Souza, Clément Stahl, Yanjun Su, Leeladarshini Sujeeun, To-Chia Ting, Vicente Vasquez, Amrutha Vijayakumar, Marcelo Vilas-Boas, Diane R. Wang, Sheng Wang, Han Wang, Jing Wang, Xin Wang, Andreas P. M. Weber, Christopher Y. S. Wong, Jin Wu, Fengqi Wu, Shengbiao Wu, Zhengbing Yan, Dedi Yang, and Yingyi Zhao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-213, https://doi.org/10.5194/essd-2025-213, 2025
Preprint under review for ESSD
Short summary
Short summary
We present the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. This repository provides a unique source of information for creating hyperspectral models for predicting photosynthetic traits and associated leaf traits in terrestrial plants.
Zitong Li, Kang Sun, Kaiyu Guan, Sheng Wang, Bin Peng, Lieven Clarisse, Martin Van Damme, Pierre-François Coheur, Karen Cady-Pereira, Mark W. Shephard, Mark Zondlo, and Daniel Moore
EGUsphere, https://doi.org/10.5194/egusphere-2025-725, https://doi.org/10.5194/egusphere-2025-725, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We estimate ammonia fluxes over the contiguous U.S. from 2008 to 2022 using a directional derivative approach applied to satellite observations from IASI and CrIS. Satellite-based flux estimates reveal that ammonia emissions deposit in nearby vegetation, with pronounced seasonal and spatial variability driven by agricultural activities, underscoring the need for improved monitoring and management strategies.
Zewei Ma, Kaiyu Guan, Bin Peng, Wang Zhou, Robert Grant, Jinyun Tang, Murugesu Sivapalan, Ming Pan, Li Li, and Zhenong Jin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-340, https://doi.org/10.5194/hess-2024-340, 2024
Preprint under review for HESS
Short summary
Short summary
We explore tile drainage’ impacts on the integrated hydrology-biogeochemistry-plant system, using ecosys with soil oxygen and microbe dynamics. We found that tile drainage lowers soil water content and improves soil oxygen levels, which helps crops grow better, especially during wet springs, and the developed root system also helps mitigate drought stress on dry summers. Overall, tile drainage increases crop resilience to climate change, making it a valuable future agricultural practice.
Licheng Liu, Shaoming Xu, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, Matthew D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar, and Zhenong Jin
Geosci. Model Dev., 15, 2839–2858, https://doi.org/10.5194/gmd-15-2839-2022, https://doi.org/10.5194/gmd-15-2839-2022, 2022
Short summary
Short summary
By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the well-known limitations of process-based models due to insufficient representations and constraints, and unlocks the “black box” of machine learning models. Therefore, KGML-ag can outperform existing approaches on capturing the hot moment and complex dynamics of N2O flux. This study will be a critical reference for the new generation of modeling paradigm for biogeochemistry and other geoscience processes.
Sheng Wang, Monica Garcia, Andreas Ibrom, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 24, 3643–3661, https://doi.org/10.5194/hess-24-3643-2020, https://doi.org/10.5194/hess-24-3643-2020, 2020
Short summary
Short summary
Remote sensing only provides snapshots of rapidly changing land surface variables; this limits its application for water resources and ecosystem management. To obtain continuous estimates of surface temperature, soil moisture, evapotranspiration, and ecosystem productivity, a simple and operational modelling scheme is presented. We demonstrate it with temporally sparse optical and thermal remote sensing data from an unmanned aerial system at a Danish bioenergy plantation eddy covariance site.
Chongya Jiang, Kaiyu Guan, Ming Pan, Youngryel Ryu, Bin Peng, and Sibo Wang
Hydrol. Earth Syst. Sci., 24, 1251–1273, https://doi.org/10.5194/hess-24-1251-2020, https://doi.org/10.5194/hess-24-1251-2020, 2020
Short summary
Short summary
Quantifying crop water use at each field every day is challenging because of the complexity of the evapotranspiration (ET) process and the unavailability of data at high spatiotemporal resolutions. We fuse multi-satellite data and employ a sophisticated model to estimate ET at 30 m resolution and a daily interval. With validation against 86 site years of ground truth in the US Corn Belt, we are confident that our ET estimation is accurate and a reliable tool for water resource management.
Related subject area
Biogeosciences and biodiversity
CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO2 fertilization
Permafrost–wildfire interactions: active layer thickness estimates for paired burned and unburned sites in northern high latitudes
Global patterns and drivers of soil dissolved organic carbon concentrations
The SahulCHAR collection: a palaeofire database for Australia, New Guinea, and New Zealand
ARGO: ARctic greenhouse Gas Observation metadata version 1
WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022
The European Forest Disturbance Atlas: a forest disturbance monitoring system using the Landsat archive
An expert survey on chamber measurement techniques and data handling procedures for methane fluxes
LegacyVegetation: Northern Hemisphere reconstruction of past plant cover and total tree cover from pollen archives of the last 14 kyr
A new habitat map of the Lena Delta in Arctic Siberia based on field and remote sensing datasets
Mapping global leaf inclination angle (LIA) based on field measurement data
A post-processed carbon flux dataset for 34 eddy covariance flux sites across the Heihe River basin, China
Century-long reconstruction of gridded phosphorus surplus across Europe (1850–2019)
High-resolution carbon cycling data from 2019 to 2021 measured at six Austrian long-term ecosystem research sites
Remote sensing of young leaf photosynthetic capacity in tropical and subtropical evergreen broadleaved forests
The JapanFlux2024 dataset for eddy covariance observations covering Japan and East Asia from 1990 to 2023
An organic matter database (OMD): consolidating global residue data from agriculture, fisheries, forestry and related industries
China's annual forest age dataset at 30 m spatial resolution from 1986 to 2022
Gas exchange velocities (k600), gas exchange rates (K600), and hydraulic geometries for streams and rivers derived from the NEON Reaeration field and lab collection data product (DP1.20190.001)
Multi-temporal high-resolution data products of ecosystem structure derived from country-wide airborne laser scanning surveys of the Netherlands
A spectral–structural characterization of European temperate, hemiboreal, and boreal forests
VODCA v2: multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring
Crop-specific management history of phosphorus fertilizer input (CMH-P) in the croplands of the United States: reconciliation of top-down and bottom-up data sources
Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation index (NDVI) with MODIS NDVI
A synthesized field survey database of vegetation and active-layer properties for the Alaskan tundra (1972–2020)
A vegetation phenology dataset by integrating multiple sources using the Reliability Ensemble Averaging method
TCSIF: a temporally consistent global Global Ozone Monitoring Experiment-2A (GOME-2A) solar-induced chlorophyll fluorescence dataset with the correction of sensor degradation
Global nitrous oxide budget (1980–2020)
National forest carbon harvesting and allocation dataset for the period 2003 to 2018
Spatial mapping of key plant functional traits in terrestrial ecosystems across China
HiQ-LAI: a high-quality reprocessed MODIS leaf area index dataset with better spatiotemporal consistency from 2000 to 2022
EUPollMap: the European atlas of contemporary pollen distribution maps derived from an integrated Kriging interpolation approach
Reference maps of soil phosphorus for the pan-Amazon region
Mapping 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020
Sensor-independent LAI/FPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022
Investigating limnological processes and modern sedimentation at Lake Żabińskie, northeast Poland: a decade-long multi-variable dataset, 2012–2021
Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022
CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada
Quantifying exchangeable base cations in permafrost: a reserve of nutrients about to thaw
Routine monitoring of western Lake Erie to track water quality changes associated with cyanobacterial harmful algal blooms
The Portuguese Large Wildfire Spread database (PT-FireSprd)
Thirty-meter map of young forest age in China
GRiMeDB: the Global River Methane Database of concentrations and fluxes
A gridded dataset of a leaf-age-dependent leaf area index seasonality product over tropical and subtropical evergreen broadleaved forests
Fire weather index data under historical and shared socioeconomic pathway projections in the 6th phase of the Coupled Model Intercomparison Project from 1850 to 2100
A remote-sensing-based dataset to characterize the ecosystem functioning and functional diversity in the Biosphere Reserve of the Sierra Nevada (southeastern Spain)
A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT
A global database on holdover time of lightning-ignited wildfires
National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the global stocktake
Yanghui Kang, Maoya Bassiouni, Max Gaber, Xinchen Lu, and Trevor F. Keenan
Earth Syst. Sci. Data, 17, 3009–3046, https://doi.org/10.5194/essd-17-3009-2025, https://doi.org/10.5194/essd-17-3009-2025, 2025
Short summary
Short summary
CEDAR-GPP provides spatiotemporally upscaled estimates of gross primary productivity (GPP) globally, uniquely incorporating the direct effect of elevated atmospheric CO2 on photosynthesis. This dataset was produced by upscaling eddy covariance data with machine learning and a broad range of satellite and climate variables. Available at monthly and 0.05° resolution from 1982 to 2020, CEDAR-GPP offers critical insights into ecosystem–climate interactions and the global carbon cycle.
Anna C. Talucci, Michael M. Loranty, Jean E. Holloway, Brendan M. Rogers, Heather D. Alexander, Natalie Baillargeon, Jennifer L. Baltzer, Logan T. Berner, Amy Breen, Leya Brodt, Brian Buma, Jacqueline Dean, Clement J. F. Delcourt, Lucas R. Diaz, Catherine M. Dieleman, Thomas A. Douglas, Gerald V. Frost, Benjamin V. Gaglioti, Rebecca E. Hewitt, Teresa Hollingsworth, M. Torre Jorgenson, Mark J. Lara, Rachel A. Loehman, Michelle C. Mack, Kristen L. Manies, Christina Minions, Susan M. Natali, Jonathan A. O'Donnell, David Olefeldt, Alison K. Paulson, Adrian V. Rocha, Lisa B. Saperstein, Tatiana A. Shestakova, Seeta Sistla, Oleg Sizov, Andrey Soromotin, Merritt R. Turetsky, Sander Veraverbeke, and Michelle A. Walvoord
Earth Syst. Sci. Data, 17, 2887–2909, https://doi.org/10.5194/essd-17-2887-2025, https://doi.org/10.5194/essd-17-2887-2025, 2025
Short summary
Short summary
Wildfires have the potential to accelerate permafrost thaw and the associated feedbacks to climate change. We assembled a dataset of permafrost thaw depth measurements from burned and unburned sites contributed by researchers from across the northern high-latitude region. We estimated maximum thaw depth for each measurement, which addresses a key challenge: the ability to assess impacts of wildfire on maximum thaw depth when measurement timing varies.
Tianjing Ren and Andong Cai
Earth Syst. Sci. Data, 17, 2873–2885, https://doi.org/10.5194/essd-17-2873-2025, https://doi.org/10.5194/essd-17-2873-2025, 2025
Short summary
Short summary
This study compiles a global database of soil dissolved organic carbon (DOC) concentrations, a key factor in soil health and climate change. Using machine learning, it identifies the most influential factors affecting soil DOC levels and maps global DOC patterns. The findings help guide soil management and climate strategies, with the dataset available for further research.
Emma Rehn, Haidee Cadd, Scott Mooney, Tim J. Cohen, Henry Munack, Alexandru T. Codilean, Matthew Adeleye, Kristen K. Beck, Mark Constantine IV, Chris Gouramanis, Johanna M. Hanson, Penelope J. Jones, A. Peter Kershaw, Lydia Mackenzie, Maame Maisie, Michela Mariani, Kia Matley, David McWethy, Keely Mills, Patrick Moss, Nicholas R. Patton, Cassandra Rowe, Janelle Stevenson, John Tibby, and Janet Wilmshurst
Earth Syst. Sci. Data, 17, 2681–2692, https://doi.org/10.5194/essd-17-2681-2025, https://doi.org/10.5194/essd-17-2681-2025, 2025
Short summary
Short summary
This paper presents SahulCHAR, a new collection of palaeofire (ancient fire) records from Australia, New Guinea, and New Zealand. SahulCHAR version 1 contains 687 records of sedimentary charcoal or black carbon, including digitized data, records from existing databases, and original author-submitted data. SahulCHAR is a much-needed update to past charcoal compilations that will also provide greater representation of records from this region in future global syntheses to understand past fire.
Judith Vogt, Martijn M. T. A. Pallandt, Luana S. Basso, Abdullah Bolek, Kseniia Ivanova, Mark Schlutow, Gerardo Celis, McKenzie Kuhn, Marguerite Mauritz, Edward A. G. Schuur, Kyle Arndt, Anna-Maria Virkkala, Isabel Wargowsky, and Mathias Göckede
Earth Syst. Sci. Data, 17, 2553–2573, https://doi.org/10.5194/essd-17-2553-2025, https://doi.org/10.5194/essd-17-2553-2025, 2025
Short summary
Short summary
We present a meta-dataset of greenhouse gas observations in the Arctic and boreal regions, including information on sites where greenhouse gases have been measured using different measurement techniques. We provide a novel repository of metadata to facilitate synthesis efforts for regions undergoing rapid environmental change. The meta-dataset shows where measurements are missing and will be updated as new measurements are published.
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
Short summary
Short summary
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.
Alba Viana-Soto and Cornelius Senf
Earth Syst. Sci. Data, 17, 2373–2404, https://doi.org/10.5194/essd-17-2373-2025, https://doi.org/10.5194/essd-17-2373-2025, 2025
Short summary
Short summary
Europe's forests are undergoing complex changes in response to increasing disturbances driven by climate and land use changes. Here, we present the European Forest Disturbance Atlas, a satellite-based approach for mapping annual forest disturbances across continental Europe from 1985 onwards. Maps provide insights into the year of disturbance occurrence, the actual frequency of disturbances, severity and the underlying causal agent, thus contributing to a future monitoring system envisioned for Europe.
Katharina Jentzsch, Lona van Delden, Matthias Fuchs, and Claire C. Treat
Earth Syst. Sci. Data, 17, 2331–2372, https://doi.org/10.5194/essd-17-2331-2025, https://doi.org/10.5194/essd-17-2331-2025, 2025
Short summary
Short summary
Methane is a greenhouse gas that contributes to global warming, but we do not fully understand how much is released from natural sources like wetlands. To measure methane over large areas, many measurements are needed, often from small chambers that are placed on the ground. However, different researchers use different measurement setups, making it hard to combine data. We surveyed 36 researchers about their methods, summarized the responses, and identified ways to make the data more comparable.
Laura Schild, Peter Ewald, Chenzhi Li, Raphaël Hébert, Thomas Laepple, and Ulrike Herzschuh
Earth Syst. Sci. Data, 17, 2007–2033, https://doi.org/10.5194/essd-17-2007-2025, https://doi.org/10.5194/essd-17-2007-2025, 2025
Short summary
Short summary
This study reconstructed vegetation and tree cover in the Northern Hemisphere from a harmonized dataset of pollen counts from sediment and peat cores for the past 14 000 years. A model was applied to correct for differences in pollen production between different plants, and modern remote-sensing forest cover was used to validate the reconstructed tree cover. Accurate data on past vegetation are invaluable for the investigation of vegetation–climate dynamics and the validation of vegetation models.
Simeon Lisovski, Alexandra Runge, Iuliia Shevtsova, Nele Landgraf, Anne Morgenstern, Ronald Reagan Okoth, Matthias Fuchs, Nikolay Lashchinskiy, Carl Stadie, Alison Beamish, Ulrike Herzschuh, Guido Grosse, and Birgit Heim
Earth Syst. Sci. Data, 17, 1707–1730, https://doi.org/10.5194/essd-17-1707-2025, https://doi.org/10.5194/essd-17-1707-2025, 2025
Short summary
Short summary
The Lena Delta is the largest river delta in the Arctic and represents a biodiversity hotspot. Here, we describe multiple field datasets and a detailed habitat classification map for the Lena Delta. We present context and methods of these openly available datasets and show how they can improve our understanding of the rapidly changing Arctic tundra system.
Sijia Li and Hongliang Fang
Earth Syst. Sci. Data, 17, 1347–1366, https://doi.org/10.5194/essd-17-1347-2025, https://doi.org/10.5194/essd-17-1347-2025, 2025
Short summary
Short summary
Leaf inclination angle (LIA) is a vital trait in radiative transfer, rainfall interception, evapotranspiration, photosynthesis, and hydrological processes. However, global LIA knowledge is still lacking. This study generated the first global 500 m LIA products by gap-filling LIA measurement data. The global LIA is 41.47° ± 9.55° and increases with latitude. LIA products could enhance our understanding of global LIA and assist remote sensing retrieval and land surface modeling studies.
Xufeng Wang, Tao Che, Jingfeng Xiao, Tonghong Wang, Junlei Tan, Yang Zhang, Zhiguo Ren, Liying Geng, Haibo Wang, Ziwei Xu, Shaomin Liu, and Xin Li
Earth Syst. Sci. Data, 17, 1329–1346, https://doi.org/10.5194/essd-17-1329-2025, https://doi.org/10.5194/essd-17-1329-2025, 2025
Short summary
Short summary
In this study, carbon flux and auxiliary meteorological data are post-processed to create an analysis-ready dataset for 34 sites across six ecosystems in the Heihe River basin. Overall, 18 sites have multi-year observations, while 16 were observed only during the 2012 growing season, totaling 1513 site months. This dataset can be used to explore carbon exchange, assess ecosystem responses to climate change, support upscaling studies, and evaluate carbon cycle models.
Masooma Batool, Fanny J. Sarrazin, and Rohini Kumar
Earth Syst. Sci. Data, 17, 881–916, https://doi.org/10.5194/essd-17-881-2025, https://doi.org/10.5194/essd-17-881-2025, 2025
Short summary
Short summary
Our paper presents a reconstruction and analysis of the gridded P surplus in European landscapes from 1850 to 2019 at a 5 arcmin resolution. By utilizing 48 different estimates, we account for uncertainties in major components of the P surplus. Our findings highlight substantial historical changes, with the total P surplus in the EU 27 tripling over 170 years. Our dataset enables flexible aggregation at various spatial scales, providing critical insights for land and water management strategies.
Thomas Dirnböck, Michael Bahn, Eugenio Diaz-Pines, Ika Djukic, Michael Englisch, Karl Gartner, Günther Gollobich, Johannes Ingrisch, Barbara Kitzler, Karl Knaebel, Johannes Kobler, Andreas Maier, Armin Malli, Ivo Offenthaler, Johannes Peterseil, Gisela Pröll, Sarah Venier, Christoph Wohner, Sophie Zechmeister-Boltenstern, Anita Zolles, and Stephan Glatzel
Earth Syst. Sci. Data, 17, 685–702, https://doi.org/10.5194/essd-17-685-2025, https://doi.org/10.5194/essd-17-685-2025, 2025
Short summary
Short summary
Long-term observation sites have been established in six Austrian locations, covering major ecosystem types such as forests, grasslands, and wetlands. The purpose of these observations is to measure baselines for assessing the impacts of extreme climate events on the carbon cycle. The collected datasets include meteorological variables, soil temperature and moisture, carbon dioxide fluxes, and tree stem growth in forests at a resolution of 15–60 min between 2019 and 2021.
Xueqin Yang, Qingling Sun, Liusheng Han, Wenping Yuan, Jie Tian, Liyang Liu, Wei Zheng, Mei Wang, Yunpeng Wang, and Xiuzhi Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-64, https://doi.org/10.5194/essd-2025-64, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Understanding how leaves absorb carbon from the atmosphere is essential for predicting changes in global forests. Young leaves play a key role in this process, but their efficiency has been difficult to measure at large scales. Using satellite data, we developed a new method to track the seasonal patterns of young leaves’ photosynthetic capacity from 2001 to 2018. Our dataset helps scientists better understand forest growth and how ecosystems respond to climate change.
Masahito Ueyama, Yuta Takao, Hiromi Yazawa, Makiko Tanaka, Hironori Yabuki, Tomo’omi Kumagai, Hiroki Iwata, Md. Abdul Awal, Mingyuan Du, Yoshinobu Harazono, Yoshiaki Hata, Takashi Hirano, Tsutom Hiura, Reiko Ide, Sachinobu Ishida, Mamoru Ishikawa, Kenzo Kitamura, Yuji Kominami, Shujiro Komiya, Ayumi Kotani, Yuta Inoue, Takashi Machimura, Kazuho Matsumoto, Yojiro Matsuura, Yasuko Mizoguchi, Shohei Murayama, Hirohiko Nagano, Taro Nakai, Tatsuro Nakaji, Ko Nakaya, Shinjiro Ohkubo, Takeshi Ohta, Keisuke Ono, Taku M. Saitoh, Ayaka Sakabe, Takanori Shimizu, Seiji Shimoda, Michiaki Sugita, Kentaro Takagi, Yoshiyuki Takahashi, Naoya Takamura, Satoru Takanashi, Takahiro Takimoto, Yukio Yasuda, Qinxue Wang, Jun Asanuma, Hideo Hasegawa, Tetsuya Hiyama, Yoshihiro Iijima, Shigeyuki Ishidoya, Masayuki Itoh, Tomomichi Kato, Hiroaki Kondo, Yoshiko Kosugi, Tomonori Kume, Takahisa Maeda, Trofim Maximov, Ryo Moriwaki, Hiroyuki Muraoka, Roman Petrov, Jun Suzuki, Shingo Taniguchi, and Kazuhito Ichii
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-615, https://doi.org/10.5194/essd-2024-615, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
The JapanFlux2024 dataset, created through collaboration across Japan and East Asia, includes eddy covariance data from 79 sites spanning 652 site-years (1990–2023). This comprehensive dataset offers valuable insights into energy, water, and CO2 fluxes, supporting research on land-atmosphere interactions and process models. Compatible with FLUXNET, it fosters global collaboration and advances research in environmental science and regional climate dynamics.
Gudeta Weldesemayat Sileshi, Edmundo Barrios, Johannes Lehmann, and Francesco Nicola Tubiello
Earth Syst. Sci. Data, 17, 369–391, https://doi.org/10.5194/essd-17-369-2025, https://doi.org/10.5194/essd-17-369-2025, 2025
Short summary
Short summary
Agricultural, fishery, forestry and agro-processing activities produce large quantities of residues, by-products and waste materials every year. Here, we present a global organic matter database (OMD), the first of its kind, consolidating estimates of residues and by-products potentially available for use in a circular bio-economy. It also provides definitions, typologies and methods to aid consistent classification, estimation and reporting of the various residues and by-products.
Rong Shang, Xudong Lin, Jing M. Chen, Yunjian Liang, Keyan Fang, Mingzhu Xu, Yulin Yan, Weimin Ju, Guirui Yu, Nianpeng He, Li Xu, Liangyun Liu, Jing Li, Wang Li, Jun Zhai, and Zhongmin Hu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-574, https://doi.org/10.5194/essd-2024-574, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Forest age is critical for carbon cycle modelling and effective forest management. Existing datasets, however, have low spatial resolutions or limited temporal coverage. This study introduces China's Annual Forest Age Dataset (CAFA), spanning 1986–2022 at 30-m resolution. By tracking forest disturbances, we annually update ages. Validation shows small errors for disturbed forests and larger for undisturbed forests. CAFA can enhance carbon cycle modelling and forest management in China.
Kelly S. Aho, Kaelin M. Cawley, Robert T. Hensley, Robert O. Hall Jr., Walter K. Dodds, and Keli J. Goodman
Earth Syst. Sci. Data, 16, 5563–5578, https://doi.org/10.5194/essd-16-5563-2024, https://doi.org/10.5194/essd-16-5563-2024, 2024
Short summary
Short summary
Gas exchange is fundamental to many biogeochemical processes in streams and depends on the degree of gas saturation and the gas transfer velocity (k). Currently, k is harder to measure than concentration. Here, we present a processing pipeline to estimate k from tracer-gas experiments conducted in 22 streams by the National Ecological Observatory Network. The processed dataset (n = 339) represents the largest compilation of standardized k estimates available.
Yifang Shi and W. Daniel Kissling
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-488, https://doi.org/10.5194/essd-2024-488, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
We present a new set of multi-temporal LiDAR metrics of ecosystem structure derived from four national ALS surveys of the Netherlands (AHN1–AHN4), capturing vegetation height, cover, and structural variability over the past two decades (1998–2022). Around 70 TB point clouds have been processed to read-to-use raster layers at 10 m resolution (~ 59 GB), enabling a wide use and uptake of ecosystem structure information in biodiversity and habitat monitoring, ecosystem and carbon dynamic modelling.
Miina Rautiainen, Aarne Hovi, Daniel Schraik, Jan Hanuš, Petr Lukeš, Zuzana Lhotáková, and Lucie Homolová
Earth Syst. Sci. Data, 16, 5069–5087, https://doi.org/10.5194/essd-16-5069-2024, https://doi.org/10.5194/essd-16-5069-2024, 2024
Short summary
Short summary
Radiative transfer models play a key role in monitoring vegetation using remote sensing data such as satellite or airborne images. The development of these models has been hindered by a lack of comprehensive ground reference data on structural and spectral characteristics of forests. Here, we reported datasets on the structural and spectral properties of temperate, hemiboreal, and boreal European forest stands. We anticipate that these data will have wide use in remote sensing applications.
Ruxandra-Maria Zotta, Leander Moesinger, Robin van der Schalie, Mariette Vreugdenhil, Wolfgang Preimesberger, Thomas Frederikse, Richard de Jeu, and Wouter Dorigo
Earth Syst. Sci. Data, 16, 4573–4617, https://doi.org/10.5194/essd-16-4573-2024, https://doi.org/10.5194/essd-16-4573-2024, 2024
Short summary
Short summary
VODCA v2 is a dataset providing vegetation indicators for long-term ecosystem monitoring. VODCA v2 comprises two products: VODCA CXKu, spanning 34 years of observations (1987–2021), suitable for monitoring upper canopy dynamics, and VODCA L (2010–2021), for above-ground biomass monitoring. VODCA v2 has lower noise levels than the previous product version and provides valuable insights into plant water dynamics and biomass changes, even in areas where optical data are limited.
Peiyu Cao, Bo Yi, Franco Bilotto, Carlos Gonzalez Fischer, Mario Herrero, and Chaoqun Lu
Earth Syst. Sci. Data, 16, 4557–4572, https://doi.org/10.5194/essd-16-4557-2024, https://doi.org/10.5194/essd-16-4557-2024, 2024
Short summary
Short summary
This article presents a spatially explicit time series dataset reconstructing crop-specific phosphorus fertilizer application rates, timing, and methods at a 4 km × 4 km resolution in the United States from 1850 to 2022. We comprehensively characterized the spatio-temporal dynamics of P fertilizer management over the last 170 years by considering cross-crop variations. This dataset will greatly contribute to the field of agricultural sustainability assessment and Earth system modeling.
Chad A. Burton, Sami W. Rifai, Luigi J. Renzullo, and Albert I. J. M. Van Dijk
Earth Syst. Sci. Data, 16, 4389–4416, https://doi.org/10.5194/essd-16-4389-2024, https://doi.org/10.5194/essd-16-4389-2024, 2024
Short summary
Short summary
Understanding vegetation response to environmental change requires accurate, long-term data on vegetation condition (VC). We evaluated existing satellite VC datasets over Australia and found them lacking, so we developed a new VC dataset for Australia, AusENDVI. It can be used for studying Australia's changing vegetation dynamics and downstream impacts on the carbon and water cycles, and it provides a reliable foundation for further research into the drivers of vegetation change.
Xiaoran Zhu, Dong Chen, Maruko Kogure, Elizabeth Hoy, Logan T. Berner, Amy L. Breen, Abhishek Chatterjee, Scott J. Davidson, Gerald V. Frost, Teresa N. Hollingsworth, Go Iwahana, Randi R. Jandt, Anja N. Kade, Tatiana V. Loboda, Matt J. Macander, Michelle Mack, Charles E. Miller, Eric A. Miller, Susan M. Natali, Martha K. Raynolds, Adrian V. Rocha, Shiro Tsuyuzaki, Craig E. Tweedie, Donald A. Walker, Mathew Williams, Xin Xu, Yingtong Zhang, Nancy French, and Scott Goetz
Earth Syst. Sci. Data, 16, 3687–3703, https://doi.org/10.5194/essd-16-3687-2024, https://doi.org/10.5194/essd-16-3687-2024, 2024
Short summary
Short summary
The Arctic tundra is experiencing widespread physical and biological changes, largely in response to warming, yet scientific understanding of tundra ecology and change remains limited due to relatively limited accessibility and studies compared to other terrestrial biomes. To support synthesis research and inform future studies, we created the Synthesized Alaskan Tundra Field Dataset (SATFiD), which brings together field datasets and includes vegetation, active-layer, and fire properties.
Yishuo Cui, Shouzhi Chen, Yufeng Gong, Mingwei Li, Zitong Jia, Yuyu Zhou, and Yongshuo H. Fu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-225, https://doi.org/10.5194/essd-2024-225, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Global changes have significantly altered vegetation phenology, affecting terrestrial carbon cycle. While various remote-sensing-based phenology datasets exist, they often suffer from inconsistencies and uncertainties. To address this, we developed a new phenology dataset spanning 1982 to 2022 using a reliability ensemble averaging method. Validated against ground data, our dataset demonstrates substantially improved accuracy, providing a novel and reliable source for global ecological studies.
Chu Zou, Shanshan Du, Xinjie Liu, and Liangyun Liu
Earth Syst. Sci. Data, 16, 2789–2809, https://doi.org/10.5194/essd-16-2789-2024, https://doi.org/10.5194/essd-16-2789-2024, 2024
Short summary
Short summary
To obtain a temporally consistent satellite solar-induced chlorophyll fluorescence
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
Hanqin Tian, Naiqing Pan, Rona L. Thompson, Josep G. Canadell, Parvadha Suntharalingam, Pierre Regnier, Eric A. Davidson, Michael Prather, Philippe Ciais, Marilena Muntean, Shufen Pan, Wilfried Winiwarter, Sönke Zaehle, Feng Zhou, Robert B. Jackson, Hermann W. Bange, Sarah Berthet, Zihao Bian, Daniele Bianchi, Alexander F. Bouwman, Erik T. Buitenhuis, Geoffrey Dutton, Minpeng Hu, Akihiko Ito, Atul K. Jain, Aurich Jeltsch-Thömmes, Fortunat Joos, Sian Kou-Giesbrecht, Paul B. Krummel, Xin Lan, Angela Landolfi, Ronny Lauerwald, Ya Li, Chaoqun Lu, Taylor Maavara, Manfredi Manizza, Dylan B. Millet, Jens Mühle, Prabir K. Patra, Glen P. Peters, Xiaoyu Qin, Peter Raymond, Laure Resplandy, Judith A. Rosentreter, Hao Shi, Qing Sun, Daniele Tonina, Francesco N. Tubiello, Guido R. van der Werf, Nicolas Vuichard, Junjie Wang, Kelley C. Wells, Luke M. Western, Chris Wilson, Jia Yang, Yuanzhi Yao, Yongfa You, and Qing Zhu
Earth Syst. Sci. Data, 16, 2543–2604, https://doi.org/10.5194/essd-16-2543-2024, https://doi.org/10.5194/essd-16-2543-2024, 2024
Short summary
Short summary
Atmospheric concentrations of nitrous oxide (N2O), a greenhouse gas 273 times more potent than carbon dioxide, have increased by 25 % since the preindustrial period, with the highest observed growth rate in 2020 and 2021. This rapid growth rate has primarily been due to a 40 % increase in anthropogenic emissions since 1980. Observed atmospheric N2O concentrations in recent years have exceeded the worst-case climate scenario, underscoring the importance of reducing anthropogenic N2O emissions.
Daju Wang, Peiyang Ren, Xiaosheng Xia, Lei Fan, Zhangcai Qin, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 16, 2465–2481, https://doi.org/10.5194/essd-16-2465-2024, https://doi.org/10.5194/essd-16-2465-2024, 2024
Short summary
Short summary
This study generated a high-precision dataset, locating forest harvested carbon and quantifying post-harvest wood emissions for various uses. It enhances our understanding of forest harvesting and post-harvest carbon dynamics in China, providing essential data for estimating the forest ecosystem carbon budget and emphasizing wood utilization's impact on carbon emissions.
Nannan An, Nan Lu, Weiliang Chen, Yongzhe Chen, Hao Shi, Fuzhong Wu, and Bojie Fu
Earth Syst. Sci. Data, 16, 1771–1810, https://doi.org/10.5194/essd-16-1771-2024, https://doi.org/10.5194/essd-16-1771-2024, 2024
Short summary
Short summary
This study generated a spatially continuous plant functional trait dataset (~1 km) in China in combination with field observations, environmental variables and vegetation indices using machine learning methods. Results showed that wood density, leaf P concentration and specific leaf area showed good accuracy with an average R2 of higher than 0.45. This dataset could provide data support for development of Earth system models to predict vegetation distribution and ecosystem functions.
Kai Yan, Jingrui Wang, Rui Peng, Kai Yang, Xiuzhi Chen, Gaofei Yin, Jinwei Dong, Marie Weiss, Jiabin Pu, and Ranga B. Myneni
Earth Syst. Sci. Data, 16, 1601–1622, https://doi.org/10.5194/essd-16-1601-2024, https://doi.org/10.5194/essd-16-1601-2024, 2024
Short summary
Short summary
Variations in observational conditions have led to poor spatiotemporal consistency in leaf area index (LAI) time series. Using prior knowledge, we leveraged high-quality observations and spatiotemporal correlation to reprocess MODIS LAI, thereby generating HiQ-LAI, a product that exhibits fewer abnormal fluctuations in time series. Reprocessing was done on Google Earth Engine, providing users with convenient access to this value-added data and facilitating large-scale research and applications.
Fabio Oriani, Gregoire Mariethoz, and Manuel Chevalier
Earth Syst. Sci. Data, 16, 731–742, https://doi.org/10.5194/essd-16-731-2024, https://doi.org/10.5194/essd-16-731-2024, 2024
Short summary
Short summary
Modern and fossil pollen data contain precious information for reconstructing the climate and environment of the past. However, these data are only achieved for single locations with no continuity in space. We present here a systematic atlas of 194 digital maps containing the spatial estimation of contemporary pollen presence over Europe. This dataset constitutes a free and ready-to-use tool to study climate, biodiversity, and environment in time and space.
João Paulo Darela-Filho, Anja Rammig, Katrin Fleischer, Tatiana Reichert, Laynara Figueiredo Lugli, Carlos Alberto Quesada, Luis Carlos Colocho Hurtarte, Mateus Dantas de Paula, and David M. Lapola
Earth Syst. Sci. Data, 16, 715–729, https://doi.org/10.5194/essd-16-715-2024, https://doi.org/10.5194/essd-16-715-2024, 2024
Short summary
Short summary
Phosphorus (P) is crucial for plant growth, and scientists have created models to study how it interacts with carbon cycle in ecosystems. To apply these models, it is important to know the distribution of phosphorus in soil. In this study we estimated the distribution of phosphorus in the Amazon region. The results showed a clear gradient of soil development and P content. These maps can help improve ecosystem models and generate new hypotheses about phosphorus availability in the Amazon.
Mengyao Zhu, Junhu Dai, Huanjiong Wang, Juha M. Alatalo, Wei Liu, Yulong Hao, and Quansheng Ge
Earth Syst. Sci. Data, 16, 277–293, https://doi.org/10.5194/essd-16-277-2024, https://doi.org/10.5194/essd-16-277-2024, 2024
Short summary
Short summary
This study utilized 24,552 in situ phenology observation records from the Chinese Phenology Observation Network to model and map 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020. These phenology maps are the first gridded, independent and reliable phenology data sources for China, offering a high spatial resolution of 0.1° and an average deviation of about 10 days. It contributes to more comprehensive research on plant phenology and climate change.
Jiabin Pu, Kai Yan, Samapriya Roy, Zaichun Zhu, Miina Rautiainen, Yuri Knyazikhin, and Ranga B. Myneni
Earth Syst. Sci. Data, 16, 15–34, https://doi.org/10.5194/essd-16-15-2024, https://doi.org/10.5194/essd-16-15-2024, 2024
Short summary
Short summary
Long-term global LAI/FPAR products provide the fundamental dataset for accessing vegetation dynamics and studying climate change. This study develops a sensor-independent LAI/FPAR climate data record based on the integration of Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products and applies advanced gap-filling techniques. The SI LAI/FPAR CDR provides a valuable resource for researchers studying vegetation dynamics and their relationship to climate change in the 21st century.
Wojciech Tylmann, Alicja Bonk, Dariusz Borowiak, Paulina Głowacka, Kamil Nowiński, Joanna Piłczyńska, Agnieszka Szczerba, and Maurycy Żarczyński
Earth Syst. Sci. Data, 15, 5093–5103, https://doi.org/10.5194/essd-15-5093-2023, https://doi.org/10.5194/essd-15-5093-2023, 2023
Short summary
Short summary
We present a dataset from the decade-long monitoring of Lake Żabińskie, a hardwater and eutrophic lake in northeast Poland. The lake contains varved sediments, which form a unique archive of past environmental variability. The monitoring program was designed to capture a pattern of relationships between meteorological conditions, limnological processes, and modern sedimentation and to verify if meteorological and limnological phenomena can be precisely tracked with varves.
Sen Cao, Muyi Li, Zaichun Zhu, Zhe Wang, Junjun Zha, Weiqing Zhao, Zeyu Duanmu, Jiana Chen, Yaoyao Zheng, Yue Chen, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4877–4899, https://doi.org/10.5194/essd-15-4877-2023, https://doi.org/10.5194/essd-15-4877-2023, 2023
Short summary
Short summary
The long-term global leaf area index (LAI) products are critical for characterizing vegetation dynamics under environmental changes. This study presents an updated GIMMS LAI product (GIMMS LAI4g; 1982−2020) based on PKU GIMMS NDVI and massive Landsat LAI samples. With higher accuracy than other LAI products, GIMMS LAI4g removes the effects of orbital drift and sensor degradation in AVHRR data. It has better temporal consistency before and after 2000 and a more reasonable global vegetation trend.
Muyi Li, Sen Cao, Zaichun Zhu, Zhe Wang, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4181–4203, https://doi.org/10.5194/essd-15-4181-2023, https://doi.org/10.5194/essd-15-4181-2023, 2023
Short summary
Short summary
Long-term global Normalized Difference Vegetation Index (NDVI) products support the understanding of changes in vegetation under environmental changes. This study generates a consistent global NDVI product (PKU GIMMS NDVI) from 1982–2022 that eliminates the issue of orbital drift and sensor degradation in Advanced Very High Resolution Radiometer (AVHRR) data. More accurate than its predecessor (GIMMS NDVI3g), it shows high temporal consistency with MODIS NDVI in describing vegetation trends.
Parisa Sarzaeim, Francisco Muñoz-Arriola, Diego Jarquin, Hasnat Aslam, and Natalia De Leon Gatti
Earth Syst. Sci. Data, 15, 3963–3990, https://doi.org/10.5194/essd-15-3963-2023, https://doi.org/10.5194/essd-15-3963-2023, 2023
Short summary
Short summary
A genomic, phenomic, and climate database for maize phenotype predictability in the US and Canada is introduced. The database encompasses climate from multiple sources and OMICS from the Genomes to Fields initiative (G2F) data from 2014 to 2021, including codes for input data quality and consistency controls. Earth system modelers and breeders can use CLIM4OMICS since it interconnects the climate and biological system sciences. CLIM4OMICS is designed to foster phenotype predictability.
Elisabeth Mauclet, Maëlle Villani, Arthur Monhonval, Catherine Hirst, Edward A. G. Schuur, and Sophie Opfergelt
Earth Syst. Sci. Data, 15, 3891–3904, https://doi.org/10.5194/essd-15-3891-2023, https://doi.org/10.5194/essd-15-3891-2023, 2023
Short summary
Short summary
Permafrost ecosystems are limited in nutrients for vegetation development and constrain the biological activity to the active layer. Upon Arctic warming, permafrost degradation exposes organic and mineral soil material that may directly influence the capacity of the soil to retain key nutrients for vegetation growth and development. Here, we demonstrate that the average total exchangeable nutrient density (Ca, K, Mg, and Na) is more than 2 times higher in the permafrost than in the active layer.
Anna G. Boegehold, Ashley M. Burtner, Andrew C. Camilleri, Glenn Carter, Paul DenUyl, David Fanslow, Deanna Fyffe Semenyuk, Casey M. Godwin, Duane Gossiaux, Thomas H. Johengen, Holly Kelchner, Christine Kitchens, Lacey A. Mason, Kelly McCabe, Danna Palladino, Dack Stuart, Henry Vanderploeg, and Reagan Errera
Earth Syst. Sci. Data, 15, 3853–3868, https://doi.org/10.5194/essd-15-3853-2023, https://doi.org/10.5194/essd-15-3853-2023, 2023
Short summary
Short summary
Western Lake Erie suffers from cyanobacterial harmful algal blooms (HABs) despite decades of international management efforts. In response, the US National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory (GLERL) and the Cooperative Institute for Great Lakes Research (CIGLR) created an annual sampling program to detect, monitor, assess, and predict HABs. Here we describe the data collected from this monitoring program from 2012 to 2021.
Akli Benali, Nuno Guiomar, Hugo Gonçalves, Bernardo Mota, Fábio Silva, Paulo M. Fernandes, Carlos Mota, Alexandre Penha, João Santos, José M. C. Pereira, and Ana C. L. Sá
Earth Syst. Sci. Data, 15, 3791–3818, https://doi.org/10.5194/essd-15-3791-2023, https://doi.org/10.5194/essd-15-3791-2023, 2023
Short summary
Short summary
We reconstructed the spread of 80 large wildfires that burned recently in Portugal and calculated metrics that describe how wildfires behave, such as rate of spread, growth rate, and energy released. We describe the fire behaviour distribution using six percentile intervals that can be easily communicated to both research and management communities. The database will help improve our current knowledge on wildfire behaviour and support better decision making.
Yuelong Xiao, Qunming Wang, Xiaohua Tong, and Peter M. Atkinson
Earth Syst. Sci. Data, 15, 3365–3386, https://doi.org/10.5194/essd-15-3365-2023, https://doi.org/10.5194/essd-15-3365-2023, 2023
Short summary
Short summary
Forest age is closely related to forest production, carbon cycles, and other ecosystem services. Existing stand age products in China derived from remote-sensing images are of a coarse spatial resolution and are not suitable for applications at the regional scale. Here, we mapped young forest ages across China at an unprecedented fine spatial resolution of 30 m. The overall accuracy (OA) of the generated map of young forest stand ages across China was 90.28 %.
Emily H. Stanley, Luke C. Loken, Nora J. Casson, Samantha K. Oliver, Ryan A. Sponseller, Marcus B. Wallin, Liwei Zhang, and Gerard Rocher-Ros
Earth Syst. Sci. Data, 15, 2879–2926, https://doi.org/10.5194/essd-15-2879-2023, https://doi.org/10.5194/essd-15-2879-2023, 2023
Short summary
Short summary
The Global River Methane Database (GRiMeDB) presents CH4 concentrations and fluxes for flowing waters and concurrent measures of CO2, N2O, and several physicochemical variables, plus information about sample locations and methods used to measure gas fluxes. GRiMeDB is intended to increase opportunities to understand variation in fluvial CH4, test hypotheses related to greenhouse gas dynamics, and reduce uncertainty in future estimates of gas emissions from world streams and rivers.
Xueqin Yang, Xiuzhi Chen, Jiashun Ren, Wenping Yuan, Liyang Liu, Juxiu Liu, Dexiang Chen, Yihua Xiao, Qinghai Song, Yanjun Du, Shengbiao Wu, Lei Fan, Xiaoai Dai, Yunpeng Wang, and Yongxian Su
Earth Syst. Sci. Data, 15, 2601–2622, https://doi.org/10.5194/essd-15-2601-2023, https://doi.org/10.5194/essd-15-2601-2023, 2023
Short summary
Short summary
We developed the first time-mapped, continental-scale gridded dataset of monthly leaf area index (LAI) in three leaf age cohorts (i.e., young, mature, and old) from 2001–2018 data (referred to as Lad-LAI). The seasonality of three LAI cohorts from the new Lad-LAI product agrees well at eight sites with very fine-scale collections of monthly LAI. The proposed satellite-based approaches can provide references for mapping finer spatiotemporal-resolution LAI products with different leaf age cohorts.
Yann Quilcaille, Fulden Batibeniz, Andreia F. S. Ribeiro, Ryan S. Padrón, and Sonia I. Seneviratne
Earth Syst. Sci. Data, 15, 2153–2177, https://doi.org/10.5194/essd-15-2153-2023, https://doi.org/10.5194/essd-15-2153-2023, 2023
Short summary
Short summary
We present a new database of four annual fire weather indicators over 1850–2100 and over all land areas. In a 3°C warmer world with respect to preindustrial times, the mean fire weather would increase on average by at least 66% in both intensity and duration and even triple for 1-in-10-year events. The dataset is a freely available resource for fire danger studies and beyond, highlighting that the best course of action would require limiting global warming as much as possible.
Beatriz P. Cazorla, Javier Cabello, Andrés Reyes, Emilio Guirado, Julio Peñas, Antonio J. Pérez-Luque, and Domingo Alcaraz-Segura
Earth Syst. Sci. Data, 15, 1871–1887, https://doi.org/10.5194/essd-15-1871-2023, https://doi.org/10.5194/essd-15-1871-2023, 2023
Short summary
Short summary
This dataset provides scientists, environmental managers, and the public in general with valuable information on the first characterization of ecosystem functional diversity based on primary production developed in the Sierra Nevada (Spain), a biodiversity hotspot in the Mediterranean basin and an exceptional natural laboratory for ecological research within the Long-Term Social-Ecological Research (LTSER) network.
Shengli Tao, Zurui Ao, Jean-Pierre Wigneron, Sassan Saatchi, Philippe Ciais, Jérôme Chave, Thuy Le Toan, Pierre-Louis Frison, Xiaomei Hu, Chi Chen, Lei Fan, Mengjia Wang, Jiangling Zhu, Xia Zhao, Xiaojun Li, Xiangzhuo Liu, Yanjun Su, Tianyu Hu, Qinghua Guo, Zhiheng Wang, Zhiyao Tang, Yi Y. Liu, and Jingyun Fang
Earth Syst. Sci. Data, 15, 1577–1596, https://doi.org/10.5194/essd-15-1577-2023, https://doi.org/10.5194/essd-15-1577-2023, 2023
Short summary
Short summary
We provide the first long-term (since 1992), high-resolution (8.9 km) satellite radar backscatter data set (LHScat) with a C-band (5.3 GHz) signal dynamic for global lands. LHScat was created by fusing signals from ERS (1992–2001; C-band), QSCAT (1999–2009; Ku-band), and ASCAT (since 2007; C-band). LHScat has been validated against independent ERS-2 signals. It could be used in a variety of studies, such as vegetation monitoring and hydrological modelling.
Jose V. Moris, Pedro Álvarez-Álvarez, Marco Conedera, Annalie Dorph, Thomas D. Hessilt, Hugh G. P. Hunt, Renata Libonati, Lucas S. Menezes, Mortimer M. Müller, Francisco J. Pérez-Invernón, Gianni B. Pezzatti, Nicolau Pineda, Rebecca C. Scholten, Sander Veraverbeke, B. Mike Wotton, and Davide Ascoli
Earth Syst. Sci. Data, 15, 1151–1163, https://doi.org/10.5194/essd-15-1151-2023, https://doi.org/10.5194/essd-15-1151-2023, 2023
Short summary
Short summary
This work describes a database on holdover times of lightning-ignited wildfires (LIWs). Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The database contains 42 datasets built with data on more than 152 375 LIWs from 13 countries in five continents from 1921 to 2020. This database is the first freely-available, harmonized and ready-to-use global source of holdover time data, which may be used to investigate LIWs and model the holdover phenomenon.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
Short summary
Short summary
Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Cited articles
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop
evapotranspiration-Guidelines for computing crop water requirements-FAO
Irrigation and drainage paper 56, Italy: Rome, available at:
http://www.fao.org/3/x0490E/x0490e00.htm (last access: 20 January 2021), 1998.
Augustine, J. A., DeLuisi, J. J., and Long, C. N.: SURFRAD – A national
surface radiation budget network for atmospheric research, B. Am.
Meteorol. Soc., 81, 2341–2357, https://doi.org/10.1175/1520-0477(2000)081<2341:SANSRB>2.3.CO;2, 2000.
Bacour, C., Maignan, F., Peylin, P., MacBean, N., Bastrikov, V., Joiner, J.,
Köhler, P., Guanter, L., and Frankenberg, C.: Differences Between OCO-2
and GOME-2 SIF Products From a Model-Data Fusion Perspective, J. Geophys.
Res.-Biogeosci., 124, 3143–3157, https://doi.org/10.1029/2018JG004938, 2019.
Badgley, G., Field, C. B., and Berry, J. A.: Canopy near-infrared reflectance
and terrestrial photosynthesis, Sci. Adv., 3, 1–6,
https://doi.org/10.1126/sciadv.1602244, 2017.
Badgley, G., Anderegg, L. D., Berry, J. A., and Field, C. B.: Terrestrial
Gross Primary Production: Using NIR V to Scale from Site to Globe, Glob.
Chang. Biol., 25, 3731–3740,, https://doi.org/10.1111/gcb.14729, 2019.
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S.,
Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein,
A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel,
W., Paw, U. K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S.,
Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: a new tool to study the
temporal and spatial variability of ecosystem-scale carbon dioxide, water
vapor, and energy flux densities, B. Am. Meteorol. Soc., 82,
2415–2434, https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001.
Baum, B. A., Menzel, W. P., Frey, R. A., Tobin, D. C., Holz, R. E.,
Ackerman, S. A., Heidinger, A. K., and Yang, P.: MODIS cloud-top property
refinements for collection 6, J. Appl. Meteorol. Climatol., 51,
1145–1163, https://doi.org/10.1175/JAMC-D-11-0203.1, 2012.
Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais,
N., Rödenbeck, C., Arain, M. A., Baldocchi, D., and Bonan, G. B.:
Terrestrial gross carbon dioxide uptake: global distribution and covariation
with climate, Science, 329, 834–838, 2010.
Bonan, G.: Climate Change and Terrestrial Ecosystem Modeling, Cambridge
University Press, Cambridge, UK, 354 pp., ISBN 978-1-107-04378-7, 2019.
Borbas, E. E., Seemann, S. W., Kern, A., Moy, L., Li, J., Gumley, L., and
Menzel, W. P.: MODIS atmospheric profile retrieval algorithm theoretical
basis document, Citeseer, available at:
http://modis-atmos.gsfc.nasa.gov/MOD07_L2/atbd.html (last access: January 2021), 2015.
Boryan, C., Yang, Z., Mueller, R., and Craig, M.: Monitoring US agriculture:
The US department of agriculture, national agricultural statistics service,
cropland data layer program, Geocarto Int., 26, 341–358,
https://doi.org/10.1080/10106049.2011.562309, 2011.
Cai, S., Liu, D., Sulla-Menashe, D., and Friedl, M. A.: Enhancing MODIS land
cover product with a spatial-temporal modeling algorithm, Remote Sens.
Environ., 147, 243–255, https://doi.org/10.1016/j.rse.2014.03.012, 2014.
Chabot, B. F. and Hicks, D. J.: The ecology of leaf life spans, Annu. Rev.
Ecol. Syst., 13, 229–259,
https://doi.org/10.1146/annurev.es.13.110182.001305, 1982.
Chang, L., Gao, G., Jin, S., He, X., Xiao, R., and Guo, L.: Calibration and
evaluation of precipitable water vapor from Modis infrared observations at
night, IEEE Trans. Geosci. Remote Sens., 53, 2612–2620,
https://doi.org/10.1109/TGRS.2014.2363089, 2015.
Dechant, B., Ryu, Y., Badgley, G., Zeng, Y., Berry, J. A., Goulas, Y., Li,
Z., Zhang, Q., Kang, M., Li, J., and Moya, I.: Canopy structure explains the
relationship between photosynthesis and sun-induced chlorophyll fluorescence
in crops, EarthArXiv Prepr., https://doi.org/10.31223/OSF.IO/CBXPQ, 2019.
Dechant, B., Ryu, Y., Badgley, G., Zeng, Y., Berry, J. A., Zhang, Y.,
Goulas, Y., Li, Z., Zhang, Q., Kang, M., Li, J., and Moya, I.: Canopy
structure explains the relationship between photosynthesis and sun-induced
chlorophyll fluorescence in crops, Remote Sens. Environ., 241, 111733,
https://doi.org/10.1016/j.rse.2020.111733, 2020.
Franch, B., Vermote, E. F., Roger, J. C., Murphy, E., Becker-reshef, I.,
Justice, C., Claverie, M., Nagol, J., Csiszar, I., Meyer, D., Baret, F.,
Masuoka, E., Wolfe, R., and Devadiga, S.: A 30+ year AVHRR land surface
re?ectance climate data record and its application to wheat yield
monitoring, Remote Sens., 9, 1–14, https://doi.org/10.3390/rs9030296, 2017.
Friedman, J. H.: Multivariate Adaptive Regression Splines, Ann. Stat.,
19, 1–67, https://doi.org/10.1214/aos/1176347963, 1991.
Gamon, J. A., Huemmrich, K. F., Wong, C. Y. S., Ensminger, I., Garrity, S.,
Hollinger, D. Y., Noormets, A., and Peñuelas, J.: A remotely sensed
pigment index reveals photosynthetic phenology in evergreen conifers, P.
Natl. Acad. Sci., 113, 201606162, https://doi.org/10.1073/pnas.1606162113, 2016.
GCOS: Systematic observation requirements for satellite-based data products
for climate, Supplemental details to the satellite-based component of the
“Implementation Plan for the Global Observing System for Climate in Support
of the UNFCCC,” Reference Number GCOS-154, available at:
http://www.wmo.int/pages/prog/gcos/Publications/gcos-154.pdf (last access: 20 January 2021), 2011.
Goldberger, J., Hinton, G. E., Roweis, S. T., and Salakhutdinov, R. R.: Neighbourhood components analysis, in Advances in neural information processing systems, 513–520, available at: http://www.cs.utoronto.ca/~rsalakhu/papers/ncanips.pdf (last access: 20 January 2021), 2005.
Green, T. R., Kipka, H., David, O., and McMaster, G. S.: Where is the USA
Corn Belt, and how is it changing?, Sci. Total Environ., 618, 1613–1618,
https://doi.org/10.1016/j.scitotenv.2017.09.325, 2018.
Guan, K., Berry, J. A., Zhang, Y., Joiner, J., Guanter, L., Badgley, G., and
Lobell, D. B.: Improving the monitoring of crop productivity using
spaceborne solar-induced fluorescence, Glob. Chang. Biol., 22, 716–726,
https://doi.org/10.1111/gcb.13136, 2016.
Guanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J. a,
Frankenberg, C., Huete, A. R., Zarco-Tejada, P., Lee, J.-E., Moran, M. S.,
Ponce-Campos, G., Beer, C., Camps-Valls, G., Buchmann, N., Gianelle, D.,
Klumpp, K., Cescatti, A., Baker, J. M., and Griffis, T. J.: Global and
time-resolved monitoring of crop photosynthesis with chlorophyll
fluorescence, P. Natl. Acad. Sci. USA, 111, E1327-33,
https://doi.org/10.1073/pnas.1320008111, 2014.
Homer, C., Huang, C., Yang, L., Wylie, B., and Coan, M.: Development of a
2001 National Land-Cover Database for the United States, Photogramm. Eng.
Remote Sens., 70, 829–840, https://doi.org/10.14358/PERS.70.7.829, 2004.
Houborg, R. and McCabe, M. F.: High-Resolution NDVI from planet's
constellation of earth observing nano-satellites: A new data source for
precision agriculture, Remote Sens., 8, 768, https://doi.org/10.3390/rs8090768, 2016.
Jiang, C. and Fang, H.: GSV: a general model for hyperspectral soil
reflectance simulation, Int. J. Appl. Earth Obs. Geoinf., 83, 101932,
https://doi.org/10.1016/j.jag.2019.101932, 2019.
Jiang, C. and Guan, K.: SLOPE Daily 250 m CONUS Gross Primary Productivity
(2000–2019), ORNL DAAC, https://doi.org/10.3334/ORNLDAAC/1786, 2020.
Jiang, C. and Ryu, Y.: Multi-scale evaluation of global gross primary
productivity and evapotranspiration products derived from Breathing Earth
System Simulator (BESS), Remote Sens. Environ., 186, 528–547,
https://doi.org/10.1016/j.rse.2016.08.030, 2016.
Jiang, C., Ryu, Y., Fang, H., Myneni, R., Claverie, M., and Zhu, Z.:
Inconsistencies of interannual variability and trends in long-term satellite
leaf area index products, Glob. Chang. Biol., 23, 4133–4146,
https://doi.org/10.1111/gcb.13787, 2017.
Jiang, C., Guan, K., Pan, M., Ryu, Y., Peng, B., and Wang, S.: BESS-STAIR: a framework to estimate daily, 30 m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt, Hydrol. Earth Syst. Sci., 24, 1251–1273, https://doi.org/10.5194/hess-24-1251-2020, 2020.
Jung, M., Reichstein, M., Schwalm, C. R., Huntingford, C., Sitch, S.,
Ahlström, A., Arneth, A., Camps-Valls, G., Ciais, P., Friedlingstein,
P., Gans, F., Ichii, K., Jain, A. K., Kato, E., Papale, D., Poulter, B.,
Raduly, B., Rödenbeck, C., Tramontana, G., Viovy, N., Wang, Y. P.,
Weber, U., Zaehle, S., and Zeng, N.: Compensatory water effects link yearly
global land CO2 sink changes to temperature, Nature, 541, 516–520,
https://doi.org/10.1038/nature20780, 2017.
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Gustau-Camps-Valls,
Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM
ensemble of global land-atmosphere energy fluxes, Sci. Data, 1–14,
https://doi.org/10.1038/s41597-019-0076-8, 2019.
Karlsson, K.-G., Anttila, K., Trentmann, J., Stengel, M., Fokke Meirink, J., Devasthale, A., Hanschmann, T., Kothe, S., Jääskeläinen, E., Sedlar, J., Benas, N., van Zadelhoff, G.-J., Schlundt, C., Stein, D., Finkensieper, S., Håkansson, N., and Hollmann, R.: CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data, Atmos. Chem. Phys., 17, 5809–5828, https://doi.org/10.5194/acp-17-5809-2017, 2017.
Kimm, H., Guan, K., Jiang, C., Peng, B., Gentry, L. F., Wilkin, S. C., Wang,
S., Cai, Y., Bernacchi, C. J., Peng, J., and Luo, Y.: Deriving
high-spatiotemporal-resolution leaf area index for agroecosystems in the
U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data, Remote Sens.
Environ., 239, 111615, https://doi.org/10.1016/j.rse.2019.111615, 2020.
Kobrick, M. and Crippen, R.: SRTMGL1: NASA Shuttle Radar Topography Mission
Global 1 arc second V003, NASA EOSDIS L. Process. DAAC, https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003, 2017.
Lark, T. J., Mueller, R. M., Johnson, D. M., and Gibbs, H. K.: Measuring
land-use and land-cover change using the U.S. department of agriculture's
cropland data layer: Cautions and recommendations, Int. J. Appl. Earth Obs.
Geoinf., 62, 224–235, https://doi.org/10.1016/j.jag.2017.06.007, 2017.
Li, W., MacBean, N., Ciais, P., Defourny, P., Lamarche, C., Bontemps, S., Houghton, R. A., and Peng, S.: Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015), Earth Syst. Sci. Data, 10, 219–234, https://doi.org/10.5194/essd-10-219-2018, 2018.
Liaw, A. and Wiener, M.: Classification and regression by randomForest, R
News, 2, 18–22, 2002.
Liu, L., Guan, L., and Liu, X.: Directly estimating diurnal changes in GPP
for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence,
Agr. Forest Meteorol., 232, 1–9, https://doi.org/10.1016/j.agrformet.2016.06.014, 2017.
Luo, Y., Guan, K., and Peng, J.: STAIR: A generic and fully-automated method
to fuse multiple sources of optical satellite data to generate a
high-resolution, daily and cloud-/gap-free surface reflectance product,
Remote Sens. Environ., 214, 87–99, https://doi.org/10.1016/j.rse.2018.04.042,
2018.
Lyapustin, A., Wang, Y., Laszlo, I., Kahn, R., Korkin, S., Remer, L., Levy,
R., and Reid, J. S.: Multiangle implementation of atmospheric correction
(MAIAC): 2. Aerosol algorithm, J. Geophys. Res.-Atmos., 116, D03211,
https://doi.org/10.1029/2010JD014986, 2011.
Magney, T. S., Bowling, D. R., Logan, B., Grossmann, K., Stutz, J., and
Blanken, P.: Mechanistic evidence for tracking the seasonality of
photosynthesis with solar-induced fluorescence, P. Natl. Acad. Sci., 116, 11640–11645, https://doi.org/10.1073/pnas.1900278116, 2019.
Monteith, J. L.: Solar Radiation and Productivity in Tropical Ecosystems, J.
Appl. Ecol., 9, 747, https://doi.org/10.2307/2401901, 1972.
Monteith, J. L. and Moss, C. J.: Climate and the Efficiency of Crop
Production in Britain, Philos. T. Roy. Soc. B Biol. Sci., 281,
277–294, https://doi.org/10.1098/rstb.1977.0140, 1977.
Remer, L. A., Mattoo, S., Levy, R. C., and Munchak, L. A.: MODIS 3 km aerosol product: algorithm and global perspective, Atmos. Meas. Tech., 6, 1829–1844, https://doi.org/10.5194/amt-6-1829-2013, 2013.
Román, M. O., Schaaf, C. B., Woodcock, C. E., Strahler, A. H., Yang, X.,
Braswell, R. H., Curtis, P. S., Davis, K. J., Dragoni, D., Goulden, M. L.,
Gu, L., Hollinger, D. Y., Kolb, T. E., Meyers, T. P., Munger, J. W.,
Privette, J. L., Richardson, A. D., Wilson, T. B., and Wofsy, S. C.: The
MODIS (Collection V005) BRDF/albedo product: Assessment of spatial
representativeness over forested landscapes, Remote Sens. Environ., 113,
2476–2498, https://doi.org/10.1016/j.rse.2009.07.009, 2009.
Running, S. W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., and
Hashimoto, H.: A continuous satellite-derived measure of global terrestrial
primary production, Bioscience, 54, 547,
https://doi.org/10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2, 2004.
Ryu, Y., Berry, J. A., and Baldocchi, D. D.: What is global photosynthesis?
History, uncertainties and opportunities, Remote Sens. Environ.,
223, 95–114, https://doi.org/10.1016/j.rse.2019.01.016,
2019.
Smith, W. K., Biederman, J. A., Scott, R. L., Moore, D. J. P., He, M.,
Kimball, J. S., Yan, D., Hudson, A., Barnes, M. L., MacBean, N., Fox, A. M.,
and Litvak, M. E.: Chlorophyll Fluorescence Better Captures Seasonal and
Interannual Gross Primary Productivity Dynamics Across Dryland Ecosystems of
Southwestern North America, Geophys. Res. Lett., 45, 748–757,
https://doi.org/10.1002/2017GL075922, 2018.
Tibshirani, R.: Regression Shrinkage and Selection Via the Lasso, J. R.
Stat. Soc. Ser. B, 58, 267–288, https://doi.org/10.1111/j.2517-6161.1996.tb02080.x,
1996.
Turner, A. J., Köhler, P., Magney, T. S., Frankenberg, C., Fung, I., and Cohen, R. C.: A double peak in the seasonality of California's photosynthesis as observed from space, Biogeosciences, 17, 405–422, https://doi.org/10.5194/bg-17-405-2020, 2020.
Vermote, E. F., El Saleous, N. Z., and Justice, C. O.: Atmospheric correction
of MODIS data in the visible to middle infrared: First results, Remote Sens.
Environ., 83, 97–111, https://doi.org/10.1016/S0034-4257(02)00089-5, 2002.
Wu, G., Guan, K., Jiang, C., Peng, B., Kimm, H., Chen, M., Yang, X., Wang,
S., Sukyer, A. E., Bernacchi, C., Moore, C. E., Zeng, Y., Berry, J., and
Cendrero-Mateo, M. P.: Radiance-based NIRv as a proxy for GPP of corn
and soybean, Environ. Res. Lett., 15, 034009, https://doi.org/10.1088/1748-9326/ab65cc, 2019.
Wu, G., Guan, K., Jiang, C., Peng, B., Kimm, H., Chen, M., Yang, X., Wang,
S., Suyker, A. E., Bernacchi, C., Moore, C. E., Zeng, Y., Berry, J., and
Cendrero-Mateo, M. P.: Radiance-based NIRv as a proxy for GPP of corn and
soybean, Environ. Res. Lett., 15, 034009, https://doi.org/10.1088/1748-9326/ab65cc,
2020.
Xiao, J., Chevallier, F., Gomez, C., Guanter, L., Hicke, J. A., Huete, A.
R., Ichii, K., Ni, W., Pang, Y., Rahman, A. F., Sun, G., Yuan, W., Zhang, L.,
and Zhang, X.: Remote sensing of the terrestrial carbon cycle: A review of
advances over 50 years, Remote Sens. Environ., 233, 111383,
https://doi.org/10.1016/j.rse.2019.111383, 2019.
Yan, L. and Roy, D. P.: Conterminous United States crop field size
quantification from multi-temporal Landsat data, Remote Sens. Environ., 172,
67–86, https://doi.org/10.1016/j.rse.2015.10.034, 2016.
Yang, X., Tang, J., Mustard, J. F., Lee, J., Rossini, M., Joiner, J.,
Munger, J. W., Kornfeld, A., and Richardson, A. D.: Solar-induced chlorophyll
fluorescence that correlates with canopy photosynthesis on diurnal and
seasonal scales in a temperate deciduous forest, Geophys. Res. Lett., 42,
2977–2987, https://doi.org/10.1002/2015GL063201, 2015.
Yang, Y., Xiao, P., Feng, X., and Li, H.: Accuracy assessment of seven global
land cover datasets over China, ISPRS J. Photogramm. Remote Sens., 125,
156–173, https://doi.org/10.1016/j.isprsjprs.2017.01.016, 2017.
Yuan, W., Liu, S., Yu, G., Bonnefond, J.-M. M., Chen, J., Davis, K., Desai,
A. R., Goldstein, A. H., Gianelle, D., Rossi, F., Suyker, A. E., and Verma,
S. B.: Global estimates of evapotranspiration and gross primary production
based on MODIS and global meteorology data, Remote Sens. Environ., 114,
1416–1431, https://doi.org/10.1016/j.rse.2010.01.022, 2010.
Zeng, Y., Badgley, G., Dechant, B., Ryu, Y., Chen, M., and Berry, J. A.: A
practical approach for estimating the escape ratio of near-infrared
solar-induced chlorophyll fluorescence, Remote Sens. Environ., 232, 111209, https://doi.org/10.1016/j.rse.2019.05.028, 2019.
Zhang, Y., Xiao, X., Wu, X., Zhou, S., Zhang, G., Qin, Y., and Dong, J.: A
global moderate resolution dataset of gross primary production of vegetation
for 2000–2016, Sci. Data, 4, 170165, https://doi.org/10.1038/sdata.2017.165, 2017.
Zhang, Y., Joiner, J., Gentine, P., and Zhou, S.: Reduced solar-induced
chlorophyll fluorescence from GOME-2 during Amazon drought caused by dataset
artifacts, Glob. Chang. Biol., 24, 2229–2230, https://doi.org/10.1111/gcb.14134,
2018.
Short summary
Photosynthesis, quantified by gross primary production (GPP), is a key Earth system process. To date, there is a lack of a high-spatiotemporal-resolution, real-time and observation-based GPP dataset. This work addresses this gap by developing a SatelLite Only Photosynthesis Estimation (SLOPE) model and generating a new GPP product, which is advanced in spatial and temporal resolutions, instantaneity, and quantitative uncertainty. The dataset will benefit a range of research and applications.
Photosynthesis, quantified by gross primary production (GPP), is a key Earth system process. To...
Altmetrics
Final-revised paper
Preprint