Articles | Volume 16, issue 1
https://doi.org/10.5194/essd-16-277-2024
© Author(s) 2024. 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-16-277-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
11 Jan 2024
Data description paper |
| 11 Jan 2024
| 11 Jan 2024
Mapping 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020
Mengyao Zhu
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 101408, China
China-Pakistan Joint Research Center on Earth Sciences, CAS-HEC, Islamabad, 45320, Pakistan
Huanjiong Wang
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Juha M. Alatalo
Environmental Science Centre, Qatar University, Doha, 2713, Qatar
Wei Liu
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 101408, China
Yulong Hao
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 101408, China
Quansheng Ge
CORRESPONDING AUTHOR
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 101408, China
Related authors
No articles found.
Yang Liu, Jingyun Zheng, Zhixin Hao, and Quansheng Ge
Earth Syst. Sci. Data, 14, 5717–5735, https://doi.org/10.5194/essd-14-5717-2022, https://doi.org/10.5194/essd-14-5717-2022, 2022
Short summary
Short summary
Proxy-based precipitation reconstruction is essential to study the inter-annual to decadal variability and underlying mechanisms beyond the instrumental period that is critical for climate modeling, prediction and attribution. We present a set of standard precipitation index reconstructions for the whole year and wet seasons over the whole of Asia since 1700, with the spatial resolution of 2.5°, based on 2912 annually resolved proxy series mainly derived from tree rings and historical documents.
Yachen Liu, Xiuqi Fang, Junhu Dai, Huanjiong Wang, and Zexing Tao
Clim. Past, 17, 929–950, https://doi.org/10.5194/cp-17-929-2021, https://doi.org/10.5194/cp-17-929-2021, 2021
Short summary
Short summary
There are controversies about whether poetry can be used as one of the evidence sources for past climate changes. We tried to discuss the reliability and validity of phenological records from poems of the Tang and Song dynasties (618–1279 CE) by analyzing their certainties and uncertainties. A standardized processing method for phenological records from poems is introduced. We hope that this study can provide a reference for the extraction and application of phenological records from poems.
Fengshan Liu, Ying Chen, Nini Bai, Dengpan Xiao, Huizi Bai, Fulu Tao, and Quansheng Ge
Biogeosciences, 18, 2275–2287, https://doi.org/10.5194/bg-18-2275-2021, https://doi.org/10.5194/bg-18-2275-2021, 2021
Short summary
Short summary
The sowing date is key to the surface biophysical processes in the winter dormancy period. The climate effect of the sowing date shift is therefore very interesting and may contribute to the mitigation of climate change. An earlier sowing date always had a higher LAI but a higher temperature in the dormancy period and a lower temperature in the growth period. The main reason was the relative contributions of the surface albedo and energy partitioning processes.
Mei Hou, Wenxiang Wu, David J. Cohen, Yang Zhou, Zhaoqi Zeng, Han Huang, Hongbo Zheng, and Quansheng Ge
Clim. Past Discuss., https://doi.org/10.5194/cp-2019-89, https://doi.org/10.5194/cp-2019-89, 2019
Manuscript not accepted for further review
Short summary
Short summary
Past climate change is of great scientific interest to deal with increasing global warmth. In this paper, we compile 47 paleoclimate records from locations around the world to document a climate anomaly at 7.5–7.0 cal ka BP (1 cal ka BP=1000 calibrated years before present). The synthesis suggests that the 7.5–7.0 cal ka BP event is of worldwide significance and four possible forcing mechanisms responsible for it, including orbital forcing, solar activity, volcanic eruption, and meltwater flux.
Q. Ge, Z. Hao, J. Zheng, and X. Shao
Clim. Past, 9, 1153–1160, https://doi.org/10.5194/cp-9-1153-2013, https://doi.org/10.5194/cp-9-1153-2013, 2013
Related subject area
Domain: ESSD – Land | Subject: Biogeosciences and biodiversity
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
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)
Spatial mapping of key plant functional traits in terrestrial ecosystems across China
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
Mammals in the Chornobyl Exclusion Zone's Red Forest: a motion-activated camera trap study
Maps with 1 km resolution reveal increases in above- and belowground forest biomass carbon pools in China over the past 20 years
AnisoVeg: anisotropy and nadir-normalized MODIS multi-angle implementation atmospheric correction (MAIAC) datasets for satellite vegetation studies in South America
TiP-Leaf: a dataset of leaf traits across vegetation types on the Tibetan Plateau
Forest structure and individual tree inventories of northeastern Siberia along climatic gradients
Global climate-related predictors at kilometer resolution for the past and future
A daily and 500 m coupled evapotranspiration and gross primary production product across China during 2000–2020
Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021
Rates and timing of chlorophyll-a increases and related environmental variables in global temperate and cold-temperate lakes
Harmonized gap-filled datasets from 20 urban flux tower sites
Holocene spatiotemporal millet agricultural patterns in northern China: a dataset of archaeobotanical macroremains
The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil
Airborne SnowSAR data at X and Ku bands over boreal forest, alpine and tundra snow cover
The Landscape Fire Scars Database: mapping historical burned area and fire severity in Chile
Aridec: an open database of litter mass loss from aridlands worldwide with recommendations on suitable model applications
LegacyPollen 1.0: a taxonomically harmonized global late Quaternary pollen dataset of 2831 records with standardized chronologies
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.
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.
Nannan An, Nan Lu, Weiliang Chen, Yongzhe Chen, Hao Shi, Fuzhong Wu, and Bojie Fu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-121, https://doi.org/10.5194/essd-2023-121, 2023
Revised manuscript accepted for ESSD
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, plant height and specific leaf area showed good accuracy with average R2 of higher than 0.45. This dataset could provide data support for developments of earth system models to predict vegetation distribution and ecosystem functions.
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.
Nicholas A. Beresford, Sergii Gashchak, Michael D. Wood, and Catherine L. Barnett
Earth Syst. Sci. Data, 15, 911–920, https://doi.org/10.5194/essd-15-911-2023, https://doi.org/10.5194/essd-15-911-2023, 2023
Short summary
Short summary
Camera traps were established in a highly contaminated area of the Chornobyl Exclusion Zone (CEZ) to capture images of mammals. Over 1 year, 14 mammal species were recorded. The number of species observed did not vary with estimated radiation exposure. The data will be of value from the perspectives of effects of radiation on wildlife and also rewilding in this large, abandoned area. They may also have value in future studies investigating impacts of recent Russian military action in the CEZ.
Yongzhe Chen, Xiaoming Feng, Bojie Fu, Haozhi Ma, Constantin M. Zohner, Thomas W. Crowther, Yuanyuan Huang, Xutong Wu, and Fangli Wei
Earth Syst. Sci. Data, 15, 897–910, https://doi.org/10.5194/essd-15-897-2023, https://doi.org/10.5194/essd-15-897-2023, 2023
Short summary
Short summary
This study presented a long-term (2002–2021) above- and belowground biomass dataset for woody vegetation in China at 1 km resolution. It was produced by combining various types of remote sensing observations with adequate plot measurements. Over 2002–2021, China’s woody biomass increased at a high rate, especially in the central and southern parts. This dataset can be applied to evaluate forest carbon sinks across China and the efficiency of ecological restoration programs in China.
Ricardo Dalagnol, Lênio Soares Galvão, Fabien Hubert Wagner, Yhasmin Mendes de Moura, Nathan Gonçalves, Yujie Wang, Alexei Lyapustin, Yan Yang, Sassan Saatchi, and Luiz Eduardo Oliveira Cruz Aragão
Earth Syst. Sci. Data, 15, 345–358, https://doi.org/10.5194/essd-15-345-2023, https://doi.org/10.5194/essd-15-345-2023, 2023
Short summary
Short summary
The AnisoVeg dataset brings 22 years of monthly satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for South America at 1 km resolution aimed at vegetation applications. It has nadir-normalized data, which is the most traditional approach to correct satellite data but also unique anisotropy data with strong biophysical meaning, explaining 55 % of Amazon forest height. We expect this dataset to help large-scale estimates of vegetation biomass and carbon.
Yili Jin, Haoyan Wang, Jie Xia, Jian Ni, Kai Li, Ying Hou, Jing Hu, Linfeng Wei, Kai Wu, Haojun Xia, and Borui Zhou
Earth Syst. Sci. Data, 15, 25–39, https://doi.org/10.5194/essd-15-25-2023, https://doi.org/10.5194/essd-15-25-2023, 2023
Short summary
Short summary
The TiP-Leaf dataset was compiled from direct field measurements and included 11 leaf traits from 468 species of 1692 individuals, covering a great proportion of species and vegetation types on the highest plateau in the world. This work is the first plant trait dataset that represents all of the alpine vegetation on the TP, which is not only an update of the Chinese plant trait database, but also a great contribution to the global trait database.
Timon Miesner, Ulrike Herzschuh, Luidmila A. Pestryakova, Mareike Wieczorek, Evgenii S. Zakharov, Alexei I. Kolmogorov, Paraskovya V. Davydova, and Stefan Kruse
Earth Syst. Sci. Data, 14, 5695–5716, https://doi.org/10.5194/essd-14-5695-2022, https://doi.org/10.5194/essd-14-5695-2022, 2022
Short summary
Short summary
We present data which were collected on expeditions to the northeast of the Russian Federation. One table describes the 226 locations we visited during those expeditions, and the other describes 40 289 trees which we recorded at these locations. We found out that important information on the forest cannot be predicted precisely from satellites. Thus, for anyone interested in distant forests, it is important to go to there and take measurements or use data (as presented here).
Philipp Brun, Niklaus E. Zimmermann, Chantal Hari, Loïc Pellissier, and Dirk Nikolaus Karger
Earth Syst. Sci. Data, 14, 5573–5603, https://doi.org/10.5194/essd-14-5573-2022, https://doi.org/10.5194/essd-14-5573-2022, 2022
Short summary
Short summary
Using mechanistic downscaling, we developed CHELSA-BIOCLIM+, a set of 15 biologically relevant, climate-related variables at unprecedented resolution, as a basis for environmental analyses. It includes monthly time series for 38+ years and 30-year averages for three future periods and three emission scenarios. Estimates matched well with station measurements, but few biases existed. The data allow for detailed assessments of climate-change impact on ecosystems and their services to societies.
Shaoyang He, Yongqiang Zhang, Ning Ma, Jing Tian, Dongdong Kong, and Changming Liu
Earth Syst. Sci. Data, 14, 5463–5488, https://doi.org/10.5194/essd-14-5463-2022, https://doi.org/10.5194/essd-14-5463-2022, 2022
Short summary
Short summary
This study developed a daily, 500 m evapotranspiration and gross primary production product (PML-V2(China)) using a locally calibrated water–carbon coupled model, PML-V2, which was well calibrated against observations at 26 flux sites across nine land cover types. PML-V2 (China) performs satisfactorily in the plot- and basin-scale evaluations compared with other mainstream products. It improved intra-annual ET and GPP dynamics, particularly in the cropland ecosystem.
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
Earth Syst. Sci. Data, 14, 5333–5347, https://doi.org/10.5194/essd-14-5333-2022, https://doi.org/10.5194/essd-14-5333-2022, 2022
Short summary
Short summary
The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing products.
Hannah Adams, Jane Ye, Bhaleka D. Persaud, Stephanie Slowinski, Homa Kheyrollah Pour, and Philippe Van Cappellen
Earth Syst. Sci. Data, 14, 5139–5156, https://doi.org/10.5194/essd-14-5139-2022, https://doi.org/10.5194/essd-14-5139-2022, 2022
Short summary
Short summary
Climate warming and land-use changes are altering the environmental factors that control the algal
productivityin lakes. To predict how environmental factors like nutrient concentrations, ice cover, and water temperature will continue to influence lake productivity in this changing climate, we created a dataset of chlorophyll-a concentrations (a compound found in algae), associated water quality parameters, and solar radiation that can be used to for a wide range of research questions.
Mathew Lipson, Sue Grimmond, Martin Best, Winston T. L. Chow, Andreas Christen, Nektarios Chrysoulakis, Andrew Coutts, Ben Crawford, Stevan Earl, Jonathan Evans, Krzysztof Fortuniak, Bert G. Heusinkveld, Je-Woo Hong, Jinkyu Hong, Leena Järvi, Sungsoo Jo, Yeon-Hee Kim, Simone Kotthaus, Keunmin Lee, Valéry Masson, Joseph P. McFadden, Oliver Michels, Wlodzimierz Pawlak, Matthias Roth, Hirofumi Sugawara, Nigel Tapper, Erik Velasco, and Helen Claire Ward
Earth Syst. Sci. Data, 14, 5157–5178, https://doi.org/10.5194/essd-14-5157-2022, https://doi.org/10.5194/essd-14-5157-2022, 2022
Short summary
Short summary
We describe a new openly accessible collection of atmospheric observations from 20 cities around the world, capturing 50 site years. The observations capture local meteorology (temperature, humidity, wind, etc.) and the energy fluxes between the land and atmosphere (e.g. radiation and sensible and latent heat fluxes). These observations can be used to improve our understanding of urban climate processes and to test the accuracy of urban climate models.
Keyang He, Houyuan Lu, Jianping Zhang, and Can Wang
Earth Syst. Sci. Data, 14, 4777–4791, https://doi.org/10.5194/essd-14-4777-2022, https://doi.org/10.5194/essd-14-4777-2022, 2022
Short summary
Short summary
Here we presented the first quantitative spatiotemporal cropping patterns spanning the Neolithic and Bronze ages in northern China. Temporally, millet agriculture underwent a dramatic transition from low-yield broomcorn to high-yield foxtail millet around 6000 cal. a BP under the influence of climate and population. Spatially, millet agriculture spread westward and northward from the mid-lower Yellow River (MLY) to the agro-pastoral ecotone (APE) around 6000 cal. a BP and diversified afterwards.
Kailiang Yu, Johan van den Hoogen, Zhiqiang Wang, Colin Averill, Devin Routh, Gabriel Reuben Smith, Rebecca E. Drenovsky, Kate M. Scow, Fei Mo, Mark P. Waldrop, Yuanhe Yang, Weize Tang, Franciska T. De Vries, Richard D. Bardgett, Peter Manning, Felipe Bastida, Sara G. Baer, Elizabeth M. Bach, Carlos García, Qingkui Wang, Linna Ma, Baodong Chen, Xianjing He, Sven Teurlincx, Amber Heijboer, James A. Bradley, and Thomas W. Crowther
Earth Syst. Sci. Data, 14, 4339–4350, https://doi.org/10.5194/essd-14-4339-2022, https://doi.org/10.5194/essd-14-4339-2022, 2022
Short summary
Short summary
We used a global-scale dataset for the surface topsoil (>3000 distinct observations of abundance of soil fungi versus bacteria) to generate the first quantitative map of soil fungal proportion across terrestrial ecosystems. We reveal striking latitudinal trends. Fungi dominated in regions with low mean annual temperature (MAT) and net primary productivity (NPP) and bacteria dominated in regions with high MAT and NPP.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
Short summary
Short summary
The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Alejandro Miranda, Rayén Mentler, Ítalo Moletto-Lobos, Gabriela Alfaro, Leonardo Aliaga, Dana Balbontín, Maximiliano Barraza, Susanne Baumbach, Patricio Calderón, Fernando Cárdenas, Iván Castillo, Gonzalo Contreras, Felipe de la Barra, Mauricio Galleguillos, Mauro E. González, Carlos Hormazábal, Antonio Lara, Ian Mancilla, Francisca Muñoz, Cristian Oyarce, Francisca Pantoja, Rocío Ramírez, and Vicente Urrutia
Earth Syst. Sci. Data, 14, 3599–3613, https://doi.org/10.5194/essd-14-3599-2022, https://doi.org/10.5194/essd-14-3599-2022, 2022
Short summary
Short summary
Achieving a local understanding of fire regimes requires high-resolution, systematic and dynamic data. High-quality information can help to transform evidence into decision-making. Taking advantage of big-data and remote sensing technics we developed a flexible workflow to reconstruct burned area and fire severity data for more than 8000 individual fires in Chile. The framework developed for the database can be applied anywhere in the world with minimal adaptation.
Agustín Sarquis, Ignacio Andrés Siebenhart, Amy Theresa Austin, and Carlos A. Sierra
Earth Syst. Sci. Data, 14, 3471–3488, https://doi.org/10.5194/essd-14-3471-2022, https://doi.org/10.5194/essd-14-3471-2022, 2022
Short summary
Short summary
Plant litter breakdown in aridlands is driven by processes different from those in more humid ecosystems. A better understanding of these processes will allow us to make better predictions of future carbon cycling. We have compiled aridec, a database of plant litter decomposition studies in aridlands and tested some modeling applications for potential users. Aridec is open for use and collaboration, and we hope it will help answer newer and more important questions as the database develops.
Ulrike Herzschuh, Chenzhi Li, Thomas Böhmer, Alexander K. Postl, Birgit Heim, Andrei A. Andreev, Xianyong Cao, Mareike Wieczorek, and Jian Ni
Earth Syst. Sci. Data, 14, 3213–3227, https://doi.org/10.5194/essd-14-3213-2022, https://doi.org/10.5194/essd-14-3213-2022, 2022
Short summary
Short summary
Pollen preserved in environmental archives such as lake sediments and bogs are extensively used for reconstructions of past vegetation and climate. Here we present LegacyPollen 1.0, a dataset of 2831 fossil pollen records from all over the globe that were collected from publicly available databases. We harmonized the names of the pollen taxa so that all datasets can be jointly investigated. LegacyPollen 1.0 is available as an open-access dataset.
Cited articles
An, S., Chen, X., Zhang, X., Lang, W., Ren, S., and Xu, L.: Precipitation and minimum temperature are primary climatic controls of alpine grassland autumn phenology on the Qinghai-Tibet Plateau, Remote Sens., 12, 431, https://doi.org/10.3390/rs12030431, 2020.
Aono, Y. and Kazui, K.: Phenological data series of cherry tree flowering in Kyoto, Japan, and its application to reconstruction of springtime temperatures since the 9th century, Int. J. Climatol., 28, 905–914, https://doi.org/10.1002/joc.1594, 2008.
Ault, T. R., Schwartz, M. D., Zurita-Milla, R., Weltzin, J. F., and Betancourt, J. L.: Trends and natural variability of spring onset in the coterminous United States as evaluated by a new gridded dataset of spring indices, J. Climate, 28, 8363–8378, https://doi.org/10.1175/jcli-d-14-00736.1, 2015.
Basler, D.: Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe, Agric. For. Meteorol., 217, 10–21, https://doi.org/10.1016/j.agrformet.2015.11.007, 2016.
Bolton, D. K., Gray, J. M., Melaas, E. K., Moon, M., Eklundh, L., and Friedl, M. A.: Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery, Remote Sens. Environ., 240, 111685, https://doi.org/10.1016/j.rse.2020.111685, 2020.
Brown, J. H.: On the Relationship between Abundance and Distribution of Species, Am. Nat., 124, 255–279, https://doi.org/10.1086/284267, 1984.
Brun, P., Zimmermann, N., Hari, C., Pellissier, L., and Karger, D.: CHELSA-BIOCLIM+ A novel set of global climate related predictors at kilometre-resolution, EnviDat [data set], https://www.doi.org/10.16904/envidat.332, last access: 9 August 2022a.
Brun, P., Zimmermann, N. E., Hari, C., Pellissier, L., and Karger, D. N.: Global climate-related predictors at kilometer resolution for the past and future, Earth Syst. Sci. Data, 14, 5573–5603, https://doi.org/10.5194/essd-14-5573-2022, 2022b.
Cai, H., Lyu, L., Shrestha, N., Tang, Z., Su, X., Xu, X., Dimitrov, D., and Wang, Z.: Geographical patterns in phylogenetic diversity of Chinese woody plants and its application for conservation planning, Divers. Distrib., 27, 179–194, https://doi.org/10.1111/ddi.13180, 2021.
Chuine, I.: A unified model for budburst of trees, J. Theor. Biol., 207, 337–347, https://doi.org/10.1006/jtbi.2000.2178, 2000.
Chuine, I., Cour, P., and Rousseau, D.: Fitting models predicting dates of flowering of temperate-zone trees using simulated annealing, Plant Cell Environ., 21, 455–466, https://doi.org/10.1046/j.1365-3040.1998.00299.x, 1998.
Chuine, I., Cambon, G., and Comtois, P.: Scaling phenology from the local to the regional level: advances from species-specific phenological models, Global Change Biol., 6, 943–952, https://doi.org/10.1046/j.1365-2486.2000.00368.x, 2000.
Cleland, E. E., Chuine, I., Menzel, A., Mooney, H. A., and Schwartz, M. D.: Shifting plant phenology in response to global change, Trends Ecol. Evol., 22, 357–365, https://doi.org/10.1016/j.tree.2007.04.003, 2007.
Dai, J., Wang, H., and Ge, Q.: Multiple phenological responses to climate change among 42 plant species in Xi'an, China, Int. J. Biometeorol., 57, 749–758, https://doi.org/10.1007/s00484-012-0602-2, 2013.
Dai, J., Wang, H., and Ge, Q.: Characteristics of spring phenological changes in China over the past 50 years, Adv. Meteorol., 2014, 843568, https://doi.org/10.1155/2014/843568, 2014.
Delbart, N., Beaubien, E., Kergoat, L., and Le Toan, T.: Comparing land surface phenology with leafing and flowering observations from the PlantWatch citizen network, Remote Sens. Environ., 160, 273–280, https://doi.org/10.1016/j.rse.2015.01.012, 2015.
Delpierre, N., Dufrêne, E., Soudani, K., Ulrich, E., Cecchini, S., Boé, J., and François, C.: Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France, Agric. For. Meteorol., 149, 938–948, https://doi.org/10.1016/j.agrformet.2008.11.014, 2009.
Didan, K. and Barreto, A.: NASA MEaSUREs Vegetation Index and Phenology (VIP) Phenology NDVI Yearly Global 0.05Deg CMG, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MEaSUREs/VIP/VIPPHEN_NDVI.004 (last access: 11 August 2022), 2016.
Dixon, D. J., Callow, J. N., Duncan, J. M., Setterfield, S. A., and Pauli, N.: Satellite prediction of forest flowering phenology, Remote Sens. Environ., 255, 112197, https://doi.org/10.1016/j.rse.2020.112197, 2021.
Donnelly, A., Yu, R., Jones, K., Belitz, M., Li, B., Duffy, K., Zhang, X., Wang, J., Seyednasrollah, B., Gerst, K. L., Li, D., Kaddoura, Y., Zhu, K., Morisette, J., Ramey, C., and Smith, K.: Exploring discrepancies between in situ phenology and remotely derived phenometrics at NEON sites, Ecosphere, 13, e3912, https://doi.org/10.1002/ecs2.3912, 2022.
Dronova, I. and Taddeo, S.: Remote sensing of phenology: Towards the comprehensive indicators of plant community dynamics from species to regional scales, J. Ecol., 110, 1460–1484, https://doi.org/10.1111/1365-2745.13897, 2022.
Estrella, N. and Menzel, A.: Responses of leaf colouring in four deciduous tree species to climate and weather in Germany, Clim. Res., 32, 253–267, https://doi.org/10.3354/cr032253, 2006.
Fang, J., Wang, Z., and Tang, Z.: Species Distribution and Climates, in: Atlas of Woody Plants in China, edited by: Fang, J., Wang, Z., and Tang, Z., Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15017-3_1, 2011.
Fielding, A. H. and Bell, J. F.: A review of methods for the assessment of prediction errors in conservation presence/absence models, Environ. Conserv., 24, 38–49, https://doi.org/10.1017/s0376892997000088, 1997.
Fisher, J. I., Mustard, J. F., and Vadeboncoeur, M. A.: Green leaf phenology at Landsat resolution: Scaling from the field to the satellite, Remote Sens. Environ., 100, 265–279, https://doi.org/10.1016/j.rse.2005.10.022, 2006.
Fitchett, J. M., Grab, S. W., and Thompson, D. I.: Plant phenology and climate change: Progress in methodological approaches and application, Prog. Phys. Geogr., 39, 460–482, https://doi.org/10.1177/0309133315578940, 2015.
Friedl, M. and Sulla-Menashe, D.: MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD12Q1.061, last access: 19 October 2022.
Fu, Y., Li, X., Zhou, X., Geng, X., Guo, Y., and Zhang, Y.: Progress in plant phenology modeling under global climate change, Sci. China Earth Sci., 63, 1237–1247, https://doi.org/10.1007/s11430-019-9622-2, 2020.
Fu, Y. H., Zhao, H., Piao, S., Peaucelle, M., Peng, S., Zhou, G., Ciais, P., Huang, M., Menzel, A., Peñuelas, J., Song, Y., Vitasse, Y., Zeng, Z., and Janssens, I. A.: Declining global warming effects on the phenology of spring leaf unfolding, Nature, 526, 104–107, https://doi.org/10.1038/nature15402, 2015.
Fu, Y. H., Piao, S., Delpierre, N., Hao, F., Hänninen, H., Liu, Y., Sun, W., Janssens, I. A., and Campioli, M.: Larger temperature response of autumn leaf senescence than spring leaf-out phenology, Global Change Biol., 24, 2159–2168, https://doi.org/10.1111/gcb.14021, 2018.
GBIF: GBIF Occurrence Download, GBIF [data set], https://doi.org/10.15468/dl.7dwjev, last access: 7 September 2022.
Ge, Q., Wang, H., and Dai, J.: Simulating changes in the leaf unfolding time of 20 plant species in China over the twenty-first century, Int. J. Biometeorol., 58, 473–484, https://doi.org/10.1007/s00484-013-0671-x, 2014.
Ge, Q., Wang, H., Rutishauser, T., and Dai, J.: Phenological response to climate change in China: a meta-analysis, Global Change Biol., 21, 265–274, https://doi.org/10.1111/gcb.12648, 2015.
Gill, A. L., Gallinat, A. S., Sanders-DeMott, R., Rigden, A. J., Short Gianotti, D. J., Mantooth, J. A., and Templer, P. H.: Changes in autumn senescence in northern hemisphere deciduous trees: a meta-analysis of autumn phenology studies, Ann. Bot., 116, 875–888, https://doi.org/10.1093/aob/mcv055, 2015.
Hufkens, K., Basler, D., Milliman, T., Melaas, E. K., and Richardson, A. D.: An integrated phenology modelling framework in R, Methods Ecol. Evol., 9, 1276–1285, https://doi.org/10.1111/2041-210x.12970, 2018.
Izquierdo-Verdiguier, E., Zurita-Milla, R., Ault, T. R., and Schwartz, M. D.: Development and analysis of spring plant phenology products: 36 years of 1-km grids over the conterminous US, Agric. For. Meteorol., 262, 34–41, https://doi.org/10.1016/j.agrformet.2018.06.028, 2018.
Jeong, S.-J. and Medvigy, D.: Macroscale prediction of autumn leaf coloration throughout the continental United States, Global Ecol. Biogeogr., 23, 1245–1254, https://doi.org/10.1111/geb.12206, 2014.
Keenan, T. F., Gray, J., Friedl, M. A., Toomey, M., Bohrer, G., Hollinger, D. Y., Munger, J. W., O’Keefe, J., Schmid, H. P., Wing, I. S., Yang, B., and Richardson, A. D.: Net carbon uptake has increased through warming-induced changes in temperate forest phenology, Nat. Clim. Change, 4, 598–604, https://doi.org/10.1038/nclimate2253, 2014.
Kowalski, K., Senf, C., Hostert, P., and Pflugmacher, D.: Characterizing spring phenology of temperate broadleaf forests using Landsat and Sentinel-2 time series, Int. J. Appl. Earth Obs. Geoinf., 92, 102172, https://doi.org/10.1016/j.jag.2020.102172, 2020.
Lang, W., Chen, X., Qian, S., Liu, G., and Piao, S.: A new process-based model for predicting autumn phenology: how is leaf senescence controlled by photoperiod and temperature coupling?, Agric. For. Meteorol., 268, 124–135, https://doi.org/10.1016/j.agrformet.2019.01.006, 2019.
Li, X., Zhou, Y., Meng, L., Asrar, G. R., Lu, C., and Wu, Q.: A dataset of 30 m annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States, Earth Syst. Sci. Data, 11, 881–894, https://doi.org/10.5194/essd-11-881-2019, 2019.
Liang, L., Schwartz, M. D., and Fei, S.: Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest, Remote Sens. Environ., 115, 143–157, https://doi.org/10.1016/j.rse.2010.08.013, 2011.
Lieth, H.: Purposes of a phenology book, in: Phenology and Seasonality Modeling, edited by: Lieth, H., Springer, Berlin, Heidelberg, 3–19, https://doi.org/10.1007/978-3-642-51863-8_1, 1974.
Liu, H., Gong, P., Wang, J., Clinton, N., Bai, Y., and Liang, S.: Annual dynamics of global land cover and its long-term changes from 1982 to 2015, Earth Syst. Sci. Data, 12, 1217–1243, https://doi.org/10.5194/essd-12-1217-2020, 2020.
Liu, Q., Fu, Y. H., Zhu, Z., Liu, Y., Liu, Z., Huang, M., Janssens, I. A., and Piao, S.: Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology, Global Change Biol., 22, 3702–3711, https://doi.org/10.1111/gcb.13311, 2016.
Liu, Y., Wu, C., Peng, D., Xu, S., Gonsamo, A., Jassal, R. S., Arain, M. A., Lu, L., Fang, B., and Chen, J. M.: Improved modeling of land surface phenology using MODIS land surface reflectance and temperature at evergreen needleleaf forests of central North America, Remote Sens. Environ., 176, 152–162, https://doi.org/10.1016/j.rse.2016.01.021, 2016.
Melaas, E. K., Sulla-Menashe, D., Gray, J. M., Black, T. A., Morin, T. H., Richardson, A. D., and Friedl, M. A.: Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat, Remote Sens. Environ., 186, 452–464, https://doi.org/10.1016/j.rse.2016.09.014, 2016.
Menzel, A.: Phenology: its importance to the global change community, Clim. Change, 54, 379, https://doi.org/10.1023/A:1016125215496, 2002.
Menzel, A., Yuan, Y., Matiu, M., Sparks, T., Scheifinger, H., Gehrig, R., and Estrella, N.: Climate change fingerprints in recent European plant phenology, Global Change Biol., 26, 2599–2612, https://doi.org/10.1111/gcb.15000, 2020.
Misra, G., Cawkwell, F., and Wingler, A.: Status of phenological research using Sentinel-2 data: A review, Remote Sens., 12, 2760, https://doi.org/10.3390/rs12172760, 2020.
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac (last access: 1 August 2022), 2019.
Park, D. S., Newman, E. A., and Breckheimer, I. K.: Scale gaps in landscape phenology: challenges and opportunities, Trends Ecol. Evol., 36, 709–721, https://doi.org/10.1016/j.tree.2021.04.008, 2021.
Peng, D., Wu, C., Li, C., Zhang, X., Liu, Z., Ye, H., Luo, S., Liu, X., Hu, Y., and Fang, B.: Spring green-up phenology products derived from MODIS NDVI and EVI: Intercomparison, interpretation and validation using National Phenology Network and AmeriFlux observations, Ecol. Indic., 77, 323–336, https://doi.org/10.1016/j.ecolind.2017.02.024, 2017.
Phillips, S. J., Anderson, R. P., and Schapire, R. E.: Maximum entropy modeling of species geographic distributions, Ecol. Modell., 190, 231–259, https://doi.org/10.1016/j.ecolmodel.2005.03.026, 2006.
Piao, S., Liu, Q., Chen, A., Janssens, I. A., Fu, Y., Dai, J., Liu, L., Lian, X., Shen, M., and Zhu, X.: Plant phenology and global climate change: Current progresses and challenges, Glob. Change Biol., 25, 1922–1940, https://doi.org/10.1111/gcb.14619, 2019.
Polgar, C. A. and Primack, R. B.: Leaf-out phenology of temperate woody plants: from trees to ecosystems, New Phytol., 191, 926–941, https://doi.org/10.1111/j.1469-8137.2011.03803.x, 2011.
Pukelsheim, F.: The three sigma rule, Am. Stat., 48, 88–91, https://doi.org/10.2307/2684253, 1994.
Richardson, A. D., Keenan, T. F., Migliavacca, M., Ryu, Y., Sonnentag, O., and Toomey, M.: Climate change, phenology, and phenological control of vegetation feedbacks to the climate system, Agric. For. Meteorol., 169, 156–173, https://doi.org/10.1016/j.agrformet.2012.09.012, 2013.
Richardson, A. D., Hufkens, K., Milliman, T., Aubrecht, D. M., Chen, M., Gray, J. M., Johnston, M. R., Keenan, T. F., Klosterman, S. T., Kosmala, M., Melaas, E. K., Friedl, M. A., and Frolking, S.: Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery, Sci. Data, 5, 180028, https://doi.org/10.1038/sdata.2018.28, 2018.
Rodriguez-Galiano, V., Dash, J., and Atkinson, P. M.: Intercomparison of satellite sensor land surface phenology and ground phenology in Europe, Geophys. Res. Lett., 42, 2253–2260, https://doi.org/10.1002/2015gl063586, 2015.
Sagarin, R. D. and Gaines, S. D.: The “abundant centre” distribution: to what extent is it a biogeographical rule?, Ecol. Lett., 5, 137–147, https://doi.org/10.1046/j.1461-0248.2002.00297.x, 2002.
Schwartz, M. D. (Ed.): Phenology: an integrative environmental science, Springer, Dordrecht, https://doi.org/10.1007/978-94-007-0632-3, 2003.
Studer, S., Stöckli, R., Appenzeller, C., and Vidale, P. L.: A comparative study of satellite and ground-based phenology, Int. J. Biometeorol., 51, 405–414, https://doi.org/10.1007/s00484-006-0080-5, 2007.
Tang, J., Körner, C., Muraoka, H., Piao, S., Shen, M., Thackeray, S. J., and Yang, X.: Emerging opportunities and challenges in phenology: a review, Ecosphere, 7, e01436, https://doi.org/10.1002/ecs2.1436, 2016.
Tao, Z., Wang, H., Dai, J., Alatalo, J., and Ge, Q.: Modeling spatiotemporal variations in leaf coloring date of three tree species across China, Agric. For. Meteorol., 249, 310–318, https://doi.org/10.1016/j.agrformet.2017.10.034, 2018.
Templ, B., Koch, E., Bolmgren, K., Ungersböck, M., Paul, A., Scheifinger, H., Rutishauser, T., Busto, M., Chmielewski, F.-M., Hájková, L., Hodzić, S., Kaspar, F., Pietragalla, B., Romero-Fresneda, R., Tolvanen, A., Vučetič, V., Zimmermann, K., and Zust, A.: Pan European Phenological database (PEP725): a single point of access for European data, Int. J. Biometeorol., 62, 1109–1113, https://doi.org/10.1007/s00484-018-1512-8, 2018.
Tian, F., Cai, Z., Jin, H., Hufkens, K., Scheifinger, H., Tagesson, T., Smets, B., Van Hoolst, R., Bonte, K., Ivits, E., Tong, X., Ardö, J., and Eklundh, L.: Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe, Remote Sens. Environ., 260, 112456, https://doi.org/10.1016/j.rse.2021.112456, 2021.
Wang, H., Dai, J., and Ge, Q.: The spatiotemporal characteristics of spring phenophase changes of Fraxinus chinensis in China from 1952 to 2007, Sci. China Earth Sci., 55, 991–1000, https://doi.org/10.1007/s11430-011-4349-0, 2012.
Wang, H., Wu, C., Ciais, P., Peñuelas, J., Dai, J., Fu, Y., and Ge, Q.: Overestimation of the effect of climatic warming on spring phenology due to misrepresentation of chilling, Nat. Commun., 11, 4945, https://doi.org/10.1038/s41467-020-18743-8, 2020.
Wu, C., Hou, X., Peng, D., Gonsamo, A., and Xu, S.: Land surface phenology of China's temperate ecosystems over 1999–2013: Spatial–temporal patterns, interaction effects, covariation with climate and implications for productivity, Agr. Forest Meteorol., 216, 177–187, https://doi.org/10.1016/j.agrformet.2015.10.015, 2016.
Wu, C., Wang, X., Wang, H., Ciais, P., Peñuelas, J., Myneni, R. B., Desai, A. R., Gough, C. M., Gonsamo, A., Black, A. T., Jassal, R. S., Ju, W., Yuan, W., Fu, Y., Shen, M., Li, S., Liu R., Chen, J. M., and Ge, Q.: Contrasting responses of autumn-leaf senescence to daytime and night-time warming, Nat. Clim. Change, 8, 1092–1096, https://doi.org/10.1038/s41558-018-0346-z, 2018.
Wu, W., Sun, Y., Xiao, K., and Xin, Q.: Development of a global annual land surface phenology dataset for 1982–2018 from the AVHRR data by implementing multiple phenology retrieving methods, Int. J. Appl. Earth Obs. Geoinf., 103, 102487, https://doi.org/10.1016/j.jag.2021.102487, 2021.
Wu, Z., Chen, S., De Boeck, H. J., Stenseth, N. C., Tang, J., Vitasse, Y., Wang, S., Zohner, C., and Fu, Y. H.: Atmospheric brightening counteracts warming-induced delays in autumn phenology of temperate trees in Europe, Global Ecol. Biogeogr., 30, 2477–2487, https://doi.org/10.1111/geb.13404, 2021.
Ye, Y., Zhang, X., Shen, Y., Wang, J., Crimmins, T., and Scheifinger, H.: An optimal method for validating satellite-derived land surface phenology using in-situ observations from national phenology networks, ISPRS J. Photogramm. Remote Sens., 194, 74–90, https://doi.org/10.1016/j.isprsjprs.2022.09.018, 2022.
Zani, D., Crowther, T. W., Mo, L., Renner, S. S., and Zohner, C. M.: Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees, Science, 370, 1066–1071, https://doi.org/10.1126/science.abd8911, 2020.
Zhang, X., Wang, J., Gao, F., Liu, Y., Schaaf, C., Friedl, M., Yu, Y., Jayavelu, S., Gray, J., Liu, L., Yan, D., and Henebry G. M.: Exploration of scaling effects on coarse resolution land surface phenology, Remote Sens. Environ., 190, 318–330, https://doi.org/10.1016/j.rse.2017.01.001, 2017.
Zhang, X., Friedl, M., and Henebry, G.: VIIRS/NPP Land Cover Dynamics Yearly L3 Global 0.05 Deg CMG V001, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/VIIRS/VNP22C2.001 (last access: 11 August 2022), 2020.
Zhu, M. and Dai J.: Species phenology and ground phenology maps over China from 1951–2020, Science Data Bank [data set], https://doi.org/10.57760/sciencedb.07995, 2023.
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.
This study utilized 24,552 in situ phenology observation records from the Chinese Phenology...
Altmetrics
Final-revised paper
Preprint