Articles | Volume 17, issue 1
https://doi.org/10.5194/essd-17-117-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-117-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
16 Jan 2025
Data description paper |
| 16 Jan 2025
| 16 Jan 2025
A flux tower site attribute dataset intended for land surface modeling
Jiahao Shi
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
Hua Yuan
CORRESPONDING AUTHOR
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
Wanyi Lin
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
Wenzong Dong
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
Hongbin Liang
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
Zhuo Liu
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
Jianxin Zeng
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
Haolin Zhang
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
Nan Wei
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
Zhongwang Wei
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
Shupeng Zhang
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
Shaofeng Liu
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
Xingjie Lu
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
Yongjiu Dai
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China
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Qianyu Li, Xingjie Lu, Yingping Wang, Xin Huang, Peter M. Cox, and Yiqi Luo
Biogeosciences, 15, 6909–6925, https://doi.org/10.5194/bg-15-6909-2018, https://doi.org/10.5194/bg-15-6909-2018, 2018
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Land-surface models have been widely used to predict the responses of terrestrial ecosystems to climate change. A better understanding of model mechanisms that govern terrestrial ecosystem responses to rising atmosphere [CO2] is needed. Our study for the first time shows that the expansion of leaf area under rising [CO2] is the most important response for the stimulation of land carbon accumulation by a land-surface model: CABLE. Processes related to leaf area should be better calibrated.
Xingjie Lu, Ying-Ping Wang, Yiqi Luo, and Lifen Jiang
Biogeosciences, 15, 6559–6572, https://doi.org/10.5194/bg-15-6559-2018, https://doi.org/10.5194/bg-15-6559-2018, 2018
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How long does C cycle through terrestrial ecosystems is a critical question for understanding land C sequestration capacity under future rising atmosphere [CO2] and climate warming. Under climate change, previous conventional concepts with a steady-state assumption will no longer be suitable for a non-steady state. Our results using the new concept, C transit time, suggest more significant responses in terrestrial C cycle under rising [CO2] and climate warming.
Yiqi Luo, Zheng Shi, Xingjie Lu, Jianyang Xia, Junyi Liang, Jiang Jiang, Ying Wang, Matthew J. Smith, Lifen Jiang, Anders Ahlström, Benito Chen, Oleksandra Hararuk, Alan Hastings, Forrest Hoffman, Belinda Medlyn, Shuli Niu, Martin Rasmussen, Katherine Todd-Brown, and Ying-Ping Wang
Biogeosciences, 14, 145–161, https://doi.org/10.5194/bg-14-145-2017, https://doi.org/10.5194/bg-14-145-2017, 2017
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Climate change is strongly regulated by land carbon cycle. However, we lack the ability to predict future land carbon sequestration. Here, we develop a novel framework for understanding what determines the direction and rate of future change in land carbon storage. The framework offers a suite of new approaches to revolutionize land carbon model evaluation and improvement.
Shengyun Chen, Wenjie Liu, Qian Zhao, Lin Zhao, Qingbai Wu, Xingjie Lu, Shichang Kang, Xiang Qin, Shilong Chen, Jiawen Ren, and Dahe Qin
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-80, https://doi.org/10.5194/tc-2016-80, 2016
Revised manuscript not accepted
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Experimental warming was manipulated using open top chambers in alpine grassland ecosystem in the permafrost regions of the Qinghai-Tibet Plateau. The results revealed variations of earlier thawing, later freezing and longer freezing-thawing periods in shallow soil. Further, the estimated permafrost table declined under the warming scenarios. The work will be helpful to evaluate the stability of Qinghai-Tibet Railway/Highway and estimate the release of carbon under the future climate warming.
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Juliette Bernard, Catherine Prigent, Carlos Jimenez, Etienne Fluet-Chouinard, Bernhard Lehner, Elodie Salmon, Philippe Ciais, Zhen Zhang, Shushi Peng, and Marielle Saunois
Earth Syst. Sci. Data, 17, 2985–3008, https://doi.org/10.5194/essd-17-2985-2025, https://doi.org/10.5194/essd-17-2985-2025, 2025
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Earth Syst. Sci. Data, 17, 2933–2952, https://doi.org/10.5194/essd-17-2933-2025, https://doi.org/10.5194/essd-17-2933-2025, 2025
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Weilin Liao, Yanman Li, Xiaoping Liu, Yuhao Wang, Yangzi Che, Ledi Shao, Guangzhao Chen, Hua Yuan, Ning Zhang, and Fei Chen
Earth Syst. Sci. Data, 17, 2535–2551, https://doi.org/10.5194/essd-17-2535-2025, https://doi.org/10.5194/essd-17-2535-2025, 2025
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The currently available urban canopy parameter (UCP) datasets are limited to just a few cities for urban climate simulations by the Weather Research and Forecasting (WRF) model. To address this gap, we develop a global 1 km spatially continuous UCP dataset (GloUCP) which provides superior spatial coverage and higher accuracy in capturing urban morphology across diverse regions. It has great potential to support further advancements in urban climate modeling and related applications.
Ruoque Shen, Qiongyan Peng, Xiangqian Li, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 17, 2193–2216, https://doi.org/10.5194/essd-17-2193-2025, https://doi.org/10.5194/essd-17-2193-2025, 2025
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Rice is a vital staple crop that plays a crucial role in food security in China. However, long-term high-resolution rice distribution maps in China are lacking. This study developed a new rice-mapping method, mitigating the impact of cloud contamination and missing data in optical remote sensing observations on rice mapping. The resulting dataset, CCD-Rice (China Crop Dataset-Rice), achieved high accuracy and showed a strong correlation with statistical data.
Yifan Cheng, Lei Zhao, TC Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang “Cathy” Li
Earth Syst. Sci. Data, 17, 2147–2174, https://doi.org/10.5194/essd-17-2147-2025, https://doi.org/10.5194/essd-17-2147-2025, 2025
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The absence of globally consistent and spatially continuous urban surface input has long hindered large-scale high-resolution urban climate modeling. Using remote sensing, cloud computing, and machine learning, we developed U-Surf, a 1 km dataset providing key urban surface properties worldwide. U-Surf enhances urban representation across scales and supports kilometer-scale urban-resolving Earth system modeling unprecedentedly, with broader applications in urban studies and beyond.
Michael MacFerrin, Christopher Amante, Kelly Carignan, Matthew Love, and Elliot Lim
Earth Syst. Sci. Data, 17, 1835–1849, https://doi.org/10.5194/essd-17-1835-2025, https://doi.org/10.5194/essd-17-1835-2025, 2025
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Here we present Earth TOPOgraphy (ETOPO) 2022, the latest iteration of NOAA's global seamless topographic–bathymetric dataset. ETOPO 2022 is a significant upgrade regarding resolution and accuracy from previous ETOPO releases and is freely available in multiple data formats and resolutions for all uses (public or private), excepting navigation.
Jingling Jiang, Hong Zhang, Ji Ge, Lijun Zuo, Lu Xu, Mingyang Song, Yinhaibin Ding, Yazhe Xie, and Wenjiang Huang
Earth Syst. Sci. Data, 17, 1781–1805, https://doi.org/10.5194/essd-17-1781-2025, https://doi.org/10.5194/essd-17-1781-2025, 2025
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This study uses temporal synthetic aperture radar (SAR) data and optical imagery to conduct rice-mapping experiments in 34 African countries with rice-planting areas exceeding 5000 ha in 2022, achieving a 20 m resolution spatial distribution mapping for 2023. The average classification accuracy based on the validation set exceeded 85 %, and the R2; values for linear fitting with existing statistical data all surpassed 0.9, demonstrating the effectiveness of the proposed mapping method.
Chao Tao, Dandan Zhong, Weiliang Mu, Zhuofei Du, and Haiyang Wu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-184, https://doi.org/10.5194/essd-2025-184, 2025
Revised manuscript accepted for ESSD
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We construct FarmSeg-VL, the first high-quality image-text dataset for farmland segmentation. It covers eight agricultural regions across four seasons in China, offering extensive spatiotemporal coverage and fine-grained annotations. This dataset fills the gap in remote sensing image-text datasets for farmland, alleviates the challenge of spatiotemporal heterogeneity in farmland segmentation, and provides valuable data to support large-scale farmland monitoring and mapping.
Zihang Lou, Dailiang Peng, Zhou Shi, Hongyan Wang, Yaqiong Zhang, Xue Yan, Zhongxing Chen, Su Ye, Le Yu, Jinkang Hu, Yulong Lv, Hao Peng, Yizhou Zhang, and Bing Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-133, https://doi.org/10.5194/essd-2025-133, 2025
Revised manuscript accepted for ESSD
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This study created the first detailed annual maps of Africa's cropland extent from 2000 to 2022 in 30 m resolution to support global efforts against hunger and sustainable farming. Our findings show Africa's cropland grew by 8.5 % over two decade, while 11.5 % of cropland was abandoned by 2018 – revealing hidden challenges in agricultural sustainability. These yearly, field-sized maps help governments track where farming grows or shrinks, plan food supplies, and protect vital cropland.
David A. Gibbs, Melissa Rose, Giacomo Grassi, Joana Melo, Simone Rossi, Viola Heinrich, and Nancy L. Harris
Earth Syst. Sci. Data, 17, 1217–1243, https://doi.org/10.5194/essd-17-1217-2025, https://doi.org/10.5194/essd-17-1217-2025, 2025
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Updated global maps of greenhouse gas (GHG) emissions and sequestration by forests from 2001 onwards using satellite-derived data show that forests are strong net carbon sinks, capturing about as much CO2 each year on average as the USA emitted from fossil fuels in 2019. After reclassifying fluxes to countries’ reporting categories for national GHG inventories, we found that roughly two-thirds of the net CO2 flux from forests is anthropogenic and one-third is non-anthropogenic.
Zhenghang Yuan, Zhitong Xiong, Lichao Mou, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 17, 1245–1263, https://doi.org/10.5194/essd-17-1245-2025, https://doi.org/10.5194/essd-17-1245-2025, 2025
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ChatEarthNet is an image–text dataset that provides high-quality, detailed natural language descriptions for global-scale satellite data. It consists of 163 488 image-text pairs with captions generated by ChatGPT-3.5 and an additional 10 000 image-text pairs with captions generated by ChatGPT-4V(ision). This dataset has significant potential for training and evaluating vision–language geo-foundation models in remote sensing.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Wenhan Zhang, Linlin Guan, Ming Bai, and Xidong Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-73, https://doi.org/10.5194/essd-2025-73, 2025
Revised manuscript accepted for ESSD
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This work describes a novel global 10 m land-cover dataset with fine classification system, which contains 30 land-cover subcategories and achieves the fulfilling performance over the globe.
Simon Boitard, Arnaud Mialon, Stéphane Mermoz, Nemesio J. Rodríguez-Fernández, Philippe Richaume, Julio César Salazar-Neira, Stéphane Tarot, and Yann H. Kerr
Earth Syst. Sci. Data, 17, 1101–1119, https://doi.org/10.5194/essd-17-1101-2025, https://doi.org/10.5194/essd-17-1101-2025, 2025
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Aboveground biomass (AGB) is a critical component of the Earth's carbon cycle. The presented dataset aims to help monitor this essential climate variable with AGB time series from 2011 onward, derived with a carefully calibrated spatial relationship between the measurements of the Soil Moisture and Ocean Salinity (SMOS) mission and pre-existing AGB maps. The produced dataset has been extensively compared with other available AGB time series and can be used in AGB studies.
Nicolas Lampach, Jon Olav Skoien, Helena Ramos, Julien Gaffuri, Renate Koeble, Linda See, and Marijn Van der Velde
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-59, https://doi.org/10.5194/essd-2025-59, 2025
Revised manuscript accepted for ESSD
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Eurostat and JRC developed a new methodology to make geospatial data from agricultural census available to users, while ensuring that no confidential information from individuals is disclosed. The geospatial data presented in the article correspond to the contextual indicators of the monitoring and evaluation framework of the Common Agricultural Policy. Our exploratory analysis reveals several interesting patterns which contribute to the broader debate on the future of European agriculture.
Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li
Earth Syst. Sci. Data, 17, 855–880, https://doi.org/10.5194/essd-17-855-2025, https://doi.org/10.5194/essd-17-855-2025, 2025
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Our study introduces GMIE, a high-resolution global map of irrigated cropland at 100 m resolution, covering 403.17 Mha and utilizing irrigation performance under drought stress. We found that 23.4 % of global cropland is irrigated, with the most extensive areas in India, China, the United States, and Pakistan. We identified the distribution of central pivot systems commonly used in the United States and Saudi Arabia. This new map can better support water management and food security globally.
Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, and Mengzi Zhou
Earth Syst. Sci. Data, 17, 773–797, https://doi.org/10.5194/essd-17-773-2025, https://doi.org/10.5194/essd-17-773-2025, 2025
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This study developed a new approach to long-time continuous annual vegetation mapping from remote sensing imagery, and mapped the vegetation of the Qinghai–Tibet Plateau (QTP) from 2000 to 2022 using the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping reached 83.3%, with the reference annual 2020 data reaching an accuracy of 83.3% and a kappa coefficient of 0.82. The study supports the use of remote sensing data to mapping long-time continuous annual vegetation.
Xuemeng Tian, Davide Consoli, Martijn Witjes, Florian Schneider, Leandro Parente, Murat Şahin, Yu-Feng Ho, Robert Minařík, and Tomislav Hengl
Earth Syst. Sci. Data, 17, 741–772, https://doi.org/10.5194/essd-17-741-2025, https://doi.org/10.5194/essd-17-741-2025, 2025
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Our study introduces a Landsat-based data cube simplifying access to detailed environmental data across Europe from 2000 to 2022, covering vegetation, water, soil, and crops. Our experiments demonstrate its effectiveness in developing environmental models and maps. Tailored feature selection is crucial for its effective use in environmental modeling. It aims to support comprehensive environmental monitoring and analysis, helping researchers and policy-makers in managing environmental resources.
Mingyang Song, Lu Xu, Ji Ge, Hong Zhang, Lijun Zuo, Jingling Jiang, Yinhaibin Ding, Yazhe Xie, and Fan Wu
Earth Syst. Sci. Data, 17, 661–683, https://doi.org/10.5194/essd-17-661-2025, https://doi.org/10.5194/essd-17-661-2025, 2025
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We created a 10 m resolution rice distribution map for East Asia in 2023 (EARice10), achieving an overall accuracy (OA) of 90.48 % on validation samples. EARice10 shows strong consistency with statistical data (coefficient of determination, R2: 0.94–0.98) and existing datasets (R2: 0.79–0.98). It is the most up-to-date map, covering the four major rice-producing countries in East Asia at 10 m resolution.
Maximilian Freudenberg, Sebastian Schnell, and Paul Magdon
Earth Syst. Sci. Data, 17, 351–367, https://doi.org/10.5194/essd-17-351-2025, https://doi.org/10.5194/essd-17-351-2025, 2025
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Classifying tree species in satellite images is an important task for environmental monitoring and forest management. Here we present a dataset containing Sentinel-2 satellite pixel time series of individual trees intended for training machine learning models. The dataset was created by merging information from the German National Forest Inventory in 2012 with satellite data. It sparsely covers the whole of Germany for the years 2015 to 2022 and comprises 48 species and 3 species groups.
Yangyang Fu, Xiuzhi Chen, Chaoqing Song, Xiaojuan Huang, Jie Dong, Qiongyan Peng, and Wenping Yuan
Earth Syst. Sci. Data, 17, 95–115, https://doi.org/10.5194/essd-17-95-2025, https://doi.org/10.5194/essd-17-95-2025, 2025
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This study proposed the Winter-Triticeae Crops Index (WTCI), which had great performance and stable spatiotemporal transferability in identifying winter-triticeae crops in 66 countries worldwide, with an overall accuracy of 87.7 %. The first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022 were further produced based on the WTCI method. The product can serve as an important basis for agricultural applications.
Yanghai Yu, Yang Lei, Paul Siqueira, Xiaotong Liu, Denuo Gu, Anmin Fu, Yong Pang, Wenli Huang, and Jiancheng Shi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-596, https://doi.org/10.5194/essd-2024-596, 2025
Revised manuscript accepted for ESSD
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This paper presents a global-to-local method to improve forest height estimates by fusing InSAR and GEDI data. The large-scale ability was tested on open-access ALOS-1 data, where a two-fold solution is used to address temporal gap between GEDI and ALOS data. Produced products of 30 m gridded forest height mosaics for the northeastern U.S. and China show improved accuracy at 3–4 m/ha and 20 % enhancement over interpolated GEDI maps. The prototype is promising to fuse GEDI and future NISAR data.
Raphaël d'Andrimont, Momchil Yordanov, Fernando Sedano, Astrid Verhegghen, Peter Strobl, Savvas Zachariadis, Flavia Camilleri, Alessandra Palmieri, Beatrice Eiselt, Jose Miguel Rubio Iglesias, and Marijn van der Velde
Earth Syst. Sci. Data, 16, 5723–5735, https://doi.org/10.5194/essd-16-5723-2024, https://doi.org/10.5194/essd-16-5723-2024, 2024
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The Land Use/Cover Area frame Survey (LUCAS) Copernicus 2022 is a large and systematic in situ field survey of 137 966 polygons over the European Union in 2022. The data contain 82 land cover classes and 40 land use classes.
Shuang Chen, Jie Wang, Qiang Liu, Xiangan Liang, Rui Liu, Peng Qin, Jincheng Yuan, Junbo Wei, Shuai Yuan, Huabing Huang, and Peng Gong
Earth Syst. Sci. Data, 16, 5449–5475, https://doi.org/10.5194/essd-16-5449-2024, https://doi.org/10.5194/essd-16-5449-2024, 2024
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The inconsistent coverage of Landsat data due to its long revisit intervals and frequent cloud cover poses challenges to large-scale land monitoring. We developed a global 30 m 23-year (2000–2022) daily seamless data cube (SDC) of surface reflectance based on Landsat 5, 7, 8, and 9 and MODIS products. The SDC exhibits enhanced capabilities for monitoring land cover changes and robust consistency in both spatial and temporal dimensions, which are important for global environmental monitoring.
Gerardo E. Soto, Steven W. Wilcox, Patrick E. Clark, Francesco P. Fava, Nathaniel D. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher B. Barrett
Earth Syst. Sci. Data, 16, 5375–5404, https://doi.org/10.5194/essd-16-5375-2024, https://doi.org/10.5194/essd-16-5375-2024, 2024
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This paper uses machine learning and linear unmixing to produce rangeland health indicators: Landsat time series of land cover classes and vegetation fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and bare ground in arid and semi-arid Kenya, Ethiopia, and Somalia. This represents the first multi-decadal Landsat-resolution dataset specifically designed for mapping and monitoring rangeland health in the arid and semi-arid rangelands of this portion of eastern Africa.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, https://doi.org/10.5194/essd-16-5357-2024, 2024
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Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.
Yuling Chen, Haitao Yang, Zekun Yang, Qiuli Yang, Weiyan Liu, Guoran Huang, Yu Ren, Kai Cheng, Tianyu Xiang, Mengxi Chen, Danyang Lin, Zhiyong Qi, Jiachen Xu, Yixuan Zhang, Guangcai Xu, and Qinghua Guo
Earth Syst. Sci. Data, 16, 5267–5285, https://doi.org/10.5194/essd-16-5267-2024, https://doi.org/10.5194/essd-16-5267-2024, 2024
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The national-scale continuous maps of arithmetic mean height and weighted mean height across China address the challenges of accurately estimating forest stand mean height using a tree-based approach. These maps produced in this study provide critical datasets for forest sustainable management in China, including climate change mitigation (e.g., terrestrial carbon estimation), forest ecosystem assessment, and forest inventory practices.
Jia Zhou, Jin Niu, Ning Wu, and Tao Lu
Earth Syst. Sci. Data, 16, 5171–5189, https://doi.org/10.5194/essd-16-5171-2024, https://doi.org/10.5194/essd-16-5171-2024, 2024
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The study provided an annual 100 m resolution glimpse into the grazing activities across the Qinghai–Tibet Plateau. The newly minted Gridded Dataset of Grazing Intensity (GDGI) not only boasts exceptional accuracy but also acts as a pivotal resource for further research and strategic planning, with the potential to shape sustainable grazing practices, guide informed environmental stewardship, and ensure the longevity of the region’s precious ecosystems.
Adrià Descals, David L. A. Gaveau, Serge Wich, Zoltan Szantoi, and Erik Meijaard
Earth Syst. Sci. Data, 16, 5111–5129, https://doi.org/10.5194/essd-16-5111-2024, https://doi.org/10.5194/essd-16-5111-2024, 2024
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This study provides a 10 m global oil palm extent layer for 2021 and a 30 m oil palm planting-year layer from 1990 to 2021. The oil palm extent layer was produced using a convolutional neural network that identified industrial and smallholder plantations using Sentinel-1 data. The oil palm planting year was developed using a methodology specifically designed to detect the early stages of oil palm development in the Landsat time series.
Ran Jia, Xiuqi Fang, Yundi Yang, Masayuki Yokozawa, and Yu Ye
Earth Syst. Sci. Data, 16, 4971–4994, https://doi.org/10.5194/essd-16-4971-2024, https://doi.org/10.5194/essd-16-4971-2024, 2024
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We reconstructed a cropland area change dataset in Northeast China over the past millennium by integrating multisource data with a unified standard using the historical and archaeological record, statistical yearbook, and national land survey. Cropland in Northeast China exhibited phases of expansion–reduction–expansion over the past millennium. This dataset can be used for improving the land use and land cover change (LUCC) dataset and assessing LUCC-induced carbon emission and climate change.
Stefania Di Tommaso, Sherrie Wang, Rob Strey, and David B. Lobell
Earth Syst. Sci. Data, 16, 4931–4947, https://doi.org/10.5194/essd-16-4931-2024, https://doi.org/10.5194/essd-16-4931-2024, 2024
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Sugarcane plays a vital role in food, biofuel, and farmer income globally, yet its cultivation faces numerous social and environmental challenges. Despite its significance, accurate mapping remains limited. Our study addresses this gap by introducing a novel 10 m global dataset of sugarcane maps spanning 2019–2022. Comparisons with field data, pre-existing maps, and official government statistics all indicate the high precision and high recall of our maps.
Jie Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Geli Zhang, Xuebin Yang, Xiaocui Wu, Chandrashekhar Biradar, and Yang Hu
Earth Syst. Sci. Data, 16, 4619–4639, https://doi.org/10.5194/essd-16-4619-2024, https://doi.org/10.5194/essd-16-4619-2024, 2024
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Existing satellite-based forest maps have large uncertainties due to different forest definitions and mapping algorithms. To effectively manage forest resources, timely and accurate annual forest maps at a high spatial resolution are needed. This study improved forest maps by integrating PALSAR-2 and Landsat images. Annual evergreen and non-evergreen forest-type maps were also generated. This critical information supports the Global Forest Resources Assessment.
Xin Zhao, Kazuya Nishina, Haruka Izumisawa, Yuji Masutomi, Seima Osako, and Shuhei Yamamoto
Earth Syst. Sci. Data, 16, 3893–3911, https://doi.org/10.5194/essd-16-3893-2024, https://doi.org/10.5194/essd-16-3893-2024, 2024
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Mapping a rice calendar in a spatially explicit manner with a consistent framework remains challenging at a global or continental scale. We successfully developed a new gridded rice calendar for monsoon Asia based on Sentinel-1 and Sentinel-2 images, which characterize transplanting and harvesting dates and the number of rice croppings in a comprehensive framework. Our rice calendar will be beneficial for rice management, production prediction, and the estimation of greenhouse gas emissions.
Yuehong Chen, Congcong Xu, Yong Ge, Xiaoxiang Zhang, and Ya'nan Zhou
Earth Syst. Sci. Data, 16, 3705–3718, https://doi.org/10.5194/essd-16-3705-2024, https://doi.org/10.5194/essd-16-3705-2024, 2024
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Population data is crucial for human–nature interactions. Gridded population data can address limitations of census data in irregular units. In China, rapid urbanization necessitates timely and accurate population grids. However, existing datasets for China are either outdated or lack recent census data. Hence, a novel approach was developed to disaggregate China’s seventh census data into 100 m population grids. The resulting dataset outperformed the existing LandScan and WorldPop datasets.
Dina Jahanianfard, Joana Parente, Oscar Gonzalez-Pelayo, and Akli Benali
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-305, https://doi.org/10.5194/essd-2024-305, 2024
Revised manuscript accepted for ESSD
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This data description paper provides details on the development of the first Portuguese burn severity atlas in Portugal from 1984 to 2022 derived from satellite imagery via Google Earth Engine platform. Moreover, a semi-automated code was also developed, which can be used to create burn severity atlas of any other region in the world. The maps of this atlas can be used not only in fields related to fire ecology and management, but also within research areas related to air, water, and soil.
Shuchao Ye, Peiyu Cao, and Chaoqun Lu
Earth Syst. Sci. Data, 16, 3453–3470, https://doi.org/10.5194/essd-16-3453-2024, https://doi.org/10.5194/essd-16-3453-2024, 2024
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We reconstructed annual cropland density and crop type maps, including nine major crop types (corn, soybean, winter wheat, spring wheat, durum wheat, cotton, sorghum, barley, and rice), from 1850 to 2021 at 1 km × 1 km resolution. We found that the US total crop acreage has increased by 118 × 106 ha (118 Mha), mainly driven by corn (30 Mha) and soybean (35 Mha). Additionally, the US cropping diversity experienced an increase in the 1850s–1960s, followed by a decline over the past 6 decades.
Fang Chen, Lei Wang, Yu Wang, Haiying Zhang, Ning Wang, Pengfei Ma, and Bo Yu
Earth Syst. Sci. Data, 16, 3369–3382, https://doi.org/10.5194/essd-16-3369-2024, https://doi.org/10.5194/essd-16-3369-2024, 2024
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Storage tanks are responsible for approximately 25 % of CH4 emissions in the atmosphere, exacerbating climate warming. Currently there is no publicly accessible storage tank inventory. We generated the first high-spatial-resolution (1–2 m) storage tank dataset (STD) over 92 typical cities in China in 2021, totaling 14 461 storage tanks with the construction year from 2000–2021. It shows significant agreement with CH4 emission spatially and temporally, promoting the CH4 control strategy proposal.
Xingyi Huang, Yuwei Yin, Luwei Feng, Xiaoye Tong, Xiaoxin Zhang, Jiangrong Li, and Feng Tian
Earth Syst. Sci. Data, 16, 3307–3332, https://doi.org/10.5194/essd-16-3307-2024, https://doi.org/10.5194/essd-16-3307-2024, 2024
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The Tibetan Plateau, with its diverse vegetation ranging from forests to alpine grasslands, plays a key role in understanding climate change impacts. Existing maps lack detail or miss unique ecosystems. Our research, using advanced satellite technology and machine learning, produced the map TP_LC10-2022. Comparisons with other maps revealed TP_LC10-2022's excellence in capturing local variations. Our map is significant for in-depth ecological studies.
Qinghang Mei, Zhao Zhang, Jichong Han, Jie Song, Jinwei Dong, Huaqing Wu, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 16, 3213–3231, https://doi.org/10.5194/essd-16-3213-2024, https://doi.org/10.5194/essd-16-3213-2024, 2024
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In order to make up for the lack of long-term soybean planting area maps in China, we firstly generated a dataset of soybean planting area with a spatial resolution of 10 m for major producing areas in China from 2017 to 2021 (ChinaSoyArea10m). Compared with existing datasets, ChinaSoyArea10m has higher consistency with census data and further improvement in spatial details. The dataset can provide reliable support for subsequent studies on yield monitoring and food security.
Yavar Pourmohamad, John T. Abatzoglou, Erin J. Belval, Erica Fleishman, Karen Short, Matthew C. Reeves, Nicholas Nauslar, Philip E. Higuera, Eric Henderson, Sawyer Ball, Amir AghaKouchak, Jeffrey P. Prestemon, Julia Olszewski, and Mojtaba Sadegh
Earth Syst. Sci. Data, 16, 3045–3060, https://doi.org/10.5194/essd-16-3045-2024, https://doi.org/10.5194/essd-16-3045-2024, 2024
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The FPA FOD-Attributes dataset provides > 300 biological, physical, social, and administrative attributes associated with > 2.3×106 wildfire incidents across the US from 1992 to 2020. The dataset can be used to (1) answer numerous questions about the covariates associated with human- and lightning-caused wildfires and (2) support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.
Ewa Grabska-Szwagrzyk, Dirk Tiede, Martin Sudmanns, and Jacek Kozak
Earth Syst. Sci. Data, 16, 2877–2891, https://doi.org/10.5194/essd-16-2877-2024, https://doi.org/10.5194/essd-16-2877-2024, 2024
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We accurately mapped 16 dominant tree species and genera in Poland using Sentinel-2 observations from short periods in spring, summer, and autumn (2018–2021). The classification achieved more than 80% accuracy in country-wide forest species mapping, with variation based on species, region, and observation frequency. Freely accessible resources, including the forest tree species map and training and test data, can be found at https://doi.org/10.5281/zenodo.10180469.
Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data, 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024, https://doi.org/10.5194/essd-16-2605-2024, 2024
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NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 120 000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. This paper summarizes those results and provides links to details on ~ 80 individual flight lines. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
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We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, https://doi.org/10.5194/essd-16-2007-2024, 2024
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This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
Hui Li, Xiaobo Wang, Shaoqiang Wang, Jinyuan Liu, Yuanyuan Liu, Zhenhai Liu, Shiliang Chen, Qinyi Wang, Tongtong Zhu, Lunche Wang, and Lizhe Wang
Earth Syst. Sci. Data, 16, 1689–1701, https://doi.org/10.5194/essd-16-1689-2024, https://doi.org/10.5194/essd-16-1689-2024, 2024
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Utilizing satellite remote sensing data, we established a multi-season rice calendar dataset named ChinaRiceCalendar. It exhibits strong alignment with field observations collected by agricultural meteorological stations across China. ChinaRiceCalendar stands as a reliable dataset for investigating and optimizing the spatiotemporal dynamics of rice phenology in China, particularly in the context of climate and land use changes.
Giulia Ronchetti, Luigi Nisini Scacchiafichi, Lorenzo Seguini, Iacopo Cerrani, and Marijn van der Velde
Earth Syst. Sci. Data, 16, 1623–1649, https://doi.org/10.5194/essd-16-1623-2024, https://doi.org/10.5194/essd-16-1623-2024, 2024
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We present a dataset of EU-wide harmonized subnational crop area, production, and yield statistics with information on data sources, processing steps, missing and derived data, and quality checks. Statistical records (344 282) collected from 1975 to 2020 for soft and durum wheat, winter and spring barley, grain maize, sunflower, and sugar beet were aligned with the EUROSTAT crop legend and the 2016 territorial classification for 961 regions. Time series have a median length of 21 years.
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
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This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Qiangqiang Sun, Ping Zhang, Xin Jiao, Xin Lin, Wenkai Duan, Su Ma, Qidi Pan, Lu Chen, Yongxiang Zhang, Shucheng You, Shunxi Liu, Jinmin Hao, Hong Li, and Danfeng Sun
Earth Syst. Sci. Data, 16, 1333–1351, https://doi.org/10.5194/essd-16-1333-2024, https://doi.org/10.5194/essd-16-1333-2024, 2024
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To provide multifaceted changes under climate change and anthropogenic impacts, we estimated monthly vegetation and soil fractions in 2001–2022, providing an accurate estimate of surface heterogeneous composition, better than vegetation index and vegetation continuous-field products. We find a greening trend on Earth except for the tropics. A combination of interactive changes in vegetation and soil can be adopted as a valuable measurement of climate change and anthropogenic impacts.
Kai Cheng, Yuling Chen, Tianyu Xiang, Haitao Yang, Weiyan Liu, Yu Ren, Hongcan Guan, Tianyu Hu, Qin Ma, and Qinghua Guo
Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024, https://doi.org/10.5194/essd-16-803-2024, 2024
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To quantify forest carbon stock and its future potential accurately, we generated a 30 m resolution forest age map for China in 2020 using multisource remote sensing datasets based on machine learning and time series analysis approaches. Validation with independent field samples indicated that the mapped forest age had an R2 of 0.51--0.63. Nationally, the average forest age is 56.1 years (standard deviation of 32.7 years).
Wolfgang Alexander Obermeier, Clemens Schwingshackl, Ana Bastos, Giulia Conchedda, Thomas Gasser, Giacomo Grassi, Richard A. Houghton, Francesco Nicola Tubiello, Stephen Sitch, and Julia Pongratz
Earth Syst. Sci. Data, 16, 605–645, https://doi.org/10.5194/essd-16-605-2024, https://doi.org/10.5194/essd-16-605-2024, 2024
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We provide and compare country-level estimates of land-use CO2 fluxes from a variety and large number of models, bottom-up estimates, and country reports for the period 1950–2021. Although net fluxes are small in many countries, they are often composed of large compensating emissions and removals. In many countries, the estimates agree well once their individual characteristics are accounted for, but in other countries, including some of the largest emitters, substantial uncertainties exist.
Cited articles
Anderson, R. G., Canadell, J. G., Randerson, J. T., Jackson, R. B., Hungate, B. A., Baldocchi, D. D., Ban-Weiss, G. A., Bonan, G. B., Caldeira, K., Cao, L., Diffenbaugh, N. S., Gurney, K. R., Kueppers, L. M., Law, B. E., Luyssaert, S., and O'Halloran, T. L.: Biophysical considerations in forestry for climate protection, Front. Ecol. Environ., 9, 174–182, https://doi.org/10.1890/090179, 2011.
Ardö, J., Mölder, M., El-Tahir, B. A., and Elkhidir, H. A. M.: Seasonal variation of carbon fluxes in a sparse savanna in semi arid Sudan, Carbon Balance Manage, 3, 7, https://doi.org/10.1186/1750-0680-3-7, 2008.
Arya, L. M. and Paris, J. F.: A Physicoempirical Model to Predict the Soil Moisture Characteristic from Particle-Size Distribution and Bulk Density Data, Soil Sci. Soc. Am. J., 45, 1023–1030, https://doi.org/10.2136/sssaj1981.03615995004500060004x, 1981.
Bagley, J. E., Kueppers, L. M., Billesbach, D. P., Williams, I. N., Biraud, S. C., and Torn, M. S.: The influence of land cover on surface energy partitioning and evaporative fraction regimes in the U.S. Southern Great Plains, J. Geophys. Res.-Atmos., 122, 5793–5807, https://doi.org/10.1002/2017JD026740, 2017.
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions, in: Progress in Photosynthesis Research, edited by: Biggins, J., Springer Netherlands, Dordrecht, 221–224, https://doi.org/10.1007/978-94-017-0519-6_48, 1987.
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and Wood, E. F.: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Sci Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214, 2018.
Berryman, E. M., Vanderhoof, M. K., Bradford, J. B., Hawbaker, T. J., Henne, P. D., Burns, S. P., Frank, J. M., Birdsey, R. A., and Ryan, M. G.: Estimating Soil Respiration in a Subalpine Landscape Using Point, Terrain, Climate, and Greenness Data, J. Geophys. Res.-Biogeo., 123, 3231–3249, https://doi.org/10.1029/2018JG004613, 2018.
Best, M. J., Abramowitz, G., Johnson, H. R., Pitman, A. J., Balsamo, G., Boone, A., Cuntz, M., Decharme, B., Dirmeyer, P. A., Dong, J., Ek, M., Guo, Z., Haverd, V., van den Hurk, B. J. J., Nearing, G. S., Pak, B., Peters-Lidard, C., Santanello, J. A., Stevens, L., and Vuichard, N.: The Plumbing of Land Surface Models: Benchmarking Model Performance, J. Hydrometeorol., 16, 1425–1442, https://doi.org/10.1175/JHM-D-14-0158.1, 2015.
Blyth, E., Gash, J., Lloyd, A., Pryor, M., Weedon, G. P., and Shuttleworth, J.: Evaluating the JULES Land Surface Model Energy Fluxes Using FLUXNET Data, J. Hydrometeorol., 11, 509–519, https://doi.org/10.1175/2009JHM1183.1, 2010.
Bonan, G. B.: Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests, Science, 320, 1444–1449, https://doi.org/10.1126/science.1155121, 2008.
Bonan, G. B., Levis, S., Kergoat, L., and Oleson, K. W.: Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models, Global Biogeochem. Cy., 16, 5-1–5-23, https://doi.org/10.1029/2000GB001360, 2002.
Brooke, J. K., Harlow, R. C., Scott, R. L., Best, M. J., Edwards, J. M., Thelen, J.-C., and Weeks, M.: Evaluating the Met Office Unified Model land surface temperature in Global Atmosphere/Land 3.1 (GA/L3.1), Global Atmosphere/Land 6.1 (GA/L6.1) and limited area 2.2 km configurations, Geosci. Model Dev., 12, 1703–1724, https://doi.org/10.5194/gmd-12-1703-2019, 2019.
Brutsaert, W.: Energy Budget and Related Methods, in: Evaporation into the Atmosphere, Springer Netherlands, Dordrecht, 209–230, https://doi.org/10.1007/978-94-017-1497-6_10, 1982.
Chu, H., Luo, X., Ouyang, Z., Chan, W. S., Dengel, S., Biraud, S. C., Torn, M. S., Metzger, S., Kumar, J., Arain, M. A., Arkebauer, T. J., Baldocchi, D., Bernacchi, C., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Bracho, R., Brown, S., Brunsell, N. A., Chen, J., Chen, X., Clark, K., Desai, A. R., Duman, T., Durden, D., Fares, S., Forbrich, I., Gamon, J. A., Gough, C. M., Griffis, T., Helbig, M., Hollinger, D., Humphreys, E., Ikawa, H., Iwata, H., Ju, Y., Knowles, J. F., Knox, S. H., Kobayashi, H., Kolb, T., Law, B., Lee, X., Litvak, M., Liu, H., Munger, J. W., Noormets, A., Novick, K., Oberbauer, S. F., Oechel, W., Oikawa, P., Papuga, S. A., Pendall, E., Prajapati, P., Prueger, J., Quinton, W. L., Richardson, A. D., Russell, E. S., Scott, R. L., Starr, G., Staebler, R., Stoy, P. C., Stuart-Haëntjens, E., Sonnentag, O., Sullivan, R. C., Suyker, A., Ueyama, M., Vargas, R., Wood, J. D., and Zona, D.: Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites, Agr. Forest Meteorol., 301–302, 108350, https://doi.org/10.1016/j.agrformet.2021.108350, 2021.
Collatz, G., Ribas-Carbo, M., and Berry, J.: Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants, Funct. Plant Biol., 19, 519, https://doi.org/10.1071/PP9920519, 1992.
Crow, W. T., Kumar, S. V., and Bolten, J. D.: On the utility of land surface models for agricultural drought monitoring, Hydrol. Earth Syst. Sci., 16, 3451–3460, https://doi.org/10.5194/hess-16-3451-2012, 2012.
Dai, Y., Zeng, X., Dickinson, R. E., Baker, I., Bonan, G. B., Bosilovich, M. G., Denning, A. S., Dirmeyer, P. A., Houser, P. R., Niu, G., Oleson, K. W., Schlosser, C. A., and Yang, Z.-L.: The Common Land Model, B. Am. Meteorol. Soc., 84, 1013–1024, https://doi.org/10.1175/BAMS-84-8-1013, 2003.
Dai, Y., Dickinson, R. E., and Wang, Y.-P.: A Two-Big-Leaf Model for Canopy Temperature, Photosynthesis, and Stomatal Conductance, J. Climate, 17, 2281–2299, https://doi.org/10.1175/1520-0442(2004)017<2281:ATMFCT>2.0.CO;2, 2004.
Dai, Y., Shangguan, W., Wei, N., Xin, Q., Yuan, H., Zhang, S., Liu, S., Lu, X., Wang, D., and Yan, F.: A review of the global soil property maps for Earth system models, SOIL, 5, 137–158, https://doi.org/10.5194/soil-5-137-2019, 2019a.
Dai, Y., Yuan, H., Xin, Q., Wang, D., Shangguan, W., Zhang, S., Liu, S., and Wei, N.: Different representations of canopy structure – A large source of uncertainty in global land surface modeling, Agr. Forest Meteorol., 269–270, 119–135, https://doi.org/10.1016/j.agrformet.2019.02.006, 2019b.
Dirmeyer, P. A.: The terrestrial segment of soil moisture-climate coupling, Geophys. Res. Lett., 38, L16702, https://doi.org/10.1029/2011GL048268, 2011.
Dy, C. Y. and Fung, J. C. H.: Updated global soil map for the Weather Research and Forecasting model and soil moisture initialization for the Noah land surface model, J. Geophys. Res.-Atmos., 121, 8777–8800, https://doi.org/10.1002/2015JD024558, 2016.
Entekhabi, D., Rodriguez-Iturbe, I., and Castelli, F.: Mutual interaction of soil moisture state and atmospheric processes, J. Hydrol., 184, 3–17, https://doi.org/10.1016/0022-1694(95)02965-6, 1996.
Essery, R.: Large-scale simulations of snow albedo masking by forests, Geophys. Res. Lett., 40, 5521–5525, https://doi.org/10.1002/grl.51008, 2013.
Farquhar, G. D., Von Caemmerer, S., and Berry, J. A.: A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149, 78–90, https://doi.org/10.1007/BF00386231, 1980.
Forzieri, G., Miralles, D. G., Ciais, P., Alkama, R., Ryu, Y., Duveiller, G., Zhang, K., Robertson, E., Kautz, M., Martens, B., Jiang, C., Arneth, A., Georgievski, G., Li, W., Ceccherini, G., Anthoni, P., Lawrence, P., Wiltshire, A., Pongratz, J., Piao, S., Sitch, S., Goll, D. S., Arora, V. K., Lienert, S., Lombardozzi, D., Kato, E., Nabel, J. E. M. S., Tian, H., Friedlingstein, P., and Cescatti, A.: Increased control of vegetation on global terrestrial energy fluxes, Nat. Clim. Change, 10, 356–362, https://doi.org/10.1038/s41558-020-0717-0, 2020.
Harper, A. B., Williams, K. E., McGuire, P. C., Duran Rojas, M. C., Hemming, D., Verhoef, A., Huntingford, C., Rowland, L., Marthews, T., Breder Eller, C., Mathison, C., Nobrega, R. L. B., Gedney, N., Vidale, P. L., Otu-Larbi, F., Pandey, D., Garrigues, S., Wright, A., Slevin, D., De Kauwe, M. G., Blyth, E., Ardö, J., Black, A., Bonal, D., Buchmann, N., Burban, B., Fuchs, K., de Grandcourt, A., Mammarella, I., Merbold, L., Montagnani, L., Nouvellon, Y., Restrepo-Coupe, N., and Wohlfahrt, G.: Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements, Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, 2021.
Harper, K. L., Lamarche, C., Hartley, A., Peylin, P., Ottlé, C., Bastrikov, V., San Martín, R., Bohnenstengel, S. I., Kirches, G., Boettcher, M., Shevchuk, R., Brockmann, C., and Defourny, P.: A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models, Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, 2023.
Humphrey, V., Berg, A., Ciais, P., Gentine, P., Jung, M., Reichstein, M., Seneviratne, S. I., and Frankenberg, C.: Soil moisture–atmosphere feedback dominates land carbon uptake variability, Nature, 592, 65–69, https://doi.org/10.1038/s41586-021-03325-5, 2021.
Lawrence, P. J. and Chase, T. N.: Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0), J. Geophys. Res., 112, G01023, https://doi.org/10.1029/2006JG000168, 2007.
Li, J., Chen, F., Zhang, G., Barlage, M., Gan, Y., Xin, Y., and Wang, C.: Impacts of Land Cover and Soil Texture Uncertainty on Land Model Simulations Over the Central Tibetan Plateau, J. Adv. Model. Earth Sy., 10, 2121–2146, https://doi.org/10.1029/2018MS001377, 2018.
Li, J., Zhang, G., Chen, F., Peng, X., and Gan, Y.: Evaluation of Land Surface Subprocesses and Their Impacts on Model Performance With Global Flux Data, J. Adv. Model. Earth Sy., 11, 1329–1348, https://doi.org/10.1029/2018MS001606, 2019.
Lin, W., Yuan, H., Dong, W., Zhang, S., Liu, S., Wei, N., Lu, X., Wei, Z., Hu, Y., and Dai, Y.: Reprocessed MODIS Version 6.1 Leaf Area Index Dataset and Its Evaluation for Land Surface and Climate Modeling, Remote Sens., 15, 1780, https://doi.org/10.3390/rs15071780, 2023.
Mariotti, A., Ruti, P. M., and Rixen, M.: Progress in subseasonal to seasonal prediction through a joint weather and climate community effort, npj Clim. Atmos. Sci., 1, 4, https://doi.org/10.1038/s41612-018-0014-z, 2018.
Melton, J. R., Arora, V. K., Wisernig-Cojoc, E., Seiler, C., Fortier, M., Chan, E., and Teckentrup, L.: CLASSIC v1.0: the open-source community successor to the Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM) – Part 1: Model framework and site-level performance, Geosci. Model Dev., 13, 2825–2850, https://doi.org/10.5194/gmd-13-2825-2020, 2020.
Ménard, C. B., Ikonen, J., Rautiainen, K., Aurela, M., Arslan, A. N., and Pulliainen, J.: Effects of Meteorological and Ancillary Data, Temporal Averaging, and Evaluation Methods on Model Performance and Uncertainty in a Land Surface Model, J. Hydrometeorol., 16, 2559–2576, https://doi.org/10.1175/JHM-D-15-0013.1, 2015.
Minasny, B. and McBratney, A. B.: Estimating the Water Retention Shape Parameter from Sand and Clay Content, Soil Sci. Soc. Am. J., 71, 1105–1110, https://doi.org/10.2136/sssaj2006.0298N, 2007.
Pastorello, G., Trotta, C., and Canfora, E.: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data, Sci. Data, 7, 225, https://doi.org/10.1038/s41597-021-00851-9, 2020.
Pitman, A. J.: The evolution of, and revolution in, land surface schemes designed for climate models, Int. J. Climatol., 23, 479–510, https://doi.org/10.1002/joc.893, 2003.
Poulter, B., Ciais, P., Hodson, E., Lischke, H., Maignan, F., Plummer, S., and Zimmermann, N. E.: Plant functional type mapping for earth system models, Geosci. Model Dev., 4, 993–1010, https://doi.org/10.5194/gmd-4-993-2011, 2011.
Purdy, A. J., Fisher, J. B., Goulden, M. L., and Famiglietti, J. S.: Ground heat flux: An analytical review of 6 models evaluated at 88 sites and globally, J. Geophys. Res.-Biogeo., 121, 3045–3059, https://doi.org/10.1002/2016JG003591, 2016.
Shangguan, W., Dai, Y., Duan, Q., Liu, B., and Yuan, H.: A global soil data set for earth system modeling, J. Adv. Model. Earth Sy., 6, 249–263, https://doi.org/10.1002/2013MS000293, 2014.
Shi, J. and Yuan, H.: Mbnl1197/Flux-tower-attribute-for-LSM: Flux-tower-attribute-for-LSM (V3.0), Zenodo [code], https://doi.org/10.5281/zenodo.13684992, 2024.
Shi, J., Yuan, H., Lin, W., Dong, W., amd Dai, Y.: A flux tower site attribute dataset intended for land surface modeling, Zenodo [data set], https://doi.org/10.5281/zenodo.12596218, 2024.
Stevens, D., Miranda, P. M. A., Orth, R., Boussetta, S., Balsamo, G., and Dutra, E.: Sensitivity of Surface Fluxes in the ECMWF Land Surface Model to the Remotely Sensed Leaf Area Index and Root Distribution: Evaluation with Tower Flux Data, Atmosphere, 11, 1362, https://doi.org/10.3390/atmos11121362, 2020.
Still, C. J., Berry, J. A., Collatz, G. J., and DeFries, R. S.: Global distribution of C 3 and C 4 vegetation: Carbon cycle implications, Global Biogeochem. Cy., 17, 6-1–6-14, https://doi.org/10.1029/2001GB001807, 2003.
Stöckli, R., Lawrence, D. M., Bonan, G. B., Denning, A. S., and Running, S. W.: Use of FLUXNET in the Community Land Model development, J. Geophys. Res., 113, G01025, https://doi.org/10.1029/2007JG000562, 2008.
Swenson, S. C., Burns, S. P., and Lawrence, D. M.: The Impact of Biomass Heat Storage on the Canopy Energy Balance and Atmospheric Stability in the Community Land Model, J. Adv. Model Earth Sy., 11, 83–98, https://doi.org/10.1029/2018MS001476, 2019.
Ukkola, A. M., De Kauwe, M. G., Pitman, A. J., Best, M. J., Abramowitz, G., Haverd, V., Decker, M., and Haughton, N.: Land surface models systematically overestimate the intensity, duration and magnitude of seasonal-scale evaporative droughts, Environ. Res. Lett., 11, 104012, https://doi.org/10.1088/1748-9326/11/10/104012, 2016a.
Ukkola, A. M., Pitman, A. J., Decker, M., De Kauwe, M. G., Abramowitz, G., Kala, J., and Wang, Y.-P.: Modelling evapotranspiration during precipitation deficits: identifying critical processes in a land surface model, Hydrol. Earth Syst. Sci., 20, 2403–2419, https://doi.org/10.5194/hess-20-2403-2016, 2016b.
Ukkola, A. M., Haughton, N., De Kauwe, M. G., Abramowitz, G., and Pitman, A. J.: FluxnetLSM R package (v1.0): a community tool for processing FLUXNET data for use in land surface modelling, Geosci. Model Dev., 10, 3379–3390, https://doi.org/10.5194/gmd-10-3379-2017, 2017.
Ukkola, A. M., Abramowitz, G., and De Kauwe, M. G.: A flux tower dataset tailored for land model evaluation, Earth Syst. Sci. Data, 14, 449–461, https://doi.org/10.5194/essd-14-449-2022, 2022.
Williams, I. N. and Torn, M. S.: Vegetation controls on surface heat flux partitioning, and land-atmosphere coupling, Geophys. Res. Lett., 42, 9416–9424, https://doi.org/10.1002/2015GL066305, 2015.
Williams, M., Richardson, A. D., Reichstein, M., Stoy, P. C., Peylin, P., Verbeeck, H., Carvalhais, N., Jung, M., Hollinger, D. Y., Kattge, J., Leuning, R., Luo, Y., Tomelleri, E., Trudinger, C. M., and Wang, Y.-P.: Improving land surface models with FLUXNET data, Biogeosciences, 6, 1341–1359, https://doi.org/10.5194/bg-6-1341-2009, 2009.
Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier, P., Bernhofer, C., Ceulemans, R., Dolman, H., Field, C., Grelle, A., Ibrom, A., Law, B. E., Kowalski, A., Meyers, T., Moncrieff, J., Monson, R., Oechel, W., Tenhunen, J., Valentini, R., and Verma, S.: Energy balance closure at FLUXNET sites, Agr. Forest Meteorol., 113, 223–243, https://doi.org/10.1016/S0168-1923(02)00109-0, 2002.
Yuan, H., Dai, Y., Xiao, Z., Ji, D., and Shangguan, W.: Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling, Remote Sens. Environ., 115, 1171–1187, https://doi.org/10.1016/j.rse.2011.01.001, 2011.
Yuan, H., Dai, Y., Dickinson, R. E., Pinty, B., Shangguan, W., Zhang, S., Wang, L., and Zhu, S.: Reexamination and further development of two-stream canopy radiative transfer models for global land modeling, J. Adv. Model Earth Sy., 9, 113–129, https://doi.org/10.1002/2016MS000773, 2017.
Zeng, X. and Dickinson, R. E.: Effect of Surface Sublayer on Surface Skin Temperature and Fluxes, J. Climate, 11, 537–550, https://doi.org/10.1175/1520-0442(1998)011<0537:EOSSOS>2.0.CO;2, 1998.
Zhang, X., Dai, Y., Cui, H., Dickinson, R. E., Zhu, S., Wei, N., Yan, B., Yuan, H., Shangguan, W., Wang, L., and Fu, W.: Evaluating common land model energy fluxes using FLUXNET data, Adv. Atmos. Sci., 34, 1035–1046, https://doi.org/10.1007/s00376-017-6251-y, 2017.
Short summary
Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally, with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Flux tower data are widely recognized as benchmarking data for land surface models, but...
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