Articles | Volume 12, issue 3
https://doi.org/10.5194/essd-12-1913-2020
© Author(s) 2020. 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-12-1913-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
28 Aug 2020
Data description paper |
| 28 Aug 2020
| 28 Aug 2020
A cultivated planet in 2010 – Part 1: The global synergy cropland map
Miao Lu
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
Wenbin Wu
CORRESPONDING AUTHOR
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
Liangzhi You
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
International Food Policy Research Institute (IFPRI), Washington, DC,
20005-3915, USA
Linda See
International Institute for Applied Systems Analysis, ESM, Laxenburg,
2361, Austria
Steffen Fritz
International Institute for Applied Systems Analysis, ESM, Laxenburg,
2361, Austria
Qiangyi Yu
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
Yanbing Wei
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
Di Chen
Institute of Environment and Sustainable Development in Agriculture,
Chinese Academy of Agricultural Sciences, Beijing, 100081, China
Peng Yang
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
Bing Xue
School of Engineering and Computer Science, Victoria University of
Wellington, Wellington, 6140, New Zealand
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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.
Zihui Zhang, Lang Xia, Fen Zhao, Yue Gu, Jing Yang, Yan Zha, Shangrong Wu, and Peng Yang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-516, https://doi.org/10.5194/essd-2024-516, 2025
Revised manuscript under review for ESSD
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We utilized multi-source data and a deep learning model to explore the annual mapping of rice for Northeast China from 1985 to 2023. First, a rice training dataset comprising 155 images was created. Then, we developed the annual result enhancement (ARE) method to diminish the impact of the limited training sample. In comparison to traditional rice mapping methods, the accuracy of results obtained using the ARE method is significantly improved.
Yuanxin Pan, Grzegorz Kłopotek, Laura Crocetti, Rudi Weinacker, Tobias Sturn, Linda See, Galina Dick, Gregor Möller, Markus Rothacher, Ian McCallum, Vicente Navarro, and Benedikt Soja
Atmos. Meas. Tech., 17, 4303–4316, https://doi.org/10.5194/amt-17-4303-2024, https://doi.org/10.5194/amt-17-4303-2024, 2024
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Crowdsourced smartphone GNSS data were processed with a dedicated data processing pipeline and could produce millimeter-level accurate estimates of zenith total delay (ZTD) – a critical atmospheric variable. This breakthrough not only demonstrates the feasibility of using ubiquitous devices for high-precision atmospheric monitoring but also underscores the potential for a global, cost-effective tropospheric monitoring network.
Kristof Van Tricht, Jeroen Degerickx, Sven Gilliams, Daniele Zanaga, Marjorie Battude, Alex Grosu, Joost Brombacher, Myroslava Lesiv, Juan Carlos Laso Bayas, Santosh Karanam, Steffen Fritz, Inbal Becker-Reshef, Belén Franch, Bertran Mollà-Bononad, Hendrik Boogaard, Arun Kumar Pratihast, Benjamin Koetz, and Zoltan Szantoi
Earth Syst. Sci. Data, 15, 5491–5515, https://doi.org/10.5194/essd-15-5491-2023, https://doi.org/10.5194/essd-15-5491-2023, 2023
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WorldCereal is a global mapping system that addresses food security challenges. It provides seasonal updates on crop areas and irrigation practices, enabling informed decision-making for sustainable agriculture. Our global products offer insights into temporary crop extent, seasonal crop type maps, and seasonal irrigation patterns. WorldCereal is an open-source tool that utilizes space-based technologies, revolutionizing global agricultural mapping.
Yating Ru, Brian Blankespoor, Ulrike Wood-Sichra, Timothy S. Thomas, Liangzhi You, and Erwin Kalvelagen
Earth Syst. Sci. Data, 15, 1357–1387, https://doi.org/10.5194/essd-15-1357-2023, https://doi.org/10.5194/essd-15-1357-2023, 2023
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Economic statistics are frequently produced at an administrative level that lacks detail to examine development patterns and the exposure to natural hazards. This paper disaggregates national and subnational administrative statistics of agricultural GDP into a global dataset at the local level using satellite-derived indicators. As an illustration, the paper estimates that the exposure of areas with extreme drought to agricultural GDP is USD 432 billion, where nearly 1.2 billion people live.
Hanqin Tian, Zihao Bian, Hao Shi, Xiaoyu Qin, Naiqing Pan, Chaoqun Lu, Shufen Pan, Francesco N. Tubiello, Jinfeng Chang, Giulia Conchedda, Junguo Liu, Nathaniel Mueller, Kazuya Nishina, Rongting Xu, Jia Yang, Liangzhi You, and Bowen Zhang
Earth Syst. Sci. Data, 14, 4551–4568, https://doi.org/10.5194/essd-14-4551-2022, https://doi.org/10.5194/essd-14-4551-2022, 2022
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Nitrogen is one of the critical nutrients for growth. Evaluating the change in nitrogen inputs due to human activity is necessary for nutrient management and pollution control. In this study, we generated a historical dataset of nitrogen input to land at the global scale. This dataset consists of nitrogen fertilizer, manure, and atmospheric deposition inputs to cropland, pasture, and rangeland at high resolution from 1860 to 2019.
Qiangyi Yu, Liangzhi You, Ulrike Wood-Sichra, Yating Ru, Alison K. B. Joglekar, Steffen Fritz, Wei Xiong, Miao Lu, Wenbin Wu, and Peng Yang
Earth Syst. Sci. Data, 12, 3545–3572, https://doi.org/10.5194/essd-12-3545-2020, https://doi.org/10.5194/essd-12-3545-2020, 2020
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SPAM makes plausible estimates of crop distribution within disaggregated units. It moves the data from coarser units such as countries and provinces to finer units such as grid cells and creates a global gridscape at the confluence between earth and agricultural-production systems. It improves spatial understanding of crop production systems and allows policymakers to better target agricultural- and rural-development policies for increasing food security with minimal environmental impacts.
Michele Ferri, Uta Wehn, Linda See, Martina Monego, and Steffen Fritz
Hydrol. Earth Syst. Sci., 24, 5781–5798, https://doi.org/10.5194/hess-24-5781-2020, https://doi.org/10.5194/hess-24-5781-2020, 2020
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As part of the flood risk management strategy of the
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C. C. Fonte, L. See, J. C. Laso-Bayas, M. Lesiv, and S. Fritz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 669–674, https://doi.org/10.5194/isprs-annals-V-3-2020-669-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-669-2020, 2020
Michele Ferri, Uta Wehn, Linda See, and Steffen Fritz
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-627, https://doi.org/10.5194/hess-2019-627, 2019
Manuscript not accepted for further review
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Citizen observatories involve citizens in making environmental observations that help to inform the decision making of local authorities. Although citizen observatories can generate many benefits, they also have an associated cost. In this paper, we undertook a cost-benefit analysis of a citizen observatory on flooding in northern Italy. The results show that the benefits outweigh the costs by 2 to 1. Implementation of other citizen observatories could help to reduce flood risk in the future.
C. C. Fonte, L. See, M. Lesiv, and S. Fritz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1213–1220, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1213-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1213-2019, 2019
O. Danylo, I. Moorthy, T. Sturn, L. See, J.-C. Laso Bayas, D. Domian, D. Fraisl, C. Giovando, B. Girardot, R. Kapur, P.-P. Matthieu, and S. Fritz
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4, 27–32, https://doi.org/10.5194/isprs-annals-IV-4-27-2018, https://doi.org/10.5194/isprs-annals-IV-4-27-2018, 2018
C. C. Fonte, M. Minghini, V. Antoniou, J. Patriarca, and L. See
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 209–215, https://doi.org/10.5194/isprs-archives-XLII-4-209-2018, https://doi.org/10.5194/isprs-archives-XLII-4-209-2018, 2018
B. Liu, Y. Shi, Y. Duan, and W. Wu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1023–1028, https://doi.org/10.5194/isprs-archives-XLII-3-1023-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1023-2018, 2018
Y. Wei, M. Lu, and W. Wu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1863–1870, https://doi.org/10.5194/isprs-archives-XLII-3-1863-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1863-2018, 2018
Myroslava Lesiv, Linda See, Juan Carlos Laso Bayas, Tobias Sturn, Dmitry Schepaschenko, Matthias Karner, Inian Moorthy, Ian McCallum, and Steffen Fritz
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2018-13, https://doi.org/10.5194/essd-2018-13, 2018
Preprint withdrawn
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Kazi Farzan Ahmed, Guiling Wang, Liangzhi You, and Miao Yu
Earth Syst. Dynam., 7, 151–165, https://doi.org/10.5194/esd-7-151-2016, https://doi.org/10.5194/esd-7-151-2016, 2016
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A prototype model LandPro was developed to study climate change impact on land use in West Africa. LandPro considers climate and socioeconomic factors in projecting anthropogenic future land use change (LULCC). The model projections reflect that relative impact of climate change on LULCC in West Africa is region dependent. Results from scenario analysis suggest that science-informed decision-making by the farmers in agricultural land use can potentially reduce crop area expansion in the region.
I. McCallum, O. Franklin, E. Moltchanova, L. Merbold, C. Schmullius, A. Shvidenko, D. Schepaschenko, and S. Fritz
Biogeosciences, 10, 6577–6590, https://doi.org/10.5194/bg-10-6577-2013, https://doi.org/10.5194/bg-10-6577-2013, 2013
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GloUCP: a global 1 km spatially continuous urban canopy parameters for the WRF model
CCD-Rice: a long-term paddy rice distribution dataset in China at 30 m resolution
U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
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Statistical Atlas of European Agriculture: Gridded Data from the Agricultural Census 2020 and the Spatial Distribution of CAP Contextual Indicators
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Zia Mehrabi, Kaitai Tong, Julie Fortin, Radost Stanimirova, Mark Friedl, and Navin Ramankutty
Earth Syst. Sci. Data, 17, 3473–3496, https://doi.org/10.5194/essd-17-3473-2025, https://doi.org/10.5194/essd-17-3473-2025, 2025
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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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Wetlands are responsible for about a third of global emissions of methane, a potent greenhouse gas. We have developed the Global Inundation Extent from Multi-Satellites-MethaneCentric (GIEMS-MC) dataset to represent the dynamics of wetland extent on a global scale (0.25° × 0.25° resolution, monthly time step). This updated resource combines satellite data and existing wetland databases, covering 1992 to 2020. Consistent maps of other methane-emitting surface waters (lakes, rivers, reservoirs, rice paddies) are also provided.
Binghong Han, Jian Bi, Shengli Tao, Tong Yang, Yongli Tang, Mengshuai Ge, Hao Wang, Zhenong Jin, Jinwei Dong, Zhibiao Nan, and Jin-Sheng He
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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The Tibetan Plateau is an important pastoral area where cultivated pastures play an increasingly important role. However, little is known about the spatial distribution of the cultivated pastures due to the difficulty in distinguishing them from natural grasslands with remote sensing. For the first time, we have mapped the cultivated pastures on the plateau at a resolution of 30 m with decent accuracy. This dataset is valuable to scientists, policymakers, conservationists, and pastoralists.
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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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.
Mauro Marty, Livia Piermattei, Lars T. Waser, and Christian Ginzler
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-428, https://doi.org/10.5194/essd-2024-428, 2025
Preprint under review for ESSD
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Millions of aerial photographs represent an enormous, resource for geoscientists. In this study, we used freely available historical stereo images covering Switzerland, allowing us to derive four countrywide DSMs at a 1 m spatial resolution across four epochs. Our DSMs achieved sub-metric accuracy compared to reference data and high image matching completeness, demonstrating the feasibility of capturing continuous surface change at a high spatial resolution over different land cover classes.
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.
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 117–134, https://doi.org/10.5194/essd-17-117-2025, https://doi.org/10.5194/essd-17-117-2025, 2025
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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.
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.
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Short summary
Global cropland distribution is critical for agricultural monitoring and food security. We propose a new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution. SASAM is based on the fusion of multiple existing cropland maps and multilevel statistics of cropland area, which is independent of training samples. The synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics.
Global cropland distribution is critical for agricultural monitoring and food security. We...
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