Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-3045-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-3045-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
28 Jun 2024
Data description paper |
| 28 Jun 2024
| 28 Jun 2024
Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset
Yavar Pourmohamad
Department of Civil Engineering, Boise State University, Boise, ID, USA
Department of Computer Science, Boise State University, Boise, ID, USA
John T. Abatzoglou
Management of Complex Systems Department, University of California, Merced, CA, USA
Erin J. Belval
USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO, USA
Erica Fleishman
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA
Karen Short
USDA Forest Service, Rocky Mountain Research Station, Missoula, Montana, USA
Matthew C. Reeves
USDA Forest Service, Rocky Mountain Research Station, Missoula, Montana, USA
Nicholas Nauslar
Storm Prediction Center, National Weather Service, Boise, ID, USA
Philip E. Higuera
Department of Ecosystem and Conservation Sciences, University of Montana, Missoula, MT, USA
Eric Henderson
Department of Computer Science, Boise State University, Boise, ID, USA
Sawyer Ball
Department of Computer Science, Boise State University, Boise, ID, USA
Amir AghaKouchak
Department of Civil and Environmental Engineering, University of California, Irvine, CA, USA
Jeffrey P. Prestemon
USDA Forest Service, Southern Research Station, Research Triangle Park, NC, USA
Julia Olszewski
USDA Forest Service, Rocky Mountain Research Station, Missoula, Montana, USA
Mojtaba Sadegh
CORRESPONDING AUTHOR
Department of Civil Engineering, Boise State University, Boise, ID, USA
United Nations University Institute for Water, Environment and Health, United Nations University, Hamilton, ON, Canada
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A. Park Williams, Caroline S. Juang, and Karen C. Short
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-366, https://doi.org/10.5194/essd-2025-366, 2025
Preprint under review for ESSD
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The WUMI2024a represents more than 22,400 large (≥1 km2) wildfires in the western United States from 1984 through 2024, including maps of fire perimeters and areas burned. It was compiled from seven government datasets and quality controlled. This dataset will aid research on the causes and effects of wildfire in a changing world.
A. Park Williams, Winslow D. Hansen, Caroline S. Juang, John T. Abatzoglou, Volker C. Radeloff, Bowen Wang, Jazlynn Hall, Jatan Buch, and Gavin D. Madakumbura
EGUsphere, https://doi.org/10.5194/egusphere-2025-2934, https://doi.org/10.5194/egusphere-2025-2934, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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The new WULFFSS is a monthly gridded forest-fire model to simulate forest fires across the western United States in response to vegetation, topographic, anthropogenic, and climate factors. This effort is motivated by the ten-fold increase in western U.S. annual forest area burned over the past 40 years. The WULFFSS is highly skillful, accounting for over 80 % of the observed variability in annual forest-fire area and capturing observed spatial, intra-annual variations, and trends.
Hossein Abbasizadeh, Petr Maca, Martin Hanel, Mads Troldborg, and Amir AghaKouchak
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-297, https://doi.org/10.5194/hess-2024-297, 2024
Revised manuscript accepted for HESS
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Here, we represented catchments as networks of variables connected by cause-and-effect relationships. By comparing the performance of statistical and machine learning methods with and without incorporating causal information to predict runoff properties, we showed that causal information can enhance models' robustness by reducing accuracy drop between training and testing phases, improving the model's interpretability, and mitigating overfitting issues, especially with small training samples.
Bhupinderjeet Singh, Mingliang Liu, John Abatzoglou, Jennifer Adam, and Kirti Rajagopalan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2284, https://doi.org/10.5194/egusphere-2024-2284, 2024
Preprint archived
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Hydrology models rely on simplistic static approaches to precipitation phase partitioning. We evaluate model skill changes for a suite of snow metrics by transitioning to a more accurate dynamic partitioning. We found that the transition resulted in a better match between modeled and observed metrics, with a 50 % reduction in model bias, emphasizing the need for the hydrological modeling community to adopt dynamic partitioning.
Matthew W. Jones, Douglas I. Kelley, Chantelle A. Burton, Francesca Di Giuseppe, Maria Lucia F. Barbosa, Esther Brambleby, Andrew J. Hartley, Anna Lombardi, Guilherme Mataveli, Joe R. McNorton, Fiona R. Spuler, Jakob B. Wessel, John T. Abatzoglou, Liana O. Anderson, Niels Andela, Sally Archibald, Dolors Armenteras, Eleanor Burke, Rachel Carmenta, Emilio Chuvieco, Hamish Clarke, Stefan H. Doerr, Paulo M. Fernandes, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Harris, Piyush Jain, Crystal A. Kolden, Tiina Kurvits, Seppe Lampe, Sarah Meier, Stacey New, Mark Parrington, Morgane M. G. Perron, Yuquan Qu, Natasha S. Ribeiro, Bambang H. Saharjo, Jesus San-Miguel-Ayanz, Jacquelyn K. Shuman, Veerachai Tanpipat, Guido R. van der Werf, Sander Veraverbeke, and Gavriil Xanthopoulos
Earth Syst. Sci. Data, 16, 3601–3685, https://doi.org/10.5194/essd-16-3601-2024, https://doi.org/10.5194/essd-16-3601-2024, 2024
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This inaugural State of Wildfires report catalogues extreme fires of the 2023–2024 fire season. For key events, we analyse their predictability and drivers and attribute them to climate change and land use. We provide a seasonal outlook and decadal projections. Key anomalies occurred in Canada, Greece, and western Amazonia, with other high-impact events catalogued worldwide. Climate change significantly increased the likelihood of extreme fires, and mitigation is required to lessen future risk.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
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As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Nadine Borduas-Dedekind, Karen C. Short, and Samuel P. Carlson
Earth Syst. Sci. Data, 15, 1437–1440, https://doi.org/10.5194/essd-15-1437-2023, https://doi.org/10.5194/essd-15-1437-2023, 2023
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This article describes the use of the open-discussion manuscript review process as an educational exercise for early career scientists.
Abby C. Lute, John Abatzoglou, and Timothy Link
Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, https://doi.org/10.5194/gmd-15-5045-2022, 2022
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We developed a snow model that can be used to quantify snowpack over large areas with a high degree of spatial detail. We ran the model over the western United States, creating a snow and climate dataset for three time periods. Compared to observations of snowpack, the model captured the key aspects of snow across time and space. The model and dataset will be useful in understanding historical and future changes in snowpack, with relevance to water resources, agriculture, and ecosystems.
Sofia Hallerbäck, Laurie S. Huning, Charlotte Love, Magnus Persson, Katarina Stensen, David Gustafsson, and Amir AghaKouchak
The Cryosphere, 16, 2493–2503, https://doi.org/10.5194/tc-16-2493-2022, https://doi.org/10.5194/tc-16-2493-2022, 2022
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Using unique data, some dating back to the 18th century, we show a significant trend in shorter ice duration, later freeze, and earlier break-up dates across Sweden. In recent observations, the mean ice durations have decreased by 11–28 d and the chance of years with an extremely short ice cover duration (less than 50 d) have increased by 800 %. Results show that even a 1 °C increase in air temperatures can result in a decrease in ice duration in Sweden of around 8–23 d.
Jianning Ren, Jennifer C. Adam, Jeffrey A. Hicke, Erin J. Hanan, Christina L. Tague, Mingliang Liu, Crystal A. Kolden, and John T. Abatzoglou
Hydrol. Earth Syst. Sci., 25, 4681–4699, https://doi.org/10.5194/hess-25-4681-2021, https://doi.org/10.5194/hess-25-4681-2021, 2021
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Mountain pine beetle outbreaks have caused widespread tree mortality. While some research shows that water yield increases after trees are killed, many others document no change or a decrease. The climatic and environmental mechanisms driving hydrologic response to tree mortality are not well understood. We demonstrated that the direction of hydrologic response is a function of multiple factors, so previous studies do not necessarily conflict with each other; they represent different conditions.
Faranak Tootoonchi, Jan Olaf Haerter, Olle Räty, Thomas Grabs, Mojtaba Sadegh, and Claudia Teutschbein
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-306, https://doi.org/10.5194/hess-2020-306, 2020
Preprint withdrawn
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The motive behind writing this paper is the growing interest in adopting copulas in hydroclimatic applications. We performed an
in-depth copula analysis on a case study in Sweden to show strength, significance, variability and non-stationarity of correlation between precipitation and temperature variables. As our final product, we illustrate a comprehensive decision support framework to support end users in adopting copulas for hydroclimatic applications.
Phu Nguyen, Mohammed Ombadi, Soroosh Sorooshian, Kuolin Hsu, Amir AghaKouchak, Dan Braithwaite, Hamed Ashouri, and Andrea Rose Thorstensen
Hydrol. Earth Syst. Sci., 22, 5801–5816, https://doi.org/10.5194/hess-22-5801-2018, https://doi.org/10.5194/hess-22-5801-2018, 2018
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The goal of this article is to first provide an overview of the available PERSIANN precipitation retrieval algorithms and their differences. We evaluate the products over CONUS at different spatial and temporal scales using CPC data. Daily scale is the finest temporal scale used for the evaluation over CONUS. We provide a comparison of the available products at a quasi-global scale. We highlight the strengths and limitations of the PERSIANN products.
Adam Luke, Brett F. Sanders, Kristen A. Goodrich, David L. Feldman, Danielle Boudreau, Ana Eguiarte, Kimberly Serrano, Abigail Reyes, Jochen E. Schubert, Amir AghaKouchak, Victoria Basolo, and Richard A. Matthew
Nat. Hazards Earth Syst. Sci., 18, 1097–1120, https://doi.org/10.5194/nhess-18-1097-2018, https://doi.org/10.5194/nhess-18-1097-2018, 2018
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In this study, engineers and social scientists explore opportunities for improving the utility of flood hazard maps through focus groups with end users. Focus groups revealed that end users preferred legends that describe flood intensity both quantitatively and with qualitative reference points, as well as flood scenario descriptions that describe the magnitude (rather than frequency) of the flood. Illustrations of pluvial flooding, or flooding caused directly by rainfall, were highly desired.
Carlos H. R. Lima, Amir AghaKouchak, and Upmanu Lall
Earth Syst. Dynam., 8, 1071–1091, https://doi.org/10.5194/esd-8-1071-2017, https://doi.org/10.5194/esd-8-1071-2017, 2017
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Floods are the main natural disaster in Brazil, causing substantial economic damage and loss of life. Here we seek to better understand the flood-generating mechanisms in the flood-prone Paraná River basin, including large-scale patterns of the ocean and atmospheric circulation. This study provides new insights for understanding causes of floods in the region and around the world and is a step forward to improve flood risk management, statistical assessments, and short-term flood forecasts.
A. AghaKouchak
Hydrol. Earth Syst. Sci., 18, 2485–2492, https://doi.org/10.5194/hess-18-2485-2014, https://doi.org/10.5194/hess-18-2485-2014, 2014
M. Sadegh and J. A. Vrugt
Hydrol. Earth Syst. Sci., 17, 4831–4850, https://doi.org/10.5194/hess-17-4831-2013, https://doi.org/10.5194/hess-17-4831-2013, 2013
A. Farahmand and A. AghaKouchak
Nat. Hazards Earth Syst. Sci., 13, 1259–1267, https://doi.org/10.5194/nhess-13-1259-2013, https://doi.org/10.5194/nhess-13-1259-2013, 2013
A. AghaKouchak, N. Nakhjiri, and E. Habib
Hydrol. Earth Syst. Sci., 17, 445–452, https://doi.org/10.5194/hess-17-445-2013, https://doi.org/10.5194/hess-17-445-2013, 2013
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Domain: ESSD – Land | Subject: Land Cover and Land Use
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CCD-Rice: a long-term paddy rice distribution dataset in China at 30 m resolution
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A large-scale image-text dataset benchmark for farmland segmentation
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Revised and updated geospatial monitoring of 21st century forest carbon fluxes
ChatEarthNet: a global-scale image–text dataset empowering vision–language geo-foundation models
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GMIE: a global maximum irrigation extent and central pivot irrigation system dataset derived via irrigation performance during drought stress and deep learning methods
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A Sentinel-2 machine learning dataset for tree species classification in Germany
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3D-GloBFP: the first global three-dimensional building footprint dataset
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Annual maps of forest and evergreen forest in the contiguous United States during 2015–2017 from analyses of PALSAR-2 and Landsat images
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A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data
Multidecadal satellite-derived Portuguese Burn Severity Atlas (1984–2022)
Annual time-series 1 km maps of crop area and types in the conterminous US (CropAT-US): cropping diversity changes during 1850–2021
Retrieval of dominant methane (CH4) emission sources, the first high-resolution (1–2 m) dataset of storage tanks of China in 2000–2021
A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types
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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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We present a global geospatial database of cropland and pastures for the year 2015. We built these data by fusing satellite-based land cover data with agricultural census data using machine learning. This database is an update to an earlier version representing the year 2000. It can be used to study issues such as land use, food security, climate change, and biodiversity loss. We provide a reproducible code base that can be used to easily update the product for future years.
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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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.
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.
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).
Cited articles
Abatzoglou, J. T.: Development of gridded surface meteorological data for ecological applications and modelling, Int. J. Climatol., 33, 121–131, 2013.
Abt, K. L., Butry, D. T., Prestemon, J. P., and Scranton, S.: Effect of fire prevention programs on accidental and incendiary wildfires on tribal lands in the United States, Int. J. Wildl. Fire, 24, 749–762, 2015.
Aldersley, A., Murray, S. J., and Cornell, S. E.: Global and regional analysis of climate and human drivers of wildfire, Sci. Total Environ., 409, 3472–3481, 2011.
Alizadeh, M. R., Abatzoglou, J. T., Luce, C. H., Adamowski, J. F., Farid, A., and Sadegh, M.: Warming enabled upslope advance in western US forest fires, P. Natl. Acad. Sci. USA, 118, e2009717118, https://doi.org/10.1073/pnas.2009717118, 2021.
Alizadeh, M. R., Abatzoglou, J. T., Adamowski, J., Modaresi Rad, A., AghaKouchak, A., Pausata, F. S. R., and Sadegh, M.: Elevation-dependent intensification of fire danger in the western United States, Nat. Commun., 14, 1773, https://doi.org/10.1038/s41467-023-37311-4, 2023.
Balch, J. K., Bradley, B. A., Abatzoglou, J. T., Chelsea Nagy, R., Fusco, E. J., and Mahood, A. L.: Human-started wildfires expand the fire niche across the United States, P. Natl. Acad. Sci. USA, 114, 2946–2951, https://doi.org/10.1073/pnas.1617394114, 2017.
Barros, A. M. G., Day, M. A., Preisler, H. K., Abatzoglou, J. T., Krawchuk, M. A., Houtman, R., and Ager, A. A.: Contrasting the role of human-and lightning-caused wildfires on future fire regimes on a Central Oregon landscape, Environ. Res. Lett., 16, 64081, https://doi.org/10.1088/1748-9326/ac03da, 2021.
Boise State University: FPA FOD-Attributes, Boise State University [data set], https://fpafod.boisestate.edu (last access: 20 June 2023), 2024.
Bowman, D. M. J. S., Balch, J. K., Artaxo, P., Bond, W. J., Carlson, J. M., Cochrane, M. A., D'Antonio, C. M., DeFries, R. S., Doyle, J. C., and Harrison, S. P.: Fire in the Earth system, Science, 324, 481–484, 2009.
Bowman, D. M. J. S., Balch, J., Artaxo, P., Bond, W. J., Cochrane, M. A., D'antonio, C. M., DeFries, R., Johnston, F. H., Keeley, J. E., and Krawchuk, M. A.: The human dimension of fire regimes on Earth, J. Biogeogr., 38, 2223–2236, 2011.
Butler, W. H., Monroe, A., and McCaffrey, S.: Collaborative implementation for ecological restoration on US public lands: implications for legal context, accountability, and adaptive management, Environ. Manage., 55, 564–577, 2015.
Chen, B. and Jin, Y.: Spatial patterns and drivers for wildfire ignitions in California, Environ. Res. Lett., 17, 055004, https://doi.org/10.1088/1748-9326/ac60da, 2022.
Chuvieco, E., Aguado, I., Salas, J., García, M., Yebra, M., and Oliva, P.: Satellite remote sensing contributions to wildland fire science and management, Curr. For. Reports, 6, 81–96, 2020.
Climate and Economic Justice Screening Tool: https://screeningtool.geoplatform.gov/, last access: 10 December 2023.
Cohen, J. D. and Deeming, J. E.: The National Fire Danger Rating System: Basic Equations (General Technical Report PSW-GTR-82), US Dep. Agric. For. Serv. Berkeley, CA, USA, 1985.
Colavito, M. M.: The role of science in the collaborative forest landscape restoration program, J. For., 115, 34–42, 2017.
Colorado Encyclopedia: East Troublesome Fire: https://coloradoencyclopedia.org/article/east-troublesome-fire, last access: 10 December 2023.
Dahal, D., Pastick, N. J., Boyte, S. P., Parajuli, S., Oimoen, M. J., and Megard, L. J.: Multi-Species Inference of Exotic Annual and Native Perennial Grasses in Rangelands of the Western United States Using Harmonized Landsat and Sentinel-2 Data, Remote Sens., 14, 807, https://doi.org/10.3390/rs14040807, 2022 (data available at: https://data.usgs.gov/datacatalog/data/USGS:61716970d34ea36449a77130, last access: 16 October 2023).
Dennison, P. E., Brewer, S. C., Arnold, J. D., and Moritz, M. A.: Large wildfire trends in the western United States, 1984–2011, Geophys. Res. Lett., 41, 2928–2933, 2014.
Dewitz, J.: National land cover database (NLCD) 2016 products, US Geol. Surv. data release, 10, P96HHBIE, https://doi.org/10.1016/j.rse.2021.112357, 2019.
Didan, K.: MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V061. NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD13A2.061, 2021.
Dissing, D. and Verbyla, D. L.: Spatial patterns of lightning strikes in interior Alaska and their relations to elevation and vegetation, Can. J. For. Res., 33, 770–782, 2003.
Douglas, J., Mills, T. J., Artly, D., Ashe, D., Bartuska, A., Black, R. L., Coloff, S., Cruz, J., Edrington, M., and Edwardson, J.: Review and update of the 1995 federal wildland fire management policy, US Dept. of the Interior; US Dept. of Agriculture, 2001.
Eidenshink, J., Schwind, B., Brewer, K., Zhu, Z.-L., Quayle, B., and Howard, S.: A project for monitoring trends in burn severity, Fire Ecol., 3, 3–21, 2007.
Finney, M. A., McHugh, C. W., Grenfell, I. C., Riley, K. L., and Short, K. C.: A simulation of probabilistic wildfire risk components for the continental United States, Stoch. Environ. Res. Risk Assess., 25, 973–1000, 2011.
Flanagan, B. E., Hallisey, E. J., Adams, E., and Lavery, A.: Measuring community vulnerability to natural and anthropogenic hazards: the Centers for Disease Control and Prevention's Social Vulnerability Index, J. Environ. Health, 80, 34–36, 2018 (data available at: https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html, last access: 16 October 2023).
Flannigan, M. D. and Wotton, B. M.: Lightning-ignited forest fires in northwestern Ontario, Can. J. For. Res., 21, 277–287, 1991.
Fowler, M., Rad, A. M., Utych, S., Adams, A., Alamian, S., Pierce, J., Dennison, P., Abatzoglou, J. T., AghaKouchak, A., and Montrose, L.: A dataset on human perception of and response to wildfire smoke, Sci. Data, 6, 1–10, 2019.
Fuquay, D. M., Baughman, R. G., Latham, D. J.: A model for predicting lightning fire ignition in wildland fuels, Intermountain Forest and Range Experiment Station, Forest Service, US, Wildfires, Paper 5, https://digitalcommons.usu.edu/govdocs_wfires/5 (last access: 16 October 2023), 1979.
Hély, C., Flannigan, M., Bergeron, Y., and McRae, D.: Role of vegetation and weather on fire behavior in the Canadian mixedwood boreal forest using two fire behavior prediction systems, Can. J. For. Res., 31, 430–441, 2001.
Hessilt, T. D., Abatzoglou, J. T., Chen, Y., Randerson, J. T., Scholten, R. C., Van Der Werf, G., and Veraverbeke, S.: Future increases in lightning ignition efficiency and wildfire occurrence expected from drier fuels in boreal forest ecosystems of western North America, Environ. Res. Lett., 17, 054008, https://doi.org/10.1088/1748-9326/ac6311, 2022.
Home Land Security: Fire Stations, https://opendata.arcgis.com/api/v3/datasets/0ccaf0c53b794eb8ac3d3de6afdb3286_0/downloads/data?format=shp&spatialRefId=3857&where=1%3D1, last access: 13 March 2023.
Houtman, R. M., Montgomery, C. A., Gagnon, A. R., Calkin, D. E., Dietterich, T. G., McGregor, S., and Crowley, M.: Allowing a wildfire to burn: estimating the effect on future fire suppression costs, Int. J. Wildl. Fire, 22, 871–882, 2013.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L. G.: Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ., 83, 195–213, 2002.
Iglesias, V., Balch, J. K., and Travis, W. R.: US fires became larger, more frequent, and more widespread in the 2000s, Sci. Adv., 8, eabc0020, https://doi.org/10.1126/sciadv.abc0020, 2022.
Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S., and Kiesecker, J.: Managing the middle: A shift in conservation priorities based on the global human modification gradient, Glob. Chang. Biol., 25, 811–826, 2019.
Khorshidi, M. S., Dennison, P. E., Nikoo, M. R., AghaKouchak, A., Luce, C. H., and Sadegh, M.: Increasing concurrence of wildfire drivers tripled megafire critical danger days in Southern California between 1982 and 2018, Environ. Res. Lett., 15, 104002, https://doi.org/10.1088/1748-9326/abae9e, 2020.
Kummu, M., Taka, M., and Guillaume, J. H. A.: Gridded global datasets for gross domestic product and Human Development Index over 1990–2015, Sci. Data, 5, 1–15, https://doi.org/10.5061/dryad.dk1j0, 2018.
Latham, D. and Williams, E.: Lightning and forest fires, in: Forest Fires, Elsevier, 375–418, 2001.
Maranghides, A., Link, E., Hawks, S., Wilson, M., Brewer, W., Brown, C., Vihnaneck, B., and Walton, W. D.: A Case Study of the Camp Fire–Fire Progression Timeline Appendix C, Community WUI Fire Hazard Evaluation Framework, 2021.
Mazzeo, G., De Santis, F., Falconieri, A., Filizzola, C., Lacava, T., Lanorte, A., Marchese, F., Nolè, G., Pergola, N., and Pietrapertosa, C.: Integrated Satellite System for Fire Detection and Prioritization, Remote Sens., 14, 335, https://doi.org/10.3390/rs14020335, 2022.
McGee, T., McFarlane, B., and Tymstra, C.: Wildfire: a Canadian perspective, in: Wildfire hazards, risks and disasters, Elsevier, 35–58, 2015.
Meisner, B. N., Chase, R. A., McCutchan, M. H., Mees, R., Benoit, J. W., Ly, B., Albright, D., Strauss, D., and Ferryman, T.: A lightning fire ignition assessment model, in: 12th Conference on Fire and Forest Meteorology'. Jekyll Island, GA, 172–178, 1993.
Miller, C. and Ager, A. A.: A review of recent advances in risk analysis for wildfire management, Int. J. Wildl. fire, 22, 1–14, 2012.
Modaresi Rad, A., Abatzoglou, J. T., Kreitler, J., Alizadeh, M. R., AghaKouchak, A., Hudyma, N., Nauslar, N. J., and Sadegh, M.: Human and infrastructure exposure to large wildfires in the United States, Nat. Sustain., 6, 1–9, 2023.
Nguyan, D., Belval, E. J., Wei, Y., Short, K. C., and Calkin, D. E.: Dataset of United States Incident Management Situation Reports, 2007–2021, Sci. Data, 11, https://doi.org/10.1038/s41597-023-02876-8, 2023.
Noonan-Wright, E. K., Opperman, T. S., Finney, M. A., Zimmerman, G. T., Seli, R. C., Elenz, L. M., Calkin, D. E., and Fiedler, J. R.: Developing the US wildland fire decision support system, J. Combust., 2011, 1–14, https://doi.org/10.1155/2011/168473, 2011.
Omernik, J. M.: Ecoregions of the conterminous United States, Annals of the Association of American Geographers 77:118-125, https://gaftp.epa.gov/EPADataCommons/ORD/Ecoregions/us/us_eco_l4_state_boundaries.zip (last access: 10 December 2023), 1987.
Pineda, N., Altube, P., Alcasena, F. J., Casellas, E., Segundo, H. S., and Montanyà, J.: Characterising the holdover phase of lightning-ignited wildfires in Catalonia, Agric. For. Meteorol., 324, 1–15, https://doi.org/10.1016/j.agrformet.2022.109111, 2022.
Pourmohamad, Y., Abatzoglou, J., Belval, E., Short, K., Fleishman, E., Reeves, M., Nauslar, N., Higuera, P., Henderson, E., Ball, S., AghaKouchak, A., Prestemon, J., Olszewski, J., and Sadegh, M. Physical, Social, and Biological Attributes for Improved Understanding and Prediction of Wildfires: FPA FOD-Attributes Dataset (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.8381129, 2023.
Prestemon, J. P. and Butry, D. T.: Time to burn: modeling wildland arson as an autoregressive crime function, Am. J. Agric. Econ., 87, 756–770, 2005.
Rad, A. M., Abatzoglou, J., Fleishman, E., Mockrin, M. H., Radeloff, V. C., Pourmohamad, Y., Cattau, M. E., Johnson, J. M., Higuera, P. E., Nauslar, N., and Sadegh, M.: Social vulnerability of the people exposed to wildfires, Sci. Adv., 9, eadh461, https://doi.org/10.1126/sciadv.adh4615, 2023.
Reeves, M. and Frid, L.: The Rangeland Vegetation Simulator: A user-driven system for quantifying production, succession, disturbance and fuels in non-forest environments, in: 10th International Rangeland Congress, 1062, 2016.
Reeves, M. C., Hanberry, B. B., Wilmer, H., Kaplan, N. E., and Lauenroth, W. K.: An assessment of production trends on the Great Plains from 1984 to 2017, Rangel. Ecol. Manag., 78, 165–179, 2021.
Riley, S. J., DeGloria, S. D., and Elliot, R.: Index that quantifies topographic heterogeneity, Intermt. J. Sci., 5, 23–27, 1999.
Roberto Barbosa, M., Carlos Sícoli Seoane, J., Guimarães Buratto, M., Santana de Oliveira Dias, L., Paulo Carvalho Raivel, J., and Lobos Martins, F.: Forest Fire Alert System: a Geo Web GIS prioritization model considering land susceptibility and hotspots – a case study in the Carajás National Forest, Brazilian Amazon, Int. J. Geogr. Inf. Sci., 24, 873–901, 2010.
Ronchi, E., Gwynne, S. M. V, Rein, G., Wadhwani, R., Intini, P., and Bergstedt, A.: e-Sanctuary: Open multi-physics framework for modelling wildfire urban evacuation, Fire Protection Research Foundation Quincy, 2017.
Rothberg, D.: “It's gone, it's gone:” Nation's largest wildfire in Nevada devastates ranches, sage grouse, Nevada Indep., 12 July 2018.
Rouse, J. W., Haas, R. H., Schell, J. A., and Deering, D. W.: Monitoring vegetation systems in the Great Plains with ERTS, NASA Spec. Publ, 351, 309, https://doi.org/10.4095/330629, 1974.
Santos, F., Bailey, J. K., and Schweitzer, J. A.: The eco-evolutionary role of fire in shaping terrestrial ecosystems, Funct. Ecol., 37, 2090–2095, 2023.
Scott, J., Helmbrecht, D., Thompson, M. P., Calkin, D. E., and Marcille, K.: Probabilistic assessment of wildfire hazard and municipal watershed exposure, Nat. Hazards, 64, 707–728, 2012.
Short, K. C.: A spatial database of wildfires in the United States, 1992–2011, Earth Syst. Sci. Data, 6, 1–27, https://doi.org/10.5194/essd-6-1-2014, 2014.
Short, K. C.: Spatial wildfire occurrence data for the United States, 1992–2018 [FPA_FOD_20210617], 5th Edition. Fort Collins, CO: Forest Service Research Data Archive, https://doi.org/10.2737/RDS-2013-0009.5, 2021.
Short, K. C.: Spatial wildfire occurrence data for the United States, 1992–2020 [FPA_FOD_20221014], 6th Edition. Fort Collins, CO: Forest Service Research Data Archive, https://doi.org/10.2737/RDS-2013-0009.6, 2022.
Short, K. C., Grenfell, I. C., Riley, K. L., and Vogler, K. C.: Pyromes of the conterminous United States, Forest Service Research Data Archive [data set], https://doi.org/10.2737/RDS-2020-0020, 2020.
Show, S. B. and Kotok, E. I.: Forest fires in California, 1911–1920: an analytical study, U.S. Dept. of Agriculture, Washington, D.C., 1923.
Shuman, J. K., Balch, J. K., Barnes, R. T., Higuera, P. E., Roos, C. I., Schwilk, D. W., Stavros, E. N., Banerjee, T., Bela, M. M., and Bendix, J.: Reimagine fire science for the anthropocene, P. Natl. Acad. Sci. USA Nexus, 1, pgac115, https://doi.org/10.1093/pnasnexus/pgac115, 2022.
Silva, F. R., Martínez, J. R. M., and González-Cabán, A.: A methodology for determining operational priorities for prevention and suppression of wildland fires, Int. J. Wildl. fire, 23, 544–554, 2014.
Silva, F. R. Y., O'Connor, C. D., Thompson, M. P., Martinez, J. R. M., and Calkin, D. E.: Modelling suppression difficulty: current and future applications, Int. J. Wildl. FIRE, 29, 752, https://doi.org/10.1071/wf19042, 2020.
St. Denis, L. A., Short, K. C., McConnell, K., Cook, M. C., Mietkiewicz, N. P., Buckland, M., and Balch, J. K.: all-hazards dataset mined from the US National Incident Management System 1999–2020, Sci. data, 10, 112, https://doi.org/10.1038/s41597-023-01955-0, 2023.
Stephens, S. L., Agee, J. K., Fule, P. Z., North, M. P., Romme, W. H., Swetnam, T. W., and Turner, M. G.: Managing forests and fire in changing climates, Science, 342, 41–42, 2013.
Tatem, A. J.: WorldPop, open data for spatial demography, Sci. Data, 4, 1–4, 2017.
TIGER: US Census Roads: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2022.html#list-tab-790442341, last access: 16 October 2023.
U.S. Department of Interior, Geological Survey, and U.S. Department of Agriculture: LANDFIRE Existing Vegetation Type layer, http://landfire.cr.usgs.gov/viewer/ (last access: 16 October 2023), last update: May 2022a.
U.S. Department of Interior, Geological Survey, and U.S. Department of Agriculture: LANDFIRE Fire Regime Groups Type layer, http://landfire.cr.usgs.gov/viewer/ (last access: 16 October 2023), last update: May 2022b.
U.S. Department of Interior, Geological Survey, and U.S. Department of Agriculture: LANDFIRE Topography Type layer, http://landfire.cr.usgs.gov/viewer/ (last access: 16 October 2023), last update: May 2022c.
U.S. Geological Survey (USGS): Gap Analysis Project (GAP): Protected Areas Database of the United States (PAD-US) 3.0: U.S. Geological Survey data release, https://doi.org/10.5066/P9Q9LQ4B, 2022.
Vermote, E.: NOAA Climate Data Record (CDR) of AVHRR normalized difference vegetation index (NDVI), version 5, NOAA National Centers for Environmental Information, https://doi.org/10.7289/V5ZG6QH9, 2019.
Viegas, D. X., Viegas, M., and Ferreira, A. D.: Moisture content of fine forest fuels and fire occurrence in central Portugal, Int. J. Wildl. Fire, 2, 69–86, 1992.
Weaver, J. F., Lindsey, D., Bikos, D., Schmidt, C. C., and Prins, E.: Fire detection using GOES rapid scan imagery, Weather Forecast., 19, 496–510, 2004.
Weiss, A.: Topographic position and landforms analysis, in: Poster presentation, ESRI user conference, 9–13 July 2001, San Diego, CA, poster no. 200, 2001.
Westerling, A. L.: Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring, Philos. T. Roy. Soc. B, 371, 20150178, https://doi.org/10.1098/rstb.2015.0178, 2016.
Wierzchowski, J., Heathcott, M., and Flannigan, M. D.: Lightning and lightning fire, central cordillera, Canada, Int. J. Wildl. Fire, 11, 41–51, 2002.
Wildland Fire Executive Council: The National Cohesive Wildland Fire Management Strategy: Phase III Western Regional Action Plan, 99 pp., https://www.frames.gov/catalog/14351 (last access: 5 January 2024), 2013.
Wildland fire perimeters full history: https://data-nifc.opendata.arcgis.com/datasets/nifc::wfigs-wildland-fire-perimeters-full-history/explore, last access: 26 February 2023.
WorldPop: Global high resolution population denominators project, Funded by Bill Melinda Gates Found. Sch. Geogr. Environ. Sci. Univ. Southampton; Dep. Geogr. Geosci. Univ. Louisville; Dep. Geogr. Univ. Namur) Cent. Int. Earth Sci. Inf. Netw.(CIESIN), Columbia Univ, 2018.
Short summary
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.
The FPA FOD-Attributes dataset provides > 300 biological, physical, social, and administrative...
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