Articles | Volume 16, issue 1
https://doi.org/10.5194/essd-16-321-2024
© 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-16-321-2024
© 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
| 
15 Jan 2024
Data description paper |
| 15 Jan 2024
| 15 Jan 2024
Annual maps of forest cover in the Brazilian Amazon from analyses of PALSAR and MODIS images
Yuanwei Qin
School of Biological Sciences, University of Oklahoma, Norman, OK 73019, USA
Xiangming Xiao
CORRESPONDING AUTHOR
School of Biological Sciences, University of Oklahoma, Norman, OK 73019, USA
Department of Geography, National University of Singapore, 1 Arts Link, Kent Ridge, 117570 Singapore
Ralph Dubayah
Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA
Russell Doughty
College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, OK 73019, USA
Diyou Liu
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Fang Liu
School of Biological Sciences, University of Oklahoma, Norman, OK 73019, USA
Yosio Shimabukuro
Brazilian National Institute for Space Research, INPE, São José dos Campos, SP 12227, Brazil
Egidio Arai
Brazilian National Institute for Space Research, INPE, São José dos Campos, SP 12227, Brazil
Xinxin Wang
Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Institute of Biodiversity Science and Institute of Eco-Chongming, School of Life Sciences, Fudan University, Shanghai 200438, China
Berrien Moore III
College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, OK 73019, USA
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Russell Doughty, Yujie Wang, Jennifer Johnson, Nicholas Parazoo, Troy Magney, Zoe Pierrat, Xiangming Xiao, Luis Guanter, Philipp Köhler, Christian Frankenberg, Peter Somkuti, Shuang Ma, Yuanwei Qin, Sean Crowell, and Berrien Moore III
EGUsphere, https://doi.org/10.22541/essoar.168167172.20799710/v1, https://doi.org/10.22541/essoar.168167172.20799710/v1, 2024
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Here we present a novel model of global photosynthesis, ChloFluo, which uses spaceborne chlorophyll fluorescence to estimate the amount of photosynthetically active radiation absorbed by chlorophyll. Potential uses of our model are to advance our understanding of the timing and magnitude of photosynthesis, its effect on atmospheric carbon dioxide fluxes, and vegetation response to climate events and change.
Jie Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Geli Zhang, Xuebin Yang, Xiaocui Wu, Chandrashekhar Biradar, and Yang Hu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-339, https://doi.org/10.5194/essd-2022-339, 2023
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Existing satellite-based forest maps remain large uncertainties, due to different forest definitions and mapping algorithms. To effectively manage the forest resources, timely and accurate annual forest maps at a high spatial resolution are demanded. This study improved the forest maps by integrating the PALSAR-2 and Landsat images. The annual evergreen and non-evergreen forest type maps were also generated. This critical information supports the Global Forest Resources Assessment.
Xinxin Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Jihua Wu, and Bo Li
Earth Syst. Sci. Data, 14, 3757–3771, https://doi.org/10.5194/essd-14-3757-2022, https://doi.org/10.5194/essd-14-3757-2022, 2022
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We generated China’s surface water bodies, Large Dams, Reservoirs, and Lakes (China-LDRL) dataset by analyzing all available Landsat imagery in 2019 (19\,338 images) in Google Earth Engine. The dataset provides accurate information on the geographical locations and sizes of surface water bodies, large dams, reservoirs, and lakes in China. The China-LDRL dataset will contribute to the understanding of water security and water resources management in China.
Russell Doughty, Yujie Wang, Jennifer Johnson, Nicholas Parazoo, Troy Magney, Zoe Pierrat, Xiangming Xiao, Luis Guanter, Philipp Köhler, Christian Frankenberg, Peter Somkuti, Shuang Ma, Yuanwei Qin, Sean Crowell, and Berrien Moore III
EGUsphere, https://doi.org/10.22541/essoar.168167172.20799710/v1, https://doi.org/10.22541/essoar.168167172.20799710/v1, 2024
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Here we present a novel model of global photosynthesis, ChloFluo, which uses spaceborne chlorophyll fluorescence to estimate the amount of photosynthetically active radiation absorbed by chlorophyll. Potential uses of our model are to advance our understanding of the timing and magnitude of photosynthesis, its effect on atmospheric carbon dioxide fluxes, and vegetation response to climate events and change.
Russell Doughty, Michael C. Wimberly, Dan Wanyama, Helene Peiro, Nicholas Parazoo, Sean Crowell, and Moses Azong Cho
EGUsphere, https://doi.org/10.5194/egusphere-2023-3022, https://doi.org/10.5194/egusphere-2023-3022, 2024
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We find West African SIF to increase during the dry season and peak prior to precipitation, similar to the Amazon. In Central Africa, we find a continental-scale bimodal seasonality in SIF, the minimum of which is synchronous with precipitation, but its maximum is likely less related to environmental drivers. We also find important differences in the seasonality of SIF and VIs, which indicates that VI-based estimates of photosynthesis could be inaccurate as they have been shown to be the Amazon.
Gregory R. McGarragh, Christopher W. O'Dell, Sean M. R. Crowell, Peter Somkuti, Eric B. Burgh, and Berrien Moore III
Atmos. Meas. Tech., 17, 1091–1121, https://doi.org/10.5194/amt-17-1091-2024, https://doi.org/10.5194/amt-17-1091-2024, 2024
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Carbon dioxide and methane are greenhouse gases that have been rapidly increasing due to human activity since the industrial revolution, leading to global warming and subsequently negative affects on the climate. It is important to measure the concentrations of these gases in order to make climate predictions that drive policy changes to mitigate climate change. GeoCarb aims to measure the concentrations of these gases from space over the Americas at unprecedented spatial and temporal scales.
Jie Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Geli Zhang, Xuebin Yang, Xiaocui Wu, Chandrashekhar Biradar, and Yang Hu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-339, https://doi.org/10.5194/essd-2022-339, 2023
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Existing satellite-based forest maps remain large uncertainties, due to different forest definitions and mapping algorithms. To effectively manage the forest resources, timely and accurate annual forest maps at a high spatial resolution are demanded. This study improved the forest maps by integrating the PALSAR-2 and Landsat images. The annual evergreen and non-evergreen forest type maps were also generated. This critical information supports the Global Forest Resources Assessment.
Sean Crowell, Tobias Haist, Michael Tscherpel, Jérôme Caron, Eric Burgh, and Berrien Moore III
Atmos. Meas. Tech., 16, 195–208, https://doi.org/10.5194/amt-16-195-2023, https://doi.org/10.5194/amt-16-195-2023, 2023
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Variations in brightness in radiance measurements cause errors that can be mitigated with hardware that scrambles the pattern of the incoming light. GeoCarb took this route to minimize this source of errors, but lab testing determined that the solution chosen was too sensitive to the the polarization of the incoming light. Modeling found that this was a predictable result of using gold coatings in the design, which is typical of spaceflight optical instruments.
Hélène Peiro, Sean Crowell, and Berrien Moore III
Atmos. Chem. Phys., 22, 15817–15849, https://doi.org/10.5194/acp-22-15817-2022, https://doi.org/10.5194/acp-22-15817-2022, 2022
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CO data can provide a powerful constraint on fire fluxes, supporting more accurate estimation of biospheric CO2 fluxes. We converted CO fire flux into CO2 fire prior, which is then used to adjust CO2 respiration. We applied this to two other fire flux products. CO2 inversions constrained by satellites or in situ data are then performed. Results show larger variations among the data assimilated than across the priors, but tropical flux from in situ inversions is sensitive to priors.
Brendan Byrne, Junjie Liu, Yonghong Yi, Abhishek Chatterjee, Sourish Basu, Rui Cheng, Russell Doughty, Frédéric Chevallier, Kevin W. Bowman, Nicholas C. Parazoo, David Crisp, Xing Li, Jingfeng Xiao, Stephen Sitch, Bertrand Guenet, Feng Deng, Matthew S. Johnson, Sajeev Philip, Patrick C. McGuire, and Charles E. Miller
Biogeosciences, 19, 4779–4799, https://doi.org/10.5194/bg-19-4779-2022, https://doi.org/10.5194/bg-19-4779-2022, 2022
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Plants draw CO2 from the atmosphere during the growing season, while respiration releases CO2 to the atmosphere throughout the year, driving seasonal variations in atmospheric CO2 that can be observed by satellites, such as the Orbiting Carbon Observatory 2 (OCO-2). Using OCO-2 XCO2 data and space-based constraints on plant growth, we show that permafrost-rich northeast Eurasia has a strong seasonal release of CO2 during the autumn, hinting at an unexpectedly large respiration signal from soils.
Xinxin Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Jihua Wu, and Bo Li
Earth Syst. Sci. Data, 14, 3757–3771, https://doi.org/10.5194/essd-14-3757-2022, https://doi.org/10.5194/essd-14-3757-2022, 2022
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We generated China’s surface water bodies, Large Dams, Reservoirs, and Lakes (China-LDRL) dataset by analyzing all available Landsat imagery in 2019 (19\,338 images) in Google Earth Engine. The dataset provides accurate information on the geographical locations and sizes of surface water bodies, large dams, reservoirs, and lakes in China. The China-LDRL dataset will contribute to the understanding of water security and water resources management in China.
Russell Doughty, Thomas P. Kurosu, Nicholas Parazoo, Philipp Köhler, Yujie Wang, Ying Sun, and Christian Frankenberg
Earth Syst. Sci. Data, 14, 1513–1529, https://doi.org/10.5194/essd-14-1513-2022, https://doi.org/10.5194/essd-14-1513-2022, 2022
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Yujie Wang, Philipp Köhler, Liyin He, Russell Doughty, Renato K. Braghiere, Jeffrey D. Wood, and Christian Frankenberg
Geosci. Model Dev., 14, 6741–6763, https://doi.org/10.5194/gmd-14-6741-2021, https://doi.org/10.5194/gmd-14-6741-2021, 2021
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We present the first step in testing a new land model as part of a new Earth system model. Our model links plant hydraulics, stomatal optimization theory, and a comprehensive canopy radiation scheme. We compared model-predicted carbon and water fluxes to flux tower observations and model-predicted sun-induced chlorophyll fluorescence to satellite retrievals. Our model quantitatively predicted the carbon and water fluxes as well as the canopy fluorescence yield.
J. Doblas, A. Carneiro, Y. Shimabukuro, S. Sant’Anna, and L. Aragão
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W12-2020, 493–498, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-493-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-493-2020, 2020
J. Doblas, A. Carneiro, Y. Shimabukuro, S. Sant’Anna, L. Aragão, and F. R. S. Pereira
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 89–96, https://doi.org/10.5194/isprs-annals-V-3-2020-89-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-89-2020, 2020
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Atmos. Meas. Tech., 12, 3317–3334, https://doi.org/10.5194/amt-12-3317-2019, https://doi.org/10.5194/amt-12-3317-2019, 2019
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A scanning strategy is proposed for the upcoming GeoCarb instrument that minimizes predicted retrieval errors of CO2 by optimizing signal-to-noise ratio with respect to air mass factor and solar zenith angle. The strategy is generated using a modified greedy algorithm that optimizes over these stationary processes while also considering operational constraints. This method increases the number of soundings with predicted CO2 retrieval error less than 2 ppm by 18–41 %.
Paul M. Montesano, Guoqing Sun, Ralph O. Dubayah, and K. Jon Ranson
Biogeosciences, 13, 3847–3861, https://doi.org/10.5194/bg-13-3847-2016, https://doi.org/10.5194/bg-13-3847-2016, 2016
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We demonstrate the potential of spaceborne data for integrating forest height and density to assess forest structure for patches in the taiga–tundra ecotone. We map forest patches at sites in northern Siberia with spaceborne data and examine the uncertainty of forest patch height estimates across these ecotone sites. Results demonstrate the opportunities for improving patch-based spaceborne estimates of forest height and forest structure patterns in the circumpolar domain.
H. Tang, S. Ganguly, G. Zhang, M. A. Hofton, R. F. Nelson, and R. Dubayah
Biogeosciences, 13, 239–252, https://doi.org/10.5194/bg-13-239-2016, https://doi.org/10.5194/bg-13-239-2016, 2016
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This paper provides a unique insight into vertical distribution of leaf area index across North American ecosystems using spaceborne lidar data. This data set of leaf area index and vertical foliage profile can help set up a baseline of canopy structure needed for evaluating climate and land use induced forest changes at continental scale in the future.
S. Crowell, P. Rayner, S. Zaccheo, and B. Moore
Atmos. Meas. Tech., 8, 2685–2697, https://doi.org/10.5194/amt-8-2685-2015, https://doi.org/10.5194/amt-8-2685-2015, 2015
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We derive a yes/no requirement for the usefulness of an O2 lidar as part of the ASCENDS mission that incorporates errors due to atmospheric state misspecification as well as instrumental noise. We find that the larger the CO2 instrument's sensitivity to surface pressure errors, the lower the precision requirement for the O2 instrument to be useful. In particular, the 2um CO2 instrument would benefit the most from the inclusion of an O2 lidar with high precision retrievals of surface pressure.
I. N. Fletcher, L. E. O. C. Aragão, A. Lima, Y. Shimabukuro, and P. Friedlingstein
Biogeosciences, 11, 1449–1459, https://doi.org/10.5194/bg-11-1449-2014, https://doi.org/10.5194/bg-11-1449-2014, 2014
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Qiangqiang Sun, Ping Zhang, Xin Jiao, Xin Lin, Wenkai Duan, Su Ma, Qidi Pan, Lu Chen, Yongxiang Zhang, Shucheng You, Shunxi Liu, Jinmin Hao, Hong Li, and Danfeng Sun
Earth Syst. Sci. Data, 16, 1333–1351, https://doi.org/10.5194/essd-16-1333-2024, https://doi.org/10.5194/essd-16-1333-2024, 2024
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To provide multifaceted changes under climate change and anthropogenic impacts, we estimated monthly vegetation and soil fractions in 2001–2022, providing an accurate estimate of surface heterogeneous composition, better than vegetation index and vegetation continuous-field products. We find a greening trend on Earth except for the tropics. A combination of interactive changes in vegetation and soil can be adopted as a valuable measurement of climate change and anthropogenic impacts.
Kai Cheng, Yuling Chen, Tianyu Xiang, Haitao Yang, Weiyan Liu, Yu Ren, Hongcan Guan, Tianyu Hu, Qin Ma, and Qinghua Guo
Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024, https://doi.org/10.5194/essd-16-803-2024, 2024
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To quantify forest carbon stock and its future potential accurately, we generated a 30 m resolution forest age map for China in 2020 using multisource remote sensing datasets based on machine learning and time series analysis approaches. Validation with independent field samples indicated that the mapped forest age had an R2 of 0.51--0.63. Nationally, the average forest age is 56.1 years (standard deviation of 32.7 years).
Wolfgang Alexander Obermeier, Clemens Schwingshackl, Ana Bastos, Giulia Conchedda, Thomas Gasser, Giacomo Grassi, Richard A. Houghton, Francesco Nicola Tubiello, Stephen Sitch, and Julia Pongratz
Earth Syst. Sci. Data, 16, 605–645, https://doi.org/10.5194/essd-16-605-2024, https://doi.org/10.5194/essd-16-605-2024, 2024
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We provide and compare country-level estimates of land-use CO2 fluxes from a variety and large number of models, bottom-up estimates, and country reports for the period 1950–2021. Although net fluxes are small in many countries, they are often composed of large compensating emissions and removals. In many countries, the estimates agree well once their individual characteristics are accounted for, but in other countries, including some of the largest emitters, substantial uncertainties exist.
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-482, https://doi.org/10.5194/essd-2023-482, 2024
Revised manuscript accepted for ESSD
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This study accurately mapped 16 dominant tree species/genera in Poland using Sentinel-2 observations from short periods in spring, summer, and autumn (2018–2021). The classification achieved 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/test data, can be found at: https://doi.org/10.5281/zenodo.10180469.
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Earth Syst. Sci. Data, 16, 525–541, https://doi.org/10.5194/essd-16-525-2024, https://doi.org/10.5194/essd-16-525-2024, 2024
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Nutrient budgets help identify the excess or insufficient use of fertilizers and other nutrient sources in agriculture. They allow the calculation of indicators, such as the nutrient balance (surplus or deficit) and nutrient use efficiency, that help to monitor agricultural productivity and sustainability. This article describes a global cropland nutrient budget that provides data on 205 countries and territories from 1961 to 2020 (data available at https://www.fao.org/faostat/en/#data/ESB).
Xiangan Liang, Qiang Liu, Jie Wang, Shuang Chen, and Peng Gong
Earth Syst. Sci. Data, 16, 177–200, https://doi.org/10.5194/essd-16-177-2024, https://doi.org/10.5194/essd-16-177-2024, 2024
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The state-of-the-art MODIS surface reflectance products suffer from temporal and spatial gaps, which make it difficult to characterize the continuous variation of the terrestrial surface. We proposed a framework for generating the first global 500 m daily seamless data cubes (SDC500), covering the period from 2000 to 2022. We believe that the SDC500 dataset can interest other researchers who study land cover mapping, quantitative remote sensing, and ecological science.
Qinghang Mei, Zhao Zhang, Jichong Han, Jie Song, Jinwei Dong, Huaqing Wu, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-467, https://doi.org/10.5194/essd-2023-467, 2023
Revised manuscript accepted for ESSD
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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 data sets, 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.
Rémy Ballot, Nicolas Guilpart, and Marie-Hélène Jeuffroy
Earth Syst. Sci. Data, 15, 5651–5666, https://doi.org/10.5194/essd-15-5651-2023, https://doi.org/10.5194/essd-15-5651-2023, 2023
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Assessing the benefits of crop diversification – a key element of agroecological transition – on a large scale requires a description of current crop sequences as a baseline, which is lacking at the scale of Europe. To fill this gap, we used a dataset that provides temporally and spatially incomplete land cover information to create a map of dominant crop sequence types for Europe over 2012–2018. This map is a useful baseline for assessing the benefits of future crop diversification.
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.
Francesco N. Tubiello, Giulia Conchedda, Leon Casse, Pengyu Hao, Giorgia De Santis, and Zhongxin Chen
Earth Syst. Sci. Data, 15, 4997–5015, https://doi.org/10.5194/essd-15-4997-2023, https://doi.org/10.5194/essd-15-4997-2023, 2023
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We describe a new dataset of cropland area circa the year 2020, with global coverage and country detail. Data are generated from geospatial information on the agreement characteristics of six high-resolution cropland maps. By helping to highlight features of cropland characteristics and underlying causes for agreement across land cover products, the dataset can be used as a tool to help guide future mapping efforts towards improved agricultural monitoring.
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 Discuss., https://doi.org/10.5194/essd-2023-430, https://doi.org/10.5194/essd-2023-430, 2023
Revised manuscript accepted for ESSD
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This dataset provides >300 biological, physical, social and administrative attributes associated with >2.3 million wildfire incidents across the US from 1992–2020.
Martin Schwartz, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cedric Vega, Jean-Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, and Ibrahim Fayad
Earth Syst. Sci. Data, 15, 4927–4945, https://doi.org/10.5194/essd-15-4927-2023, https://doi.org/10.5194/essd-15-4927-2023, 2023
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As forests play a key role in climate-related issues, their accurate monitoring is critical to reduce global carbon emissions effectively. Based on open-access remote-sensing sensors, and artificial intelligence methods, we created high-resolution tree height, wood volume, and biomass maps of metropolitan France that outperform previous products. This study, based on freely available data, provides essential information to support climate-efficient forest management policies at a low cost.
Zhuohong Li, Wei He, Mofan Cheng, Jingxin Hu, Guangyi Yang, and Hongyan Zhang
Earth Syst. Sci. Data, 15, 4749–4780, https://doi.org/10.5194/essd-15-4749-2023, https://doi.org/10.5194/essd-15-4749-2023, 2023
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Nowadays, a very-high-resolution land-cover (LC) map with national coverage is still unavailable in China, hindering efficient resource allocation. To fill this gap, the first 1 m resolution LC map of China, SinoLC-1, was built. The results showed that SinoLC-1 had an overall accuracy of 73.61 % and conformed to the official survey reports. Comparison with other datasets suggests that SinoLC-1 can be a better support for downstream applications and provide more accurate LC information to users.
Johannes H. Uhl, Dominic Royé, Keith Burghardt, José A. Aldrey Vázquez, Manuel Borobio Sanchiz, and Stefan Leyk
Earth Syst. Sci. Data, 15, 4713–4747, https://doi.org/10.5194/essd-15-4713-2023, https://doi.org/10.5194/essd-15-4713-2023, 2023
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Historical, fine-grained geospatial datasets on built-up areas are rarely available, constraining studies of urbanization, settlement evolution, or the dynamics of human–environment interactions to recent decades. In order to provide such historical data, we used publicly available cadastral building data for Spain and created a series of gridded surfaces, measuring age, physical, and land-use-related features of the built environment in Spain and the evolution of settlements from 1900 to 2020.
Christopher G. Marston, Aneurin W. O'Neil, R. Daniel Morton, Claire M. Wood, and Clare S. Rowland
Earth Syst. Sci. Data, 15, 4631–4649, https://doi.org/10.5194/essd-15-4631-2023, https://doi.org/10.5194/essd-15-4631-2023, 2023
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The UK Land Cover Map 2021 (LCM2021) is a UK-wide land cover data set, with 21- and 10-class versions. It is intended to support a broad range of UK environmental research, including ecological and hydrological research. LCM2021 was produced by classifying Sentinel-2 satellite imagery. LCM2021 is distributed as a suite of products to facilitate easy use for a range of applications. To support research at different spatial scales it includes 10 m, 25 m and 1 km resolution products.
Xingyi Huang, Yuwei Yin, Luwei Feng, Xiaoye Tong, Xiaoxin Zhang, Jiangrong Li, and Feng Tian
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-327, https://doi.org/10.5194/essd-2023-327, 2023
Revised manuscript accepted for ESSD
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The Tibetan Plateau, with its diverse vegetation 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 TP_LC10-2022 map. Comparisons with other maps revealed TP_LC10-2022's excellence in capturing local variations. Our map is significant for in-depth ecological studies.
Yu Zhao, Shaoyu Han, Jie Zheng, Hanyu Xue, Zhenhai Li, Yang Meng, Xuguang Li, Xiaodong Yang, Zhenhong Li, Shuhong Cai, and Guijun Yang
Earth Syst. Sci. Data, 15, 4047–4063, https://doi.org/10.5194/essd-15-4047-2023, https://doi.org/10.5194/essd-15-4047-2023, 2023
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In the present study, we generated a 30 m Chinese winter wheat yield dataset from 2016 to 2021, called ChinaWheatYield30m. The dataset has high spatial resolution and great accuracy. It is the highest-resolution yield dataset known. Such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling or regional climate evaluation.
Shuchao Ye, Peiyu Cao, and Chaoqun Lu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-195, https://doi.org/10.5194/essd-2023-195, 2023
Revised manuscript accepted for ESSD
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In this study, we reconstructed the annual cropland density and crop type map from 1850 to 2021 at 1 km by 1 km resolution. The cropland density map presents the distribution and percentage of planted area in each 1 km by 1 km pixel. The crop type map displays the distribution of nine major crop types (corn, soybean, winter wheat, spring wheat, durum wheat, cotton, sorghum, barley, and rice) and one type of “others” (all remaining crop types excluding idle/fallow farm land, and cropland pasture).
Feng Yang and Zhenzhong Zeng
Earth Syst. Sci. Data, 15, 4011–4021, https://doi.org/10.5194/essd-15-4011-2023, https://doi.org/10.5194/essd-15-4011-2023, 2023
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We generated a 4.77 m resolution annual tree cover map product for Southeast Asia (SEA) for 2016–2021 using Planet-NICFI and Sentinel-1 imagery. Maps were created with good accuracy and high consistency during 2016–2021. The baseline maps at 4.77 m can be converted to forest cover maps for SEA at various resolutions to meet different users’ needs. Our products can help resolve rounding errors in forest cover mapping by counting isolated trees and monitoring long, narrow forest cover removal.
Adrià Descals, Serge Wich, Zoltan Szantoi, Matthew J. Struebig, Rona Dennis, Zoe Hatton, Thina Ariffin, Nabillah Unus, David L. A. Gaveau, and Erik Meijaard
Earth Syst. Sci. Data, 15, 3991–4010, https://doi.org/10.5194/essd-15-3991-2023, https://doi.org/10.5194/essd-15-3991-2023, 2023
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The spatial extent of coconut palm is understudied despite its increasing demand and associated impacts. We present the first global coconut palm layer at 20 m resolution. The layer was produced using deep learning and remotely sensed data. The global coconut area estimate is 12.31 Mha for dense coconut palm, but the estimate is 3 times larger when sparse coconut palm is considered. This means that coconut production can likely increase on the lands currently allocated to coconut palm.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data, 15, 3819–3852, https://doi.org/10.5194/essd-15-3819-2023, https://doi.org/10.5194/essd-15-3819-2023, 2023
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This paper introduces the new high-resolution land use and land cover change dataset LUCAS LUC for Europe (version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Wanru He, Xuecao Li, Yuyu Zhou, Zitong Shi, Guojiang Yu, Tengyun Hu, Yixuan Wang, Jianxi Huang, Tiecheng Bai, Zhongchang Sun, Xiaoping Liu, and Peng Gong
Earth Syst. Sci. Data, 15, 3623–3639, https://doi.org/10.5194/essd-15-3623-2023, https://doi.org/10.5194/essd-15-3623-2023, 2023
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Most existing global urban products with future projections were developed in urban and non-urban categories, which ignores the gradual change of urban development at the local scale. Using annual global urban extent data from 1985 to 2015, we forecasted global urban fractional changes under eight scenarios throughout 2100. The developed dataset can provide spatially explicit information on urban fractions at 1 km resolution, which helps support various urban studies (e.g., urban heat island).
Zeping Liu, Hong Tang, Lin Feng, and Siqing Lyu
Earth Syst. Sci. Data, 15, 3547–3572, https://doi.org/10.5194/essd-15-3547-2023, https://doi.org/10.5194/essd-15-3547-2023, 2023
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Large-scale maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In this paper, we propose a deep-learning method for high-resolution BRA mapping (2.5 m) from Sentinel-2 imagery (10 m). The resulting China building rooftop area dataset (CBRA) is the first multi-annual (2016–2021) and high-resolution (2.5 m) BRA dataset in China. Cross-comparisons show that the CBRA achieves the best performance in capturing the spatiotemporal information.
Ruoque Shen, Baihong Pan, Qiongyan Peng, Jie Dong, Xuebing Chen, Xi Zhang, Tao Ye, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, https://doi.org/10.5194/essd-15-3203-2023, 2023
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Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. This study developed a new rice-mapping method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data, and we found that the distribution maps have good accuracy.
Charles R. Lane, Ellen D'Amico, Jay R. Christensen, Heather E. Golden, Qiusheng Wu, and Adnan Rajib
Earth Syst. Sci. Data, 15, 2927–2955, https://doi.org/10.5194/essd-15-2927-2023, https://doi.org/10.5194/essd-15-2927-2023, 2023
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Non-floodplain wetlands (NFWs) – wetlands located outside floodplains – confer watershed-scale resilience to hydrological, biogeochemical, and biotic disturbances. Although they are frequently unmapped, we identified ~ 33 million NFWs covering > 16 × 10 km2 across the globe. NFWs constitute the majority of the world's wetlands (53 %). Despite their small size (median 0.039 km2), these imperiled systems have an outsized impact on watershed functions and sustainability and require protection.
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data, 15, 2347–2373, https://doi.org/10.5194/essd-15-2347-2023, https://doi.org/10.5194/essd-15-2347-2023, 2023
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A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.
Richard A. Houghton and Andrea Castanho
Earth Syst. Sci. Data, 15, 2025–2054, https://doi.org/10.5194/essd-15-2025-2023, https://doi.org/10.5194/essd-15-2025-2023, 2023
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We update a previous analysis of carbon emissions (annual and national) from land use, land-use change, and forestry from 1850 to 2020. We use data from the latest (2020) Global Forest Resources Assessment, incorporate shifting cultivation, and include improvements to the bookkeeping model and recent estimates of emissions from peatlands. Net global emissions declined steadily over the decade from 2011 to 2020 (mean of 0.96 Pg C yr−1), falling below 1.0 Pg C yr−1 for the first time in 30 years.
Charles H. Simpson, Oscar Brousse, Nahid Mohajeri, Michael Davies, and Clare Heaviside
Earth Syst. Sci. Data, 15, 1521–1541, https://doi.org/10.5194/essd-15-1521-2023, https://doi.org/10.5194/essd-15-1521-2023, 2023
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Adding plants to roofs of buildings can reduce indoor and outdoor temperatures and so can reduce urban overheating, which is expected to increase due to climate change and urban growth. To better understand the effect this has on the urban environment, we need data on how many buildings have green roofs already.
We used a computer vision model to find green roofs in aerial imagery in London, producing a dataset identifying what buildings have green roofs and improving on previous methods.
Chunling Sun, Hong Zhang, Lu Xu, Ji Ge, Jingling Jiang, Lijun Zuo, and Chao Wang
Earth Syst. Sci. Data, 15, 1501–1520, https://doi.org/10.5194/essd-15-1501-2023, https://doi.org/10.5194/essd-15-1501-2023, 2023
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Over 90 % of the world’s rice is produced in the Asia–Pacific region. In this study, a rice-mapping method based on Sentinel-1 data for mainland Southeast Asia is proposed. A combination of spatiotemporal features with strong generalization is selected and input into the U-Net model to obtain a 20 m resolution rice area map of mainland Southeast Asia in 2019. The accuracy of the proposed method is 92.20 %. The rice area map is concordant with statistics and other rice area maps.
Kandice L. Harper, Céline Lamarche, Andrew Hartley, Philippe Peylin, Catherine Ottlé, Vladislav Bastrikov, Rodrigo San Martín, Sylvia I. Bohnenstengel, Grit Kirches, Martin Boettcher, Roman Shevchuk, Carsten Brockmann, and Pierre Defourny
Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, https://doi.org/10.5194/essd-15-1465-2023, 2023
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We built a spatially explicit annual plant-functional-type (PFT) dataset for 1992–2020 exhibiting intra-class spatial variability in PFT fractional cover at 300 m. For each year, 14 maps of percentage cover are produced: bare soil, water, permanent snow/ice, built, managed grasses, natural grasses, and trees and shrubs, each split into leaf type and seasonality. Model simulations indicate significant differences in simulated carbon, water, and energy fluxes in some regions using this new set.
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.
Elena Aragoneses, Mariano García, Michele Salis, Luís M. Ribeiro, and Emilio Chuvieco
Earth Syst. Sci. Data, 15, 1287–1315, https://doi.org/10.5194/essd-15-1287-2023, https://doi.org/10.5194/essd-15-1287-2023, 2023
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We present a new hierarchical fuel classification system with a total of 85 fuels that is useful for preventing fire risk at different spatial scales. Based on this, we developed a European fuel map (1 km resolution) using land cover datasets, biogeographic datasets, and bioclimatic modelling. We validated the map by comparing it to high-resolution data, obtaining high overall accuracy. Finally, we developed a crosswalk for standard fuel models as a first assignment of fuel parameters.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
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Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Xiaoyong Li, Hanqin Tian, Chaoqun Lu, and Shufen Pan
Earth Syst. Sci. Data, 15, 1005–1035, https://doi.org/10.5194/essd-15-1005-2023, https://doi.org/10.5194/essd-15-1005-2023, 2023
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We reconstructed land use and land cover (LULC) history for the conterminous United States during 1630–2020 by integrating multi-source data. The results show the widespread expansion of cropland and urban land and the shrinking of natural vegetation in the past four centuries. Forest planting and regeneration accelerated forest recovery since the 1920s. The datasets can be used to assess the LULC impacts on the ecosystem's carbon, nitrogen, and water cycles.
Huifang Zhang, Zhonggang Tang, Binyao Wang, Hongcheng Kan, Yi Sun, Yu Qin, Baoping Meng, Meng Li, Jianjun Chen, Yanyan Lv, Jianguo Zhang, Shuli Niu, and Shuhua Yi
Earth Syst. Sci. Data, 15, 821–846, https://doi.org/10.5194/essd-15-821-2023, https://doi.org/10.5194/essd-15-821-2023, 2023
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The accuracy of regional grassland aboveground biomass (AGB) is always limited by insufficient ground measurements and large spatial gaps with satellite pixels. This paper used more than 37 000 UAV images as bridges to successfully obtain AGB values matching MODIS pixels. The new AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m2. Our new dataset provides important input parameters for understanding the Qinghai–Tibet Plateau during global climate change.
Huaqing Wu, Jing Zhang, Zhao Zhang, Jichong Han, Juan Cao, Liangliang Zhang, Yuchuan Luo, Qinghang Mei, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 15, 791–808, https://doi.org/10.5194/essd-15-791-2023, https://doi.org/10.5194/essd-15-791-2023, 2023
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High-spatiotemporal-resolution rice yield datasets are limited over a large region. We proposed an explicit method to predict rice yield based on machine learning methods and generated a seasonal 4 km resolution rice yield dataset across Asia (AsiaRiceYield4km) for 1995–2015. The seasonal rice yield accuracy of AsiaRiceYield4km is high and much improved compared with previous datasets. AsiaRiceYield4km will fill the current data gap and better support agricultural monitoring systems.
Steve Ahlswede, Christian Schulz, Christiano Gava, Patrick Helber, Benjamin Bischke, Michael Förster, Florencia Arias, Jörn Hees, Begüm Demir, and Birgit Kleinschmit
Earth Syst. Sci. Data, 15, 681–695, https://doi.org/10.5194/essd-15-681-2023, https://doi.org/10.5194/essd-15-681-2023, 2023
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Imagery from air and space is the primary source of large-scale forest mapping. Our study introduces a new dataset with over 50000 image patches prepared for deep learning tasks. We show how the information for 20 European tree species can be extracted from different remote sensing sensors. Our algorithms can detect single species with precision scores up to 88 %. With a pixel size of 20×20 cm, forestry administration can now derive large-scale tree species maps at a very high resolution.
Qian Shi, Mengxi Liu, Andrea Marinoni, and Xiaoping Liu
Earth Syst. Sci. Data, 15, 555–577, https://doi.org/10.5194/essd-15-555-2023, https://doi.org/10.5194/essd-15-555-2023, 2023
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A large-scale and high-resolution urban green space (UGS) product with 1 m of 31 major cities in China (UGS-1m) is generated based on a deep learning framework to provide basic UGS information for relevant UGS research, such as distribution, area, and UGS rate. Moreover, an urban green space dataset (UGSet) with a total of 4454 samples of 512 × 512 in size are also supplied as the benchmark to support model training and algorithm comparison.
Raphaël d'Andrimont, Martin Claverie, Pieter Kempeneers, Davide Muraro, Momchil Yordanov, Devis Peressutti, Matej Batič, and François Waldner
Earth Syst. Sci. Data, 15, 317–329, https://doi.org/10.5194/essd-15-317-2023, https://doi.org/10.5194/essd-15-317-2023, 2023
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AI4boundaries is an open AI-ready data set to map field boundaries with Sentinel-2 and aerial photography provided with harmonised labels covering seven countries and 2.5 M parcels in Europe.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Xidong Chen, Shangrong Lin, Jinqing Wang, Jun Mi, and Wendi Liu
Earth Syst. Sci. Data, 15, 265–293, https://doi.org/10.5194/essd-15-265-2023, https://doi.org/10.5194/essd-15-265-2023, 2023
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An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping and generates the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal wetland sub-categories (mangrove, salt marsh and tidal flats).
Chong Liu, Xiaoqing Xu, Xuejie Feng, Xiao Cheng, Caixia Liu, and Huabing Huang
Earth Syst. Sci. Data, 15, 133–153, https://doi.org/10.5194/essd-15-133-2023, https://doi.org/10.5194/essd-15-133-2023, 2023
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Rapid Arctic changes are increasingly influencing human society, both locally and globally. Land cover offers a basis for characterizing the terrestrial world, yet spatially detailed information on Arctic land cover is lacking. We employ multi-source data to develop a new land cover map for the circumpolar Arctic. Our product reveals regionally contrasting biome distributions not fully documented in existing studies and thus enhances our understanding of the Arctic’s terrestrial system.
Jingliang Hu, Rong Liu, Danfeng Hong, Andrés Camero, Jing Yao, Mathias Schneider, Franz Kurz, Karl Segl, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 15, 113–131, https://doi.org/10.5194/essd-15-113-2023, https://doi.org/10.5194/essd-15-113-2023, 2023
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Multimodal data fusion is an intuitive strategy to break the limitation of individual data in Earth observation. Here, we present a multimodal data set, named MDAS, consisting of synthetic aperture radar (SAR), multispectral, hyperspectral, digital surface model (DSM), and geographic information system (GIS) data for the city of Augsburg, Germany, along with baseline models for resolution enhancement, spectral unmixing, and land cover classification, three typical remote sensing applications.
Furong Li, Marie-José Gaillard, Xianyong Cao, Ulrike Herzschuh, Shinya Sugita, Jian Ni, Yan Zhao, Chengbang An, Xiaozhong Huang, Yu Li, Hongyan Liu, Aizhi Sun, and Yifeng Yao
Earth Syst. Sci. Data, 15, 95–112, https://doi.org/10.5194/essd-15-95-2023, https://doi.org/10.5194/essd-15-95-2023, 2023
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The objective of this study is present the first gridded and temporally continuous quantitative plant-cover reconstruction for temperate and northern subtropical China over the last 12 millennia. The reconstructions are based on 94 pollen records and include estimates for 27 plant taxa, 10 plant functional types, and 3 land-cover types. The dataset is suitable for palaeoclimate modelling and the evaluation of simulated past vegetation cover and anthropogenic land-cover change from models.
Jose Luis Gómez-Dans, Philip Edward Lewis, Feng Yin, Kofi Asare, Patrick Lamptey, Kenneth Kobina Yedu Aidoo, Dilys Sefakor MacCarthy, Hongyuan Ma, Qingling Wu, Martin Addi, Stephen Aboagye-Ntow, Caroline Edinam Doe, Rahaman Alhassan, Isaac Kankam-Boadu, Jianxi Huang, and Xuecao Li
Earth Syst. Sci. Data, 14, 5387–5410, https://doi.org/10.5194/essd-14-5387-2022, https://doi.org/10.5194/essd-14-5387-2022, 2022
Short summary
Short summary
We provide a data set to support mapping croplands in smallholder landscapes in Ghana. The data set contains information on crop location on three agroecological zones for 2 years, temporal series of measurements of leaf area index and leaf chlorophyll concentration for maize canopies and yield. We demonstrate the use of these data to validate cropland masks, create a maize mask using satellite data and explore the relationship between satellite measurements and yield.
Cited articles
Achard, F., Beuchle, R., Mayaux, P., Stibig, H. J., Bodart, C., Brink, A., Carboni, S., Desclee, B., Donnay, F., Eva, H. D., Lupi, A., Rasi, R., Seliger, R., and Simonetti, D.: Determination of tropical deforestation rates and related carbon losses from 1990 to 2010, Global Change Biol., 20, 2540–2554, https://doi.org/10.1111/gcb.12605, 2014.
Almeida, C. T., Oliveira-Júnior, J. F., Delgado, R. C., Cubo, P., and Ramos, M. C.: Spatiotemporal rainfall and temperature trends throughout the Brazilian Legal Amazon, 1973–2013, Int. J. Climatol., 37, 2013–2026, https://doi.org/10.1002/joc.4831, 2017.
Aragão, L. E. O. C., Anderson, L. O., Fonseca, M. G., Rosan, T. M., Vedovato, L. B., Wagner, F. H., Silva, C. V. J., Silva Junior, C. H. L., Arai, E., Aguiar, A. P., Barlow, J., Berenguer, E., Deeter, M. N., Domingues, L. G., Gatti, L., Gloor, M., Malhi, Y., Marengo, J. A., Miller, J. B., Phillips, O. L., and Saatchi, S.: 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions, Nat. Commun., 9, 536, https://doi.org/10.1038/s41467-017-02771-y, 2018.
Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P. S. A., Dubayah, R., Friedl, M. A., Samanta, S., and Houghton, R. A.: Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps, Nat. Clim. Change, 2, 182–185, https://doi.org/10.1038/Nclimate1354, 2012.
Chen, B. Q., Xiao, X. M., Ye, H. C., Ma, J., Doughty, R., Li, X. P., Zhao, B., Wu, Z. X., Sun, R., Dong, J. W., Qin, Y. W., and Xie, G. S.: Mapping Forest and Their Spatial-Temporal Changes From 2007 to 2015 in Tropical Hainan Island by Integrating ALOS/ALOS-2 L-Band SAR and Landsat Optical Images, Ieee J.-Stars, 11, 852–867, https://doi.org/10.1109/jstars.2018.2795595, 2018.
Dubayah, R., Blair, J. B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S., Hofton, M., Hurtt, G., Kellner, J., Luthcke, S., Armston, J., Tang, H., Duncanson, L., Hancock, S., Jantz, P., Marselis, S., Patterson, P. L., Qi, W., and Silva, C.: The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth's forests and topography, Sci. Remote Sens., 1, 100002, https://doi.org/10.1016/j.srs.2020.100002, 2020.
Espírito-Santo, F. D. B., Gloor, M., Keller, M., Malhi, Y., Saatchi, S., Nelson, B., Junior, R. C. O., Pereira, C., Lloyd, J., Frolking, S., Palace, M., Shimabukuro, Y. E., Duarte, V., Mendoza, A. M., López-González, G., Baker, T. R., Feldpausch, T. R., Brienen, R. J. W., Asner, G. P., Boyd, D. S., and Phillips, O. L.: Size and frequency of natural forest disturbances and the Amazon forest carbon balance, Nat. Commun., 5, 3434, https://doi.org/10.1038/ncomms4434, 2014.
Fan, L., Wigneron, J. P., Ciais, P., Chave, J., Brandt, M., Fensholt, R., Saatchi, S. S., Bastos, A., Al-Yaari, A., Hufkens, K., Qin, Y., Xiao, X., Chen, C., Myneni, R. B., Fernandez-Moran, R., Mialon, A., Rodriguez-Fernandez, N. J., Kerr, Y., Tian, F., and Penuelas, J.: Satellite-observed pantropical carbon dynamics, Nat. Plants, 5, 944–951, https://doi.org/10.1038/s41477-019-0478-9, 2019.
Fanin, T. and van der Werf, G. R.: Relationships between burned area, forest cover loss, and land cover change in the Brazilian Amazon based on satellite data, Biogeosciences, 12, 6033–6043, https://doi.org/10.5194/bg-12-6033-2015, 2015.
FAO: Expert Meeting on Harmonizing Forest-Related Definitions for Use by Various Stakeholders, FAO, https://www.fao.org/3/cb7646en/cb7646en.pdf (last access: 23 October 2023), 2002.
FAO: Global Forest Resources Assessment 2020: Main report, Rome, https://doi.org/10.4060/ca9825en, 2020.
Fearnside, P. M.: Deforestation in Brazilian Amazonia: History, rates, and consequences, Conserv. Biol., 19, 680–688, https://doi.org/10.1111/j.1523-1739.2005.00697.x, 2005.
FRA: Global Forest Resources Assessment: Terms and Definitions, edited by: Pekkarinen, A., https://www.fao.org/3/I8661EN/i8661en.pdf (last access: 23 October 2023), 2020.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X. M.: MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets, Remote Sens. Environ., 114, 168–182, https://doi.org/10.1016/j.rse.2009.08.016, 2010.
Fritz, S., McCallum, I., Schill, C., Perger, C., See, L., Schepaschenko, D., van der Velde, M., Kraxner, F., and Obersteiner, M.: Geo-Wiki: An online platform for improving global land cover, Environ. Modell. Softw., 31, 110–123, https://doi.org/10.1016/j.envsoft.2011.11.015, 2012.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Hansen, M. C., DeFries, R. S., Townshend, J. R. G., Sohlberg, R., Dimiceli, C., and Carroll, M.: Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data, Remote Sens. Environ., 83, 303–319, https://doi.org/10.1016/S0034-4257(02)00079-2, 2002.
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R.: High-resolution global maps of 21st-century forest cover change, Science, 342, 850–853, https://doi.org/10.1126/science.1244693, 2013 (data available at: http://earthenginepartners.appspot.com/science-2013-global-forest, last access: 23 October 2023).
Hudak, A. T., Lefsky, M. A., Cohen, W. B., and Berterretche, M.: Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height, Remote Sens. Environ., 82, 397–416, https://doi.org/10.1016/S0034-4257(02)00056-1, 2002.
INPE: PRODES Legal Amazon Deforestation Monitoring System, http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes (last access: 23 October 2023), 2023.
Jenkins, C. N., Pimm, S. L., and Joppa, L. N.: Global patterns of terrestrial vertebrate diversity and conservation, P. Natl. Acad. Sci. USA, 110, E2602–E2610, https://doi.org/10.1073/pnas.1302251110, 2013.
Leitold, V., Morton, D. C., Longo, M., dos-Santos, M. N., Keller, M., and Scaranello, M.: El Niño drought increased canopy turnover in Amazon forests, New Phytol., 219, 959–971, https://doi.org/10.1111/nph.15110, 2018.
Li, G., Lu, D., Moran, E., Calvi, M. F., Dutra, L. V., and Batistella, M.: Examining deforestation and agropasture dynamics along the Brazilian TransAmazon Highway using multitemporal Landsat imagery, Gisci Remote Sens,, 56, 161–183, https://doi.org/10.1080/15481603.2018.1497438, 2019.
Lovejoy, T. E. and Nobre, C.: Amazon tipping point: Last chance for action, Sci, Adv,, 5, eaba2949, https://doi.org/10.1126/sciadv.aba2949, 2019.
Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B., Farrell, S., Fricker, H., Gardner, A., Harding, D., Jasinski, M., Kwok, R., Magruder, L., Lubin, D., Luthcke, S., Morison, J., Nelson, R., Neuenschwander, A., Palm, S., Popescu, S., Shum, C. K., Schutz, B. E., Smith, B., Yang, Y., and Zwally, J.: The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): Science requirements, concept, and implementation, Remote Sens. Environ., 190, 260–273, https://doi.org/10.1016/j.rse.2016.12.029, 2017.
Matricardi, E. A. T., Skole, D. L., Costa, O. B., Pedlowski, M. A., Samek, J. H., and Miguel, E. P.: Long-term forest degradation surpasses deforestation in the Brazilian Amazon, Science, 369, 1378–1382, https://doi.org/10.1126/science.abb3021, 2020.
Mitchard, E. T. A.: The tropical forest carbon cycle and climate change, Nature, 559, 527–534, https://doi.org/10.1038/s41586-018-0300-2, 2018.
Nepstad, D., McGrath, D., Stickler, C., Alencar, A., Azevedo, A., Swette, B., Bezerra, T., DiGiano, M., Shimada, J., da Motta, R. S., Armijo, E., Castello, L., Brando, P., Hansen, M. C., McGrath-Horn, M., Carvalho, O., and Hess, L.: Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains, Science, 344, 1118–1123, https://doi.org/10.1126/science.1248525, 2014.
Ochoa-Quintero, J. M., Gardner, T. A., Rosa, I., Ferraz, S. F. D., and Sutherland, W. J.: Thresholds of species loss in Amazonian deforestation frontier landscapes, Conserv. Biol., 29, 440–451, https://doi.org/10.1111/cobi.12446, 2015.
Olofsson, P., Stehman, S. V., Woodcock, C. E., Sulla-Menashe, D., Sibley, A. M., Newell, J. D., Friedl, M. A., and Herold, M.: A global land-cover validation data set, part I: fundamental design principles, Int. J. Remote Sens., 33, 5768–5788, https://doi.org/10.1080/01431161.2012.674230, 2012.
Qin, Y. and Xiao, X.: Codes for forest and evergreen forest mapping in the Brazilian Amazon, figshare [code], https://doi.org/10.6084/m9.figshare.21445626.v1, 2022a.
Qin, Y. and Xiao, X.: Annual PALSAR/MODIS forest and evergreen forest maps in the Brazilian Amazon, figshare [data set], https://doi.org/10.6084/m9.figshare.21445590.v1, 2022b.
Qin, Y., Xiao, X., Dong, J., Zhang, G., Roy, P. S., Joshi, P. K., Gilani, H., Murthy, M. S., Jin, C., Wang, J., Zhang, Y., Chen, B., Menarguez, M. A., Biradar, C. M., Bajgain, R., Li, X., Dai, S., Hou, Y., Xin, F., and Moore, B., 3rd: Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010, Sci. Rep., 6, 20880, https://doi.org/10.1038/srep20880, 2016.
Qin, Y. W., Xiao, X. M., Dong, J. W., Zhang, G. L., Shimada, M., Liu, J. Y., Li, C. G., Kou, W. L., and Moore, B.: Forest cover maps of China in 2010 from multiple approaches and data sources: PALSAR, Landsat, MODIS, FRA, and NFI, Isprs J. Photogramm. Remote, 109, 1–16, https://doi.org/10.1016/j.isprsjprs.2015.08.010, 2015.
Qin, Y. W., Xiao, X. M., Dong, J. W., Zhang, G. L., Roy, P. S., Joshi, P. K., Gilani, H., Murthy, M. S. R., Jin, C., Wang, J., Zhang, Y., Chen, B. Q., Menarguez, M. A., Biradar, C. M., Bajgain, R., Li, X. P., Dai, S. Q., Hou, Y., Xin, F. F., and Moore, B.: Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010, Sci. Rep., 6, 20880, https://doi.org/10.1038/srep20880, 2016.
Qin, Y. W., Xiao, X. M., Dong, J. W., Zhou, Y. T., Wang, J., Doughty, R. B., Chen, Y., Zou, Z. H., and Moore, B.: Annual dynamics of forest areas in South America during 2007–2010 at 50 m spatial resolution, Remote Sens. Environ., 201, 73–87, https://doi.org/10.1016/j.rse.2017.09.005, 2017.
Qin, Y. W., Xiao, X. M., Dong, J. W., Zhang, Y., Wu, X. C., Shimabukuro, Y., Arai, E., Biradar, C., Wang, J., Zou, Z. H., Liu, F., Shi, Z., Doughty, R., and Moore, B.: Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017, Nat. Sustainabil., 2, 764–772, https://doi.org/10.1038/s41893-019-0336-9, 2019.
Qin, Y. W., Xiao, X. M., Wigneron, J. P., Ciais, P., Brandt, M., Fan, L., Li, X. J., Crowell, S., Wu, X. C., Doughty, R., Zhang, Y., Liu, F., Sitch, S., and Moore, B.: Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon, Nat. Clim. Change, 11, 442–448, https://doi.org/10.1038/s41558-021-01026-5, 2021.
Reiche, J., Lucas, R., Mitchell, A. L., Verbesselt, J., Hoekman, D. H., Haarpaintner, J., Kellndorfer, J. M., Rosenqvist, A., Lehmann, E. A., Woodcock, C. E., Seifert, F. M., and Herold, M.: Combining satellite data for better tropical forest monitoring, Nat. Clim. Change, 6, 120–122, https://doi.org/10.1038/nclimate2919, 2016.
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A., Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., Petrova, S., White, L., Silman, M., and Morel, A.: Benchmark map of forest carbon stocks in tropical regions across three continents, P. Natl. Acad. Sci. USA, 108, 9899–9904, https://doi.org/10.1073/pnas.1019576108, 2011.
Sexton, J. O., Noojipady, P., Song, X.-P., Feng, M., Song, D.-X., Kim, D.-H., Anand, A., Huang, C., Channan, S., Pimm, S. L., and Townshend, J. R.: Conservation policy and the measurement of forests, Nat. Clim. Change, 6, 192–196, https://doi.org/10.1038/nclimate2816, 2015.
Shimada, M., Isoguchi, O., Tadono, T., and Isono, K.: PALSAR Radiometric and Geometric Calibration, IEEE T. Geosci. Remote, 47, 3915–3932, https://doi.org/10.1109/Tgrs.2009.2023909, 2009.
Shimada, M., Itoh, T., Motooka, T., Watanabe, M., Shiraishi, T., Thapa, R., and Lucas, R.: New global forest/non-forest maps from ALOS PALSAR data (2007–2010), Remote Sens. Environ., 155, 13–31, https://doi.org/10.1016/j.rse.2014.04.014, 2014.
Skole, D. and Tucker, C.: Tropical Deforestation and Habitat Fragmentation in the Amazon – Satellite Data from 1978 to 1988, Science, 260, 1905–1910, https://doi.org/10.1126/science.260.5116.1905, 1993.
Sonter, L. J., Herrera, D., Barrett, D. J., Galford, G. L., Moran, C. J., and Soares-Filho, B. S.: Mining drives extensive deforestation in the Brazilian Amazon, Nat. Commun., 8, 1013, https://doi.org/10.1038/s41467-017-00557-w, 2017.
Souza, C. M., Z. Shimbo, J., Rosa, M. R., Parente, L. L., A. Alencar, A., Rudorff, B. F. T., Hasenack, H., Matsumoto, M., G. Ferreira, L., Souza-Filho, P. W. M., de Oliveira, S. W., Rocha, W. F., Fonseca, A. V., Marques, C. B., Diniz, C. G., Costa, D., Monteiro, D., Rosa, E. R., Vélez-Martin, E., Weber, E. J., Lenti, F. E. B., Paternost, F. F., Pareyn, F. G. C., Siqueira, J. V., Viera, J. L., Neto, L. C. F., Saraiva, M. M., Sales, M. H., Salgado, M. P. G., Vasconcelos, R., Galano, S., Mesquita, V. V., and Azevedo, T.: Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine, Remote Sens.-Basel, 12, 2735, https://doi.org/10.3390/rs12172735, 2020.
Stehman, S. V., Olofsson, P., Woodcock, C. E., Herold, M., and Friedl, M. A.: A global land-cover validation data set, II: augmenting a stratified sampling design to estimate accuracy by region and land-cover class, Int. J. Remote Sens., 33, 6975–6993, https://doi.org/10.1080/01431161.2012.695092, 2012.
Tang, H., Armston, J., Hancock, S., Marselis, S., Goetz, S., and Dubayah, R.: Characterizing global forest canopy cover distribution using spaceborne lidar, Remote Sens. Environ., 231, 111262, https://doi.org/10.1016/j.rse.2019.111262, 2019a.
Tang, H., Song, X.-P., Zhao, F. A., Strahler, A. H., Schaaf, C. L., Goetz, S., Huang, C., Hansen, M. C., and Dubayah, R.: Definition and measurement of tree cover: A comparative analysis of field-, lidar- and landsat-based tree cover estimations in the Sierra national forests, USA, Agr. Forest Meteorol., 268, 258–268, https://doi.org/10.1016/j.agrformet.2019.01.024, 2019b.
Thapa, R. B., Itoh, T., Shimada, M., Watanabe, M., Takeshi, M., and Shiraishi, T.: Evaluation of ALOS PALSAR sensitivity for characterizing natural forest cover in wider tropical areas, Remote Sens. Environ., 155, 32–41, https://doi.org/10.1016/j.rse.2013.04.025, 2014.
Tyukavina, A., Hansen, M. C., Potapov, P. V., Stehman, S. V., Smith-Rodriguez, K., Okpa, C., and Aguilar, R.: Types and rates of forest disturbance in Brazilian Legal Amazon, 2000–2013, Sci. Adv., 3, e1601047, https://doi.org/10.1126/sciadv.1601047, 2017.
Woodcock, C. E., Allen, R., Anderson, M., Belward, A., Bindschadler, R., Cohen, W., Gao, F., Goward, S. N., Helder, D., Helmer, E., Nemani, R., Oreopoulos, L., Schott, J., Thenkabail, P. S., Vermote, E. F., Vogelmann, J., Wulder, M. A., Wynne, R., and Team, L. S.: Free access to Landsat imagery, Science, 320, 1011–1011, 2008.
Xiao, X., Boles, S., Frolking, S., Salas, W., Moore, B., Li, C., He, L., and Zhao, R.: Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data, Int. J. Remote Sens., 23, 3009–3022, https://doi.org/10.1080/01431160110107734, 2002.
Xiao, X., Dorovskoy, P., Biradar, C., and Bridge, E.: A library of georeferenced photos from the field, Eos, Transactions American Geophysical Union, 92, 453–454, https://doi.org/10.1029/2011eo490002, 2011.
Xiao, X. M., Biradar, C. M., Czarnecki, C., Alabi, T., and Keller, M.: A Simple Algorithm for Large-Scale Mapping of Evergreen Forests in Tropical America, Africa and Asia, Remote Sens.-Basel, 1, 355–374, https://doi.org/10.3390/Rs1030355, 2009.
Short summary
Forest definition has two major biophysical parameters, i.e., canopy height and canopy coverage. However, few studies have assessed forest cover maps in terms of these two parameters at a large scale. Here, we assessed the annual forest cover maps in the Brazilian Amazon using 1.1 million footprints of canopy height and canopy coverage. Over 93 % of our forest cover maps are consistent with the FAO forest definition, showing the high accuracy of these forest cover maps in the Brazilian Amazon.
Forest definition has two major biophysical parameters, i.e., canopy height and canopy coverage....
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