Articles | Volume 15, issue 6
https://doi.org/10.5194/essd-15-2347-2023
© Author(s) 2023. 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-15-2347-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
07 Jun 2023
Data description paper |
| 07 Jun 2023
| 07 Jun 2023
An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multisource product-fusion approach
Bingjie Li
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Xiaocong Xu
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Xiaoping Liu
CORRESPONDING AUTHOR
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, 519080, China
Qian Shi
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Haoming Zhuang
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Yaotong Cai
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Da He
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
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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.
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China’s forests play a crucial role in storing carbon and mitigating climate change, yet long-term, high-resolution data on their biomass have been limited. We developed a 30-m annual forest aboveground biomass dataset from 1985 to 2023 using satellite data and deep learning. Our results reveal significant biomass gains, regional variations, and the impact of forest policies. This dataset provides valuable insights for climate research, conservation planning, and sustainable forest management.
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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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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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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1203–1211, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1203-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1203-2017, 2017
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The Earth Topography 2022 (ETOPO 2022) global DEM dataset
The 20 m Africa rice distribution map of 2023
A 30m resolution annual cropland extent dataset of Africa in recent decades of the 21st century
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Aboveground biomass dataset from SMOS L-band vegetation optical depth and reference maps
Statistical Atlas of European Agriculture: Gridded Data from the Agricultural Census 2020 and the Spatial Distribution of CAP Contextual Indicators
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Annual vegetation maps in the Qinghai–Tibet Plateau (QTP) from 2000 to 2022 based on MODIS series satellite imagery
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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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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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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.
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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
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Eurostat and JRC developed a new methodology to make geospatial data from agricultural census available to users, while ensuring that no confidential information from individuals is disclosed. The geospatial data presented in the article correspond to the contextual indicators of the monitoring and evaluation framework of the Common Agricultural Policy. Our exploratory analysis reveals several interesting patterns which contribute to the broader debate on the future of European agriculture.
Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li
Earth Syst. Sci. Data, 17, 855–880, https://doi.org/10.5194/essd-17-855-2025, https://doi.org/10.5194/essd-17-855-2025, 2025
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Our study introduces GMIE, a high-resolution global map of irrigated cropland at 100 m resolution, covering 403.17 Mha and utilizing irrigation performance under drought stress. We found that 23.4 % of global cropland is irrigated, with the most extensive areas in India, China, the United States, and Pakistan. We identified the distribution of central pivot systems commonly used in the United States and Saudi Arabia. This new map can better support water management and food security globally.
Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, and Mengzi Zhou
Earth Syst. Sci. Data, 17, 773–797, https://doi.org/10.5194/essd-17-773-2025, https://doi.org/10.5194/essd-17-773-2025, 2025
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This study developed a new approach to long-time continuous annual vegetation mapping from remote sensing imagery, and mapped the vegetation of the Qinghai–Tibet Plateau (QTP) from 2000 to 2022 using the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping reached 83.3%, with the reference annual 2020 data reaching an accuracy of 83.3% and a kappa coefficient of 0.82. The study supports the use of remote sensing data to mapping long-time continuous annual vegetation.
Xuemeng Tian, Davide Consoli, Martijn Witjes, Florian Schneider, Leandro Parente, Murat Şahin, Yu-Feng Ho, Robert Minařík, and Tomislav Hengl
Earth Syst. Sci. Data, 17, 741–772, https://doi.org/10.5194/essd-17-741-2025, https://doi.org/10.5194/essd-17-741-2025, 2025
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Our study introduces a Landsat-based data cube simplifying access to detailed environmental data across Europe from 2000 to 2022, covering vegetation, water, soil, and crops. Our experiments demonstrate its effectiveness in developing environmental models and maps. Tailored feature selection is crucial for its effective use in environmental modeling. It aims to support comprehensive environmental monitoring and analysis, helping researchers and policy-makers in managing environmental resources.
Mingyang Song, Lu Xu, Ji Ge, Hong Zhang, Lijun Zuo, Jingling Jiang, Yinhaibin Ding, Yazhe Xie, and Fan Wu
Earth Syst. Sci. Data, 17, 661–683, https://doi.org/10.5194/essd-17-661-2025, https://doi.org/10.5194/essd-17-661-2025, 2025
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We created a 10 m resolution rice distribution map for East Asia in 2023 (EARice10), achieving an overall accuracy (OA) of 90.48 % on validation samples. EARice10 shows strong consistency with statistical data (coefficient of determination, R2: 0.94–0.98) and existing datasets (R2: 0.79–0.98). It is the most up-to-date map, covering the four major rice-producing countries in East Asia at 10 m resolution.
Maximilian Freudenberg, Sebastian Schnell, and Paul Magdon
Earth Syst. Sci. Data, 17, 351–367, https://doi.org/10.5194/essd-17-351-2025, https://doi.org/10.5194/essd-17-351-2025, 2025
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Classifying tree species in satellite images is an important task for environmental monitoring and forest management. Here we present a dataset containing Sentinel-2 satellite pixel time series of individual trees intended for training machine learning models. The dataset was created by merging information from the German National Forest Inventory in 2012 with satellite data. It sparsely covers the whole of Germany for the years 2015 to 2022 and comprises 48 species and 3 species groups.
Yangyang Fu, Xiuzhi Chen, Chaoqing Song, Xiaojuan Huang, Jie Dong, Qiongyan Peng, and Wenping Yuan
Earth Syst. Sci. Data, 17, 95–115, https://doi.org/10.5194/essd-17-95-2025, https://doi.org/10.5194/essd-17-95-2025, 2025
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This study proposed the Winter-Triticeae Crops Index (WTCI), which had great performance and stable spatiotemporal transferability in identifying winter-triticeae crops in 66 countries worldwide, with an overall accuracy of 87.7 %. The first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022 were further produced based on the WTCI method. The product can serve as an important basis for agricultural applications.
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.
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 Discuss., https://doi.org/10.5194/essd-2024-620, https://doi.org/10.5194/essd-2024-620, 2025
Revised manuscript accepted for ESSD
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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 pasture 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 meters with decent accuracy. This data set is valuable to scientists, policy makers, conservationists and pastoralists.
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.
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 Discuss., https://doi.org/10.5194/essd-2024-466, https://doi.org/10.5194/essd-2024-466, 2024
Revised manuscript accepted for ESSD
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Wetlands are responsible for about a third of global emissions of methane, a potent greenhouse gas. We have developed the GIEMS-MethaneCentric (GIEMS-MC) dataset to represent the dynamics of wetland extent on a global scale (0.25°x0.25° 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.
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.
Zia Mehrabi, Kaitai Tong, Julie Fortin, Radost Stanimirova, Mark Friedl, and Navin Ramankutty
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-279, https://doi.org/10.5194/essd-2024-279, 2024
Revised manuscript accepted for ESSD
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We present a global geospatial database of cropland and pastures representing the year 2015. We made it using machine learning models to merge satellite-based land cover data with agricultural census data that we compiled. 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 to easily update the product for future years.
Shuchao Ye, Peiyu Cao, and Chaoqun Lu
Earth Syst. Sci. Data, 16, 3453–3470, https://doi.org/10.5194/essd-16-3453-2024, https://doi.org/10.5194/essd-16-3453-2024, 2024
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We reconstructed annual cropland density and crop type maps, including nine major crop types (corn, soybean, winter wheat, spring wheat, durum wheat, cotton, sorghum, barley, and rice), from 1850 to 2021 at 1 km × 1 km resolution. We found that the US total crop acreage has increased by 118 × 106 ha (118 Mha), mainly driven by corn (30 Mha) and soybean (35 Mha). Additionally, the US cropping diversity experienced an increase in the 1850s–1960s, followed by a decline over the past 6 decades.
Fang Chen, Lei Wang, Yu Wang, Haiying Zhang, Ning Wang, Pengfei Ma, and Bo Yu
Earth Syst. Sci. Data, 16, 3369–3382, https://doi.org/10.5194/essd-16-3369-2024, https://doi.org/10.5194/essd-16-3369-2024, 2024
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Storage tanks are responsible for approximately 25 % of CH4 emissions in the atmosphere, exacerbating climate warming. Currently there is no publicly accessible storage tank inventory. We generated the first high-spatial-resolution (1–2 m) storage tank dataset (STD) over 92 typical cities in China in 2021, totaling 14 461 storage tanks with the construction year from 2000–2021. It shows significant agreement with CH4 emission spatially and temporally, promoting the CH4 control strategy proposal.
Xingyi Huang, Yuwei Yin, Luwei Feng, Xiaoye Tong, Xiaoxin Zhang, Jiangrong Li, and Feng Tian
Earth Syst. Sci. Data, 16, 3307–3332, https://doi.org/10.5194/essd-16-3307-2024, https://doi.org/10.5194/essd-16-3307-2024, 2024
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The Tibetan Plateau, with its diverse vegetation ranging from forests to alpine grasslands, plays a key role in understanding climate change impacts. Existing maps lack detail or miss unique ecosystems. Our research, using advanced satellite technology and machine learning, produced the map TP_LC10-2022. Comparisons with other maps revealed TP_LC10-2022's excellence in capturing local variations. Our map is significant for in-depth ecological studies.
Qinghang Mei, Zhao Zhang, Jichong Han, Jie Song, Jinwei Dong, Huaqing Wu, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 16, 3213–3231, https://doi.org/10.5194/essd-16-3213-2024, https://doi.org/10.5194/essd-16-3213-2024, 2024
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In order to make up for the lack of long-term soybean planting area maps in China, we firstly generated a dataset of soybean planting area with a spatial resolution of 10 m for major producing areas in China from 2017 to 2021 (ChinaSoyArea10m). Compared with existing datasets, ChinaSoyArea10m has higher consistency with census data and further improvement in spatial details. The dataset can provide reliable support for subsequent studies on yield monitoring and food security.
Yavar Pourmohamad, John T. Abatzoglou, Erin J. Belval, Erica Fleishman, Karen Short, Matthew C. Reeves, Nicholas Nauslar, Philip E. Higuera, Eric Henderson, Sawyer Ball, Amir AghaKouchak, Jeffrey P. Prestemon, Julia Olszewski, and Mojtaba Sadegh
Earth Syst. Sci. Data, 16, 3045–3060, https://doi.org/10.5194/essd-16-3045-2024, https://doi.org/10.5194/essd-16-3045-2024, 2024
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The FPA FOD-Attributes dataset provides > 300 biological, physical, social, and administrative attributes associated with > 2.3×106 wildfire incidents across the US from 1992 to 2020. The dataset can be used to (1) answer numerous questions about the covariates associated with human- and lightning-caused wildfires and (2) support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.
Ewa Grabska-Szwagrzyk, Dirk Tiede, Martin Sudmanns, and Jacek Kozak
Earth Syst. Sci. Data, 16, 2877–2891, https://doi.org/10.5194/essd-16-2877-2024, https://doi.org/10.5194/essd-16-2877-2024, 2024
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We accurately mapped 16 dominant tree species and genera in Poland using Sentinel-2 observations from short periods in spring, summer, and autumn (2018–2021). The classification achieved more than 80% accuracy in country-wide forest species mapping, with variation based on species, region, and observation frequency. Freely accessible resources, including the forest tree species map and training and test data, can be found at https://doi.org/10.5281/zenodo.10180469.
Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data, 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024, https://doi.org/10.5194/essd-16-2605-2024, 2024
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NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 120 000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. This paper summarizes those results and provides links to details on ~ 80 individual flight lines. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
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We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, https://doi.org/10.5194/essd-16-2007-2024, 2024
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This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
Hui Li, Xiaobo Wang, Shaoqiang Wang, Jinyuan Liu, Yuanyuan Liu, Zhenhai Liu, Shiliang Chen, Qinyi Wang, Tongtong Zhu, Lunche Wang, and Lizhe Wang
Earth Syst. Sci. Data, 16, 1689–1701, https://doi.org/10.5194/essd-16-1689-2024, https://doi.org/10.5194/essd-16-1689-2024, 2024
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Utilizing satellite remote sensing data, we established a multi-season rice calendar dataset named ChinaRiceCalendar. It exhibits strong alignment with field observations collected by agricultural meteorological stations across China. ChinaRiceCalendar stands as a reliable dataset for investigating and optimizing the spatiotemporal dynamics of rice phenology in China, particularly in the context of climate and land use changes.
Giulia Ronchetti, Luigi Nisini Scacchiafichi, Lorenzo Seguini, Iacopo Cerrani, and Marijn van der Velde
Earth Syst. Sci. Data, 16, 1623–1649, https://doi.org/10.5194/essd-16-1623-2024, https://doi.org/10.5194/essd-16-1623-2024, 2024
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We present a dataset of EU-wide harmonized subnational crop area, production, and yield statistics with information on data sources, processing steps, missing and derived data, and quality checks. Statistical records (344 282) collected from 1975 to 2020 for soft and durum wheat, winter and spring barley, grain maize, sunflower, and sugar beet were aligned with the EUROSTAT crop legend and the 2016 territorial classification for 961 regions. Time series have a median length of 21 years.
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
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This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Qiangqiang Sun, Ping Zhang, Xin Jiao, Xin Lin, Wenkai Duan, Su Ma, Qidi Pan, Lu Chen, Yongxiang Zhang, Shucheng You, Shunxi Liu, Jinmin Hao, Hong Li, and Danfeng Sun
Earth Syst. Sci. Data, 16, 1333–1351, https://doi.org/10.5194/essd-16-1333-2024, https://doi.org/10.5194/essd-16-1333-2024, 2024
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To provide multifaceted changes under climate change and anthropogenic impacts, we estimated monthly vegetation and soil fractions in 2001–2022, providing an accurate estimate of surface heterogeneous composition, better than vegetation index and vegetation continuous-field products. We find a greening trend on Earth except for the tropics. A combination of interactive changes in vegetation and soil can be adopted as a valuable measurement of climate change and anthropogenic impacts.
Kai Cheng, Yuling Chen, Tianyu Xiang, Haitao Yang, Weiyan Liu, Yu Ren, Hongcan Guan, Tianyu Hu, Qin Ma, and Qinghua Guo
Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024, https://doi.org/10.5194/essd-16-803-2024, 2024
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To quantify forest carbon stock and its future potential accurately, we generated a 30 m resolution forest age map for China in 2020 using multisource remote sensing datasets based on machine learning and time series analysis approaches. Validation with independent field samples indicated that the mapped forest age had an R2 of 0.51--0.63. Nationally, the average forest age is 56.1 years (standard deviation of 32.7 years).
Wolfgang Alexander Obermeier, Clemens Schwingshackl, Ana Bastos, Giulia Conchedda, Thomas Gasser, Giacomo Grassi, Richard A. Houghton, Francesco Nicola Tubiello, Stephen Sitch, and Julia Pongratz
Earth Syst. Sci. Data, 16, 605–645, https://doi.org/10.5194/essd-16-605-2024, https://doi.org/10.5194/essd-16-605-2024, 2024
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We provide and compare country-level estimates of land-use CO2 fluxes from a variety and large number of models, bottom-up estimates, and country reports for the period 1950–2021. Although net fluxes are small in many countries, they are often composed of large compensating emissions and removals. In many countries, the estimates agree well once their individual characteristics are accounted for, but in other countries, including some of the largest emitters, substantial uncertainties exist.
Cameron I. Ludemann, Nathan Wanner, Pauline Chivenge, Achim Dobermann, Rasmus Einarsson, Patricio Grassini, Armelle Gruere, Kevin Jackson, Luis Lassaletta, Federico Maggi, Griffiths Obli-Laryea, Martin K. van Ittersum, Srishti Vishwakarma, Xin Zhang, and Francesco N. Tubiello
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).
Cited articles
Ban, Y., Gong, P., and Giri, C.: Global land cover mapping using Earth
observation satellite data: Recent progresses and challenges, ISPRS J.
Photogramm., 103, 1–6, https://doi.org/10.1016/j.isprsjprs.2015.01.001, 2015.
Bartholomé, E. and Belward, A. S.: GLC2000: A new approach to global
land cover mapping from Earth observation data, Int. J. Remote Sens., 26,
1959–1977, https://doi.org/10.1080/01431160412331291297, 2005.
Bounoua, L., DeFries, R., Collatz, G. J., Sellers, P., and Khan, H.: Effects
of land cover conversion on surface climate, Climatic Change, 52, 29–64,
https://doi.org/10.1023/A:1013051420309, 2002.
Bunting, P., Rosenqvist, A., Lucas, R. M., Rebelo, L.-M., Hilarides, L., Thomas, N., Hardy, A., Itoh, T., Shimada, M., and Finlayson, C. M.: The Global Mangrove Watch-A new 2010 global baseline of mangrove extent, Remote Sens., 10, 1669,https://doi.org/10.3390/rs10101669, 2018.
Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R. M., Thomas, N., Tadono, T., Worthington, T. A., Spalding, M., Murray, N. J., and Rebelo, L.-M.: Global mangrove extent change 1996-2020: Global Mangrove Watch version 3.0, Remote Sens., 14, 3657, https://doi.org/10.3390/rs14153657, 2022.
Chapin, F. S., Zavaleta, E. S., Eviner, V. T., Naylor, R. L., Vitousek,
P. M., Reynolds, H. L., Hooper, D. U., Lavorel, S., Sala, O. E., Hobbie, S.
E., Mack, M. C., and Díaz, S.: Consequences of changing biodiversity,
Nature, 405, 234–242, https://doi.org/10.1038/35012241, 2000.
Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G.,
Peng, S., Lu, M., Zhang, W., Tong, X., and Mills, J.: Global land cover
mapping at 30 m resolution: A POK-based operational approach, ISPRS J.
Photogramm., 103, 7–27, https://doi.org/10.1016/j.isprsjprs.2014.09.002, 2015.
Chen, T. M. and Venkataramanan, V.: Dempster-Shafer theory for intrusion
detection in ad hoc networks, IEEE Internet Comput., 9, 35–41, https://doi.org/10.1109/MIC.2005.123, 2005.
Clinton, N., Yu, L., and Gong, P.: Geographic stacking: Decision fusion to
increase global land cover map accuracy, ISPRS J. Photogramm., 103, 57–65,
https://doi.org/10.1016/j.isprsjprs.2015.02.010, 2015.
Defourny, P., Kirches, G., Brockmann, C., Boettcher, M., Peters, M., Bontemps, S., Lamarche, C.,
Schlerf, M., and Santoro, M.: Land Cover CCI: Product User Guide Version 2,
https://www.esa-landcover-cci.org/?q=webfm_send/84 (last access: 15 January 2022), 2018.
DeFries, R. S., Houghton, R. A., Hansen, M. C., Field, C. B., Skole, D., and
Townshend, J.: Carbon emissions from tropical deforestation and regrowth
based on satellite observations for the 1980s and 1990s, P. Natl. Acad.
Sci. USA, 99, 14256–14261, https://doi.org/10.1073/pnas.182560099,
2002.
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter,
S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J.
H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A.,
Prentice, I. C., Ramankutty, N., and Snyder, P. K.: Global consequences of
land use, Science, 309, 570–574, https://doi.org/10.1126/science.1111772, 2005.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N.,
Sibley, A., and Huang, X.: 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., You, L., Bun, A., See, L., McCallum, I., Schill, C., Perger, C.,
Liu, J., Hansen, M., and Obersteiner, M.: Cropland for sub-Saharan Africa: A
synergistic approach using five land cover data sets, Geophys. Res. Lett.,
38, L04404, https://doi.org/10.1029/2010GL046213, 2011.
Gao, Y., Liu, L., Zhang, X., Chen, X., Mi, J., and Xie, S.: Consistency
analysis and accuracy assessment of three global 30 m land-cover products
over the European Union using the LUCAS dataset, Remote Sens., 12, 3479,
https://doi.org/10.3390/rs12213479, 2020.
Gengler, S. and Bogaert, P.: Combining land cover products using a minimum
divergence and a Bayesian data fusion approach, Int. J. Geogr. Inf. Sci.,
32, 806–826, https://doi.org/10.1080/13658816.2017.1413577,
2018.
Giri, C., Zhu, Z., and Reed, B.: A comparative analysis of the Global Land
Cover 2000 and MODIS land cover datasets, Remote Sens. Environ., 94,
123–132, https://doi.org/10.1016/j.rse.2004.09.005, 2005.
Giri, C., Pengra, B., Long, J., and Loveland, T. R.: Next generation of
global land cover characterization, mapping, and monitoring, Int. J. Appl.
Earth Obs., 25, 30–37, https://doi.org/10.1016/j.jag.2013.03.005, 2013.
Gómez, C., White, J. C., and Wulder, M. A.: Optical remotely sensed time
series data for land cover classification: A review, ISPRS J. Photogramm.,
116, 55–72, https://doi.org/10.1016/j.isprsjprs.2016.03.008,
2016.
Gong, P.: Remote sensing of environmental change over China: A review, Sci.
Bull., 57, 2793–2801, https://doi.org/10.1007/s11434-012-5268-y, 2012.
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang,
X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X.,
Cheng, Q., Hu, L., Yao, W., Zhang, H., Zhu, P., Zhao, Z., Zhang, H., Zheng,
Y., Ji, L., Zhang, Y., Chen, H., Yan, A., Guo, J., Yu, L., Wang, L., Liu,
X., Shi, T., Zhu, M., Chen, Y., Yang, G., Tang, P., Xu, B., Giri, C.,
Clinton, N., Zhu, Z., Chen, J., and Chen, J.: Finer resolution observation
and monitoring of global land cover: first mapping results with Landsat TM
and ETM+ data, Int. J. Remote Sens., 34, 2607–2654, https://doi.org/10.1080/01431161.2012.748992, 2013.
Gong, P., Yu, L., Li, C., Wang, J., Liang, L., Li, X., Ji, L., Bai, Y.,
Cheng, Y., and Zhu, Z.: A new research paradigm for global land cover
mapping, Ann. GIS, 22, 87–102, https://doi.org/10.1080/19475683.2016.1164247, 2016.
Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., Liu, X., Xu, B.,
Yang, J., Zhang, W., and Zhou, Y.: Annual maps of global artificial
impervious area (GAIA) between 1985 and 2018, Remote Sens. Environ., 236,
111510, https://doi.org/10.1016/j.rse.2019.111510, 2020.
Grekousis, G., Mountrakis, G., and Kavouras, M.: An overview of 21 global
and 43 regional land-cover mapping products, Int. J. Remote Sens., 36,
5309–5335, https://doi.org/10.1080/01431161.2015.1093195, 2015.
Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai,
X., and Briggs, J. M.: Global change and the ecology of cities, Science,
319, 756–760, https://doi.org/10.1126/science.1150195, 2008.
Hansen, M. C., Defries, R. S., Townshend, J. R. G., and Sohlberg, R.: Global
land cover classification at 1 km spatial resolution using a classification
tree approach, Int. J. Remote Sens., 21, 1331–1364, https://doi.org/10.1080/014311600210209, 2000.
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.
G.: High-resolution global maps of 21st-century forest cover change,
Science, 342, 850–853, https://doi.org/10.1126/science.1244693,
2013.
Herold, M., Mayaux, P., Woodcock, C. E., Baccini, A., and Schmullius, C.:
Some challenges in global land cover mapping: An assessment of agreement and
accuracy in existing 1 km datasets, Remote Sens. Environ., 112, 2538–2556,
https://doi.org/10.1016/j.rse.2007.11.013, 2008.
Hu, L., Chen, Y., Xu, Y., Zhao, Y., Yu, L., Wang, J., and Gong, P.: A 30
meter land cover mapping of China with an efficient clustering algorithm
CBEST, Sci. China Earth Sci., 57, 2293–2304, https://doi.org/10.1007/s11430-014-4917-1, 2014.
Hu, S., Niu, Z., Chen, Y., Li, L., and Zhang, H.: Global wetlands: Potential
distribution, wetland loss, and status, Sci. Total Environ., 586, 319–327,
https://doi.org/10.1016/j.scitotenv.2017.02.001, 2017.
Huang, X., Li, J., Yang, J., Zhang, Z., Li, D., and Liu, X.: 30 m global
impervious surface area dynamics and urban expansion pattern observed by
Landsat satellites: From 1972 to 2019, Sci. China Earth Sci., 64,
1922–1933, https://doi.org/10.1007/s11430-020-9797-9, 2021.
Huang, X., Yang, J., Wang, W., and Liu, Z.: Mapping 10 m global impervious surface area (GISA-10m) using multi-source geospatial data, Earth Syst. Sci. Data, 14, 3649–3672, https://doi.org/10.5194/essd-14-3649-2022, 2022.
Iwao, K., Nasahara, K. N., Kinoshita, T., Yamagata, Y., Patton, D., and
Tsuchida, S.: Creation of new global land cover map with map integration, J.
Geogr. Inf. Syst., 3, 160–165, https://doi.org/10.4236/jgis.2011.32013, 2011.
Jin, Q., Xu, E., and Zhang, X.: A fusion method for multisource land cover
products based on superpixels and statistical extraction for enhancing
resolution and improving accuracy, Remote Sens., 14, 1676, https://doi.org/10.3390/rs14071676, 2022.
Jung, M., Henkel, K., Herold, M., and Churkina, G.: Exploiting synergies of
global land cover products for carbon cycle modeling, Remote Sens. Environ.,
101, 534–553, https://doi.org/10.1016/j.rse.2006.01.020, 2006.
Kang, J., Wang, Z., Sui, L., Yang, X., Ma, Y., and Wang, J.: Consistency
analysis of remote sensing land cover products in the tropical rainforest
climate region: A case study of Indonesia, Remote Sens., 12, 1410,
https://doi.org/10.3390/rs12091410, 2020.
Kim, D., Lim, C.-H., Song, C., Lee, W.-K., Piao, D., Heo, S., and Jeon, S.:
Estimation of future carbon budget with climate change and reforestation
scenario in North Korea, Adv. Space Res., 58, 1002–1016, https://doi.org/10.1016/j.asr.2016.05.049, 2016.
Li, B., Xu, X., Liu, X., Shi, Q., Zhuang, H., Cai, Y., and He, D.: An
improved global land cover mapping in 2015 with 30 m resolution (GLC-2015)
based on a multi-source product fusion approach, figshare [data set], https://doi.org/10.6084/m9.figshare.22358143.v2, 2023.
Li, C., Gong, P., Wang, J., Zhu, Z., Biging, G. S., Yuan, C., Hu, T., Zhang,
H., Wang, Q., Li, X., Liu, X., Xu, Y., Guo, J., Liu, C., Hackman, K. O.,
Zhang, M., Cheng, Y., Yu, L., Yang, J., Huang, H., and Clinton, N.: The
first all-season sample set for mapping global land cover with Landsat-8
data, Sci. Bull., 62, 508–515, https://doi.org/10.1016/j.scib.2017.03.011, 2017.
Liao, A., Chen, L., Chen, J., He, C., Cao, X., Chen, J., Peng, S., Sun, F.,
and Gong, P.: High-resolution remote sensing mapping of global land water,
Sci. China Earth Sci., 57, 2305–2316, https://doi.org/10.1007/s11430-014-4918-0, 2014.
Liu, H., Gong, P., Wang, J., Clinton, N., Bai, Y., and Liang, S.: Annual dynamics of global land cover and its long-term changes from 1982 to 2015, Earth Syst. Sci. Data, 12, 1217–1243, https://doi.org/10.5194/essd-12-1217-2020, 2020.
Liu, H., Gong, P., Wang, J., Wang, X., Ning, G., and Xu, B.: Production of
global daily seamless data cubes and quantification of global land cover
change from 1985 to 2020 – iMap World 1.0, Remote Sens. Environ., 258,
112364, https://doi.org/10.1016/j.rse.2021.112364, 2021.
Liu, J., Kuang, W., Zhang, Z., Xu, X., Qin, Y., Ning, J., Zhou, W., Zhang,
S., Li, R., Yan, C., Wu, S., Shi, X., Jiang, N., Yu, D., Pan, X., and Chi,
W.: Spatiotemporal characteristics, patterns and causes of land use changes
in China since the late 1980s, Dili Xuebao/Acta Geogr. Sin., 69, 3–14,
https://doi.org/10.11821/dlxb201401001, 2014.
Liu, K. and Xu, E.: Fusion and correction of multi-source land cover
products based on spatial detection and uncertainty reasoning methods in
Central Asia, Remote Sens., 13, 244, https://doi.org/10.3390/rs13020244, 2021.
Liu, L., Zhang, X., Gao, Y., Chen, X., Shuai, X., and Mi, J.:
Finer-resolution mapping of global land cover: Recent developments,
consistency analysis, and prospects, J. Remote Sens., 2021,
5289697, https://doi.org/10.34133/2021/5289697, 2021.
Liu, X., Huang, Y., Xu, X., Li, X., Li, X., Ciais, P., Lin, P., Gong, K.,
Ziegler, A. D., Chen, A., Gong, P., Chen, J., Hu, G., Chen, Y., Wang, S.,
Wu, Q., Huang, K., Estes, L., and Zeng, Z.: High-spatiotemporal-resolution
mapping of global urban change from 1985 to 2015, Nat. Sustain., 3,
564–570, https://doi.org/10.1038/s41893-020-0521-x, 2020.
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L.,
and Merchant, J. W.: Development of a global land cover characteristics
database and IGBP DISCover from 1 km AVHRR data, Int. J. Remote Sens., 21,
1303–1330, https://doi.org/10.1080/014311600210191, 2000.
Lu, M., Wu, W., You, L., See, L., Fritz, S., Yu, Q., Wei, Y., Chen, D., Yang, P., and Xue, B.: A cultivated planet in 2010 – Part 1: The global synergy cropland map, Earth Syst. Sci. Data, 12, 1913–1928, https://doi.org/10.5194/essd-12-1913-2020, 2020.
Ludwig, C., Walli, A., Schleicher, C., Weichselbaum, J., and Riffler, M.: A
highly automated algorithm for wetland detection using multi-temporal
optical satellite data, Remote Sens. Environ., 224, 333–351, https://doi.org/10.1016/j.rse.2019.01.017, 2019.
Mayaux, P., Bartholomé, E., Fritz, S., and Belward, A.: A new land-cover
map of Africa for the year 2000, J. Biogeogr., 31, 861–877, https://doi.org/10.1111/j.1365-2699.2004.01073.x, 2004.
McCallum, I., Obersteiner, M., Nilsson, S., and Shvidenko, A.: A spatial
comparison of four satellite derived 1 km global land cover datasets, Int. J.
Appl. Earth Obs., 8, 246–255, https://doi.org/10.1016/j.jag.2005.12.002, 2006.
Meng, Z., Dong, J., Ellis, E. C., Metternicht, G., Qin, Y., Song, X.-P.,
Löfqvist, S., Garrett, R. D., Jia, X., and Xiao, X.: Post-2020
biodiversity framework challenged by cropland expansion in protected areas,
Nat. Sustain., 1–11, https://doi.org/10.1038/s41893-023-01093-w, online first, 2023.
Meyer, M. F., Labou, S. G., Cramer, A. N., Brousil, M. R., and Luff, B. T.:
The global lake area, climate, and population dataset, Sci. Data, 7, 174,
https://doi.org/10.1038/s41597-020-0517-4, 2020.
Moody, A. and Woodcock, C.: Scale-dependent errors in the estimation of
land-cover proportions: Implications for global land-cover datasets,
Photogramm. Eng. Rem. S., 60, 585–594, 1994.
Pekel, J. F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution
mapping of global surface water and its long-term changes, Nature, 540,
418–422, https://doi.org/10.1038/nature20584, 2016.
Pengra, B. W., Stehman, S. V., Horton, J. A., Dockter, D. J., Schroeder, T.
A., Yang, Z., Cohen, W. B., Healey, S. P., and Loveland, T. R.: Quality
control and assessment of interpreter consistency of annual land cover
reference data in an operational national monitoring program, Remote Sens.
Environ., 238, 111261, https://doi.org/10.1016/j.rse.2019.111261, 2020.
Pickens, A. H., Hansen, M. C., Hancher, M., Stehman, S. V., Tyukavina, A.,
Potapov, P., Marroquin, B., and Sherani, Z.: Mapping and sampling to
characterize global inland water dynamics from 1999 to 2018 with full
Landsat time-series, Remote Sens. Environ., 243, 111792, https://doi.org/10.1016/j.rse.2020.111792, 2020.
Razi, S., Mollaei, M. R. K., and Ghasemi, J.: A novel method for
classification of BCI multi-class motor imagery task based on
Dempster–Shafer theory, Inform. Sciences, 484, 14–26, https://doi.org/10.1016/j.ins.2019.01.053, 2019.
Rottensteiner, F., Trinder, J., Clode, S., and Kubik, K.: Using the
Dempster-Shafer method for the fusion of LIDAR data and multi-spectral
images for building detection, Inform. Fusion, 6, 283–300, https://doi.org/10.1016/j.inffus.2004.06.004, 2005.
Running, S. W.: Ecosystem disturbance, carbon, and climate, Science, 321,
652–653, https://doi.org/10.1126/science.1159607, 2008.
Schewe, J., Gosling, S. N., Reyer, C., Zhao, F., Ciais, P., Elliott, J.,
Francois, L., Huber, V., Lotze, H. K., Seneviratne, S. I., van Vliet, M. T.
H., Vautard, R., Wada, Y., Breuer, L., Büchner, M., Carozza, D. A.,
Chang, J., Coll, M., Deryng, D., de Wit, A., Eddy, T. D., Folberth, C.,
Frieler, K., Friend, A. D., Gerten, D., Gudmundsson, L., Hanasaki, N., Ito,
A., Khabarov, N., Kim, H., Lawrence, P., Morfopoulos, C., Müller, C.,
Müller Schmied, H., Orth, R., Ostberg, S., Pokhrel, Y., Pugh, T. A. M.,
Sakurai, G., Satoh, Y., Schmid, E., Stacke, T., Steenbeek, J., Steinkamp,
J., Tang, Q., Tian, H., Tittensor, D. P., Volkholz, J., Wang, X., and
Warszawski, L.: State-of-the-art global models underestimate impacts from
climate extremes, Nat. Commun., 10, 1005, https://doi.org/10.1038/s41467-019-08745-6, 2019.
See, L., Schepaschenko, D., Lesiv, M., McCallum, I., Fritz, S., Comber, A.,
Perger, C., Schill, C., Zhao, Y., Maus, V., Siraj, M. A., Albrecht, F.,
Cipriani, A., Vakolyuk, M. y., Garcia, A., Rabia, A. H., Singha, K.,
Marcarini, A. A., Kattenborn, T., Hazarika, R., Schepaschenko, M., van der
Velde, M., Kraxner, F., and Obersteiner, M.: Building a hybrid land cover
map with crowdsourcing and geographically weighted regression, ISPRS J.
Photogramm., 103, 48–56, https://doi.org/10.1016/j.isprsjprs.2014.06.016, 2015.
Shafizadeh-Moghadam, H., Minaei, M., Feng, Y., and Pontius, R. G.:
GlobeLand30 maps show four times larger gross than net land change from 2000
to 2010 in Asia, Int. J. Appl. Earth Obs., 78, 240–248, https://doi.org/10.1016/j.jag.2019.01.003, 2019.
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.
Song, X.-P., Hansen, M. C., Stehman, S. V., Potapov, P. V., Tyukavina, A.,
Vermote, E. F., and Townshend, J. R.: Global land change from 1982 to 2016,
Nature, 560, 639–643, https://doi.org/10.1038/s41586-018-0411-9, 2018.
Sun, B., Chen, X., and Zhou, Q.: UNCERTAINTY ASSESSMENT OF GLOBELAND30 LAND COVER DATA SET OVER CENTRAL ASIA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 1313–1317, https://doi.org/10.5194/isprs-archives-XLI-B8-1313-2016, 2016.
Teluguntla, P., Thenkabail, P. S., Oliphant, A., Xiong, J., Gumma, M. K.,
Congalton, R. G., Yadav, K., and Huete, A.: A 30-m landsat-derived cropland
extent product of Australia and China using random forest machine learning
algorithm on Google Earth Engine cloud computing platform, ISPRS J.
Photogramm., 144, 325–340, https://doi.org/10.1016/j.isprsjprs.2018.07.017, 2018.
Verburg, P. H., Neumann, K., and Nol, L.: Challenges in using land use and
land cover data for global change studies, Glob. Change Biol., 17, 974–989,
https://doi.org/10.1111/j.1365-2486.2010.02307.x, 2011.
Verburg, P. H., Mertz, O., Erb, K.-H., Haberl, H., and Wu, W.: Land system
change and food security: towards multi-scale land system solutions, Curr.
Opin. Env. Sust., 5, 494–502, https://doi.org/10.1016/j.cosust.2013.07.003, 2013.
Wickham, J., Stehman, S. V., Sorenson, D. G., Gass, L., and Dewitz, J. A.:
Thematic accuracy assessment of the NLCD 2016 land cover for the
conterminous United States, Remote Sens. Environ., 257, 112357, https://doi.org/10.1016/j.rse.2021.112357, 2021.
Wu, J., Wang, X., Zhong, B., Yang, A., Jue, K., Wu, J., Zhang, L., Xu, W.,
Wu, S., Zhang, N., and Liu, Q.: Ecological environment assessment for
Greater Mekong Subregion based on Pressure-State-Response framework by
remote sensing, Ecol. Indic., 117, 106521, https://doi.org/10.1016/j.ecolind.2020.106521, 2020.
Wulder, M. A., Li, Z., Campbell, E. M., White, J. C., Hobart, G.,
Hermosilla, T., and Coops, N. C.: A national assessment of wetland status
and trends for Canada's forested ecosystems using 33 years of earth
observation satellite data, Remote Sens., 10, 1623, https://doi.org/10.3390/rs10101623, 2018.
Xiong, J., Thenkabail, P. S., Tilton, J. C., Gumma, M. K., Teluguntla, P.,
Oliphant, A., Congalton, R. G., Yadav, K., and Gorelick, N.: Nominal 30-m
cropland extent map of continental Africa by integrating pixel-based and
object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth
Engine, Remote Sens., 9, 1065, https://doi.org/10.3390/rs9101065, 2017.
Xu, G., Zhang, H., Chen, B., Zhang, H., Yan, J., Chen, J., Che, M., Lin, X.,
and Dou, X.: A Bayesian based method to generate a synergetic land-cover map
from existing land-cover products, Remote Sens., 6, 5589–5613, https://doi.org/10.3390/rs606558910.3390/rs6065589, 2014.
Xue, J., Wang, Y., Teng, H., Wang, N., Li, D., Peng, J., Biswas, A., and
Shi, Z.: Dynamics of vegetation greenness and its response to climate
change in Xinjiang over the past two decades, Remote Sens., 13, 4063,
https://doi.org/10.3390/rs13204063, 2021.
Yang, J. and Huang, X.: The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019, Earth Syst. Sci. Data, 13, 3907–3925, https://doi.org/10.5194/essd-13-3907-2021, 2021.
Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., Xu, B., Shi, J.,
and Dickinson, R.: The role of satellite remote sensing in climate change
studies, Nat. Clim. Change, 3, 875–883, https://doi.org/10.1038/nclimate1908, 2013.
Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Bender, S. M., Case,
A., Costello, C., Dewitz, J., Fry, J., Funk, M., Granneman, B., Liknes, G.
C., Rigge, M., and Xian, G.: A new generation of the United States National
Land Cover Database: Requirements, research priorities, design, and
implementation strategies, ISPRS J. Photogramm., 146, 108–123, https://doi.org/10.1016/j.isprsjprs.2018.09.006, 2018.
Yang, Y., Xiao, P., Feng, X., and Li, H.: Accuracy assessment of seven
global land cover datasets over China, ISPRS J. Photogramm., 125, 156–173,
https://doi.org/10.1016/j.isprsjprs.2017.01.016, 2017.
Yu, L., Wang, J., Clinton, N., Xin, Q., Zhong, L., Chen, Y., and Gong, P.:
FROM-GC: 30 m global cropland extent derived through multisource data
integration, Int. J. Digit. Earth, 6, 521–533, https://doi.org/10.1080/17538947.2013.822574, 2013.
Zhang, C., Dong, J., and Ge, Q.: Quantifying the accuracies of six 30-m
cropland datasets over China: A comparison and evaluation analysis, Comput.
Electron. Agr., 197, 106946, https://doi.org/10.1016/j.compag.2022.106946, 2022.
Zhang, M., Ma, M., De Maeyer, P., and Kurban, A.: Uncertainties in
classification system conversion and an analysis of inconsistencies in
global land cover products, ISPRS Int. J. Geo-Inf., 6, 112, https://doi.org/10.3390/ijgi6040112, 2017.
Zhang, X., Long, T., He, G., Guo, Y., Yin, R., Zhang, Z., Xiao, H., Li, M.,
and Cheng, B.: Rapid generation of global forest cover map using Landsat based
on the forest ecological zones, J. Appl. Remote Sens., 14, 022211, https://doi.org/10.1117/1.JRS.14.022211, 2020.
Zhang, X., Liu, L., Chen, X., Gao, Y., Xie, S., and Mi, J.: GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery, Earth Syst. Sci. Data, 13, 2753–2776, https://doi.org/10.5194/essd-13-2753-2021, 2021.
Zhang, X., Liu, L., Zhao, T., Chen, X., Lin, S., Wang, J., Mi, J., and Liu, W.: GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020, Earth Syst. Sci. Data, 15, 265–293, https://doi.org/10.5194/essd-15-265-2023, 2023.
Zhao, J., Yu, L., Liu, H., Huang, H., Wang, J., and Gong, P.: Towards an
open and synergistic framework for mapping global land cover, PeerJ, 9,
e11877, https://doi.org/10.7717/peerj.11877, 2021.
Zheng, W., Liu, Y., Yang, X., and Fan, W.: Spatiotemporal variations of
forest vegetation phenology and its response to climate change in northeast
China, Remote Sens., 14, 2909, https://doi.org/10.3390/rs14122909, 2022.
Short summary
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.
A global land cover map with fine spatial resolution is important for climate and environmental...
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