Articles | Volume 16, issue 7
https://doi.org/10.5194/essd-16-3213-2024
© 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-16-3213-2024
© 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
| 
10 Jul 2024
Data description paper |
| 10 Jul 2024
| 10 Jul 2024
ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021
Qinghang Mei
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Zhao Zhang
CORRESPONDING AUTHOR
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
Jichong Han
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
School of Systems Science, Beijing Normal University, Beijing 100875, China
Jie Song
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
School of Systems Science, Beijing Normal University, Beijing 100875, China
Jinwei Dong
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Huaqing Wu
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
Fulu Tao
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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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.
Mingyang Song, Lu Xu, Ji Ge, Hong Zhang, Lijun Zuo, Jingling Jiang, Yinhaibin Ding, Yazhe Xie, and Fan Wu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-331, https://doi.org/10.5194/essd-2024-331, 2024
Revised manuscript accepted for ESSD
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We designed to generate the 10 m resolution rice distribution map of EA in 2023 (EARice10). The generated EARice10 has an OA of 90.48 % on the validation samples, showing good consistency with statistical data and existing datasets, with R2 values ranging between 0.94 and 0.98 with statistical data, and between 0.79 and 0.98 with existing datasets. Moreover, EARice10 is the most up-to-date rice distribution map that comprehensively covers four rice production countries of EA in 10 m resolution.
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.
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.
Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, and Mengzi Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-193, https://doi.org/10.5194/essd-2024-193, 2024
Revised manuscript accepted for ESSD
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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 through the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping reached 80.9%, with the reference annual 2020 reaching an accuracy of 86.5% and a Kappa coefficient of 0.85. The study supports the use of remote sensing data to mapping a long-time continuous annual vegetation.
Maximilian Freudenberg, Sebastian Schnell, and Paul Magdon
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-206, https://doi.org/10.5194/essd-2024-206, 2024
Revised manuscript accepted for ESSD
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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 entire Germany for the years 2015 to 2022 and comprises 51 species and species groups.
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.
Zhenghang Yuan, Zhitong Xiong, Lichao Mou, and Xiao Xiang Zhu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-140, https://doi.org/10.5194/essd-2024-140, 2024
Revised manuscript accepted for ESSD
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ChatEarthNet is an image-text dataset that provides high-quality, detailed natural language descriptions for global-scale satellite data. It consists of 163,488 image-text pairs with captions generated by ChatGPT-3.5, and an additional 10,000 image-text pairs with captions generated by ChatGPT-4V(ision). This dataset has significant potential for training and evaluating vision-language geo-foundation models in remote sensing.
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).
Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-536, https://doi.org/10.5194/essd-2023-536, 2024
Revised manuscript accepted for ESSD
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Our team has developed an irrigation map with 100 m resolution, which is more detailed than existing one. We used satellite images and focused on the crop status during the dry conditions. We found that 23.4 % of global cropland is irrigated, with the most extensive areas in India, China, the US, and Pakistan. We also explored the distribution of central pivot systems, which are commonly used in the US and Saudi Arabia. This new map can better support water management and food security globally.
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).
Yuanwei Qin, Xiangming Xiao, Hao Tang, Ralph Dubayah, Russell Doughty, Diyou Liu, Fang Liu, Yosio Shimabukuro, Egidio Arai, Xinxin Wang, and Berrien Moore III
Earth Syst. Sci. Data, 16, 321–336, https://doi.org/10.5194/essd-16-321-2024, https://doi.org/10.5194/essd-16-321-2024, 2024
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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.
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.
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.
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.
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.
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.
Cited articles
Ahmed, M., Seraj, R., and Islam, S. M. S.: The k-means Algorithm: A Comprehensive Survey and Performance Evaluation, Electronics, 9, 1295, https://doi.org/10.3390/electronics9081295, 2020.
Arthur, D. and Vassilvitskii, S.: k-means++: The advantages of careful seeding, Stanford InfoLab Technical Report, No. 2006-13, Stanford University, http://ilpubs.stanford.edu:8090/778/ (last access: 3 July 2024), 2006.
Bach, F. and Jordan, M.: Learning Spectral Clustering, in: Advances in Neural Information Processing Systems, Proceedings of the 16th International Conference on Neural Information Processing Systems (NIPS 2003), Vancouver, Canada, 9–12 December 2003, MIT Press, https://proceedings.neurips.cc/paper_files/paper/2003/file/d04863f100d59b3eb688a11f95b0ae60-Paper.pdf (last access: 3 July 2024), 2003.
Chabalala, Y., Adam, E., and Ali, K. A.: Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data towards Mapping Fruit Plantations in Highly Heterogenous Landscapes, Remote Sens., 14, 2621, https://doi.org/10.3390/rs14112621, 2022.
Chen, D., Huang, J., and Jackson, T. J.: Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands, Remote Sens. Environ., 98, 225–236, https://doi.org/10.1016/j.rse.2005.07.008, 2005.
Chen, H., Li, H., Liu, Z., Zhang, C., Zhang, S., and Atkinson, P. M.: A novel Greenness and Water Content Composite Index (GWCCI) for soybean mapping from single remotely sensed multispectral images, Remote Sens. Environ., 295, 113679, https://doi.org/10.1016/j.rse.2023.113679, 2023.
Cui, K. and Shoemaker, S. P.: A look at food security in China, npj Sci. Food, 2, 4, https://doi.org/10.1038/s41538-018-0012-x, 2018.
Di Tommaso, S., Wang, S., and Lobell, D. B.: Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops, Environ. Res. Lett., 16, 125002, https://doi.org/10.1088/1748-9326/ac358c, 2021.
Dong, J., Fu, Y., Wang, J., Tian, H., Fu, S., Niu, Z., Han, W., Zheng, Y., Huang, J., and Yuan, W.: Early-season mapping of winter wheat in China based on Landsat and Sentinel images, Earth Syst. Sci. Data, 12, 3081–3095, https://doi.org/10.5194/essd-12-3081-2020, 2020.
Du, J., Han, T., Gai, J., Yong, T., Sun, X., Wang, X., Yang, F., Liu, J., Shu, K., Liu, W., and Yang, W.: Maize-soybean strip intercropping: Achieved a balance between high productivity and sustainability, J. Integr. Agric., 17, 747–754, https://doi.org/10.1016/S2095-3119(17)61789-1, 2018.
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise, KDD, Portland, Oregon, USA, 2–4 August 1996, 226–231, https://cdn.aaai.org/KDD/1996/KDD96-037.pdf (last access: 3 July 2024), 1996.
FAOSTAT: Countries by commodity, https://www.fao.org/faostat/en/#rankings/countries_by_commodity, last access: 10 October 2023.
Fensholt, R. and Sandholt, I.: Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment, Remote Sens. Environ., 87, 111–121, https://doi.org/10.1016/j.rse.2003.07.002, 2003.
Gong, L., Tian, B., Li, Y., and Wu, S.: Phenological Changes of Soybean in Response to Climate Conditions in Frigid Region in China over the Past Decades, Int. J. Plant Prod., 15, 363–375, https://doi.org/10.1007/s42106-021-00145-5, 2021.
Graesser, J. and Ramankutty, N.: Detection of cropland field parcels from Landsat imagery, Remote Sens. Environ., 201, 165–180, https://doi.org/10.1016/j.rse.2017.08.027, 2017.
Guan, X., Huang, C., Liu, G., Meng, X., and Liu, Q.: Mapping Rice Cropping Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement Based on DTW Distance, Remote Sens., 8, 19, https://doi.org/10.3390/rs8010019, 2016.
Guo, W., Ren, J., Liu, X., Chen, Z., Wu, S., and Pan, H.: Winter wheat mapping with globally optimized threshold under total quantity constraint of statistical data, J. Remote Sens., 22, 1023–1041, https://doi.org/10.11834/jrs.20187468, 2018.
Hamano, M., Shiozawa, S., Yamamoto, S., Suzuki, N., Kitaki, Y., and Watanabe, O.: Development of a method for detecting the planting and ridge areas in paddy fields using AI, GIS, and precise DEM, Precision Agric, 24, 1862–1888, https://doi.org/10.1007/s11119-023-10021-z, 2023.
Han, J., Zhang, Z., Luo, Y., Cao, J., Zhang, L., Zhang, J., and Li, Z.: The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data, Earth Syst. Sci. Data, 13, 2857–2874, https://doi.org/10.5194/essd-13-2857-2021, 2021.
Han, J., Zhang, Z., Luo, Y., Cao, J., Zhang, L., Zhuang, H., Cheng, F., Zhang, J., and Tao, F.: Annual paddy rice planting area and cropping intensity datasets and their dynamics in the Asian monsoon region from 2000 to 2020, Agric. Syst., 200, 103437, https://doi.org/10.1016/j.agsy.2022.103437, 2022.
Hartman, G. L., West, E. D., and Herman, T. K.: Crops that feed the World 2. Soybean – worldwide production, use, and constraints caused by pathogens and pests, Food Secur., 3, 5–17, https://doi.org/10.1007/s12571-010-0108-x, 2011.
Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H.: The elements of statistical learning: data mining, inference, and prediction, Springer, ISBN 978-1-4899-0519-2, ISBN 978-0-387-21606-5 (eBook), https://doi.org/10.1007/978-0-387-21606-5, 2009.
Housman, I. W., Chastain, R. A., and Finco, M. V.: An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States, Remote Sens., 10, 1184, https://doi.org/10.3390/rs10081184, 2018.
Huang, Y., Qiu, B., Chen, C., Zhu, X., Wu, W., Jiang, F., Lin, D., and Peng, Y.: Automated soybean mapping based on canopy water content and chlorophyll content using Sentinel-2 images, Int. J. Appl. Earth Obs., 109, 102801, https://doi.org/10.1016/j.jag.2022.102801, 2022.
Kerdprasop, K., Kerdprasop, N., and Sattayatham, P.: Weighted K-means for density-biased clustering, in: International conference on data warehousing and knowledge discovery, Copenhagen, Denmark, 22–26 August 2005, 488–497, https://doi.org/10.1007/11546849_48, 2005.
Konduri, V. S., Kumar, J., Hargrove, W. W., Hoffman, F. M., and Ganguly, A. R.: Mapping crops within the growing season across the United States, Remote Sens. Environ., 251, 112048, https://doi.org/10.1016/j.rse.2020.112048, 2020.
Kumari, M., Murthy, C. S., Pandey, V., and Bairagi, G. D.: Soybean Cropland Mapping Using Multi-Temporal Sentinel-1 Data, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W6, 109–114, https://doi.org/10.5194/isprs-archives-XLII-3-W6-109-2019, 2019.
Kwak, G.-H. and Park, N.-W.: Unsupervised Domain Adaptation with Adversarial Self-Training for Crop Classification Using Remote Sensing Images, Remote Sens., 14, 4639, https://doi.org/10.3390/rs14184639, 2022.
Li, B. and Yang, L.: Clustering accuracy analysis of building area in high spatial resolution remote sensing images based on k-means algorithm, in: 2017 2nd International Conference on Frontiers of Sensors Technologies (ICFST), 2017 2nd International Conference on Frontiers of Sensors Technologies (ICFST), Shenzhen, China, 14–16 April 2017, 174–178, https://doi.org/10.1109/ICFST.2017.8210497, 2017.
Li, H., Song, X.-P., Hansen, M. C., Becker-Reshef, I., Adusei, B., Pickering, J., Wang, L., Wang, L., Lin, Z., Zalles, V., Potapov, P., Stehman, S. V., and Justice, C.: Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation, Remote Sens. Environ., 294, 113623, https://doi.org/10.1016/j.rse.2023.113623, 2023.
Li, L., Friedl, M. A., Xin, Q., Gray, J., Pan, Y., and Frolking, S.: Mapping Crop Cycles in China Using MODIS-EVI Time Series, Remote Sens., 6, 2473–2493, https://doi.org/10.3390/rs6032473, 2014.
Li, T., Johansen, K., and McCabe, M. F.: A machine learning approach for identifying and delineating agricultural fields and their multi-temporal dynamics using three decades of Landsat data, ISPRS J. Photogramm. Remote Sens., 186, 83–101, https://doi.org/10.1016/j.isprsjprs.2022.02.002, 2022.
Li, Y. and Qu, H.: LSD and Skeleton Extraction Combined with Farmland Ridge Detection, in: Advances in Intelligent, Interactive Systems and Applications, Cham, 446–453, https://doi.org/10.1007/978-3-030-02804-6_59, 2019.
Liu, H., Zhang, J., Pan, Y., Shuai, G., Zhu, X., and Zhu, S.: An Efficient Approach Based on UAV Orthographic Imagery to Map Paddy With Support of Field-Level Canopy Height From Point Cloud Data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 11, 2034–2046, https://doi.org/10.1109/JSTARS.2018.2829218, 2018.
Liu, J., Wang, L., Yang, F., Yao, B., and Yang, L.: Recognition ability of red edge and short wave infrared spectrum on maize and soybean, Chinese Agricultural Science Bulletin, 34, 120–129, 2018 (in Chinese).
Liu, L., Xiao, X., Qin, Y., Wang, J., Xu, X., Hu, Y., and Qiao, Z.: Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine, Remote Sens. Environ., 239, 111624, https://doi.org/10.1016/j.rse.2019.111624, 2020.
Liu, M. and Fan, Q.: Study on the Current Situation and Problems of Soybean Consumption, Production and Import in China, Grain Science And Technology And Economy, 46, 28–35, https://doi.org/10.16465/j.gste.cn431252ts.20210606, 2021 (in Chinese).
Liu, Z., Ying, H., Chen, M., Bai, J., Xue, Y., Yin, Y., Batchelor, W. D., Yang, Y., Bai, Z., Du, M., Guo, Y., Zhang, Q., Cui, Z., Zhang, F., and Dou, Z.: Optimization of China's maize and soy production can ensure feed sufficiency at lower nitrogen and carbon footprints, Nat. Food, 2, 426–433, https://doi.org/10.1038/s43016-021-00300-1, 2021.
Lowder, S. K., Skoet, J., and Raney, T.: The Number, Size, and Distribution of Farms, Smallholder Farms, and Family Farms Worldwide, World Dev., 87, 16–29, https://doi.org/10.1016/j.worlddev.2015.10.041, 2016.
Luo, C., Liu, H., Lu, L., Liu, Z., Kong, F., and Zhang, X.: Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine, J. Integr. Agr., 20, 1944–1957, https://doi.org/10.1016/S2095-3119(20)63329-9, 2021.
Luo, Y., Zhang, Z., Li, Z., Chen, Y., Zhang, L., Cao, J., and Tao, F.: Identifying the spatiotemporal changes of annual harvesting areas for three staple crops in China by integrating multi-data sources, Environ. Res. Lett., 15, 074003, https://doi.org/10.1088/1748-9326/ab80f0, 2020.
Luo, Y., Zhang, Z., Zhang, L., Han, J., Cao, J., and Zhang, J.: Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data, Remote Sens., 14, 1809, https://doi.org/10.3390/rs14081809, 2022.
Ma, Z., Liu, Z., Zhao, Y., Zhang, L., Liu, D., Ren, T., Zhang, X., and Li, S.: An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning, ISPRS Int. J. Geo-Inf., 9, 648, https://doi.org/10.3390/ijgi9110648, 2020.
Marshall, M., Belgiu, M., Boschetti, M., Pepe, M., Stein, A., and Nelson, A.: Field-level crop yield estimation with PRISMA and Sentinel-2, ISPRS J. Photogramm. Remote Sens., 187, 191–210, https://doi.org/10.1016/j.isprsjprs.2022.03.008, 2022.
Mei, Q., Zhang, Z., Han, J., Song, J., Dong, J., Wu, H., Xu, J., and Tao, F.: ChinaSoyArea10m: a dataset of soybean planting areas with a spatial resolution of 10 m across China from 2017 to 2021 (V1), Zenodo [data set], https://doi.org/10.5281/zenodo.10071427, 2023.
National Bureau of Statistics of China: National Data, https://data.stats.gov.cn/english/, last access: 6 July 2024.
Olsen, J. L., Stisen, S., Proud, S. R., and Fensholt, R.: Evaluating EO-based canopy water stress from seasonally detrended NDVI and SIWSI with modeled evapotranspiration in the Senegal River Basin, Remote Sens. Environ., 159, 57–69, https://doi.org/10.1016/j.rse.2014.11.029, 2015.
Oreopoulos, L., Wilson, M. J., and Várnai, T.: Implementation on Landsat Data of a Simple Cloud-Mask Algorithm Developed for MODIS Land Bands, IEEE Geosci. Remote. Sens. Lett., 8, 597–601, https://doi.org/10.1109/LGRS.2010.2095409, 2011.
Pan, B. and Yuan, W.: Dataset of 10-meter resolution planting distribution of double-season rice in China from 2016 to 2020, National Ecosystem Research Network Data Center [data set], https://doi.org/10.12199/nesdc.ecodb.rs.2022.012, 2022 (in Chinese).
Pan, B., Zheng, Y., Shen, R., Ye, T., Zhao, W., Dong, J., Ma, H., and Yuan, W.: High Resolution Distribution Dataset of Double-Season Paddy Rice in China, Remote Sens., 13, 4609, https://doi.org/10.3390/rs13224609, 2021.
Potapov, P., Turubanova, S., Hansen, M. C., Tyukavina, A., Zalles, V., Khan, A., Song, X.-P., Pickens, A., Shen, Q., and Cortez, J.: Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century, Nat. Food, 3, 19–28, https://doi.org/10.1038/s43016-021-00429-z, 2022.
Raykov, Y. P., Boukouvalas, A., Baig, F., and Little, M. A.: What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm, PLOS ONE, 11, e0162259, https://doi.org/10.1371/journal.pone.0162259, 2016.
Rivera, A. J., Pérez-Godoy, M. D., Elizondo, D., Deka, L., and del Jesus, M. J.: Analysis of clustering methods for crop type mapping using satellite imagery, Neurocomputing, 492, 91–106, https://doi.org/10.1016/j.neucom.2022.04.002, 2022.
Shangguan, Y., Li, X., Lin, Y., Deng, J., and Yu, L.: Mapping spatial-temporal nationwide soybean planting area in Argentina using Google Earth Engine, Int. J. Remote Sens., 43, 1724–1748, https://doi.org/10.1080/01431161.2022.2049913, 2022.
Shen, R., Dong, J., Yuan, W., Han, W., Ye, T., and Zhao, W.: A 30 m Resolution Distribution Map of Maize for China Based on Landsat and Sentinel Images, J. Remote Sens., 2022, 2022/9846712, https://doi.org/10.34133/2022/9846712, 2022.
Shen, R., Pan, B., Peng, Q., Dong, J., Chen, X., Zhang, X., Ye, T., Huang, J., and Yuan, W.: High-resolution distribution maps of single-season rice in China from 2017 to 2022, Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, 2023a.
Shen, R., Pan, B., Peng, Q.,Dong, J., Chen, X., Zhang X., Ye, T., Huang, J., and Yuan, W.: High-resolution distribution maps of single-season rice in China from 2017 to 2022, Science Data Bank [data set], https://doi.org/10.57760/sciencedb.06963, 2023b.
Sobejano-Paz, V., Mikkelsen, T. N., Baum, A., Mo, X., Liu, S., Köppl, C. J., Johnson, M. S., Gulyas, L., and García, M.: Hyperspectral and Thermal Sensing of Stomatal Conductance, Transpiration, and Photosynthesis for Soybean and Maize under Drought, Remote Sens., 12, 3182, https://doi.org/10.3390/rs12193182, 2020.
Song, X.-P., Potapov, P. V., Krylov, A., King, L., Di Bella, C. M., Hudson, A., Khan, A., Adusei, B., Stehman, S. V., and Hansen, M. C.: National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey, Remote Sens. Environ., 190, 383–395, https://doi.org/10.1016/j.rse.2017.01.008, 2017.
Tan, P.-N., Steinbach, M., and Kumar, V.: Introduction to data mining, Pearson Education India, ISBN 978-93-325-8605-5, 2016.
Wang, S., Azzari, G., and Lobell, D. B.: Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques, Remote Sens. Environ., 222, 303–317, https://doi.org/10.1016/j.rse.2018.12.026, 2019.
Wang, S., Di Tommaso, S., Deines, J. M., and Lobell, D. B.: Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive, Sci. Data, 7, 307, https://doi.org/10.1038/s41597-020-00646-4, 2020.
Wang, Y. and Gai, J.: Study on the ecological regions of soybean in China II – Ecological environment and representative varieties, Chinese Journal of Applied Ecology, 71–75, 2002 (in Chinese).
Wang, Y., Feng, L., Sun, W., Zhang, Z., Zhang, H., Yang, G., and Meng, X.: Exploring the potential of multi-source unsupervised domain adaptation in crop mapping using Sentinel-2 images, Gisci. Remote Sens., 59, 2247–2265, https://doi.org/10.1080/15481603.2022.2156123, 2022.
Wang, Y., Ling, X., Ma, C., Liu, C., Zhang, W., Huang, J., Peng, S., and Deng, N.: Can China get out of soy dilemma? A yield gap analysis of soybean in China, Agron. Sustain. Dev., 43, 47, https://doi.org/10.1007/s13593-023-00897-6, 2023.
Xiong, J., Thenkabail, P. S., Gumma, M. K., Teluguntla, P., Poehnelt, J., Congalton, R. G., Yadav, K., and Thau, D.: Automated cropland mapping of continental Africa using Google Earth Engine cloud computing, ISPRS J. Photogramm. Remote Sens., 126, 225–244, https://doi.org/10.1016/j.isprsjprs.2017.01.019, 2017.
Yang, F., Huang, S., Gao, R., Liu, W., Yong, T., Wang, X., Wu, X., and Yang, W.: Growth of soybean seedlings in relay strip intercropping systems in relation to light quantity and red:far-red ratio, Field Crops Res., 155, 245–253, https://doi.org/10.1016/j.fcr.2013.08.011, 2014.
You, N. and Dong, J.: Examining earliest identifiable timing of crops using all available Sentinel
You, N., Dong, J., Huang, J., Du, G., Zhang, G., He, Y., Yang, T., Di, Y., and Xiao, X.: The 10-m crop type maps in Northeast China during 2017–2019, Sci. Data, 8, 41, https://doi.org/10.1038/s41597-021-00827-9, 2021.
You, N., Dong, J., Li, J., Huang, J., and Jin, Z.: Rapid early-season maize mapping without crop labels, Remote Sens. Environ., 290, 113496, https://doi.org/10.1016/j.rse.2023.113496, 2023.
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, G., Xiao, X., Biradar, C. M., Dong, J., Qin, Y., Menarguez, M. A., Zhou, Y., Zhang, Y., Jin, C., Wang, J., Doughty, R. B., Ding, M., and Moore, B.: Spatiotemporal patterns of paddy rice croplands in China and India from 2000 to 2015, Sci. Total Environ., 579, 82–92, https://doi.org/10.1016/j.scitotenv.2016.10.223, 2017.
Zhong, L., Hu, L., Yu, L., Gong, P., and Biging, G. S.: Automated mapping of soybean and corn using phenology, ISPRS J. Photogramm. Remote Sens., 119, 151–164, https://doi.org/10.1016/j.isprsjprs.2016.05.014, 2016.
Zhou, W., Wei, H., Chen, Y., Zhang, X., Hu, J., Cai, Z., Yang, J., Hu, Q., Xiong, H., Yin, G., and Xu, B.: Monitoring intra-annual and interannual variability in spatial distribution of plastic-mulched citrus in cloudy and rainy areas using multisource remote sensing data, Eur. J. Agron., 151, 126981, https://doi.org/10.1016/j.eja.2023.126981, 2023.
Short summary
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.
In order to make up for the lack of long-term soybean planting area maps in China, we firstly...
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