Articles | Volume 15, issue 2
https://doi.org/10.5194/essd-15-791-2023
© 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-15-791-2023
© 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
| 
14 Feb 2023
Data description paper |
| 14 Feb 2023
| 14 Feb 2023
AsiaRiceYield4km: seasonal rice yield in Asia from 1995 to 2015
Huaqing Wu
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR China
Jing Zhang
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
Faculty of Geographical Science, Beijing Normal University, Beijing
100875, PR China
Zhao Zhang
CORRESPONDING AUTHOR
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR China
Jichong Han
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR China
Juan Cao
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR China
Liangliang Zhang
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR China
Yuchuan Luo
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR China
Qinghang Mei
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, PR China
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR China
Jialu Xu
School of National Safety and Emergency Management, Beijing Normal
University, Beijing 100875, PR 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, PR China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, PR China
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One of the major manifestations of global change is the alteration of natural vegetation landscapes by human reclamation. Here we reconstruct a unified set of long-term time series cropland area change datasets with standardized criteria. The cropland in Northeast China exhibited phase changes of expansion-reduction-expansion over the past millennium. Compared to global historical LUCC datasets, our dataset has significant time resolution and reliability.
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).
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.
Gerardo E. Soto, Steven Wilcox, Patrick E. Clark, Francesco P. Fava, Nathan M. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher Barrett
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-217, https://doi.org/10.5194/essd-2023-217, 2023
Preprint under review for ESSD
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Using machine learning classification and linear unmixing, this paper produced Landsat-based time series of land cover classes and vegetation fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and bare ground. This dataset represents a first multi-decadal high-resolution dataset specifically designed for mapping and monitoring rangelands health in East Africa including Kenya, Ethiopia, and Somalia, which are dominated by arid and semi-arid extensive rangeland systems.
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.
Ruoque Shen, Baihong Pan, Qiongyan Peng, Jie Dong, Xuebing Chen, Xi Zhang, Tao Ye, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, https://doi.org/10.5194/essd-15-3203-2023, 2023
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Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. This study developed a new rice-mapping method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data, and we found that the distribution maps have good accuracy.
Charles R. Lane, Ellen D'Amico, Jay R. Christensen, Heather E. Golden, Qiusheng Wu, and Adnan Rajib
Earth Syst. Sci. Data, 15, 2927–2955, https://doi.org/10.5194/essd-15-2927-2023, https://doi.org/10.5194/essd-15-2927-2023, 2023
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Non-floodplain wetlands (NFWs) – wetlands located outside floodplains – confer watershed-scale resilience to hydrological, biogeochemical, and biotic disturbances. Although they are frequently unmapped, we identified ~ 33 million NFWs covering > 16 × 10 km2 across the globe. NFWs constitute the majority of the world's wetlands (53 %). Despite their small size (median 0.039 km2), these imperiled systems have an outsized impact on watershed functions and sustainability and require protection.
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data, 15, 2347–2373, https://doi.org/10.5194/essd-15-2347-2023, https://doi.org/10.5194/essd-15-2347-2023, 2023
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A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.
Richard A. Houghton and Andrea Castanho
Earth Syst. Sci. Data, 15, 2025–2054, https://doi.org/10.5194/essd-15-2025-2023, https://doi.org/10.5194/essd-15-2025-2023, 2023
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We update a previous analysis of carbon emissions (annual and national) from land use, land-use change, and forestry from 1850 to 2020. We use data from the latest (2020) Global Forest Resources Assessment, incorporate shifting cultivation, and include improvements to the bookkeeping model and recent estimates of emissions from peatlands. Net global emissions declined steadily over the decade from 2011 to 2020 (mean of 0.96 Pg C yr−1), falling below 1.0 Pg C yr−1 for the first time in 30 years.
Charles H. Simpson, Oscar Brousse, Nahid Mohajeri, Michael Davies, and Clare Heaviside
Earth Syst. Sci. Data, 15, 1521–1541, https://doi.org/10.5194/essd-15-1521-2023, https://doi.org/10.5194/essd-15-1521-2023, 2023
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Adding plants to roofs of buildings can reduce indoor and outdoor temperatures and so can reduce urban overheating, which is expected to increase due to climate change and urban growth. To better understand the effect this has on the urban environment, we need data on how many buildings have green roofs already.
We used a computer vision model to find green roofs in aerial imagery in London, producing a dataset identifying what buildings have green roofs and improving on previous methods.
Chunling Sun, Hong Zhang, Lu Xu, Ji Ge, Jingling Jiang, Lijun Zuo, and Chao Wang
Earth Syst. Sci. Data, 15, 1501–1520, https://doi.org/10.5194/essd-15-1501-2023, https://doi.org/10.5194/essd-15-1501-2023, 2023
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Over 90 % of the world’s rice is produced in the Asia–Pacific region. In this study, a rice-mapping method based on Sentinel-1 data for mainland Southeast Asia is proposed. A combination of spatiotemporal features with strong generalization is selected and input into the U-Net model to obtain a 20 m resolution rice area map of mainland Southeast Asia in 2019. The accuracy of the proposed method is 92.20 %. The rice area map is concordant with statistics and other rice area maps.
Kandice L. Harper, Céline Lamarche, Andrew Hartley, Philippe Peylin, Catherine Ottlé, Vladislav Bastrikov, Rodrigo San Martín, Sylvia I. Bohnenstengel, Grit Kirches, Martin Boettcher, Roman Shevchuk, Carsten Brockmann, and Pierre Defourny
Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, https://doi.org/10.5194/essd-15-1465-2023, 2023
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We built a spatially explicit annual plant-functional-type (PFT) dataset for 1992–2020 exhibiting intra-class spatial variability in PFT fractional cover at 300 m. For each year, 14 maps of percentage cover are produced: bare soil, water, permanent snow/ice, built, managed grasses, natural grasses, and trees and shrubs, each split into leaf type and seasonality. Model simulations indicate significant differences in simulated carbon, water, and energy fluxes in some regions using this new set.
Yating Ru, Brian Blankespoor, Ulrike Wood-Sichra, Timothy S. Thomas, Liangzhi You, and Erwin Kalvelagen
Earth Syst. Sci. Data, 15, 1357–1387, https://doi.org/10.5194/essd-15-1357-2023, https://doi.org/10.5194/essd-15-1357-2023, 2023
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Economic statistics are frequently produced at an administrative level that lacks detail to examine development patterns and the exposure to natural hazards. This paper disaggregates national and subnational administrative statistics of agricultural GDP into a global dataset at the local level using satellite-derived indicators. As an illustration, the paper estimates that the exposure of areas with extreme drought to agricultural GDP is USD 432 billion, where nearly 1.2 billion people live.
Elena Aragoneses, Mariano García, Michele Salis, Luís M. Ribeiro, and Emilio Chuvieco
Earth Syst. Sci. Data, 15, 1287–1315, https://doi.org/10.5194/essd-15-1287-2023, https://doi.org/10.5194/essd-15-1287-2023, 2023
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We present a new hierarchical fuel classification system with a total of 85 fuels that is useful for preventing fire risk at different spatial scales. Based on this, we developed a European fuel map (1 km resolution) using land cover datasets, biogeographic datasets, and bioclimatic modelling. We validated the map by comparing it to high-resolution data, obtaining high overall accuracy. Finally, we developed a crosswalk for standard fuel models as a first assignment of fuel parameters.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
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Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Xiaoyong Li, Hanqin Tian, Chaoqun Lu, and Shufen Pan
Earth Syst. Sci. Data, 15, 1005–1035, https://doi.org/10.5194/essd-15-1005-2023, https://doi.org/10.5194/essd-15-1005-2023, 2023
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We reconstructed land use and land cover (LULC) history for the conterminous United States during 1630–2020 by integrating multi-source data. The results show the widespread expansion of cropland and urban land and the shrinking of natural vegetation in the past four centuries. Forest planting and regeneration accelerated forest recovery since the 1920s. The datasets can be used to assess the LULC impacts on the ecosystem's carbon, nitrogen, and water cycles.
Huifang Zhang, Zhonggang Tang, Binyao Wang, Hongcheng Kan, Yi Sun, Yu Qin, Baoping Meng, Meng Li, Jianjun Chen, Yanyan Lv, Jianguo Zhang, Shuli Niu, and Shuhua Yi
Earth Syst. Sci. Data, 15, 821–846, https://doi.org/10.5194/essd-15-821-2023, https://doi.org/10.5194/essd-15-821-2023, 2023
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The accuracy of regional grassland aboveground biomass (AGB) is always limited by insufficient ground measurements and large spatial gaps with satellite pixels. This paper used more than 37 000 UAV images as bridges to successfully obtain AGB values matching MODIS pixels. The new AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m2. Our new dataset provides important input parameters for understanding the Qinghai–Tibet Plateau during global climate change.
Jie Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Geli Zhang, Xuebin Yang, Xiaocui Wu, Chandrashekhar Biradar, and Yang Hu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-339, https://doi.org/10.5194/essd-2022-339, 2023
Revised manuscript accepted for ESSD
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Existing satellite-based forest maps remain large uncertainties, due to different forest definitions and mapping algorithms. To effectively manage the forest resources, timely and accurate annual forest maps at a high spatial resolution are demanded. This study improved the forest maps by integrating the PALSAR-2 and Landsat images. The annual evergreen and non-evergreen forest type maps were also generated. This critical information supports the Global Forest Resources Assessment.
Steve Ahlswede, Christian Schulz, Christiano Gava, Patrick Helber, Benjamin Bischke, Michael Förster, Florencia Arias, Jörn Hees, Begüm Demir, and Birgit Kleinschmit
Earth Syst. Sci. Data, 15, 681–695, https://doi.org/10.5194/essd-15-681-2023, https://doi.org/10.5194/essd-15-681-2023, 2023
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Imagery from air and space is the primary source of large-scale forest mapping. Our study introduces a new dataset with over 50000 image patches prepared for deep learning tasks. We show how the information for 20 European tree species can be extracted from different remote sensing sensors. Our algorithms can detect single species with precision scores up to 88 %. With a pixel size of 20×20 cm, forestry administration can now derive large-scale tree species maps at a very high resolution.
Cited articles
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C.: Monthly climate and climatic water balance for global terrestrial surfaces from 1958–2015, Northwest Knowledge Network [data set], https://doi.org/10.7923/G43J3B0R, 2017.
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C.:
TerraClimate, a high-resolution global dataset of monthly climate and
climatic water balance from 1958–2015, Sci. Data, 5, 170191,
https://doi.org/10.1038/sdata.2017.191, 2018.
Alexandratos, N. and Bruinsma, J.: World agriculture towards 2030/2050: the 2012 revision, Food and Agriculture Organization of the United Nations, https://doi.org/10.22004/ag.econ.288998, 2012.
Arumugam, P., Chemura, A., Schauberger, B., and Gornott, C.: Remote Sensing
Based Yield Estimation of Rice (Oryza Sativa L.) Using Gradient Boosted
Regression in India, Remote Sens., 13, 2379,
https://doi.org/10.3390/rs13122379, 2021.
Bandumula, N.: Rice production in Asia: Key to global food security, Proc.
Natl. Acad. Sci. India Sect. B Biol. Sci., 88, 1323–1328,
https://doi.org/10.1007/s40011-017-0867-7, 2018.
Birla, D. S., Malik, K., Sainger, M., Chaudhary, D., Jaiwal, R., and Jaiwal,
P. K.: Progress and challenges in improving the nutritional quality of rice
(Oryza sativa L.), Crit. Rev. Food Sci. Nutr., 57, 2455–2481,
https://doi.org/10.1080/10408398.2015.1084992, 2017.
Blomqvist, L., Yates, L., and Brook, B. W.: Drivers of increasing global
crop production: A decomposition analysis, Environ. Res. Lett., 15, 0940b6,
https://doi.org/10.1088/1748-9326/ab9e9c, 2020.
Breiman, L.: Bagging predictors, Mach. Learn., 24, 123–140,
https://doi.org/10.1007/BF00058655, 1996.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001.
Cai, Y., Guan, K., Lobell, D., Potgieter, A. B., Wang, S., Peng, J., Xu, T.,
Asseng, S., Zhang, Y., and You, L.: Integrating satellite and climate data
to predict wheat yield in Australia using machine learning approaches,
Agric. For. Meteorol., 274, 144–159,
https://doi.org/10.1016/j.agrformet.2019.03.010, 2019.
Cao, J., Zhang, Z., Tao, F., Zhang, L., Luo, Y., Han, J., and Li, Z.:
Identifying the Contributions of Multi-Source Data for Winter Wheat Yield
Prediction in China, Remote Sens., 12, 750,
https://doi.org/10.3390/rs12050750, 2020.
Cao, J., Zhang, Z., Tao, F., Zhang, L., Luo, Y., Zhang, J., Han, J., and
Xie, J.: Integrating Multi-Source Data for Rice Yield Prediction across
China using Machine Learning and Deep Learning Approaches, Agric. For.
Meteorol., 297, 108275, https://doi.org/10.1016/j.agrformet.2020.108275,
2021.
Chang, K.-W., Shen, Y., and Lo, J.-C.: Predicting rice yield using canopy
reflectance measured at booting stage, Agron. J., 97, 872–878,
https://doi.org/10.2134/agronj2004.0162, 2005.
Chen, H., Zhu, G., Zhang, K., Bi, J., Jia, X., Ding, B., Zhang, Y., Shang,
S., Zhao, N., and Qin, W.: Evaluation of evapotranspiration models using
different LAI and meteorological forcing data from 1982 to 2017, Remote
Sens., 12, 2473, https://doi.org/10.3390/rs12152473, 2020.
Chen, S., Liu, W., Feng, P., Ye, T., Ma, Y., and Zhang, Z.: Improving
Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with
Multisource Data: A Case Study of Chinese Maize Yield, Remote Sens., 14,
2340, https://doi.org/10.3390/rs14102340, 2022.
Chen, T. and Guestrin, C.: Xgboost: A scalable tree boosting system, in:
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016,
785–794, https://doi.org/10.1145/2939672.2939785, 2016.
Chen, Y., Song, X., Wang, S., Huang, J., and Mansaray, L. R.: Impacts of
spatial heterogeneity on crop area mapping in Canada using MODIS data, ISPRS
J. Photogramm. Remote Sens., 119, 451–461,
https://doi.org/10.1016/j.isprsjprs.2016.07.007, 2016.
Chen, Y., Zhang, Z., Tao, F., Palosuo, T., and Rötter, R. P.: Impacts of
heat stress on leaf area index and growth duration of winter wheat in the
North China Plain, Field Crops Res., 222, 230–237,
https://doi.org/10.1016/j.fcr.2017.06.007, 2018.
Chlingaryan, A., Sukkarieh, S., and Whelan, B.: Machine learning approaches
for crop yield prediction and nitrogen status estimation in precision
agriculture: A review, Comput. Electron. Agric., 151, 61–69,
https://doi.org/10.1016/j.compag.2018.05.012, 2018.
Dawe, D., Pandey, S., and Nelson, A.: Emerging trends and spatial patterns
of rice production, in: Rice in the global economy: Strategic research and
policy issues for food security, edited by: Sushil, P., Derek, B., David,
D., Achim, D., Samarendu, M., Scott, R., and Bill, H., International Rice
Research Institute (IRRI), Los Baños, Philippines, 15–36, ISBN 978-971-22-0258-2, 2010.
Dinh, T. L. A. and Aires, F.: Nested leave-two-out cross-validation for the optimal crop yield model selection, Geosci. Model Dev., 15, 3519–3535, https://doi.org/10.5194/gmd-15-3519-2022, 2022.
Fang, H., Zhang, Y., Wei, S., Li, W., Ye, Y., Sun, T., and Liu, W.:
Validation of global moderate resolution leaf area index (LAI) products over
croplands in northeastern China, Remote Sens. Environ., 233, 111377,
https://doi.org/10.1016/j.rse.2019.111377, 2019.
Fernandez-Beltran, R., Baidar, T., Kang, J., and Pla, F.: Rice-yield
prediction with multi-temporal sentinel-2 data and 3D CNN: A case study in
Nepal, Remote Sens., 13, 1391, https://doi.org/10.3390/rs13071391, 2021.
Folberth, C., Skalský, R., Moltchanova, E., Balkovič, J., Azevedo, L.
B., Obersteiner, M., and Van Der Velde, M.: Uncertainty in soil data can
outweigh climate impact signals in global crop yield simulations, Nat.
Commun., 7, 11872, https://doi.org/10.1038/ncomms11872, 2016.
Folberth, C., Khabarov, N., Balkovič, J., Skalsk?, R., Visconti, P.,
Ciais, P., Janssens, I. A., Peñuelas, J., and Obersteiner, M.: The
global cropland-sparing potential of high-yield farming, Nat. Sustain., 3,
281–289, https://doi.org/10.1038/s41893-020-0505-x, 2020.
Food and Agriculture Organization of the United Nations (FAO): Crops and livestock products,
https://www.fao.org/faostat/en/#data/QCL/visualize, last access: 6 April
2022.
Fritz, S., See, L., Bayas, J. C. L., Waldner, F., Jacques, D.,
Becker-Reshef, I., Whitcraft, A., Baruth, B., Bonifacio, R., and
Crutchfield, J.: A comparison of global agricultural monitoring systems and
current gaps, Agric. Syst., 168, 258–272,
https://doi.org/10.1016/j.agsy.2018.05.010, 2019.
GLOBE Task Team and others: The Global Land One-kilometer Base Elevation
(GLOBE) Digital Elevation Model, Version 1.0., National Oceanic and Atmospheric Administration, National Geophysical Data Center [data set],
http://www.ngdc.noaa.gov/mgg/topo/globe.html (last access: 13 February 2023), 1999.
Han, J., Zhang, Z., Luo, Y., Cao, J., Zhang, L., Cheng, F., Zhuang, H., and
Zhang, J.: APRA500: a 500 m annual paddy rice dataset for monsoon Asia using
multisource remote sensing data, Zenodo [data set],
https://doi.org/10.5281/zenodo.5555721, 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.
He, T., Xie, C., Liu, Q., Guan, S., and Liu, G.: Evaluation and comparison
of random forest and A-LSTM networks for large-scale winter wheat
identification, Remote Sens., 11, 1665, https://doi.org/10.3390/rs11141665,
2019.
Hochreiter, S. and Schmidhuber, J.: Long short-term memory, Neural Comput.,
9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997.
Huang, J., Wang, X., Li, X., Tian, H., and Pan, Z.: Remotely sensed rice
yield prediction using multi-temporal NDVI data derived from NOAA's-AVHRR,
PloS ONE, 8, e70816, https://doi.org/10.1371/journal.pone.0070816, 2013.
Huntington, T., Cui, X., Mishra, U., and Scown, C. D.: Machine learning to
predict biomass sorghum yields under future climate scenarios, Biofuels
Bioprod. Biorefining, 14, 566–577, https://doi.org/10.1002/bbb.2087, 2020.
Iizumi, T. and Sakai, T.: The global dataset of historical yields for major
crops 1981–2016, Sci. Data, 7, 97,
https://doi.org/10.1038/s41597-020-0433-7, 2020.
Iizumi, T., Yokozawa, M., Sakurai, G., Travasso, M. I., Romanenkov, V.,
Oettli, P., Newby, T., Ishigooka, Y., and Furuya, J.: Historical changes in
global yields: major cereal and legume crops from 1982 to 2006, Glob. Ecol.
Biogeogr., 23, 346–357, https://doi.org/10.1111/geb.12120, 2014.
Iizumi, T., Hosokawa, N., and Wagai, R.: Soil carbon-food synergy: sizable
contributions of small-scale farmers, CABI Agric. Biosci., 2, 43,
https://doi.org/10.1186/s43170-021-00063-6, 2021.
Jeong, S., Ko, J., and Yeom, J.-M.: Predicting rice yield at pixel scale
through synthetic use of crop and deep learning models with satellite data
in South and North Korea, Sci. Total Environ., 802, 149726,
https://doi.org/10.1016/j.scitotenv.2021.149726, 2022.
Kaltenegger, K. and Winiwarter, W.: Global gridded nitrogen indicators:
influence of crop maps, Glob. Biogeochem. Cycles, 34, e2020GB006634,
https://doi.org/10.1029/2020GB006634, 2020.
Kim, K.-H., Doi, Y., Ramankutty, N., and Iizumi, T.: A review of global
gridded cropping system data products, Environ. Res. Lett., 16, 093005,
https://doi.org/10.1088/1748-9326/ac20f4, 2021.
Laborte, A. G., Gutierrez, M. A., Balanza, J. G., Saito, K., Zwart, S. J.,
Boschetti, M., Murty, M. V. R., Villano, L., Aunario, J. K., Reinke, R.,
Koo, J., Hijmans, R. J., and Nelson, A.: RiceAtlas, a spatial database of
global rice calendars and production, Sci. Data, 4, 170074,
https://doi.org/10.1038/sdata.2017.74, 2017.
Lambin, E. F. and Meyfroidt, P.: Global land use change, economic
globalization, and the looming land scarcity, P. Natl. Acad. Sci. USA, 108,
3465–3472, https://doi.org/10.1073/pnas.1100480108, 2011.
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444,
https://doi.org/10.1038/nature14539, 2015.
Li, X., Lu, H., Yu, L., and Yang, K.: Comparison of the spatial
characteristics of four remotely sensed leaf area index products over China:
Direct validation and relative uncertainties, Remote Sens., 10, 148, https://doi.org/10.3390/rs10010148, 2018.
Liang, S., Cheng, J., Jia, K., Jiang, B., Liu, Q., Xiao, Z., Yao, Y., Yuan,
W., Zhang, X., and Zhao, X.: The global land surface satellite (GLASS)
product suite, B. Am. Meteorol. Soc., 102, E323–E337,
https://doi.org/10.1175/BAMS-D-18-0341.1, 2021.
Lin, T.-S., Song, Y., Lawrence, P., Kheshgi, H. S., and Jain, A. K.:
Worldwide Maize and Soybean Yield Response to Environmental and Management
Factors Over the 20th and 21st Centuries, J. Geophys. Res.-Biogeo.,
126, e2021JG006304, https://doi.org/10.1029/2021JG006304, 2021.
Liu, C., Huang, H., and Sun, F.: A Pixel-Based Vegetation Greenness Trend
Analysis over the Russian Tundra with All Available Landsat Data from 1984
to 2018, Remote Sens., 13, 4933, https://doi.org/10.3390/rs13234933, 2021.
Liu, W., Dugar, S., McCallum, I., Thapa, G., See, L., Khadka, P.,
Budhathoki, N., Brown, S., Mechler, R., Fritz, S., and Shakya, P.:
Integrated Participatory and Collaborative Risk Mapping for Enhancing
Disaster Resilience, Isprs Int. J. Geo-Inf., 7, 68,
https://doi.org/10.3390/ijgi7020068, 2018.
Liu, W., Ye, T., Jägermeyr, J., Müller, C., Chen, S., Liu, X., and
Shi, P.: Future climate change significantly alters interannual wheat yield
variability over half of harvested areas, Environ. Res. Lett.,
https://doi.org/10.1088/1748-9326/ac1fbb, 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, Y., Zhang, Z., Chen, Y., Li, Z., and Tao, F.: ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products, Earth Syst. Sci. Data, 12, 197–214, https://doi.org/10.5194/essd-12-197-2020, 2020a.
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, 2020b.
Luo, Y., Zhang, Z., Cao, J., Zhang, L., Zhang, J., Han, J., Zhuang, H.,
Cheng, F., and Tao, F.: Accurately mapping global wheat production system
using deep learning algorithms, Int. J. Appl. Earth Obs. Geoinformation,
110, 102823, https://doi.org/10.1016/j.jag.2022.102823, 2022.
Maclean, J. L., Dawe, D. C., Hettel, G. P., and Hettel, G. P. (Eds.): Rice
almanac: Source book for the most important economic activity on earth, 3rd
edn., CABI Publishing, Wallingford, UK, ISBN 0851996361, 2002.
Manfron, G., Delmotte, S., Busetto, L., Hossard, L., Ranghetti, L., Brivio,
P. A., and Boschetti, M.: Estimating inter-annual variability in winter
wheat sowing dates from satellite time series in Camargue, France, Int. J.
Appl. Earth Obs. Geoinformation, 57, 190–201,
https://doi.org/10.1016/j.jag.2017.01.001, 2017.
Meroni, M., Waldner, F., Seguini, L., Kerdiles, H., and Rembold, F.: Yield
forecasting with machine learning and small data: what gains for grains?,
Agric. For. Meteorol., 308, 108555,
https://doi.org/10.1016/j.agrformet.2021.108555, 2021.
Monfreda, C., Ramankutty, N., and Foley, J. A.: Farming the planet: 2.
Geographic distribution of crop areas, yields, physiological types, and net
primary production in the year 2000, Glob. Biogeochem. Cycles, 22, 259–277,
https://doi.org/10.1029/2007GB002947, 2008.
Mosleh, M. K., Hassan, Q. K., and Chowdhury, E. H.: Application of remote
sensors in mapping rice area and forecasting its production: A review,
Sensors, 15, 769–791, https://doi.org/10.3390/s150100769, 2015.
Muehe, E. M., Wang, T., Kerl, C. F., Planer-Friedrich, B., and Fendorf, S.:
Rice production threatened by coupled stresses of climate and soil arsenic,
Nat. Commun., 10, 4985, https://doi.org/10.1038/s41467-019-12946-4, 2019.
Müller, C., Elliott, J., Kelly, D., Arneth, A., Balkovic, J., Ciais, P.,
Deryng, D., Folberth, C., Hoek, S., Izaurralde, R. C., Jones, C. D.,
Khabarov, N., Lawrence, P., Liu, W., Olin, S., Pugh, T. A. M., Reddy, A.,
Rosenzweig, C., Ruane, A. C., Sakurai, G., Schmid, E., Skalsky, R., Wang,
X., de Wit, A., and Yang, H.: The Global Gridded Crop Model Intercomparison
phase 1 simulation dataset, Sci. Data, 6, 50,
https://doi.org/10.1038/s41597-019-0023-8, 2019.
Muruganantham, P., Wibowo, S., Grandhi, S., Samrat, N. H., and Islam, N.: A
Systematic Literature Review on Crop Yield Prediction with Deep Learning and
Remote Sensing, Remote Sens., 14, 1990, https://doi.org/10.3390/rs14091990,
2022.
Nazir, A., Ullah, S., Saqib, Z. A., Abbas, A., Ali, A., Iqbal, M. S.,
Hussain, K., Shakir, M., Shah, M., and Butt, M. U.: Estimation and
Forecasting of Rice Yield Using Phenology-Based Algorithm and Linear
Regression Model on Sentinel-II Satellite Data, Agriculture, 11, 1026,
https://doi.org/10.3390/agriculture11101026, 2021.
Obsie, E. Y., Qu, H., and Drummond, F.: Wild blueberry yield prediction
using a combination of computer simulation and machine learning algorithms,
Comput. Electron. Agric., 178, 105778,
https://doi.org/10.1016/j.compag.2020.105778, 2020.
Qian, H., Huang, S., Chen, J., Wang, L., Hungate, B. A., van Kessel, C.,
Zhang, J., Deng, A., Jiang, Y., and van Groenigen, K. J.:
Lower-than-expected CH4 emissions from rice paddies with rising CO2
concentrations, Glob. Change Biol., 26, 2368–2376,
https://doi.org/10.1111/gcb.14984, 2020.
Ray, D. K., West, P. C., Clark, M., Gerber, J. S., Prishchepov, A. V., and
Chatterjee, S.: Climate change has likely already affected global food
production, PloS One, 14, e0217148,
https://doi.org/10.1371/journal.pone.0217148, 2019.
Ripley, B. D.: Pattern recognition and neural networks, 1st edn., Cambridge University Press, New York, USA,
ISBN 9780521717700, 2007.
Sak, H., Senior, A., and Beaufays, F.: Long short-term memory based
recurrent neural network architectures for large vocabulary speech
recognition, arXiv [preprint], https://doi.org/10.48550/arXiv.1402.1128, 5 February 2014.
Sakamoto, T.: Incorporating environmental variables into a MODIS-based crop
yield estimation method for United States corn and soybeans through the use
of a random forest regression algorithm, ISPRS J. Photogramm. Remote Sens.,
160, 208–228, https://doi.org/10.1016/j.isprsjprs.2019.12.012, 2020.
Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., and
Ohno, H.: A crop phenology detection method using time-series MODIS data,
Remote Sens. Environ., 96, 366–374,
https://doi.org/10.1016/j.rse.2005.03.008, 2005.
Salvacion, A. R.: Chapter 11 – Multiscale drought hazard assessment in the
Philippines, in: Computers in Earth and Environmental Sciences, edited by:
Pourghasemi, H. R., Elsevier, Amsterdam, Netherlands, 169–179, https://doi.org/10.1016/B978-0-323-89861-4.00024-5, 2022.
Shahhosseini, M., Hu, G., and Archontoulis, S. V.: Forecasting Corn Yield
With Machine Learning Ensembles, Front. Plant Sci., 11, 1120,
https://doi.org/10.3389/fpls.2020.01120, 2020.
Shahhosseini, M., Hu, G., Huber, I., and Archontoulis, S. V.: Coupling
machine learning and crop modeling improves crop yield prediction in the US
Corn Belt, Sci. Rep., 11, 1606, https://doi.org/10.1038/s41598-020-80820-1,
2021.
Son, N. T., Chen, C. F., Chen, C. R., Chang, L. Y., Duc, H. N., and Nguyen,
L. D.: Prediction of rice crop yield using MODIS EVI- LAI data in the Mekong
Delta, Vietnam, Int. J. Remote Sens., 34, 7275–7292,
https://doi.org/10.1080/01431161.2013.818258, 2013.
Son, N.-T., Chen, C.-F., Chen, C.-R., Guo, H.-Y., Cheng, Y.-S., Chen, S.-L.,
Lin, H.-S., and Chen, S.-H.: Machine learning approaches for rice crop yield
predictions using time-series satellite data in Taiwan, Int. J. Remote
Sens., 41, 7868–7888, https://doi.org/10.1080/01431161.2020.1766148, 2020.
Srivastava, V. C. and Mahapatra, I. C.: Advances in Rice Production
Technology: Theory and Practice, Agrobios Publishing, Jodhpur, India, ISBN 9788177544626, 2012.
Tian, H., Wang, P., Tansey, K., Zhang, J., Zhang, S., and Li, H.: An LSTM
neural network for improving wheat yield estimates by integrating remote
sensing data and meteorological data in the Guanzhong Plain, PR China,
Agric. For. Meteorol., 310, 108629,
https://doi.org/10.1016/j.agrformet.2021.108629, 2021.
van Klompenburg, T., Kassahun, A., and Catal, C.: Crop yield prediction
using machine learning: A systematic literature review, Comput. Electron.
Agric., 177, 105709, https://doi.org/10.1016/j.compag.2020.105709, 2020.
van Oort, P. A. and Zwart, S. J.: Impacts of climate change on rice
production in Africa and causes of simulated yield changes, Glob. Change
Biol., 24, 1029–1045, https://doi.org/10.1111/gcb.13967, 2018.
Wang, C., Zhang, Z., Chen, Y., Tao, F., Zhang, J., and Zhang, W.: Comparing
different smoothing methods to detect double-cropping rice phenology based
on LAI products – a case study in the Hunan province of China, Int. J. Remote
Sens., 39, 6405–6428, https://doi.org/10.1080/01431161.2018.1460504, 2018.
Wieder, W. R., Boehnert, J., Bonan, G. B., and Langseth, M.: Regridded Harmonized World Soil Database v1.2, ORNL DAAC [data set],
https://doi.org/10.3334/ORNLDAAC/1247, 2014.
Wu, H., Zhang, J., Zhang, Z., Han, J., Cao, J., Zhang, L., Luo, Y., Mei, Q.,
Xu, J., and Tao, F.: AsiaRiceYield4km: Seasonal Rice Yield in Asia from 1995
to 2015, Zenodo [data set], https://doi.org/10.5281/zenodo.6901968, 2022.
Xiao, Z., Liang, S., Wang, J., Chen, P., Yin, X., Zhang, L., and Song, J.:
Use of general regression neural networks for generating the GLASS leaf area
index product from time-series MODIS surface reflectance, IEEE T. Geosci. Remote, 52, 209–223,
https://doi.org/10.1109/TGRS.2013.2237780, 2013.
Xiao, Z., Liang, S., Wang, J., Xiang, Y., Zhao, X., and Song, J.:
Long-time-series global land surface satellite leaf area index product
derived from MODIS and AVHRR surface reflectance, IEEE T. Geosci. Remote, 54, 5301–5318, https://doi.org/10.1109/TGRS.2016.2560522, 2016.
Xiao, Z., Liang, S., and Jiang, B.: Evaluation of four long time-series
global leaf area index products, Agric. For. Meteorol., 246, 218–230, 2017.
You, L. and Wood, S.: An entropy approach to spatial disaggregation of
agricultural production, Agric. Syst., 90, 329–347,
https://doi.org/10.1016/j.agsy.2006.01.008, 2006.
Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth Syst. Sci. Data, 12, 3545–3572, https://doi.org/10.5194/essd-12-3545-2020, 2020.
Zhang, G., Xiao, X., Dong, J., Xin, F., Zhang, Y., Qin, Y., Doughty, R. B.,
and Moore, B.: Fingerprint of rice paddies in spatial–temporal dynamics of
atmospheric methane concentration in monsoon Asia, Nat. Commun., 11, 554,
https://doi.org/10.1038/s41467-019-14155-5,
2020.
Zhang, J., Wu, H., Zhang, Z., Zhang, L., Luo, Y., Han, J., and Tao, F.:
Asian Rice Calendar Dynamics Detected by Remote Sensing and Their Climate
Drivers, Remote Sens., 13, 4189, https://doi.org/10.3390/rs14174189, 2022.
Zhang, L., Zhang, Z., Luo, Y., Cao, J., and Tao, F.: Combining Optical,
Fluorescence, Thermal Satellite, and Environmental Data to Predict
County-Level Maize Yield in China Using Machine Learning Approaches, Remote
Sens., 12, 21, https://doi.org/10.3390/rs12010021, 2019.
Zhang, L., Zhang, Z., Luo, Y., Cao, J., Xie, R., and Li, S.: Integrating
satellite-derived climatic and vegetation indices to predict smallholder
maize yield using deep learning, Agric. For. Meteorol., 311, 108666,
https://doi.org/10.1016/j.agrformet.2021.108666, 2021.
Zhang, T., Yang, X., Wang, H., Li, Y., and Ye, Q.: Climatic and
technological ceilings for C hinese rice stagnation based on yield gaps and
yield trend pattern analysis, Glob. Change Biol., 20, 1289–1298,
https://doi.org/10.1111/gcb.12428, 2014.
Zhang, Z., Li, Z., Chen, Y., Zhang, L., and Tao, F.: Improving regional
wheat yields estimations by multi-step-assimilating of a crop model with
multi-source data, Agric. For. Meteorol., 290, 107993,
https://doi.org/10.1016/j.agrformet.2020.107993, 2020.
Short summary
High-spatiotemporal-resolution rice yield datasets are limited over a large region. We proposed an explicit method to predict rice yield based on machine learning methods and generated a seasonal 4 km resolution rice yield dataset across Asia (AsiaRiceYield4km) for 1995–2015. The seasonal rice yield accuracy of AsiaRiceYield4km is high and much improved compared with previous datasets. AsiaRiceYield4km will fill the current data gap and better support agricultural monitoring systems.
High-spatiotemporal-resolution rice yield datasets are limited over a large region. We proposed...
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