Articles | Volume 17, issue 7
https://doi.org/10.5194/essd-17-3375-2025
© Author(s) 2025. 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-17-3375-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
14 Jul 2025
Data description paper |
| 14 Jul 2025
| 14 Jul 2025
Global patterns of soil organic carbon distribution in the 20–100 cm soil profile for different ecosystems: a global meta-analysis
Haiyan Wang
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Department of Plant Nutrition, Sanya Institute of China Agricultural University, 572025 Sanya, China
Tingyao Cai
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Xingshuai Tian
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Zhong Chen
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Kai He
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Zihan Wang
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Haiqing Gong
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Qi Miao
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Yingcheng Wang
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Yiyan Chu
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Qingsong Zhang
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Minghao Zhuang
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Yulong Yin
CORRESPONDING AUTHOR
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
Zhenling Cui
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, 100193 Beijing, China
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G. L. Wang, Y. L. Ye, X. P. Chen, and Z. L. Cui
Biogeosciences, 11, 3031–3041, https://doi.org/10.5194/bg-11-3031-2014, https://doi.org/10.5194/bg-11-3031-2014, 2014
Z. L. Cui, L. Wu, Y. L. Ye, W. Q. Ma, X. P. Chen, and F. S. Zhang
Biogeosciences, 11, 2287–2294, https://doi.org/10.5194/bg-11-2287-2014, https://doi.org/10.5194/bg-11-2287-2014, 2014
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Domain: ESSD – Land | Subject: Pedology
Mapping global distributions, environmental controls, and uncertainties of apparent topsoil and subsoil organic carbon turnover times
Distribution and sources of fallout 137Cs and 239+240Pu in equatorial and Southern Hemisphere reference soils
A China dataset of soil properties for land surface modelling (version 2, CSDLv2)
An integrated dataset of ground hydrothermal regimes and soil nutrients monitored in some previously burned areas in hemiboreal forests in Northeast China during 2016–2022
Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023)
BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands
European topsoil bulk density and organic carbon stock database (0–20 cm) using machine-learning-based pedotransfer functions
Improving the Latin America and Caribbean Soil Information System (SISLAC) database enhances its usability and scalability
The patterns of soil nitrogen stocks and C : N stoichiometry under impervious surfaces in China
Mapping of peatlands in the forested landscape of Sweden using lidar-based terrain indices
Harmonized Soil Database of Ecuador (HESD): data from 2009 to 2015
ChinaCropSM1 km: a fine 1 km daily soil moisture dataset for dryland wheat and maize across China during 1993–2018
Colombian soil texture: building a spatial ensemble model
SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022
A high spatial resolution soil carbon and nitrogen dataset for the northern permafrost region based on circumpolar land cover upscaling
A repository of measured soil freezing characteristic curves: 1921 to 2021
A compiled soil respiration dataset at different time scales for forest ecosystems across China from 2000 to 2018
Lei Zhang, Lin Yang, Thomas W. Crowther, Constantin M. Zohner, Sebastian Doetterl, Gerard B. M. Heuvelink, Alexandre M. J.-C. Wadoux, A.-Xing Zhu, Yue Pu, Feixue Shen, Haozhi Ma, Yibiao Zou, and Chenghu Zhou
Earth Syst. Sci. Data, 17, 2605–2623, https://doi.org/10.5194/essd-17-2605-2025, https://doi.org/10.5194/essd-17-2605-2025, 2025
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Current understandings of depth-dependent variations and controls of soil organic carbon turnover time (τ) at global, biome, and local scales remain incomplete. We used the state-of-the-art soil and root profile databases and satellite observations to generate new spatially explicit global maps of topsoil and subsoil τ, with quantified uncertainties for better user applications. The new insights from the resulting maps will facilitate efforts to model the carbon cycle and will support effective carbon management.
Gerald Dicen, Floriane Guillevic, Surya Gupta, Pierre-Alexis Chaboche, Katrin Meusburger, Pierre Sabatier, Olivier Evrard, and Christine Alewell
Earth Syst. Sci. Data, 17, 1529–1549, https://doi.org/10.5194/essd-17-1529-2025, https://doi.org/10.5194/essd-17-1529-2025, 2025
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Fallout radionuclides (FRNs) such as 137Cs and 239+240Pu are considered to be critical tools in various environmental research. Here, we compiled reference soil data on these FRNs from the literature to build a comprehensive database. Using this database, we determined the distribution and sources of 137Cs and 239+240Pu. We also demonstrated how the database can be used to identify the environmental factors that influence their distribution using a machine learning algorithm.
Gaosong Shi, Wenye Sun, Wei Shangguan, Zhongwang Wei, Hua Yuan, Lu Li, Xiaolin Sun, Ye Zhang, Hongbin Liang, Danxi Li, Feini Huang, Qingliang Li, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 517–543, https://doi.org/10.5194/essd-17-517-2025, https://doi.org/10.5194/essd-17-517-2025, 2025
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In this study, we developed the second version of China's high-resolution soil information grid using legacy soil samples and advanced machine learning. This version predicts over 20 soil properties at six depths, providing accurate soil variation maps across China. It outperforms previous versions and global products, offering valuable data for hydrological and ecological analyses and Earth system modelling, enhancing our understanding of soil roles in environmental processes.
Xiaoying Li, Huijun Jin, Qi Feng, Qingbai Wu, Hongwei Wang, Ruixia He, Dongliang Luo, Xiaoli Chang, Raul-David Şerban, and Tao Zhan
Earth Syst. Sci. Data, 16, 5009–5026, https://doi.org/10.5194/essd-16-5009-2024, https://doi.org/10.5194/essd-16-5009-2024, 2024
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In Northeast China, the permafrost is more sensitive to climate warming and fire disturbances than the boreal and Arctic permafrost. Since 2016, a continuous ground hydrothermal regime and soil nutrient content observation system has been gradually established in Northeast China. The integrated dataset includes soil moisture content, soil organic carbon, total nitrogen, total phosphorus, total potassium, ground temperatures at depths of 0–20 m, and active layer thickness from 2016 to 2022.
Niels H. Batjes, Luis Calisto, and Luis M. de Sousa
Earth Syst. Sci. Data, 16, 4735–4765, https://doi.org/10.5194/essd-16-4735-2024, https://doi.org/10.5194/essd-16-4735-2024, 2024
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Soils are an important provider of ecosystem services. This dataset provides quality-assessed and standardised soil data to support digital soil mapping and environmental applications at a broad scale. The underpinning soil profiles were shared by a wide range of data providers. Special attention was paid to the standardisation of soil property definitions, analytical method descriptions and property values. We present three measures to assess "fitness for intended use" of the standardised data.
Anatol Helfenstein, Vera L. Mulder, Mirjam J. D. Hack-ten Broeke, Maarten van Doorn, Kees Teuling, Dennis J. J. Walvoort, and Gerard B. M. Heuvelink
Earth Syst. Sci. Data, 16, 2941–2970, https://doi.org/10.5194/essd-16-2941-2024, https://doi.org/10.5194/essd-16-2941-2024, 2024
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Earth system models and decision support systems greatly benefit from high-resolution soil information with quantified accuracy. Here we introduce BIS-4D, a statistical modeling platform that predicts nine essential soil properties and their uncertainties at 25 m resolution in surface 2 m across the Netherlands. Using machine learning informed by up to 856 000 soil observations coupled with 366 spatially explicit environmental variables, prediction accuracy was the highest for clay, sand and pH.
Songchao Chen, Zhongxing Chen, Xianglin Zhang, Zhongkui Luo, Calogero Schillaci, Dominique Arrouays, Anne Christine Richer-de-Forges, and Zhou Shi
Earth Syst. Sci. Data, 16, 2367–2383, https://doi.org/10.5194/essd-16-2367-2024, https://doi.org/10.5194/essd-16-2367-2024, 2024
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A new dataset for topsoil bulk density (BD) and soil organic carbon (SOC) stock (0–20 cm) across Europe using machine learning was generated. The proposed approach performed better in BD prediction and slightly better in SOC stock prediction than earlier-published PTFs. The outcomes present a meaningful advancement in enhancing the accuracy of BD, and the resultant topsoil BD and SOC stock datasets across Europe enable more precise soil hydrological and biological modeling.
Sergio Díaz-Guadarrama, Viviana M. Varón-Ramírez, Iván Lizarazo, Mario Guevara, Marcos Angelini, Gustavo A. Araujo-Carrillo, Jainer Argeñal, Daphne Armas, Rafael A. Balta, Adriana Bolivar, Nelson Bustamante, Ricardo O. Dart, Martin Dell Acqua, Arnulfo Encina, Hernán Figueredo, Fernando Fontes, Joan S. Gutiérrez-Díaz, Wilmer Jiménez, Raúl S. Lavado, Jesús F. Mansilla-Baca, Maria de Lourdes Mendonça-Santos, Lucas M. Moretti, Iván D. Muñoz, Carolina Olivera, Guillermo Olmedo, Christian Omuto, Sol Ortiz, Carla Pascale, Marco Pfeiffer, Iván A. Ramos, Danny Ríos, Rafael Rivera, Lady M. Rodriguez, Darío M. Rodríguez, Albán Rosales, Kenset Rosales, Guillermo Schulz, Víctor Sevilla, Leonardo M. Tenti, Ronald Vargas, Gustavo M. Vasques, Yusuf Yigini, and Yolanda Rubiano
Earth Syst. Sci. Data, 16, 1229–1246, https://doi.org/10.5194/essd-16-1229-2024, https://doi.org/10.5194/essd-16-1229-2024, 2024
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In this work, the Latin America and Caribbean Soil Information System (SISLAC) database (https://54.229.242.119/sislac/es) was revised to generate an improved version of the data. Rules for data enhancement were defined. In addition, other datasets available in the region were included. Subsequently, through a principal component analysis (PCA), the main soil characteristics for the region were analyzed. We hope this dataset can help mitigate problems such as food security and global warming.
Qian Ding, Hua Shao, Chi Zhang, and Xia Fang
Earth Syst. Sci. Data, 15, 4599–4612, https://doi.org/10.5194/essd-15-4599-2023, https://doi.org/10.5194/essd-15-4599-2023, 2023
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A soil survey in 41 Chinese cities showed the soil nitrogen (N) in impervious surface areas (ISA; NISA) was 0.59±0.35 kg m−2, lower than in pervious soils. Eastern China had the highest NISA but the lowest natural soil N in China. Soil N decreased linearly with depth in ISA but nonlinearly in natural ecosystems. Temperature was negatively correlated with C : NISA but positively correlated with natural soil C : N. The unique NISA patterns imply intensive disturbance in N cycle by soil sealing.
Lukas Rimondini, Thomas Gumbricht, Anders Ahlström, and Gustaf Hugelius
Earth Syst. Sci. Data, 15, 3473–3482, https://doi.org/10.5194/essd-15-3473-2023, https://doi.org/10.5194/essd-15-3473-2023, 2023
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Peatlands have historically sequestrated large amounts of carbon and contributed to atmospheric cooling. However, human activities and climate change may instead turn them into considerable carbon emitters. In this study, we produced high-quality maps showing the extent of peatlands in the forests of Sweden, one of the most peatland-dense countries in the world. The maps are publicly available and may be used to support work promoting sustainable peatland management and combat their degradation.
Daphne Armas, Mario Guevara, Fernando Bezares, Rodrigo Vargas, Pilar Durante, Víctor Osorio, Wilmer Jiménez, and Cecilio Oyonarte
Earth Syst. Sci. Data, 15, 431–445, https://doi.org/10.5194/essd-15-431-2023, https://doi.org/10.5194/essd-15-431-2023, 2023
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The global need for updated soil datasets has increased. Our main objective was to synthesize and harmonize soil profile information collected by two different projects in Ecuador between 2009 and 2015.The main result was the development of the Harmonized Soil Database of Ecuador (HESD) that includes information from 13 542 soil profiles with over 51 713 measured soil horizons, including 92 different edaphic variables, and follows international standards for archiving and sharing soil data.
Fei Cheng, Zhao Zhang, Huimin Zhuang, Jichong Han, Yuchuan Luo, Juan Cao, Liangliang Zhang, Jing Zhang, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 15, 395–409, https://doi.org/10.5194/essd-15-395-2023, https://doi.org/10.5194/essd-15-395-2023, 2023
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We generated a 1 km daily soil moisture dataset for dryland wheat and maize across China (ChinaCropSM1 km) over 1993–2018 through random forest regression, based on in situ observations. Our improved products have a remarkably better quality compared with the public global products in terms of both spatial and time dimensions by integrating an irrigation module (crop type, phenology, soil depth). The dataset may be useful for agriculture drought monitoring and crop yield forecasting studies.
Viviana Marcela Varón-Ramírez, Gustavo Alfonso Araujo-Carrillo, and Mario Antonio Guevara Santamaría
Earth Syst. Sci. Data, 14, 4719–4741, https://doi.org/10.5194/essd-14-4719-2022, https://doi.org/10.5194/essd-14-4719-2022, 2022
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These are the first national soil texture maps obtained via digital soil mapping. We built clay, sand, and silt maps using spatial assembling with the best possible predictions at different depths. Also, we identified the better model for each pixel. This work was done to address the lack of soil texture maps in Colombia, and it can provide soil information for water-related applications, ecosystem services, and agricultural and crop modeling.
Qiang Zhang, Qiangqiang Yuan, Taoyong Jin, Meiping Song, and Fujun Sun
Earth Syst. Sci. Data, 14, 4473–4488, https://doi.org/10.5194/essd-14-4473-2022, https://doi.org/10.5194/essd-14-4473-2022, 2022
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Compared to previous seamless global daily soil moisture (SGD-SM 1.0) products, SGD-SM 2.0 enlarges the temporal scope from 2002 to 2022. By fusing auxiliary precipitation information with the long short-term memory convolutional neural network (LSTM-CNN) model, SGD-SM 2.0 can consider sudden extreme weather conditions for 1 d in global daily soil moisture products and is significant for full-coverage global daily hydrologic monitoring, rather than averaging monthly–quarterly–yearly results.
Juri Palmtag, Jaroslav Obu, Peter Kuhry, Andreas Richter, Matthias B. Siewert, Niels Weiss, Sebastian Westermann, and Gustaf Hugelius
Earth Syst. Sci. Data, 14, 4095–4110, https://doi.org/10.5194/essd-14-4095-2022, https://doi.org/10.5194/essd-14-4095-2022, 2022
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The northern permafrost region covers 22 % of the Northern Hemisphere and holds almost twice as much carbon as the atmosphere. This paper presents data from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. We use this dataset together with ESA's global land cover dataset to estimate soil organic carbon and total nitrogen storage up to 300 cm soil depth, with estimated values of 813 Pg for carbon and 55 Pg for nitrogen.
Élise G. Devoie, Stephan Gruber, and Jeffrey M. McKenzie
Earth Syst. Sci. Data, 14, 3365–3377, https://doi.org/10.5194/essd-14-3365-2022, https://doi.org/10.5194/essd-14-3365-2022, 2022
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Soil freezing characteristic curves (SFCCs) relate the temperature of a soil to its ice content. SFCCs are needed in all physically based numerical models representing freezing and thawing soils, and they affect the movement of water in the subsurface, biogeochemical processes, soil mechanics, and ecology. Over a century of SFCC data exist, showing high variability in SFCCs based on soil texture, water content, and other factors. This repository summarizes all available SFCC data and metadata.
Hongru Sun, Zhenzhu Xu, and Bingrui Jia
Earth Syst. Sci. Data, 14, 2951–2961, https://doi.org/10.5194/essd-14-2951-2022, https://doi.org/10.5194/essd-14-2951-2022, 2022
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We compiled a new soil respiration (Rs) database of China's forests from 568 studies published up to 2018. The hourly, monthly, and annual samples were 8317, 5003, and 634, respectively. Most of the Rs data are shown in figures but were seldom exploited. For the first time, these data were digitized, accounting for 82 % of samples. Rs measured with common methods was selected (Li-6400, Li-8100, Li-8150, gas chromatography) and showed small differences of ~10 %. Bamboo had the highest Rs.
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Short summary
Accurately quantifying the distribution of soil profile SOC (soil organic carbon) stocks is crucial for carbon sequestration and mitigation. The detailed spatial subsoil SOC data are the scientific basis for environmental protection, as well as for the development of Earth system models. Based on multiple environmental variables and soil profile data, this study used machine learning approaches to evaluate the SOC stocks and their spatial distribution at a depth interval of 20–100 cm in various ecosystems.
Accurately quantifying the distribution of soil profile SOC (soil organic carbon) stocks is...
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