Articles | Volume 13, issue 1
https://doi.org/10.5194/essd-13-83-2021
© Author(s) 2021. 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-13-83-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
20 Jan 2021
Data description paper |
| 20 Jan 2021
| 20 Jan 2021
EstSoil-EH: a high-resolution eco-hydrological modelling parameters dataset for Estonia
Department of Geography, Institute of Ecology and Earth Sciences,
University of Tartu, Vanemuise 46, Tartu, 51003, Estonia
Arno Kanal
Department of Geography, Institute of Ecology and Earth Sciences,
University of Tartu, Vanemuise 46, Tartu, 51003, Estonia
deceased, 7 May 2019
Alar Astover
Chair of Soil Science, Institute of Agricultural and Environmental
Sciences, Estonian University of Life Sciences, Fr.R. Kreutzwaldi 5, Tartu,
51014, Estonia
Department of Geography, Institute of Ecology and Earth Sciences,
University of Tartu, Vanemuise 46, Tartu, 51003, Estonia
Holger Virro
Department of Geography, Institute of Ecology and Earth Sciences,
University of Tartu, Vanemuise 46, Tartu, 51003, Estonia
Aveliina Helm
Department of Botany, Institute of Ecology and Earth Sciences,
University of Tartu, Lai 40, Tartu, 51005, Estonia
Meelis Pärtel
Department of Botany, Institute of Ecology and Earth Sciences,
University of Tartu, Lai 40, Tartu, 51005, Estonia
Ivika Ostonen
Department of Geography, Institute of Ecology and Earth Sciences,
University of Tartu, Vanemuise 46, Tartu, 51003, Estonia
Evelyn Uuemaa
Department of Geography, Institute of Ecology and Earth Sciences,
University of Tartu, Vanemuise 46, Tartu, 51003, Estonia
Related authors
Alexander Kmoch, Benoit Bovy, Justus Magin, Ryan Abernathey, Alejandro Coca-Castro, Peter Strobl, Anne Fouilloux, Daniel Loos, Evelyn Uuemaa, Wai Tik Chan, Jean-Marc Delouis, and Tina Odaka
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W12-2024, 75–80, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-75-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-75-2024, 2024
Nikita Murin, Alexander Kmoch, and Evelyn Uuemaa
AGILE GIScience Ser., 4, 34, https://doi.org/10.5194/agile-giss-4-34-2023, https://doi.org/10.5194/agile-giss-4-34-2023, 2023
Tahmin Sitab, Oleksandr Karasov, and Alexander Kmoch
AGILE GIScience Ser., 4, 43, https://doi.org/10.5194/agile-giss-4-43-2023, https://doi.org/10.5194/agile-giss-4-43-2023, 2023
Evelyn Uuemaa and Alexander Kmoch
AGILE GIScience Ser., 3, 65, https://doi.org/10.5194/agile-giss-3-65-2022, https://doi.org/10.5194/agile-giss-3-65-2022, 2022
Holger Virro, Alexander Kmoch, Marko Vainu, and Evelyn Uuemaa
AGILE GIScience Ser., 3, 66, https://doi.org/10.5194/agile-giss-3-66-2022, https://doi.org/10.5194/agile-giss-3-66-2022, 2022
Alexander Kmoch, Oleksandr Matsibora, Ivan Vasilyev, and Evelyn Uuemaa
AGILE GIScience Ser., 3, 41, https://doi.org/10.5194/agile-giss-3-41-2022, https://doi.org/10.5194/agile-giss-3-41-2022, 2022
Holger Virro, Giuseppe Amatulli, Alexander Kmoch, Longzhu Shen, and Evelyn Uuemaa
Earth Syst. Sci. Data, 13, 5483–5507, https://doi.org/10.5194/essd-13-5483-2021, https://doi.org/10.5194/essd-13-5483-2021, 2021
Short summary
Short summary
Water quality modeling is essential for understanding and mitigating water quality deterioration in river networks due to agricultural and industrial pollution. Improving the availability and usability of open data is vital to support global water quality modeling efforts. The GRQA extends the spatial and temporal coverage of previously available water quality data and provides a reproducible workflow for combining multi-source water quality datasets.
Laura Kuusemets, Ülo Mander, Jordi Escuer-Gatius, Alar Astover, Karin Kauer, Kaido Soosaar, and Mikk Espenberg
SOIL, 11, 1–15, https://doi.org/10.5194/soil-11-1-2025, https://doi.org/10.5194/soil-11-1-2025, 2025
Short summary
Short summary
We investigated relationships between mineral nitrogen (N) fertilisation rates and additional manure amendment with different crop types through an analysis of soil environmental characteristics and microbiomes, soil N2O and N2 emissions as well as biomass production. The results show that wheat grew well at a fertilisation rate of 80 kg N ha−1, and newly introduced sorghum showed good potential for cultivation in temperate climates.
Arta Bārdule, Raija Laiho, Jyrki Jauhiainen, Kaido Soosaar, Andis Lazdiņš, Kęstutis Armolaitis, Aldis Butlers, Dovilė Čiuldienė, Andreas Haberl, Ain Kull, Milda Muraškienė, Ivika Ostonen, Gristin Rohula-Okunev, Muhammad Kamil-Sardar, Thomas Schindler, Hanna Vahter, Egidijus Vigricas, and Ieva Līcīte
EGUsphere, https://doi.org/10.5194/egusphere-2024-2523, https://doi.org/10.5194/egusphere-2024-2523, 2024
Short summary
Short summary
Estimates of CO2 fluxes from drained nutrient-rich organic soils in cropland and grassland in the hemiboreal region of Europe revealed that annual net CO2 fluxes were lower than the latest (2014) IPCC emission factors provided for the whole temperate zone including hemiboreal region. Contribution of CO2 fluxes from shallow highly decomposed organic soils, former peatlands that no longer meet the IPCC criterion for organic soils, to total emissions can be high and should not be underestimated.
Evelyn Uuemaa, Marco Ciolli, and Marco Minghini
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W12-2024, 1–2, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-1-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-1-2024, 2024
Alexander Kmoch, Benoit Bovy, Justus Magin, Ryan Abernathey, Alejandro Coca-Castro, Peter Strobl, Anne Fouilloux, Daniel Loos, Evelyn Uuemaa, Wai Tik Chan, Jean-Marc Delouis, and Tina Odaka
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W12-2024, 75–80, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-75-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-75-2024, 2024
Nikita Murin, Alexander Kmoch, and Evelyn Uuemaa
AGILE GIScience Ser., 4, 34, https://doi.org/10.5194/agile-giss-4-34-2023, https://doi.org/10.5194/agile-giss-4-34-2023, 2023
Tahmin Sitab, Oleksandr Karasov, and Alexander Kmoch
AGILE GIScience Ser., 4, 43, https://doi.org/10.5194/agile-giss-4-43-2023, https://doi.org/10.5194/agile-giss-4-43-2023, 2023
Niel Verbrigghe, Niki I. W. Leblans, Bjarni D. Sigurdsson, Sara Vicca, Chao Fang, Lucia Fuchslueger, Jennifer L. Soong, James T. Weedon, Christopher Poeplau, Cristina Ariza-Carricondo, Michael Bahn, Bertrand Guenet, Per Gundersen, Gunnhildur E. Gunnarsdóttir, Thomas Kätterer, Zhanfeng Liu, Marja Maljanen, Sara Marañón-Jiménez, Kathiravan Meeran, Edda S. Oddsdóttir, Ivika Ostonen, Josep Peñuelas, Andreas Richter, Jordi Sardans, Páll Sigurðsson, Margaret S. Torn, Peter M. Van Bodegom, Erik Verbruggen, Tom W. N. Walker, Håkan Wallander, and Ivan A. Janssens
Biogeosciences, 19, 3381–3393, https://doi.org/10.5194/bg-19-3381-2022, https://doi.org/10.5194/bg-19-3381-2022, 2022
Short summary
Short summary
In subarctic grassland on a geothermal warming gradient, we found large reductions in topsoil carbon stocks, with carbon stocks linearly declining with warming intensity. Most importantly, however, we observed that soil carbon stocks stabilised within 5 years of warming and remained unaffected by warming thereafter, even after > 50 years of warming. Moreover, in contrast to the large topsoil carbon losses, subsoil carbon stocks remained unaffected after > 50 years of soil warming.
Evelyn Uuemaa and Alexander Kmoch
AGILE GIScience Ser., 3, 65, https://doi.org/10.5194/agile-giss-3-65-2022, https://doi.org/10.5194/agile-giss-3-65-2022, 2022
Holger Virro, Alexander Kmoch, Marko Vainu, and Evelyn Uuemaa
AGILE GIScience Ser., 3, 66, https://doi.org/10.5194/agile-giss-3-66-2022, https://doi.org/10.5194/agile-giss-3-66-2022, 2022
Alexander Kmoch, Oleksandr Matsibora, Ivan Vasilyev, and Evelyn Uuemaa
AGILE GIScience Ser., 3, 41, https://doi.org/10.5194/agile-giss-3-41-2022, https://doi.org/10.5194/agile-giss-3-41-2022, 2022
Holger Virro, Giuseppe Amatulli, Alexander Kmoch, Longzhu Shen, and Evelyn Uuemaa
Earth Syst. Sci. Data, 13, 5483–5507, https://doi.org/10.5194/essd-13-5483-2021, https://doi.org/10.5194/essd-13-5483-2021, 2021
Short summary
Short summary
Water quality modeling is essential for understanding and mitigating water quality deterioration in river networks due to agricultural and industrial pollution. Improving the availability and usability of open data is vital to support global water quality modeling efforts. The GRQA extends the spatial and temporal coverage of previously available water quality data and provides a reproducible workflow for combining multi-source water quality datasets.
Related subject area
Pedology
A China dataset of soil properties for land surface modelling (version 2, CSDLv2)
Distribution and sources of fallout 137Cs and 239+240Pu in Equatorial and Southern Hemisphere reference soils
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
New gridded dataset of rainfall erosivity (1950–2020) on the Tibetan Plateau
An hourly ground temperature dataset for 16 high-elevation sites (3493–4377 m a.s.l.) in the Bale Mountains, Ethiopia (2017–2020)
Rainfall erosivity mapping over mainland China based on high-density hourly rainfall records
The Boreal–Arctic Wetland and Lake Dataset (BAWLD)
A first investigation of hydrogeology and hydrogeophysics of the Maqu catchment in the Yellow River source region
Radionuclide contamination in flood sediment deposits in the coastal rivers draining the main radioactive pollution plume of Fukushima Prefecture, Japan (2011–2020)
Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019
An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018
A new dataset of soil carbon and nitrogen stocks and profiles from an instrumented Greenlandic fen designed to evaluate land-surface models
Spatial radionuclide deposition data from the 60 km radial area around the Chernobyl Nuclear Power Plant: results from a sampling survey in 1987
Generalized models to estimate carbon and nitrogen stocks of organic soil horizons in Interior Alaska
Soil moisture and matric potential – an open field comparison of sensor systems
CHLSOC: the Chilean Soil Organic Carbon database, a multi-institutional collaborative effort
An open-source database for the synthesis of soil radiocarbon data: International Soil Radiocarbon Database (ISRaD) version 1.0
Analysis of soil hydraulic and thermal properties for land surface modeling over the Tibetan Plateau
Spatial datasets of radionuclide contamination in the Ukrainian Chernobyl Exclusion Zone
A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves
WoSIS: providing standardised soil profile data for the world
Post-Chernobyl surveys of radiocaesium in soil, vegetation, wildlife and fungi in Great Britain
A new data set for estimating organic carbon storage to 3 m depth in soils of the northern circumpolar permafrost region
The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions
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
Short summary
Short summary
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.
Gerald Dicen, Floriane Guillevic, Surya Gupta, Pierre-Alexis Chaboche, Katrin Meusburger, Pierre Sabatier, Olivier Evrard, and Christine Alewell
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-509, https://doi.org/10.5194/essd-2024-509, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Fallout radionuclides such as 137Cs and 239+240Pu are considered as critical tools in various environmental research. Here, we compiled reference soil data on these fallout radionuclides 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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Yueli Chen, Xingwu Duan, Minghu Ding, Wei Qi, Ting Wei, Jianduo Li, and Yun Xie
Earth Syst. Sci. Data, 14, 2681–2695, https://doi.org/10.5194/essd-14-2681-2022, https://doi.org/10.5194/essd-14-2681-2022, 2022
Short summary
Short summary
We reconstructed the first annual rainfall erosivity dataset for the Tibetan Plateau in China. The dataset covers 71 years in a 0.25° grid. The reanalysis precipitation data are employed in combination with the densely spaced in situ precipitation observations to generate the dataset. The dataset can supply fundamental data for quantifying the water erosion, and extend our knowledge of the rainfall-related hazard prediction on the Tibetan Plateau.
Alexander R. Groos, Janik Niederhauser, Bruk Lemma, Mekbib Fekadu, Wolfgang Zech, Falk Hänsel, Luise Wraase, Naki Akçar, and Heinz Veit
Earth Syst. Sci. Data, 14, 1043–1062, https://doi.org/10.5194/essd-14-1043-2022, https://doi.org/10.5194/essd-14-1043-2022, 2022
Short summary
Short summary
Continuous observations and measurements from high elevations are necessary to monitor recent climate and environmental changes in the tropical mountains of eastern Africa, but meteorological and ground temperature data from above 3000 m are very rare. Here we present a comprehensive ground temperature monitoring network that has been established between 3493 and 4377 m in the Bale Mountains (Ethiopian Highlands) to monitor and study the afro-alpine climate and ecosystem in this region.
Tianyu Yue, Shuiqing Yin, Yun Xie, Bofu Yu, and Baoyuan Liu
Earth Syst. Sci. Data, 14, 665–682, https://doi.org/10.5194/essd-14-665-2022, https://doi.org/10.5194/essd-14-665-2022, 2022
Short summary
Short summary
This paper provides new rainfall erosivity maps over mainland China based on hourly data from 2381 stations (available at https://doi.org/10.12275/bnu.clicia.rainfallerosivity.CN.001). The improvement from the previous work was also assessed. The improvement in the R-factor map occurred mainly in the western region, because of an increase in the number of stations and an increased temporal resolution from daily to hourly data.
David Olefeldt, Mikael Hovemyr, McKenzie A. Kuhn, David Bastviken, Theodore J. Bohn, John Connolly, Patrick Crill, Eugénie S. Euskirchen, Sarah A. Finkelstein, Hélène Genet, Guido Grosse, Lorna I. Harris, Liam Heffernan, Manuel Helbig, Gustaf Hugelius, Ryan Hutchins, Sari Juutinen, Mark J. Lara, Avni Malhotra, Kristen Manies, A. David McGuire, Susan M. Natali, Jonathan A. O'Donnell, Frans-Jan W. Parmentier, Aleksi Räsänen, Christina Schädel, Oliver Sonnentag, Maria Strack, Suzanne E. Tank, Claire Treat, Ruth K. Varner, Tarmo Virtanen, Rebecca K. Warren, and Jennifer D. Watts
Earth Syst. Sci. Data, 13, 5127–5149, https://doi.org/10.5194/essd-13-5127-2021, https://doi.org/10.5194/essd-13-5127-2021, 2021
Short summary
Short summary
Wetlands, lakes, and rivers are important sources of the greenhouse gas methane to the atmosphere. To understand current and future methane emissions from northern regions, we need maps that show the extent and distribution of specific types of wetlands, lakes, and rivers. The Boreal–Arctic Wetland and Lake Dataset (BAWLD) provides maps of five wetland types, seven lake types, and three river types for northern regions and will improve our ability to predict future methane emissions.
Mengna Li, Yijian Zeng, Maciek W. Lubczynski, Jean Roy, Lianyu Yu, Hui Qian, Zhenyu Li, Jie Chen, Lei Han, Han Zheng, Tom Veldkamp, Jeroen M. Schoorl, Harrie-Jan Hendricks Franssen, Kai Hou, Qiying Zhang, Panpan Xu, Fan Li, Kai Lu, Yulin Li, and Zhongbo Su
Earth Syst. Sci. Data, 13, 4727–4757, https://doi.org/10.5194/essd-13-4727-2021, https://doi.org/10.5194/essd-13-4727-2021, 2021
Short summary
Short summary
The Tibetan Plateau is the source of most of Asia's major rivers and has been called the Asian Water Tower. Due to its remoteness and the harsh environment, there is a lack of field survey data to investigate its hydrogeology. Borehole core lithology analysis, an altitude survey, soil thickness measurement, hydrogeological surveys, and hydrogeophysical surveys were conducted in the Maqu catchment within the Yellow River source region to improve a full–picture understanding of the water cycle.
Olivier Evrard, Caroline Chartin, J. Patrick Laceby, Yuichi Onda, Yoshifumi Wakiyama, Atsushi Nakao, Olivier Cerdan, Hugo Lepage, Hugo Jaegler, Rosalie Vandromme, Irène Lefèvre, and Philippe Bonté
Earth Syst. Sci. Data, 13, 2555–2560, https://doi.org/10.5194/essd-13-2555-2021, https://doi.org/10.5194/essd-13-2555-2021, 2021
Short summary
Short summary
This dataset provides an original compilation of radioactive dose rates and artificial radionuclide activities in sediment deposited after floods in the rivers draining the main radioactive pollution plume in Fukushuma, Japan, between November
2011 and November 2020. In total, 782 sediment samples collected from 27 to 71 locations during 16 fieldwork campaigns were analysed. This provides a unique post-accidental dataset to better understand the environmental fate of radionuclides.
Qiang Zhang, Qiangqiang Yuan, Jie Li, Yuan Wang, Fujun Sun, and Liangpei Zhang
Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, https://doi.org/10.5194/essd-13-1385-2021, 2021
Short summary
Short summary
Acquired daily soil moisture products are always incomplete globally (just about 30 %–80 % coverage ratio) due to the satellite orbit coverage and the limitations of soil moisture retrieval algorithms. To solve this inevitable problem, we generate long-term seamless global daily (SGD) AMSR2 soil moisture productions from 2013 to 2019. These productions are significant for full-coverage global daily hydrologic monitoring, rather than averaging as the monthly–quarter–yearly results.
Yongzhe Chen, Xiaoming Feng, and Bojie Fu
Earth Syst. Sci. Data, 13, 1–31, https://doi.org/10.5194/essd-13-1-2021, https://doi.org/10.5194/essd-13-1-2021, 2021
Short summary
Short summary
Soil moisture can greatly influence the ecosystem but is hard to monitor at the global scale. By calibrating and combining 11 different products derived from satellite observation, we developed a new global surface soil moisture dataset spanning from 2003 to 2018 with high accuracy. Using this new dataset, not only can the global long-term trends be derived, but also the seasonal variation and spatial distribution of surface soil moisture at different latitudes can be better studied.
Xavier Morel, Birger Hansen, Christine Delire, Per Ambus, Mikhail Mastepanov, and Bertrand Decharme
Earth Syst. Sci. Data, 12, 2365–2380, https://doi.org/10.5194/essd-12-2365-2020, https://doi.org/10.5194/essd-12-2365-2020, 2020
Short summary
Short summary
Nuuk fen site is a well-instrumented Greenlandic site where soil physical variables and greenhouse gas fluxes are monitored. But knowledge of soil carbon stocks and profiles is missing. This is a crucial shortcoming for a complete evaluation of models. For the first time we measured soil carbon and nitrogen density, profiles, and stocks in the Nuuk peatland. This new dataset can contribute to further develop joint modeling of greenhouse gas emissions and soil carbon in land-surface models.
Valery Kashparov, Sviatoslav Levchuk, Marina Zhurba, Valentyn Protsak, Nicholas A. Beresford, and Jacqueline S. Chaplow
Earth Syst. Sci. Data, 12, 1861–1875, https://doi.org/10.5194/essd-12-1861-2020, https://doi.org/10.5194/essd-12-1861-2020, 2020
Short summary
Short summary
Sampling and analysis methodology and spatial radionuclide deposition data from the 60 km area around the Chernobyl Nuclear Power Plant, sampled in 1987 by the Ukrainian Institute of Agricultural Radiology, are useful for reconstructing doses to human and wildlife populations, answering the current lack of scientific consensus on the effects of radiation on wildlife in the Chernobyl Exclusion Zone and evaluating future management options for the Chernobyl-impacted areas of Ukraine and Belarus.
Kristen Manies, Mark Waldrop, and Jennifer Harden
Earth Syst. Sci. Data, 12, 1745–1757, https://doi.org/10.5194/essd-12-1745-2020, https://doi.org/10.5194/essd-12-1745-2020, 2020
Short summary
Short summary
Boreal ecosystems are unique in that their mineral soil is covered by what can be quite thick layers of organic soil. Layers within this organic soil have different bulk densities, carbon composition, and nitrogen composition. We summarize these properties by soil layer and examine if and how they are affected by soil drainage and stand age. These values can be used to initialize and validate models as well as gap fill when these important soil properties are not measured.
Conrad Jackisch, Kai Germer, Thomas Graeff, Ines Andrä, Katrin Schulz, Marcus Schiedung, Jaqueline Haller-Jans, Jonas Schneider, Julia Jaquemotte, Philipp Helmer, Leander Lotz, Andreas Bauer, Irene Hahn, Martin Šanda, Monika Kumpan, Johann Dorner, Gerrit de Rooij, Stefan Wessel-Bothe, Lorenz Kottmann, Siegfried Schittenhelm, and Wolfgang Durner
Earth Syst. Sci. Data, 12, 683–697, https://doi.org/10.5194/essd-12-683-2020, https://doi.org/10.5194/essd-12-683-2020, 2020
Short summary
Short summary
Soil water content and matric potential are central hydrological state variables. A large variety of automated probes and sensor systems for field monitoring exist. In a field experiment under idealised conditions we compared 15 systems for soil moisture and 14 systems for matric potential. The individual records of one system agree well with the others. Most records are also plausible. However, the absolute values of the different measuring systems span a very large range of possible truths.
Marco Pfeiffer, José Padarian, Rodrigo Osorio, Nelson Bustamante, Guillermo Federico Olmedo, Mario Guevara, Felipe Aburto, Francisco Albornoz, Monica Antilén, Elías Araya, Eduardo Arellano, Maialen Barret, Juan Barrera, Pascal Boeckx, Margarita Briceño, Sally Bunning, Lea Cabrol, Manuel Casanova, Pablo Cornejo, Fabio Corradini, Gustavo Curaqueo, Sebastian Doetterl, Paola Duran, Mauricio Escudey, Angelina Espinoza, Samuel Francke, Juan Pablo Fuentes, Marcel Fuentes, Gonzalo Gajardo, Rafael García, Audrey Gallaud, Mauricio Galleguillos, Andrés Gomez, Marcela Hidalgo, Jorge Ivelic-Sáez, Lwando Mashalaba, Francisco Matus, Francisco Meza, Maria de la Luz Mora, Jorge Mora, Cristina Muñoz, Pablo Norambuena, Carolina Olivera, Carlos Ovalle, Marcelo Panichini, Aníbal Pauchard, Jorge F. Pérez-Quezada, Sergio Radic, José Ramirez, Nicolás Riveras, Germán Ruiz, Osvaldo Salazar, Iván Salgado, Oscar Seguel, Maria Sepúlveda, Carlos Sierra, Yasna Tapia, Francisco Tapia, Balfredo Toledo, José Miguel Torrico, Susana Valle, Ronald Vargas, Michael Wolff, and Erick Zagal
Earth Syst. Sci. Data, 12, 457–468, https://doi.org/10.5194/essd-12-457-2020, https://doi.org/10.5194/essd-12-457-2020, 2020
Short summary
Short summary
The CHLSOC database is the biggest soil organic carbon (SOC) database that has been compiled for Chile yet, comprising 13 612 data points. This database is the product of the compilation of numerous sources including unpublished and difficult-to-access data, allowing us to fill numerous spatial gaps where no SOC estimates were publicly available before. The values of SOC compiled in CHLSOC have a wide range, reflecting the variety of ecosystems that exists in Chile.
Corey R. Lawrence, Jeffrey Beem-Miller, Alison M. Hoyt, Grey Monroe, Carlos A. Sierra, Shane Stoner, Katherine Heckman, Joseph C. Blankinship, Susan E. Crow, Gavin McNicol, Susan Trumbore, Paul A. Levine, Olga Vindušková, Katherine Todd-Brown, Craig Rasmussen, Caitlin E. Hicks Pries, Christina Schädel, Karis McFarlane, Sebastian Doetterl, Christine Hatté, Yujie He, Claire Treat, Jennifer W. Harden, Margaret S. Torn, Cristian Estop-Aragonés, Asmeret Asefaw Berhe, Marco Keiluweit, Ágatha Della Rosa Kuhnen, Erika Marin-Spiotta, Alain F. Plante, Aaron Thompson, Zheng Shi, Joshua P. Schimel, Lydia J. S. Vaughn, Sophie F. von Fromm, and Rota Wagai
Earth Syst. Sci. Data, 12, 61–76, https://doi.org/10.5194/essd-12-61-2020, https://doi.org/10.5194/essd-12-61-2020, 2020
Short summary
Short summary
The International Soil Radiocarbon Database (ISRaD) is an an open-source archive of soil data focused on datasets including radiocarbon measurements. ISRaD includes data from bulk or
whole soils, distinct soil carbon pools isolated in the laboratory by a variety of soil fractionation methods, samples of soil gas or water collected interstitially from within an intact soil profile, CO2 gas isolated from laboratory soil incubations, and fluxes collected in situ from a soil surface.
Hong Zhao, Yijian Zeng, Shaoning Lv, and Zhongbo Su
Earth Syst. Sci. Data, 10, 1031–1061, https://doi.org/10.5194/essd-10-1031-2018, https://doi.org/10.5194/essd-10-1031-2018, 2018
Short summary
Short summary
The Tibet-Obs soil properties dataset was compiled based on in situ and laboratory measurements of soil profiles across three climate zones on the Tibetan Plateau. The appropriate parameterization schemes of soil hydraulic and thermal properties were discussed for their applicability in land surface modeling. The uncertainties of existing soil datasets were evaluated. This paper contributes to land surface modeling and hydro-climatology communities for their studies of the third pole region.
Valery Kashparov, Sviatoslav Levchuk, Marina Zhurba, Valentyn Protsak, Yuri Khomutinin, Nicholas A. Beresford, and Jacqueline S. Chaplow
Earth Syst. Sci. Data, 10, 339–353, https://doi.org/10.5194/essd-10-339-2018, https://doi.org/10.5194/essd-10-339-2018, 2018
Short summary
Short summary
Spatial datasets of radionuclide contamination in the Ukrainian Chernobyl Exclusion Zone describe data from analysis of samples collected by the Ukrainian Institute of Agricultural Radiology after the Chernobyl nuclear accident between May 1986 and 2014 at sites inside the Chernobyl Exclusion Zone and other areas of interest. The data and supporting documentation are freely available from the Environmental Information Data Centre: https://doi.org/10.5285/782ec845-2135-4698-8881-b38823e533bf.
Carsten Montzka, Michael Herbst, Lutz Weihermüller, Anne Verhoef, and Harry Vereecken
Earth Syst. Sci. Data, 9, 529–543, https://doi.org/10.5194/essd-9-529-2017, https://doi.org/10.5194/essd-9-529-2017, 2017
Short summary
Short summary
Global climate models require adequate parameterization of soil hydraulic properties, but typical resampling to the model grid introduces uncertainties. Here we present a method to scale hydraulic parameters to individual model grids and provide a global data set that overcomes the problems. It preserves the information of sub-grid variability of the water retention curve by deriving local scaling parameters that enables modellers to perturb hydraulic parameters for model ensemble generation.
Niels H. Batjes, Eloi Ribeiro, Ad van Oostrum, Johan Leenaars, Tom Hengl, and Jorge Mendes de Jesus
Earth Syst. Sci. Data, 9, 1–14, https://doi.org/10.5194/essd-9-1-2017, https://doi.org/10.5194/essd-9-1-2017, 2017
Short summary
Short summary
Soil is an important provider of ecosystem services. Yet this natural resource is being threatened. Professionals, scientists, and decision makers require quality-assessed soil data to address issues such as food security, land degradation, and climate change. Procedures for safeguarding, standardising, and subsequently serving of consistent soil data to underpin broad-scale mapping and modelling are described. The data are freely accessible at doi:10.17027/isric-wdcsoils.20160003.
J. S. Chaplow, N. A. Beresford, and C. L. Barnett
Earth Syst. Sci. Data, 7, 215–221, https://doi.org/10.5194/essd-7-215-2015, https://doi.org/10.5194/essd-7-215-2015, 2015
Short summary
Short summary
The data set ‘Post Chernobyl surveys of radiocaesium in soil, vegetation, wildlife and fungi in Great Britain’ was developed to enable data collected by the Natural Environment Research Council after the Chernobyl accident to be made publicly available. Data for samples collected between May 1986 (immediately after Chernobyl) to spring 1997 are freely available for non-commercial use under Open Government Licence terms and conditions. doi: 10.5285/d0a6a8bf-68f0-4935-8b43-4e597c3bf251.
G. Hugelius, J. G. Bockheim, P. Camill, B. Elberling, G. Grosse, J. W. Harden, K. Johnson, T. Jorgenson, C. D. Koven, P. Kuhry, G. Michaelson, U. Mishra, J. Palmtag, C.-L. Ping, J. O'Donnell, L. Schirrmeister, E. A. G. Schuur, Y. Sheng, L. C. Smith, J. Strauss, and Z. Yu
Earth Syst. Sci. Data, 5, 393–402, https://doi.org/10.5194/essd-5-393-2013, https://doi.org/10.5194/essd-5-393-2013, 2013
G. Hugelius, C. Tarnocai, G. Broll, J. G. Canadell, P. Kuhry, and D. K. Swanson
Earth Syst. Sci. Data, 5, 3–13, https://doi.org/10.5194/essd-5-3-2013, https://doi.org/10.5194/essd-5-3-2013, 2013
Cited articles
Abbaspour, K. C., Vaghefi, S. A., Yang, H. and Srinivasan, R.: Global soil,
landuse, evapotranspiration, historical and future weather databases for
SWAT Applications, Sci. Data, 6, 263, https://doi.org/10.1038/s41597-019-0282-4,
2019.
Abdelbaki, A. M.: Evaluation of pedotransfer functions for predicting soil
bulk density for U.S. soils, Ain Shams Eng. J., 9, 1611–1619,
https://doi.org/10.1016/j.asej.2016.12.002, 2018.
Adams, W. A.: The Effect of Organic Matter on the bulk and true Densities of
some Uncultivated Podzolic Soils, J. Soil Sci., 24, 10–17,
https://doi.org/10.1111/j.1365-2389.1973.tb00737.x, 1973.
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing
area model of basin hydrology, Hydrol. Sci. B., 24, 43–69,
https://doi.org/10.1080/02626667909491834, 1979.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001.
Calhoun, T. E., Ellermäe, O., Kõlli, R., Lemetti, I., Penu, P., and
Smith, C. W.: Benchmark Soils of Estonia Researched thru Baltic – American
Collaboration, Problems of Estonian Soil Classification, Trans. Est. Agric.
Univ., 198, 76–114, 1998.
Caruana, R. and Niculescu-Mizil, A.: An Empirical Comparison of Supervised
Learning Algorithms, in: Proceedings of the 23rd International Conference on
Machine Learning, 161–168, ACM, New York, NY, USA, 25–29 June 2006.
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015.
Dipak, S. and Abhijit, H.: Physical and Chemical Methods in Soil Analysis,
New Age International Ltd., New Delhi, 2005.
Ditzler, C., Scheffe, K., and Monger, H. C.: Soil survey manual. USDA
Handbook 18, Soil Science Division, Government Printing Office, Washington,
D.C., 2017.
Estonian Landboard: Soilmap of Estonia – Mullastiku kaart, National Soilmap of Estonia, Dataset deposit,
https://doi.org/10.15155/re-72, 2017.
Eswaran, H., Van Den Berg, E., and Reich, P.: Organic Carbon in Soils of the
World, Soil Sci. Soc. Am. J., 57, 192,
https://doi.org/10.2136/sssaj1993.03615995005700010034x, 1993.
FAO: World reference base for soil resources, 2014 International soil classification system for naming soils and creating legends for soil maps, World Soil Resources Reports No. 106. FAO, Rome Italy,available at: http://www.fao.org/3/i3794en/I3794en.pdf (last access: 1 January 2021), 2015.
Fischer, G., Nachtergaele, F., Prieler, S., van Velthuizen, H. T., Verelst,
L., and Wiberg, D.: Global Agro-ecological Zones Assessment for Agriculture
(GAEZ 2008), in: IIASA, Laxenburg, Austria and FAO, Rome, Italy, 2008.
Gafni, A., Malterer, T., Verry, E., Nichols, D., Boelter, D., and
Päivänen, J.: Physical Properties of Organic Soils, in Peatland
Biogeochemistry and Watershed Hydrology at the Marcell Experimental Forest,
edited by: Kolka, R., Sebestyen, S., Verry, E. S., and Brooks, K., 135–176,
CRC Press, Boca Raton, FL, 2011.
Gunarathna, M. H. J. P., Sakai, K., Nakandakari, T., Momii, K., and Kumari,
M. K. N.: Machine learning approaches to develop pedotransfer functions for
tropical Sri Lankan soils, Water, 11, 1940 https://doi.org/10.3390/w11091940, 2019.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M.,
Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global gridded soil information based on machine
learning, edited by: Bond-Lamberty, B., PLoS One, 12, e0169748,
https://doi.org/10.1371/journal.pone.0169748, 2017.
Hiederer, R., Michéli, E., and Durrant, T.: Evaluation of the BioSoil
DemonstrationProject, Ispra, European Commission Joint Research Centre Institute for Environment and Sustainability, 2011.
Kachinsky, N.: Fizika potchv, Soil physics, Vol. 1, Moscow
University Press, Moscow, 1965 (in Russian).
Kask, R.: On the English Equivalents of the Estonian Terms for the Textural
Classes of Estonian Soils, J. Agr. Sci., 14, 93–96, 2001.
Kauer, K., Astover, A., Viiralt, R., Raave, H., and Kätterer, T.:
Evolution of soil organic carbon in a carbonaceous glacial till as an effect
of crop and fertility management over 50 years in a field experiment, Agr.
Ecosyst. Environ., 283, 106562, https://doi.org/10.1016/j.agee.2019.06.001, 2019.
Keesstra, S., Mol, G., de Leeuw, J., Okx, J., Molenaar, C., de Cleen, M., and
Visser, S.: Soil-related sustainable development goals: Four concepts to
make land degradation neutrality and restoration work, Land, 7, 133,
https://doi.org/10.3390/land7040133, 2018.
Keesstra, S. D., Bouma, J., Wallinga, J., Tittonell, P., Smith, P., Cerdà, A., Montanarella, L., Quinton, J. N., Pachepsky, Y., van der Putten, W. H., Bardgett, R. D., Moolenaar, S., Mol, G., Jansen, B., and Fresco, L. O.: The significance of soils and soil science towards realization of the United Nations Sustainable Development Goals, SOIL, 2, 111–128, https://doi.org/10.5194/soil-2-111-2016, 2016.
Kmoch, A., Kanal, A., Astover, A., Kull, A., Virro, H., Helm, A.,
Pärtel, M., Ostonen, I., and Uuemaa, E.: EstSoil-EH: An eco-hydrological
modelling parameters dataset derived from the Soil Map of Estonia (data
deposit), Zenodo, https://doi.org/10.5281/zenodo.3473289, 2019a.
Kmoch, A., Virro, H., and Uuemaa, E.: EstSoil-EH software supplement, Zenodo,
https://doi.org/10.5281/zenodo.3473210, 2019b.
Kõlli, R., Ellermäe, O., Köster, T., Lemetti, I., Asi, E., and
Kauer, K.: Stocks of organic carbon in Estonian soils, Est. J. Earth Sci.,
58, 95–108, https://doi.org/10.3176/earth.2009.2.01, 2009.
Kriiska, K., Frey, J., Asi, E., Kabral, N., Uri, V., Aosaar, J., Varik, M.,
Napa, Ü., Apuhtin, V., Timmusk, T., and Ostonen, I.: Variation in annual
carbon fluxes affecting the SOC pool in hemiboreal coniferous forests in
Estonia, Forest Ecol. Manag., 433, 419–430,
https://doi.org/10.1016/j.foreco.2018.11.026, 2019.
Laas, A. and Kull, A.: Sustainable Planning and Development, edited by: Beriatos, A. G.
K. E., Brebbia, C. A., and Coccossis, H., Boston, Wessex Institute of
Techonology Press, Southampton, 2003.
Michielsen, A., Kalantari, Z., Lyon, S. W., and Liljegren, E.: Predicting and
communicating flood risk of transport infrastructure based on watershed
characteristics, J. Environ. Manage., 182, 505–518, https://doi.org/10.1016/j.jenvman.2016.07.051,
2016.
Minasny, B. and Hartemink, A. E.: Predicting soil properties in the tropics,
Earth-Sci. Rev., 106, 52–62, https://doi.org/10.1016/j.earscirev.2011.01.005,
2011.
Mokarram, M., Roshan, G. and Negahban, S.: Landform classification using
topography position index (case study: salt dome of Korsia-Darab plain,
Iran), Model. Earth Syst. Environ., 1, 40, https://doi.org/10.1007/s40808-015-0055-9, 2015.
Moore, I. D., Grayson, R. B., and Ladson, A. R.: Digital terrain modelling: A
review of hydrological, geomorphological, and biological applications,
Hydrol. Process., 5, 3–30, https://doi.org/10.1002/hyp.3360050103, 1991.
Noreika, N., Helm, A., Öpik, M., Jairus, T., Vasar, M., Reier, Ü.,
Kook, E., Riibak, K., Kasari, L., Tullus, H., Tullus, T., Lutter, R., Oja,
E., Saag, A., Randlane, T., and Pärtel, M.: Forest biomass, soil and
biodiversity relationships originate from biogeographic affinity and direct
ecological effects, Oikos, 128, 1653–1665, https://doi.org/10.1111/oik.06693, 2019.
Nussbaum, M., Spiess, K., Baltensweiler, A., Grob, U., Keller, A., Greiner, L., Schaepman, M. E., and Papritz, A.: Evaluation of digital soil mapping approaches with large sets of environmental covariates, SOIL, 4, 1–22, https://doi.org/10.5194/soil-4-1-2018, 2018.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12,
2825–2830, 2011.
Prévost, M.: Predicting Soil Properties from Organic Matter Content
following Mechanical Site Preparation of Forest Soils, Soil Sci. Soc. Am.
J., 68, 943, https://doi.org/10.2136/sssaj2004.9430, 2004.
Putku, E.: Prediction models of soil organic carbon and bulk density of
arable mineral soils, Doctoral Thesis, Estonian University of Life
Sciences, 2016.
Reintam, L., Kull, A., Palang, H. and Rooma, I.: Large-Scale Soil Maps and a
Supplementary Database for Land Use Planning in Estonia, J. Plant Nutr. Soil
Sc., 166, 225–231, 2003.
Reintam, L., Rooma, I., Kull, A., and Kõlli, R.: Soil information and its
application in Estonia, Research report, European Soil Bureau, 9,
121–132, 2005.
Suuster, E., Ritz, C., Roostalu, H., Reintam, E., Kõlli, R., and Astover,
A.: Soil bulk density pedotransfer functions of the humus horizon in arable
soils, Geoderma, 163, 74–82, https://doi.org/10.1016/j.geoderma.2011.04.005,
2011.
Tarnocai, C., Canadell, J. G., Schuur, E. A. G., Kuhry, P., Mazhitova, G.,
and Zimov, S.: Soil organic carbon pools in the northern circumpolar
permafrost region, Global Biogeochem. Cy., 23, GB2023,
https://doi.org/10.1029/2008GB003327, 2009.
Tóth, B., Weynants, M., Pásztor, L., and Hengl, T.: 3D soil hydraulic
database of Europe at 250 m resolution, Hydrol. Process., 31, 2662–2666,
https://doi.org/10.1002/hyp.11203, 2017.
Uuemaa, E., Hughes, A. O., and Tanner, C. C.: Identifying feasible locations
for wetland creation or restoration in catchments by suitability modelling
using light detection and ranging (LiDAR) Digital Elevation Model (DEM), Water,
10, 464, https://doi.org/10.3390/w10040464, 2018.
Van Looy, K., Bouma, J., Herbst, M., Koestel, J., Minasny, B., Mishra, U.,
Montzka, C., Nemes, A., Pachepsky, Y. A., Padarian, J., Schaap, M. G.,
Tóth, B., Verhoef, A., Vanderborght, J., van der Ploeg, M. J.,
Weihermüller, L., Zacharias, S., Zhang, Y., and Vereecken, H.:
Pedotransfer Functions in Earth System Science: Challenges and Perspectives,
Rev. Geophys., 55, 1199–1256, https://doi.org/10.1002/2017RG000581, 2017.
Vitharana, U. W. A., Mishra, U., Jastrow, J. D., Matamala, R., and Fan, Z.:
Observational needs for estimating Alaskan soil carbon stocks under current
and future climate, J. Geophys. Res.-Biogeo., 122, 415–429,
https://doi.org/10.1002/2016JG003421, 2017.
Yigini, Y. and Panagos, P.: Assessment of soil organic carbon stocks under
future climate and land cover changes in Europe, Sci. Total Environ.,
557–558, 838–850, https://doi.org/10.1016/J.SCITOTENV.2016.03.085, 2016.
Zhang, Y. and Schaap, M. G.: Weighted recalibration of the Rosetta
pedotransfer model with improved estimates of hydraulic parameter
distributions and summary statistics (Rosetta3), J. Hydrol., 547, 39–53,
https://doi.org/10.1016/j.jhydrol.2017.01.004, 2017.
Short summary
The Soil Map of Estonia is the most detailed and information-rich dataset for soils in Estonia. But its information is not immediately usable for analyses or modelling. We derived parameters including soil layering, soil texture (clay, silt, and sand content), coarse fragments, and rock content and aggregated and predicted physical variables related to water and carbon cycles (bulk density, hydraulic conductivity, organic carbon content, available water capacity).
The Soil Map of Estonia is the most detailed and information-rich dataset for soils in Estonia....
Altmetrics
Final-revised paper
Preprint
Similar articles
A China dataset of soil properties for...
Shi et al.
An integrated dataset of ground...
Li et al.
Providing quality-assessed and...
Batjes et al.