Articles | Volume 16, issue 3
https://doi.org/10.5194/essd-16-1229-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-1229-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
11 Mar 2024
Data description paper |
| 11 Mar 2024
| 11 Mar 2024
Improving the Latin America and Caribbean Soil Information System (SISLAC) database enhances its usability and scalability
Sergio Díaz-Guadarrama
CORRESPONDING AUTHOR
Departamento de Agronomía, Facultad de Ciencias Agrarias. Universidad Nacional de Colombia, Bogotá, Colombia
Viviana M. Varón-Ramírez
Centro de Geociencias, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, 76230, Mexico
Corporación colombiana de investigación agropecuaria AGROSAVIA, C.I. Tibaitatá, Bogotá, CO-0571, Colombia
Iván Lizarazo
Departamento de Agronomía, Facultad de Ciencias Agrarias. Universidad Nacional de Colombia, Bogotá, Colombia
Mario Guevara
CORRESPONDING AUTHOR
Centro de Geociencias, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, 76230, Mexico
Department of Environmental Sciences, University of California, Riverside, Riverside, CA 92507, USA
United States Department of Agriculture, Soil Salinity National Laboratory, Riverside, CA 92507, USA
Marcos Angelini
FAO, Vialle de Terme di Caracalla, Rome, Italy
Gustavo A. Araujo-Carrillo
Corporación colombiana de investigación agropecuaria AGROSAVIA, C.I. Tibaitatá, Bogotá, CO-0571, Colombia
Jainer Argeñal
Facultad de Ciencias, Universidad Nacional Autónoma de Honduras, Tegucigalpa, Honduras
Daphne Armas
Departamento de Agronomía, Edif. CITEIIB, Universidad de Almería, 04120 Almería, Spain
Rafael A. Balta
Dirección General de Asuntos Ambientales Agrarios, Ministerio de Desarrollo Agrario y Riego, Lima, Peru
Adriana Bolivar
Subdirección Agrología, Instituto Geográfico Agustín Codazzi, Bogotá, Colombia
Nelson Bustamante
Servicio Agrícola y Ganadero, Santiago de Chile, Chile
Ricardo O. Dart
Embrapa Solos, Rio de Janeiro, 22460-000, Brazil
Martin Dell Acqua
Direccion General de Recursos Naturales, Ministerio de Ganadería, Agricultura y Pesca, Montevideo, Uruguay
Arnulfo Encina
Facultad de Ciencias Agrarias, Universidad Nacional de Asunción, Asunción, Paraguay
Hernán Figueredo
Sociedad Boliviana de la Ciencia del Suelo, La Paz, Bolivia
Fernando Fontes
Direccion General de Recursos Naturales, Ministerio de Ganadería, Agricultura y Pesca, Montevideo, Uruguay
Joan S. Gutiérrez-Díaz
Department of Agroecology, Faculty of Science and Technology, Aarhus University, Tjele, 8830, Denmark
Wilmer Jiménez
Ministerio de Agricultura y Ganadería, Quito, 170516, Ecuador
Raúl S. Lavado
Facultad de Agronomía e INBA (CONICET/UBA), Universidad de Buenos Aires, Buenos Aires, 1417, Argentina
Jesús F. Mansilla-Baca
Embrapa Solos, Rio de Janeiro, 22460-000, Brazil
Maria de Lourdes Mendonça-Santos
Embrapa Solos, Rio de Janeiro, 22460-000, Brazil
Lucas M. Moretti
Estación Experimental Agropecuaria Cerro Azul, Instituto Nacional de Tecnología Agropecuaria, Misiones, Argentina
Iván D. Muñoz
Subdirección Agrología, Instituto Geográfico Agustín Codazzi, Bogotá, Colombia
Carolina Olivera
FAO, Vialle de Terme di Caracalla, Rome, Italy
Guillermo Olmedo
FAO, Vialle de Terme di Caracalla, Rome, Italy
Christian Omuto
FAO, Vialle de Terme di Caracalla, Rome, Italy
Sol Ortiz
Secretaría de Agricultura y Desarrollo Rural, Mexico City, Mexico
Carla Pascale
Ministerio de Agricultura, Ganadería y Pesca (MAGYP), Buenos Aires, Argentina
Marco Pfeiffer
Departamento de Ingeniería y Suelos, Facultad de Ciencias Agronómicas, Universidad de Chile, Santiago, Chile
Iván A. Ramos
Instituto de Investigación Agropecuaria de Panamá, Panama City, Panama
Danny Ríos
Departamento de Ciencias del Suelo y Ordenamiento Territorial, Universidad Nacional de Asunción, Asunción, Paraguay
Rafael Rivera
Ministerio de Medio Ambiente, Santo Domingo, Dominican Republic
Lady M. Rodriguez
Subdirección Agrología, Instituto Geográfico Agustín Codazzi, Bogotá, Colombia
Darío M. Rodríguez
Instituto de Investigación Suelos, Centro de Investigación de Recursos Naturales (CIRN), Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Buenos Aires, B1686, Argentina
Albán Rosales
Instituto Nacional de Innovación y Transferencia en Tecnología Agropecuaria, San José, Costa Rica
Kenset Rosales
Ministerio de Ambiente y Recursos Naturales, Guatemala City, Guatemala
Guillermo Schulz
Instituto de Investigación Suelos, Centro de Investigación de Recursos Naturales (CIRN), Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Buenos Aires, B1686, Argentina
Víctor Sevilla
Facultad de Agronomía, Universidad Central de Venezuela, Maracay, Venezuela
Leonardo M. Tenti
Instituto de Investigación Suelos, Centro de Investigación de Recursos Naturales (CIRN), Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Buenos Aires, B1686, Argentina
Ronald Vargas
FAO, Vialle de Terme di Caracalla, Rome, Italy
Gustavo M. Vasques
Embrapa Solos, Rio de Janeiro, 22460-000, Brazil
Yusuf Yigini
FAO, Vialle de Terme di Caracalla, Rome, Italy
Yolanda Rubiano
Departamento de Agronomía, Facultad de Ciencias Agrarias. Universidad Nacional de Colombia, Bogotá, Colombia
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Pilar Durante, Juan Miguel Requena-Mullor, Rodrigo Vargas, Mario Guevara, Domingo Alcaraz-Segura, and Cecilio Oyonarte
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-431, https://doi.org/10.5194/essd-2024-431, 2024
Preprint under review for ESSD
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Human activities have disrupted the global carbon cycle, increasing CO2 levels. Soils are the largest carbon stores on land, making it essential to understand how much carbon they hold to fight climate change. Our study improved estimates of soil carbon in peninsular Spain by integrating historical soil data and using machine-learning methods to create detailed maps of carbon content. These maps will help manage soil carbon better and support efforts to track carbon emissions globally.
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.
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.
Y. M. Montilla, C. León-Sánchez, and I. Lizarazo Salcedo
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-W2-2022, 201–208, https://doi.org/10.5194/isprs-annals-X-4-W2-2022-201-2022, https://doi.org/10.5194/isprs-annals-X-4-W2-2022-201-2022, 2022
Katherine E. O. Todd-Brown, Rose Z. Abramoff, Jeffrey Beem-Miller, Hava K. Blair, Stevan Earl, Kristen J. Frederick, Daniel R. Fuka, Mario Guevara Santamaria, Jennifer W. Harden, Katherine Heckman, Lillian J. Heran, James R. Holmquist, Alison M. Hoyt, David H. Klinges, David S. LeBauer, Avni Malhotra, Shelby C. McClelland, Lucas E. Nave, Katherine S. Rocci, Sean M. Schaeffer, Shane Stoner, Natasja van Gestel, Sophie F. von Fromm, and Marisa L. Younger
Biogeosciences, 19, 3505–3522, https://doi.org/10.5194/bg-19-3505-2022, https://doi.org/10.5194/bg-19-3505-2022, 2022
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Research data are becoming increasingly available online with tantalizing possibilities for reanalysis. However harmonizing data from different sources remains challenging. Using the soils community as an example, we walked through the various strategies that researchers currently use to integrate datasets for reanalysis. We find that manual data transcription is still extremely common and that there is a critical need for community-supported informatics tools like vocabularies and ontologies.
V. Angulo-Morales, J. Rodríguez-Galvis, I. Lizarazo-Salcedo, and E. Gaona-García
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 269–276, https://doi.org/10.5194/isprs-annals-V-1-2020-269-2020, https://doi.org/10.5194/isprs-annals-V-1-2020-269-2020, 2020
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
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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.
Marwa Tifafi, Marta Camino-Serrano, Christine Hatté, Hector Morras, Lucas Moretti, Sebastián Barbaro, Sophie Cornu, and Bertrand Guenet
Geosci. Model Dev., 11, 4711–4726, https://doi.org/10.5194/gmd-11-4711-2018, https://doi.org/10.5194/gmd-11-4711-2018, 2018
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The role of soil carbon in climate dynamics becomes one of the major uncertainties in land surface models. This work is a presentation of a new version of the land surface model called ORCHIDEE incorporating the radiocarbon (14C) used as integrator of the soil carbon dynamics. It has been possible to highlight an underestimation of the age of carbon in the soil and that model improvements should focus more on a depth-dependent parameterization mainly for the diffusion.
Mario Guevara, Guillermo Federico Olmedo, Emma Stell, Yusuf Yigini, Yameli Aguilar Duarte, Carlos Arellano Hernández, Gloria E. Arévalo, Carlos Eduardo Arroyo-Cruz, Adriana Bolivar, Sally Bunning, Nelson Bustamante Cañas, Carlos Omar Cruz-Gaistardo, Fabian Davila, Martin Dell Acqua, Arnulfo Encina, Hernán Figueredo Tacona, Fernando Fontes, José Antonio Hernández Herrera, Alejandro Roberto Ibelles Navarro, Veronica Loayza, Alexandra M. Manueles, Fernando Mendoza Jara, Carolina Olivera, Rodrigo Osorio Hermosilla, Gonzalo Pereira, Pablo Prieto, Iván Alexis Ramos, Juan Carlos Rey Brina, Rafael Rivera, Javier Rodríguez-Rodríguez, Ronald Roopnarine, Albán Rosales Ibarra, Kenset Amaury Rosales Riveiro, Guillermo Andrés Schulz, Adrian Spence, Gustavo M. Vasques, Ronald R. Vargas, and Rodrigo Vargas
SOIL, 4, 173–193, https://doi.org/10.5194/soil-4-173-2018, https://doi.org/10.5194/soil-4-173-2018, 2018
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We provide a reproducible multi-modeling approach for SOC mapping across Latin America on a country-specific basis as required by the Global Soil Partnership of the United Nations. We identify key prediction factors for SOC across each country. We compare and test different methods to generate spatially explicit predictions of SOC and conclude that there is no best method on a quantifiable basis.
Luca Montanarella, Daniel Jon Pennock, Neil McKenzie, Mohamed Badraoui, Victor Chude, Isaurinda Baptista, Tekalign Mamo, Martin Yemefack, Mikha Singh Aulakh, Kazuyuki Yagi, Suk Young Hong, Pisoot Vijarnsorn, Gan-Lin Zhang, Dominique Arrouays, Helaina Black, Pavel Krasilnikov, Jaroslava Sobocká, Julio Alegre, Carlos Roberto Henriquez, Maria de Lourdes Mendonça-Santos, Miguel Taboada, David Espinosa-Victoria, Abdullah AlShankiti, Sayed Kazem AlaviPanah, Elsiddig Ahmed El Mustafa Elsheikh, Jon Hempel, Marta Camps Arbestain, Freddy Nachtergaele, and Ronald Vargas
SOIL, 2, 79–82, https://doi.org/10.5194/soil-2-79-2016, https://doi.org/10.5194/soil-2-79-2016, 2016
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The Intergovernmental Technical Panel on Soils has completed the first State of the World's Soil Resources Report. The gravest threats were identified for all the regions of the world. This assessment forms a basis for future soil monitoring. The quality of soil information available for policy formulation must be improved.
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Domain: ESSD – Land | Subject: Pedology
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
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
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.
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.
Cited articles
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Angelini, M., Rodriguez, D. M., Olmedo, G. F., and Schulz, G.: Sistema de Información de Suelos del INTA (SISINTA): presente y futuro, in: XXVI Congreso Argentino de la Ciencia del Suelo, Tucumán, Argentina, 15–18 May 2018, 5 pp., https://www.researchgate.net/publication/325607030_Sistema_de_informacion_de_suelos_del_INTA_SISINTA_Presente_y_futuro (last access: 6 March 2024), 2018.
Araujo-Carrillo, G. A., Varón-Ramírez, V. M., Jaramillo-Barrios, C. I., Estupiñan-Casallas, J. M., Silva-Arero, E. A., Gómez-Latorre, D. A., and Martínez-Maldonado, F. E.: IRAKA: The first Colombian soil information system with digital soil mapping products, Catena, 196, 104940, https://doi.org/10.1016/j.catena.2020.104940, 2021.
Armas, D., Guevara, M., Alcaraz-Segura, D., Vargas, R., Soriano-Luna, Á., Durante, P., and Oyonarte, C: Digital map of the organic carbon profile in the soils of Andalusia, Spain, Ecosistemas, 26, 80–88, https://doi.org/10.7818/ecos.2017.26-3.10, 2017.
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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.
In this work, the Latin America and Caribbean Soil Information System (SISLAC) database (
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