Articles | Volume 17, issue 2
https://doi.org/10.5194/essd-17-517-2025
© 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-17-517-2025
© 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
| 
07 Feb 2025
Data description paper |
| 07 Feb 2025
| 07 Feb 2025
A China dataset of soil properties for land surface modelling (version 2, CSDLv2)
Gaosong Shi
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Wenye Sun
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Zhongwang Wei
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Hua Yuan
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Lu Li
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Xiaolin Sun
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Ye Zhang
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Hongbin Liang
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Danxi Li
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Feini Huang
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
Qingliang Li
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China
Yongjiu Dai
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
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Shulei Zhang, Hongbin Liang, Fang Li, Xingjie Lu, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2024-4093, https://doi.org/10.5194/egusphere-2024-4093, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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This study enhances irrigation modeling in the Common Land Model by capturing the full irrigation process, detailing water supplies from various sources, and enabling bidirectional coupling between water demand and supply. The proposed model accurately simulates irrigation water withdrawals, energy fluxes, river flow, and crop yields. It offers insights into irrigation-related climate impacts and water scarcity, contributing to sustainable water management and improved Earth system modeling.
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 117–134, https://doi.org/10.5194/essd-17-117-2025, https://doi.org/10.5194/essd-17-117-2025, 2025
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Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally, with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, https://doi.org/10.5194/essd-16-5357-2024, 2024
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Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.
Weilin Liao, Yanman Li, Xiaoping Liu, Yuhao Wang, Yangzi Che, Ledi Shao, Guangzhao Chen, Hua Yuan, Ning Zhang, and Fei Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-408, https://doi.org/10.5194/essd-2024-408, 2024
Revised manuscript under review for ESSD
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The currently available urban canopy parameter (UCP) datasets are limited to just a few cities for urban climate simulations by the Weather Research and Forecasting (WRF) model. To address this gap, we develop a global 1 km spatially continuous UCP dataset (GloUCP), which provides superior spatial coverage and higher accuracy in capturing urban morphology across diverse regions. It has great potential to support further advancements in urban climate modeling and related applications.
Chen Yang, Zitong Jia, Wenjie Xu, Zhongwang Wei, Xiaolang Zhang, Yiguang Zou, Jeffrey McDonnell, Laura Condon, Yongjiu Dai, and Reed Maxwell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-292, https://doi.org/10.5194/hess-2024-292, 2024
Revised manuscript under review for HESS
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We developed the first high-resolution, integrated surface water-groundwater hydrologic model of the entire continental China using ParFlow. The model shows good performance of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.
Liqing Peng, Justin Sheffield, Zhongwang Wei, Michael Ek, and Eric F. Wood
Earth Syst. Dynam., 15, 1277–1300, https://doi.org/10.5194/esd-15-1277-2024, https://doi.org/10.5194/esd-15-1277-2024, 2024
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Integrating evaporative demand into drought indicators is effective, but the choice of method and the effectiveness of surface features remain undocumented. We evaluate various methods and surface features for predicting soil moisture dynamics. Using minimal ancillary information alongside meteorological and vegetation data, we develop a simple land-cover-based method that improves soil moisture drought predictions, especially in forests, showing promise for better real-time drought forecasting.
Qingliang Li, Gaosong Shi, Wei Shangguan, Vahid Nourani, Jianduo Li, Lu Li, Feini Huang, Ye Zhang, Chunyan Wang, Dagang Wang, Jianxiu Qiu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 14, 5267–5286, https://doi.org/10.5194/essd-14-5267-2022, https://doi.org/10.5194/essd-14-5267-2022, 2022
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SMCI1.0 is a 1 km resolution dataset of daily soil moisture over China for 2000–2020 derived through machine learning trained with in situ measurements of 1789 stations, meteorological forcings, and land surface variables. It contains 10 soil layers with 10 cm intervals up to 100 cm deep. Evaluated by in situ data, the error (ubRMSE) ranges from 0.045 to 0.051, and the correlation (R) range is 0.866-0.893. Compared with ERA5-Land, SMAP-L4, and SoMo.ml, SIMI1.0 has higher accuracy and resolution.
Yi Nan, Zhihua He, Fuqiang Tian, Zhongwang Wei, and Lide Tian
Hydrol. Earth Syst. Sci., 26, 4147–4167, https://doi.org/10.5194/hess-26-4147-2022, https://doi.org/10.5194/hess-26-4147-2022, 2022
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Tracer-aided hydrological models are useful tool to reduce uncertainty of hydrological modeling in cold basins, but there is little guidance on the sampling strategy for isotope analysis, which is important for large mountainous basins. This study evaluated the reliance of the tracer-aided modeling performance on the availability of isotope data in the Yarlung Tsangpo river basin, and provides implications for collecting water isotope data for running tracer-aided hydrological models.
Ziqi Lin, Yongjiu Dai, Umakant Mishra, Guocheng Wang, Wei Shangguan, Wen Zhang, and Zhangcai Qin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-232, https://doi.org/10.5194/essd-2022-232, 2022
Manuscript not accepted for further review
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Spatial soil organic carbon (SOC) data is critical for predictions in carbon climate feedbacks and future climate trends, but no conclusion has yet been reached on which dataset to be used for specific purposes. We evaluated the SOC estimates from five widely used global soil datasets and a regional permafrost dataset, and identify uncertainties of SOC estimates by region, biome, and data sources, hoping to help improve SOC/soil data in the future.
Yaoping Wang, Jiafu Mao, Mingzhou Jin, Forrest M. Hoffman, Xiaoying Shi, Stan D. Wullschleger, and Yongjiu Dai
Earth Syst. Sci. Data, 13, 4385–4405, https://doi.org/10.5194/essd-13-4385-2021, https://doi.org/10.5194/essd-13-4385-2021, 2021
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We developed seven global soil moisture datasets (1970–2016, monthly, half-degree, and multilayer) by merging a wide range of data sources, including in situ and satellite observations, reanalysis, offline land surface model simulations, and Earth system model simulations. Given the great value of long-term, multilayer, gap-free soil moisture products to climate research and applications, we believe this paper and the presented datasets would be of interest to many different communities.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
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Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Yongjiu Dai, Wei Shangguan, Nan Wei, Qinchuan Xin, Hua Yuan, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, Dagang Wang, and Fapeng Yan
SOIL, 5, 137–158, https://doi.org/10.5194/soil-5-137-2019, https://doi.org/10.5194/soil-5-137-2019, 2019
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Soil data are widely used in various Earth science fields. We reviewed soil property maps for Earth system models, which can also offer insights to soil data developers and users. Old soil datasets are often based on limited observations and have various uncertainties. Updated and comprehensive soil data are made available to the public and can benefit related research. Good-quality soil data are identified and suggestions on how to improve and use them are provided.
Qinchuan Xin, Yongjiu Dai, and Xiaoping Liu
Biogeosciences, 16, 467–484, https://doi.org/10.5194/bg-16-467-2019, https://doi.org/10.5194/bg-16-467-2019, 2019
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Terrestrial biosphere models that simulate both leaf dynamics and canopy photosynthesis are required to understand vegetation–climate interactions. A time-stepping scheme is proposed to simulate leaf area index, phenology, and gross primary production via climate variables. The method performs well on simulating deciduous broadleaf forests across the eastern United States; it provides a simplified and improved version of the growing production day model for use in land surface modeling.
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, https://doi.org/10.5194/gmd-11-5027-2018, 2018
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This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
Fapeng Yan, Wei Shangguan, Jing Zhang, and Bifeng Hu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2018-103, https://doi.org/10.5194/essd-2018-103, 2018
Revised manuscript not accepted
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A depth-to-bedrock map of China was produced at 100-meter resolution based on 6,382 observations and 133 covariates.
This map is the most detailed and accurate one at the national scale.
The uncertainty map was provided as a map quality reference.
The ensemble machine learning model explains 57% of variation in spatial distribution.
The four most important covariates for the map production are topographic wetness index, landform, topographic openness index, and slope.
Related subject area
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
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European topsoil bulk density and organic carbon stock database (0–20 cm) using machine-learning-based pedotransfer functions
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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.
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.
Cited articles
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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.
In this study, we developed the second version of China's high-resolution soil information grid...
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