Articles | Volume 16, issue 5
https://doi.org/10.5194/essd-16-2367-2024
© 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-16-2367-2024
© 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
| 
16 May 2024
Data description paper |
| 16 May 2024
| 16 May 2024
European topsoil bulk density and organic carbon stock database (0–20 cm) using machine-learning-based pedotransfer functions
Songchao Chen
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311215, China
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
Zhongxing Chen
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311215, China
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
Xianglin Zhang
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311215, China
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
UMR ECOSYS, AgroParisTech, INRAE, Universiteé Paris-Saclay, Palaiseau 91120, France
Zhongkui Luo
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
Calogero Schillaci
European Commission, Joint Research Centre, Ispra, 21026, Italy
Dominique Arrouays
INRAE, Info&Sols, Orléans 45075, France
Anne Christine Richer-de-Forges
INRAE, Info&Sols, Orléans 45075, France
Zhou Shi
CORRESPONDING AUTHOR
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
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Zhige Wang, Ce Zhang, Kejian Shi, Yulin Shangguan, Bifeng Hu, Xueyao Chen, Danqing Wei, Songchao Chen, Peter M. Atkinson, and Qiang Zhang
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The irreversible trend in global warming underscores the necessity for accurate monitoring of atmospheric carbon dynamics on a global scale. This study generated a global dataset of column-averaged dry-air mole fraction of CO2 (XCO2) at 0.05° resolution with full coverage using carbon satellite data and a deep learning model. The dataset accurately depicts global and regional XCO2 patterns, advancing the monitoring of carbon emissions and understanding of global carbon dynamics.
Songchao Chen, Qi Shuai, Dominique Arrouays, Zhongxing Chen, Lingju Dai, Yongsheng Hong, Bifeng Hu, Yuyang Huang, Wenjun Ji, Shuo Li, Zongzheng Liang, Yuxin Ma, Anne C. Richer-de-Forges, Calogero Schillaci, Yang Su, Hongfen Teng, Nan Wang, Xi Wang, Yanyu Wang, Zheng Wang, Zhige Wang, Dongyun Xu, Jie Xue, Su Ye, Xianglin Zhang, Yin Zhou, Peng Zhu, and Zhou Shi
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Amicie A. Delahaie, Lauric Cécillon, Marija Stojanova, Samuel Abiven, Pierre Arbelet, Dominique Arrouays, François Baudin, Antonio Bispo, Line Boulonne, Claire Chenu, Jussi Heinonsalo, Claudy Jolivet, Kristiina Karhu, Manuel Martin, Lorenza Pacini, Christopher Poeplau, Céline Ratié, Pierre Roudier, Nicolas P. A. Saby, Florence Savignac, and Pierre Barré
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Preprint under review for ESSD
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The irreversible trend in global warming underscores the necessity for accurate monitoring of atmospheric carbon dynamics on a global scale. This study generated a global dataset of column-averaged dry-air mole fraction of CO2 (XCO2) at 0.05° resolution with full coverage using carbon satellite data and a deep learning model. The dataset accurately depicts global and regional XCO2 patterns, advancing the monitoring of carbon emissions and understanding of global carbon dynamics.
Songchao Chen, Qi Shuai, Dominique Arrouays, Zhongxing Chen, Lingju Dai, Yongsheng Hong, Bifeng Hu, Yuyang Huang, Wenjun Ji, Shuo Li, Zongzheng Liang, Yuxin Ma, Anne C. Richer-de-Forges, Calogero Schillaci, Yang Su, Hongfen Teng, Nan Wang, Xi Wang, Yanyu Wang, Zheng Wang, Zhige Wang, Dongyun Xu, Jie Xue, Su Ye, Xianglin Zhang, Yin Zhou, Peng Zhu, and Zhou Shi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-373, https://doi.org/10.5194/essd-2024-373, 2024
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Yang Yan, Wenjun Ji, Baoguo Li, Guiman Wang, Songchao Chen, Dehai Zhu, and Zhong Liu
SOIL, 9, 351–364, https://doi.org/10.5194/soil-9-351-2023, https://doi.org/10.5194/soil-9-351-2023, 2023
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The response rate of soil organic matter (SOM) to the amount of straw return was inversely proportional to the initial SOM and the sand contents. From paddy to dryland, the SOM loss decreased with the increased amount of straw return. The SOM even increased by 1.84 g kg-1 when the straw return amount reached 60–100%. The study revealed that straw return is beneficial to carbon sink in farmland and is a way to prevent a C source caused by the change of paddy field to upland.
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Guocheng Wang, Zhongkui Luo, Yao Huang, Wenjuan Sun, Yurong Wei, Liujun Xiao, Xi Deng, Jinhuan Zhu, Tingting Li, and Wen Zhang
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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
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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
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We generated a 1 km daily soil moisture dataset for dryland wheat and maize across China (ChinaCropSM1 km) over 1993–2018 through random forest regression, based on in situ observations. Our improved products have a remarkably better quality compared with the public global products in terms of both spatial and time dimensions by integrating an irrigation module (crop type, phenology, soil depth). The dataset may be useful for agriculture drought monitoring and crop yield forecasting studies.
Viviana Marcela Varón-Ramírez, Gustavo Alfonso Araujo-Carrillo, and Mario Antonio Guevara Santamaría
Earth Syst. Sci. Data, 14, 4719–4741, https://doi.org/10.5194/essd-14-4719-2022, https://doi.org/10.5194/essd-14-4719-2022, 2022
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These are the first national soil texture maps obtained via digital soil mapping. We built clay, sand, and silt maps using spatial assembling with the best possible predictions at different depths. Also, we identified the better model for each pixel. This work was done to address the lack of soil texture maps in Colombia, and it can provide soil information for water-related applications, ecosystem services, and agricultural and crop modeling.
Qiang Zhang, Qiangqiang Yuan, Taoyong Jin, Meiping Song, and Fujun Sun
Earth Syst. Sci. Data, 14, 4473–4488, https://doi.org/10.5194/essd-14-4473-2022, https://doi.org/10.5194/essd-14-4473-2022, 2022
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Compared to previous seamless global daily soil moisture (SGD-SM 1.0) products, SGD-SM 2.0 enlarges the temporal scope from 2002 to 2022. By fusing auxiliary precipitation information with the long short-term memory convolutional neural network (LSTM-CNN) model, SGD-SM 2.0 can consider sudden extreme weather conditions for 1 d in global daily soil moisture products and is significant for full-coverage global daily hydrologic monitoring, rather than averaging monthly–quarterly–yearly results.
Juri Palmtag, Jaroslav Obu, Peter Kuhry, Andreas Richter, Matthias B. Siewert, Niels Weiss, Sebastian Westermann, and Gustaf Hugelius
Earth Syst. Sci. Data, 14, 4095–4110, https://doi.org/10.5194/essd-14-4095-2022, https://doi.org/10.5194/essd-14-4095-2022, 2022
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The northern permafrost region covers 22 % of the Northern Hemisphere and holds almost twice as much carbon as the atmosphere. This paper presents data from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. We use this dataset together with ESA's global land cover dataset to estimate soil organic carbon and total nitrogen storage up to 300 cm soil depth, with estimated values of 813 Pg for carbon and 55 Pg for nitrogen.
Élise G. Devoie, Stephan Gruber, and Jeffrey M. McKenzie
Earth Syst. Sci. Data, 14, 3365–3377, https://doi.org/10.5194/essd-14-3365-2022, https://doi.org/10.5194/essd-14-3365-2022, 2022
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Soil freezing characteristic curves (SFCCs) relate the temperature of a soil to its ice content. SFCCs are needed in all physically based numerical models representing freezing and thawing soils, and they affect the movement of water in the subsurface, biogeochemical processes, soil mechanics, and ecology. Over a century of SFCC data exist, showing high variability in SFCCs based on soil texture, water content, and other factors. This repository summarizes all available SFCC data and metadata.
Hongru Sun, Zhenzhu Xu, and Bingrui Jia
Earth Syst. Sci. Data, 14, 2951–2961, https://doi.org/10.5194/essd-14-2951-2022, https://doi.org/10.5194/essd-14-2951-2022, 2022
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We compiled a new soil respiration (Rs) database of China's forests from 568 studies published up to 2018. The hourly, monthly, and annual samples were 8317, 5003, and 634, respectively. Most of the Rs data are shown in figures but were seldom exploited. For the first time, these data were digitized, accounting for 82 % of samples. Rs measured with common methods was selected (Li-6400, Li-8100, Li-8150, gas chromatography) and showed small differences of ~10 %. Bamboo had the highest Rs.
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Short summary
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.
A new dataset for topsoil bulk density (BD) and soil organic carbon (SOC) stock (0–20 cm) across...
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