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
Related authors
Zhige Wang, Ce Zhang, Kejian Shi, Yulin Shangguan, Bifeng Hu, Xueyao Chen, Danqing Wei, Songchao Chen, Peter M. Atkinson, and Qiang Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-315, https://doi.org/10.5194/essd-2024-315, 2024
Preprint under review for ESSD
Short summary
Short summary
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
Preprint under review for ESSD
Short summary
Short summary
The impact of land use and land cover change (LULCC) on soil organic carbon stock (SOCS) is uncertain due to limited global data. Despite regional efforts, a comprehensive global SOCS database has been lacking. This study introduces the Global Soil Organic Carbon Stock dataset after LULCC (GSOCS-LULCC), compiled from 639 articles covering 1,206 sites and 5,982 records across five major land uses. This open-access database enables global assessment of LULCC's effects on SOCS dynamics.
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
Short summary
Short summary
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.
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
SOIL, 10, 795–812, https://doi.org/10.5194/soil-10-795-2024, https://doi.org/10.5194/soil-10-795-2024, 2024
Short summary
Short summary
This paper compares the soil organic carbon fractions obtained from a new thermal fractionation scheme and a well-known physical fractionation scheme on an unprecedented dataset of French topsoil samples. For each fraction, we use a machine learning model to determine its environmental drivers (pedology, climate, and land cover). Our results suggest that these two fractionation schemes provide different fractions, which means they provide complementary information.
Zhige Wang, Ce Zhang, Kejian Shi, Yulin Shangguan, Bifeng Hu, Xueyao Chen, Danqing Wei, Songchao Chen, Peter M. Atkinson, and Qiang Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-315, https://doi.org/10.5194/essd-2024-315, 2024
Preprint under review for ESSD
Short summary
Short summary
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
Preprint under review for ESSD
Short summary
Short summary
The impact of land use and land cover change (LULCC) on soil organic carbon stock (SOCS) is uncertain due to limited global data. Despite regional efforts, a comprehensive global SOCS database has been lacking. This study introduces the Global Soil Organic Carbon Stock dataset after LULCC (GSOCS-LULCC), compiled from 639 articles covering 1,206 sites and 5,982 records across five major land uses. This open-access database enables global assessment of LULCC's effects on SOCS dynamics.
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
Short summary
Short summary
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.
Amicie A. Delahaie, Pierre Barré, François Baudin, Dominique Arrouays, Antonio Bispo, Line Boulonne, Claire Chenu, Claudy Jolivet, Manuel P. Martin, Céline Ratié, Nicolas P. A. Saby, Florence Savignac, and Lauric Cécillon
SOIL, 9, 209–229, https://doi.org/10.5194/soil-9-209-2023, https://doi.org/10.5194/soil-9-209-2023, 2023
Short summary
Short summary
We characterized organic matter in French soils by analysing samples from the French RMQS network using Rock-Eval thermal analysis. We found that thermal analysis is appropriate to characterize large set of samples (ca. 2000) and provides interpretation references for Rock-Eval parameter values. This shows that organic matter in managed soils is on average more oxidized and more thermally stable and that some Rock-Eval parameters are good proxies for organic matter biogeochemical stability.
Zhongkui Luo, Raphael A. Viscarra-Rossel, and Tian Qian
Biogeosciences, 18, 2063–2073, https://doi.org/10.5194/bg-18-2063-2021, https://doi.org/10.5194/bg-18-2063-2021, 2021
Short summary
Short summary
Using the data from 141 584 whole-soil profiles across the globe, we disentangled the relative importance of biotic, climatic and edaphic variables in controlling global SOC stocks. The results suggested that soil properties and climate contributed similarly to the explained global variance of SOC in four sequential soil layers down to 2 m. However, the most important individual controls are consistently soil-related, challenging current climate-driven framework of SOC dynamics.
Guocheng Wang, Zhongkui Luo, Yao Huang, Wenjuan Sun, Yurong Wei, Liujun Xiao, Xi Deng, Jinhuan Zhu, Tingting Li, and Wen Zhang
Atmos. Chem. Phys., 21, 3059–3071, https://doi.org/10.5194/acp-21-3059-2021, https://doi.org/10.5194/acp-21-3059-2021, 2021
Short summary
Short summary
We simulate the spatiotemporal dynamics of aboveground biomass (AGB) in Inner Mongolian grasslands using a machine-learning-based approach. Under climate change, on average, compared with the historical AGB (average of 1981–2019), the AGB at the end of this century (average of 2080–2100) would decrease by 14 % under RCP4.5 and 28 % under RCP8.5. The decrease in AGB might be mitigated or even reversed by positive carbon dioxide enrichment effects on plant growth.
Ziqiang Ma, Jintao Xu, Siyu Zhu, Jun Yang, Guoqiang Tang, Yuanjian Yang, Zhou Shi, and Yang Hong
Earth Syst. Sci. Data, 12, 1525–1544, https://doi.org/10.5194/essd-12-1525-2020, https://doi.org/10.5194/essd-12-1525-2020, 2020
Short summary
Short summary
Focusing on the potential drawbacks in generating the state-of-the-art IMERG data in both the TRMM and GPM era, a new daily calibration algorithm on IMERG was proposed, as well as a new AIMERG precipitation dataset (0.1°/half-hourly, 2000–2015, Asia) with better quality than IMERG for Asian scientific research and applications. The proposed daily calibration algorithm for GPM is promising and applicable in generating the future IMERG in either an operational scheme or a retrospective manner.
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
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
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
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.
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.
Cited articles
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, 1973.
Armas, D., Guevara, M., Bezares, F., Vargas, R., Durante, P., Osorio, V., Jiménez, W., and Oyonarte, C.: Harmonized Soil Database of Ecuador (HESD): data from 2009 to 2015, Earth Syst. Sci. Data, 15, 431–445, https://doi.org/10.5194/essd-15-431-2023, 2023.
Atwood, T. B., Connolly, R. M., Almahasheer, H., Carnell, P. E., Duarte, C. M., Ewers Lewis, C. J., and Lovelock, C. E.: Global patterns in mangrove soil carbon stocks and losses, Nat. Clim. Change, 7, 523–528, https://doi.org/10.1038/nclimate3326, 2017.
Augusto, L. and Boča, A.: Tree functional traits, forest biomass, and tree species diversity interact with site properties to drive forest soil carbon, Nat. Commun., 13, 1097, https://doi.org/10.1038/s41467-022-28748-0, 2022.
Bates, D. M. and Watts, D. G.: Nonlinear regression analysis and its applications: Nonlinear regression analysis and its applications, Wiley Series in Probability and Statistics, John Wiley & Sons, Inc., New York, United States, https://doi.org/10.1002/9780470316757, 1988.
Batjes, N. H., Ribeiro, E., and van Oostrum, A.: Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019), Earth Syst. Sci. Data, 12, 299–320, https://doi.org/10.5194/essd-12-299-2020, 2020.
Benites, V. M., Machado, P. L. O. A., Fidalgo, E. C. C., Coelho, M. R., and Madari, B. E.: Pedotransfer functions for estimating soil bulk density from existing soil survey reports in Brazil, Geoderma, 139, 90–97, https://doi.org/10.1016/j.geoderma.2007.01.005, 2007.
Bondi, G., Creamer, R., Ferrari, A., Fenton, O., and Wall, D.: Using machine learning to predict soil bulk density on the basis of visual parameters: Tools for in-field and post-field evaluation, Geoderma, 318, 137–147, https://doi.org/10.1016/j.geoderma.2017.11.035, 2018.
Chen, S., Richer-de-Forges, A. C., Saby, N. P. A., Martin, M. P., Walter, C., and Arrouays, D.: Building a pedotransfer function for soil bulk density on regional dataset and testing its validity over a larger area, Geoderma, 312, 52–63, https://doi.org/10.1016/j.geoderma.2017.10.009, 2018.
Chen, S., Xu, H., Xu, D., Ji, W., Li, S., Yang, M., Hu, B., Zhou, Y., Wang, N., Arrouays, D., and Shi, Z.: Evaluating validation strategies on the performance of soil property prediction from regional to continental spectral data, Geoderma, 400, 115159, https://doi.org/10.1016/j.geoderma.2021.115159, 2021.
Chen, S., Arrouays, D., Mulder, V. L., Poggio, L., Minasny, B., Roudier, P., Libohova, Z., Lagacherie, P., Shi, Z., Hannam, J., Meersmans, J., Richer-de-Forges, A. C., and Walter, C.: Digital mapping of GlobalSoilMap soil properties at a broad scale: A review, Geoderma, 409, 115567, https://doi.org/10.1016/j.geoderma.2021.115567, 2022.
Chen, S., Chen, Z., Zhang, X., Luo, Z., Schillaci, C., Arrouays, D., Richer-de-Forges, A. C., and Shi, Z.: European soil bulk density and organic carbon stock database using LUCAS Soil 2018 [Data set], Zenodo, https://doi.org/10.5281/zenodo.10211884, 2023.
Chen, Z., Shuai, Q., Shi, Z., Arrouays, D., Richer-de-Forges, A. C., and Chen, S.: National-scale mapping of soil organic carbon stock in France: New insights and lessons learned by direct and indirect approaches, Soil Environ. Health, 1, 100049, https://doi.org/10.1016/j.seh.2023.100049, 2023.
Cotrufo, M. F., Ranalli, M. G., Haddix, M. L., Six, J., and Lugato, E.: Soil carbon storage informed by particulate and mineral-associated organic matter, Nat. Geosci., 12, 989–994, https://doi.org/10.1038/s41561-019-0484-6, 2019.
Dam, R. F., Mehdi, B. B., Burgess, M. S. E., Madramootoo, C. A., Mehuys, G. R., and Callum, I. R.: Soil bulk density and crop yield under eleven consecutive years of corn with different tillage and residue practices in a sandy loam soil in central Canada, Soil Till. Res., 84, 41–53, https://doi.org/10.1016/j.still.2004.08.006, 2005.
Dawson, J. J. C. and Smith, P.: Carbon losses from soil and its consequences for land-use management, Sci. Total Environ., 382, 165–190, https://doi.org/10.1016/j.scitotenv.2007.03.023, 2007.
De Rosa, D., Ballabio, C., Lugato, E., Fasiolo, M., Jones, A., and Panagos, P.: Soil organic carbon stocks in European croplands and grasslands: How much have we lost in the past decade?, Glob. Change Biol., 30, e16992, https://doi.org/10.1111/gcb.16992, 2023.
Elzhov, T. V., Mullen, K. M., Spiess, A. N., and Bolker, B.: minpack.lm: R Interface to the Levenberg–Marquardt Nonlinear Least-Squares Algorithm Found in MINPACK, Plus Support for Bounds, https://cran.r-project.org/web/packages/minpack.lm/index.html (last access: 15 August 2023), 2015.
European Commission: LUCAS 2009 TOPSOIL data, European Commission [data set], https://esdac.jrc.ec.europa.eu/content/lucas-2009-topsoil-data (last access: 1 June 2023), 2013.
European Commission: LUCAS 2015 TOPSOIL data, European Commission [data set], https://esdac.jrc.ec.europa.eu/content/lucas2015-topsoil-data (last access: 1 June 2023), 2020.
European Commission: LUCAS 2018 TOPSOIL data, European Commission [data set], https://esdac.jrc.ec.europa.eu/content/lucas-2018-topsoil-data (last access: 1 June 2023), 2022.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The shuttle radar topography mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007.
Fernández-Ugalde O., Orgiazzi A., Jones A., Lugato E., Panagos P.: LUCAS 2018 – SOIL COMPONENT: Sampling Instructions for Surveyors, JRC technical report, EUR 28501 EN, European Commission, Joint Research Centre, Ispra, Italy, https://doi.org/10.2760/023673, 2017.
Fernández-Ugalde, O., Orgiazzi, A., Marechal, A., Jones, A., Scarpa, S., Panagos, P., and Van Liedekerke, M.: LUCAS 2018 soil module: presentation of dataset and results, Publications Office of the European Union, Luxembourg, https://doi.org/10.2760/215013, 2022.
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315, https://doi.org/10.1002/joc.5086, 2017.
Ghehi, N. G., Nemes, A., Verdoodt, A., Van Ranst, E., Cornelis, W. M., and Boeckx, P.: Nonparametric techniques for predicting soil bulk density of tropical rainforest topsoils in Rwanda, Soil Sci. Soc. Am. J., 76, 1172–1183, https://doi.org/10.2136/sssaj2011.0330, 2012.
Gupta, A., Vasava, H. B., Das, B. S., and Choubey, A. K.: Local modeling approaches for estimating soil properties in selected Indian soils using diffuse reflectance data over visible to near-infrared region, Geoderma, 325, 59–71, https://doi.org/10.1016/j.geoderma.2018.03.025, 2018.
Gupta, S. C. and Larson, W. E.: Estimating soil-waster retention characteristics from particle-size distribution, organic-matter percent, and bulk-density, Water Resour. Res., 15, 1633–1635, https://doi.org/10.1029/WR015i006p01633, 1979.
Hollis, J. M., Hannam, J., and Bellamy, P. H.: Empirically-derived pedotransfer functions for predicting bulk density in European soils, Eur. J. Soil Sci., 63, 96–109, https://doi.org/10.1111/j.1365-2389.2011.01412.x, 2012.
Hu, B., Xie, M., Shi, Z., Li, H., Chen, S., Wang, Z., Zhou, Y., Ni, H., Geng, Y., Zhu, Q., and Zhang, X.: Fine-resolution mapping of cropland topsoil pH of Southern China and its environmental application, Geoderma, 442, 116798, https://doi.org/10.1016/j.geoderma.2024.116798, 2024.
Jalabert, S. S. M., Martin, M. P., Renaud, J. P., Boulonne, L., Jolivet, C., Montanarella, L., and Arrouays, D.: Estimating forest soil bulk density using boosted regression modelling, Soil Use Manage., 26, 516–528, https://doi.org/10.1111/j.1475-2743.2010.00305.x, 2010.
Katuwal, S., Knadel, M., Norgaard, T., Moldrup, P., Greve, M. H., and de Jonge, L. W.: Predicting the dry bulk density of soils across Denmark: Comparison of single-parameter, multi-parameter, and vis-NIR based models, Geoderma, 361, 114080, https://doi.org/10.1016/j.geoderma.2019.114080, 2020.
Lal, R.: Digging deeper: A holistic perspective of factors affecting soil organic carbon sequestration in agroecosystems, Glob. Change Biol., 24, 3285–3301, https://doi.org/10.1111/gcb.14054, 2018.
Lark, R. M., Rawlins, B. G., Robinson, D. A., Lebron, I., and Tye, A. M.: Implications of short-range spatial variation of soil bulk density for adequate field-sampling protocols: methodology and results from two contrasting soils, Eur. J. Soil Sci., 65, 803–814, https://doi.org/10.1111/ejss.12178, 2014.
Lemercier, B., Lagacherie, P., Amelin, J., Sauter, J., Pichelin, P., Richer-de-Forges, A. C., and Arrouays, D.: Multiscale evaluations of global, national and regional digital soil mapping products in France, Geoderma, 425, 116052, https://doi.org/10.1016/j.geoderma.2022.116052, 2022.
Li, S., Li, Q., Wang, C., Li, B., Gao, X., Li, Y., and Wu, D.: Spatial variability of soil bulk density and its controlling factors in an agricultural intensive area of Chengdu Plain, Southwest China, J. Integr. Agr., 18, 290–300, https://doi.org/10.1016/S2095-3119(18)61930-6, 2019.
Liu, Y., Chen, S., Yu, Q., Cai, Z., Zhou, Q., Bellingrath-Kimura, S. D., and Wu, W.: Improving digital mapping of soil organic matter in cropland by incorporating crop rotation, Geoderma, 438, 116620, https://doi.org/10.1016/j.geoderma.2023.116620, 2023.
Maestre, F. T., Benito, B. M., Berdugo, M., Concostrina-Zubiri, L., Delgado-Baquerizo, M., Eldridge, D. J., Guirado, E., Gross, N., Kéfi, S., Bagousse-Pinguet, Y. L., Ochoa-Hueso, R., and Soliveres, S.: Biogeography of global drylands, New Phytol., 231, 540–558, https://doi.org/10.1111/nph.17395, 2021.
Makovníková, J., Širáň, M., Houšková, B., Pálka, B., and Jones, A.: Comparison of different models for predicting soil bulk density. Case study–Slovakian agricultural soils, Int. Agrophys., 31, 491–498, https://doi.org/10.1515/intag-2016-0079, 2017.
Meyer, H. and Pebesma, E.: Predicting into unknown space? Estimating the area of applicability of spatial prediction models, Methods Ecol. Evol., 12, 1620–1633, https://doi.org/10.1111/2041-210X.13650, 2021.
Munera-Echeverri, J.-L., Martin, M. P., Boulonne, L., Saby, N. P. A., and Arrouays, D.: Assessing carbon stock changes in French top soils in croplands and grasslands: comparison of fixed depth and equivalent soil mass. 22th World Congress of Soil Sciences, Jul 2022, Glasgow, United Kingdom, https://doi.org/10.1111/ejss.12002, 2022.
Nasta, P., Palladino, M., Sica, B., Pizzolante, A., Trifuoggi, M., Toscanesi, M., Giarra, A., D'Auria, J., Nicodemo, F., Mazzitelli, C., Lazzaro, U., Fiore, D. P., and Romano, N.: Evaluating pedotransfer functions for predicting soil bulk density using hierarchical mapping information in Campania, Italy, Geoderma Reg., 21, e00267, https://doi.org/10.1016/j.geodrs.2020.e00267, 2020.
Nocita, M., Stevens, A., Toth, G., Panagos, P., van Wesemael, B., and Montanarella, L.: Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach, Soil Biol. Biochem., 68, 337–347, https://doi.org/10.1016/j.soilbio.2013.10.022, 2014.
Orgiazzi, A., Panagos, P., Fernández-Ugalde, O., Wojda, P., Labouyrie, M., Ballabio, C., Franco, A., Pistocchi, A., Montanarella, L and Jones, A.: LUCAS Soil Biodiversity and LUCAS Soil Pesticides, new tools for research and policy development, Eur. J. Soil Sci., 73, e13299, https://doi.org/10.1111/ejss.13299, 2022.
Pacini, L., Yunta, F., Jones, A., Montanarella, L., Barrè, P., Saia, S., Chen, S., and Schillaci, C.: Fine earth soil bulk density at 0.2 m depth from Land Use and Coverage Area Frame Survey (LUCAS) soil 2018, Eur. J. Soil Sci., 74, e13391, https://doi.org/10.1111/ejss.13391, 2023.
Padarian, J., Minasny, B., and McBratney, A. B.: Transfer learning to localise a continental soil vis-NIR calibration model, Geoderma, 340, 279–288, https://doi.org/10.1016/j.geoderma.2019.01.009, 2019.
Palladino, M., Romano, N., Pasolli, E., and Nasta, P.: Developing pedotransfer functions for predicting soil bulk density in Campania, Geoderma, 412, 115726, https://doi.org/10.1016/j.geoderma.2022.115726, 2022.
Palmtag, J., Obu, J., Kuhry, P., Richter, A., Siewert, M. B., Weiss, N., Westermann, S., and Hugelius, G.: A high spatial resolution soil carbon and nitrogen dataset for the northern permafrost region based on circumpolar land cover upscaling, Earth Syst. Sci. Data, 14, 4095–4110, https://doi.org/10.5194/essd-14-4095-2022, 2022.
Panagos, P., Van Liedekerke, M., Borrelli, P., Köninger, J., Ballabio, C., Orgiazzi, A., Lugato, E., Liakos, L., Hervas, J., Jones, A and Montanarella, L.: European Soil Data Centre 2.0: Soil data and knowledge in support of the EU policies, Eur. J. Soil Sci., 73, e13315, https://doi.org/10.1111/ejss.13315, 2022.
Panagos, P., De Rosa, D., Liakos, L., Labouyrie, M., Borrelli, P., and Ballabio, C.: Soil bulk density assessment in Europe, Agr. Ecosyst. Environ., 364, 108907, https://doi.org/10.1016/j.agee.2024.108907, 2024.
Patton, N. R., Lohse, K. A., Seyfried, M., Will, R., and Benner, S. G.: Lithology and coarse fraction adjusted bulk density estimates for determining total organic carbon stocks in dryland soils, Geoderma, 337, 844–852, https://doi.org/10.1016/j.geoderma.2018.10.036, 2019.
Perie, C. and Ouimet, R.: Organic carbon, organic matter and bulk density relationships in boreal forest soils, Can. J. Soil Sci., 88, 315–325, https://doi.org/10.4141/cjss06008, 2008.
Poeplau, C., Vos, C., and Don, A.: Soil organic carbon stocks are systematically overestimated by misuse of the parameters bulk density and rock fragment content, SOIL, 3, 61–66, https://doi.org/10.5194/soil-3-61-2017, 2017.
Pribyl, D. W.: A critical review of the conventional SOC to SOM conversion factor, Geoderma, 156, 75–83, https://doi.org/10.1016/j.geoderma.2010.02.003, 2010.
Rabot, E., Wiesmeier, M., Schlüter, S., and Vogel, H. J.: Soil structure as an indicator of soil functions: A review, Geoderma, 314, 122–137, https://doi.org/10.1016/j.geoderma.2017.11.009, 2018.
Ramcharan, A., Hengl, T., Beaudette, D., and Wills, S.: A soil bulk density pedotransfer function based on machine learning: A case study with the ncss soil characterization database, Soil Sci. Soc. Am. J., 81, 1279–1287, https://doi.org/10.2136/sssaj2016.12.0421, 2017.
Rawls, W. J. and Brakensiek, D. L.: Prediction of soil water properties for hydrologic modeling, ASCE, in: Proceedings of a Symposium Watershed Management in the Eighties, New York, 30 April–1 May 1985, edited by: Jones, E. B. and Ward, T. J., 293–299, 1985.
Richer-de-Forges, A. C., Arrouays, D., Poggio, L., Chen, S., Lacoste, M., and Minasny, B.: Hand-feel soil texture observations to evaluate the accuracy of digital soil maps for local prediction of particle size distribution. A case study in central France, Pedosphere, 33, 731–743, https://doi.org/10.1016/j.pedsph.2022.07.009, 2023.
Sanderman, J., Savage, K., and Dangal, S. R.: Mid-infrared spectroscopy for prediction of soil health indicators in the United States, Soil Sci. Soc. Am. J., 84, 251–261, https://doi.org/10.1002/saj2.20009, 2020.
Schillaci, C., Perego, A., Valkama, E., Märker, M., Saia, S., Veronesi, F., Lipani, A., Lombardo, L., Tadiello, T., Gamper, G. A., Tedone, L., Moss, C., Pareja-Serrano, E., Amato, G., Kühl, K., Dămătîrcă, C., Cogato, A., Mzid, N., Eeswaran, R., Rabelo, M., Sperandio, G., Bosino, A., Bufalini, M., Tunçay, T., Ding, J., Fiorentini, M., Tiscornia, G., Conradt, S., Botta, M., and Acutis, M.: New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems, Sci. Total Environ., 780, 146609, https://doi.org/10.1016/j.scitotenv.2021.146609, 2021.
Schrumpf, M., Schulze, E. D., Kaiser, K., and Schumacher, J.: How accurately can soil organic carbon stocks and stock changes be quantified by soil inventories?, Biogeosciences, 8, 1193–1212, https://doi.org/10.5194/bg-8-1193-2011, 2011.
Shiri, J., Keshavarzi, A., Kisi, O., Karimi, S., and Iturraran-Viveros, U.: Modeling soil bulk density through a complete data scanning procedure: Heuristic alternatives, J. Hydrol., 549, 592–602, https://doi.org/10.1016/j.jhydrol.2017.04.035, 2017.
Sprengel, C.: Ueber Pflanzenhumus, Humussaüre und humussaure Salze, Archiv für die Gesammte Naturlehre, 8, 145–220, 1826.
Sun, W., Canadell, J. G., Yu, L., Yu, L., Zhang, W., Smith, P., Fischer, T., and Huang, Y.: Climate drives global soil carbon sequestration and crop yield changes under conservation agriculture, Glob. Change Biol., 26, 3325–3335, https://doi.org/10.1111/gcb.15001, 2020.
Taalab, K., Corstanje, R., Mayr, T. M., Whelan, M. J., and Creamer, R. E: The application of expert knowledge in Bayesian networks to predict soil bulk density at the landscape scale, Eur. J. Soil Sci., 66, 930–941, https://doi.org/10.1111/ejss.12282, 2015.
Tao, F., Huang, Y., Hungate, B. A., Manzoni, S., Frey, S. D., Schmidt, M. W. I., Reichstein, M., Carvalhais, N., Ciais, P., Jiang, L., Lehmann, J., Wang, Y., Houlton, B. Z., Ahrens, B., Mishra, U., Hugelius, G., Hocking, T. D., Lu, X., Shi, Z., Viatkin, K., Vargas, R., Yigini, Y., Omutom C., Malik, A. A., Peralta, G., Cuevas-Corona, R., Paolo, L. E. D., Luotto, I., Liao, C., Liang, Y., Saynes, V. S., Huang, X., and Luo, Y.: Microbial carbon use efficiency promotes global soil carbon storage, Nature, 618, 981–985, https://doi.org/10.1038/s41586-023-06042-3, 2023.
Tautges, N. E., Chiartas, J. L., Gaudin, A. C., O'Geen, A. T., Herrera, I., and Scow, K. M.: Deep soil inventories reveal that impacts of cover crops and compost on soil carbon sequestration differ in surface and subsurface soils, Glob. Change Biol., 25, 3753–3766, https://doi.org/10.1111/gcb.14762, 2019.
Tifafi, M., Guenet, B., and Hatté, C.: Large Differences in Global and Regional Total Soil Carbon Stock Estimates Based on SoilGrids, HWSD, and NCSCD: Intercomparison and Evaluation Based on Field Data From USA, England, Wales, and France: Differences in total SOC stock estimates, Global Biogeochem. Cy., 32, 42–56, https://doi.org/10.1002/2017GB005678, 2018.
Van Bemmelen, J. M.: Über die Bestimmung des Wassers, des Humus, des Schwefels, der in den colloïdalen Silikaten gebundenen Kieselsäure, des Mangans u.s.w. im Ackerboden, Die Landwirthschaftlichen Versuchs-Stationen, 37, 279–290, 1890.
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.
Wang, M., Guo, X., Zhang, S., Xiao, L., Mishra, U., Yang, Y., Zhu, B., Wang, G., Mao, X., Qian, T., Jiang, T., Shi, Z., and Luo, Z.: Global soil profiles indicate depth-dependent soil carbon losses under a warmer climate, Nat. Commun., 13, 5514, https://doi.org/10.1038/s41467-022-33278-w, 2022.
Wang, N., Chen, S., Huang, J., Frappart, F., Taghizadeh, R., Zhang, X., Wigneron, J. P., Xue, J., Xiao, Y., Peng, J., and Shi, Z.: Global Soil Salinity Estimation at 10 m Using Multi-source Remote Sensing, J. Remote Sens., https://doi.org/10.34133/remotesensing.0130, 2024.
Wang, Y., Luo, G., Li, C., Ye, H., Shi, H., Fan, B., Zhang, W., Zhang, C., Xie, M., and Zhang, Y.: Effects of land clearing for agriculture on soil organic carbon stocks in drylands: A meta-analysis, Glob. Change Biol., 29, 547–562, https://doi.org/10.1111/gcb.16481, 2023.
Wendt, J. W. and Hauser, S.: An equivalent soil mass procedure for monitoring soil organic carbon in multiple soil layers, Eur. J. Soil Sci., 64, 58–65, https://doi.org/10.1111/ejss.12002, 2013.
Wiesmeier, M., Spörlein, P., Geuß, U., Hangen, E., Haug, S., Reischl, A., Schilling, B., von Lützow, M., and Kögel-Knabner, I.: Soil organic carbon stocks in southeast Germany (Bavaria) as affected by land use, soil type and sampling depth, Glob. Change Biol., 18, 2233–2245, https://doi.org/10.1111/j.1365-2486.2012.02699.x, 2012.
Xiao, Y., Xue, J., Zhang, X., Wang, N., Hong, Y., Jiang, Y., Zhou, Y., Teng, H., Hu, B., Lugato, E., Richer-de-Forges, A. C., Arrouays, D., Shi, Z., and Chen, S.: Improving pedotransfer functions for predicting soil mineral associated organic carbon by ensemble machine learning, Geoderma, 428, 116208, https://doi.org/10.1016/j.geoderma.2022.116208, 2022a.
Xiao, Y., Xue, J., Zhang, X., Wang, N., Hong, Y., Jiang, Y., Zhou, Y., Teng, H., Hu, B., Lugato, E., Richer-de-Forges, A.C., Arrouays, D., Shi, Z., and Chen, S.: Forward Recursive Feature Selection, Zenodo [software], https://doi.org/10.5281/zenodo.7141020, 2022b.
Yi, X., Li, G., and Yin, Y.: Pedotransfer functions for estimating soil bulk density: A case study in the three-river headwater region of Qinghai Province, China, Pedosphere, 26, 362–373, https://doi.org/10.1016/S1002-0160(15)60049-2, 2016.
Yost, J. L. and Hartemink, A. E.: How deep is the soil studied–an analysis of four soil science journals, Plant Soil, 452, 5–18, https://doi.org/10.1007/s11104-020-04550-z, 2020.
Zhang, X., Chen, S., Xue, J., Wang, N., Xiao, Y., Chen, Q., Hong, Y., Zhou, Y., Teng, H., Hu, B., Zhuo, Z., Ji, W., Huang, Y., Gou, Y., Richer-de-Forges, A. C., Arrouays, D., and Shi, Z.: Improving model parsimony and accuracy by modified greedy feature selection in digital soil mapping, Geoderma, 432, 116383, https://doi.org/10.1016/j.geoderma.2023.116383, 2023.
Zhu, C., Byrd, R. H., Lu, P., and Nocedal, J.: Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization, ACM T. Math. Software, 23, 550–560, https://doi.org/10.1145/279232.279236, 1997.
Zomer, R. J., Xu, J., and Trabucco, A.: Version 3 of the global aridity index and potential evapotranspiration database, Sci. Data, 9, 409–409, https://doi.org/10.1038/s41597-022-01493-1, 2022.
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...
Altmetrics
Final-revised paper
Preprint