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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Shapley values reveal the drivers of soil organic carbon stock prediction
Alexandre M.J.‐C. Wadoux, Nicolas Saby, Manuel Martín
SOIL (2023) Vol. 9, Iss. 1, pp. 21-38
Open Access | Times Cited: 28

Showing 1-25 of 28 citing articles:

Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI)
Hanyu Li, Stenka Vulova, Alby Duarte Rocha, et al.
The Science of The Total Environment (2024) Vol. 916, pp. 170330-170330
Open Access | Times Cited: 24
Three-dimensional space and time mapping reveals soil organic matter decreases across anthropogenic landscapes in the Netherlands
Anatol Helfenstein, Vera Leatitia Mulder, G.B.M. Heuvelink, et al.
Communications Earth & Environment (2024) Vol. 5, Iss. 1
Open Access | Times Cited: 16
Modelling and prediction of major soil chemical properties with Random Forest: Machine learning as tool to understand soil-environment relationships in Antarctica
Rafael Gomes Siqueira, Cássio Marques Moquedace dos Santos, Elpídio Inácio Fernandes Filho, et al.
CATENA (2023) Vol. 235, pp. 107677-107677
Closed Access | Times Cited: 25
Enhancing soil organic carbon prediction by unraveling the role of crop residue coverage using interpretable machine learning
Yi Dong, Xinting Wang, Sheng Wang, et al.
Geoderma (2025) Vol. 455, pp. 117225-117225
Open Access
Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland
Jan Skála, Daniel Žížala, Robert Minařík
Journal of Environmental Management (2025) Vol. 380, pp. 125035-125035
Closed Access
Digital Mapping of Soil pH and Driving Factor Analysis Based on Environmental Variable Screening
He Huang, Yaolin Liu, Yanfang Liu, et al.
Sustainability (2025) Vol. 17, Iss. 7, pp. 3173-3173
Open Access
Assessment of the spatial and temporal heterogeneity of available phosphorus content in arable soil on the scale of one field (using the example of the Republic of Tatarstan, Russia)
Ilnas Sakhabiev, K. G. Giniyatullin, Е. В. Смирнова
E3S Web of Conferences (2025) Vol. 623, pp. 02014-02014
Open Access
Artificial intelligence in soil science
Alexandre M.J.‐C. Wadoux
European Journal of Soil Science (2025) Vol. 76, Iss. 2
Open Access
A novel approach of generating pseudo revisited soil sample data based on environmental similarity for space-time soil organic carbon modelling
Wei Cui, Lin Yang, Lei Zhang, et al.
International Journal of Applied Earth Observation and Geoinformation (2025) Vol. 139, pp. 104542-104542
Closed Access
Impact of Updating Vegetation Information on Land Surface Model Performance
Melissa Ruiz‐Vásquez, O Sungmin, Gabriele Arduini, et al.
Journal of Geophysical Research Atmospheres (2023) Vol. 128, Iss. 21
Open Access | Times Cited: 9
Biplots for understanding machine learning predictions in digital soil mapping
Stephan van der Westhuizen, G.B.M. Heuvelink, Sugnet Gardner‐Lubbe, et al.
Ecological Informatics (2024), pp. 102892-102892
Open Access | Times Cited: 3
Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia
А. В. Чинилин, I. Yu. Savin
The Egyptian Journal of Remote Sensing and Space Science (2023) Vol. 26, Iss. 3, pp. 666-675
Open Access | Times Cited: 7
Identifying compound weather drivers of forest biomass loss with generative deep learning
Mohit Anand, Friedrich J. Bohn, Gustau Camps‐Valls, et al.
Environmental Data Science (2024) Vol. 3
Open Access | Times Cited: 1
Assessing and mapping of soil organic carbon at multiple depths in the semi-arid Trans-Ural steppe zone
Suleymanov Azamat, Asylbaev Ilgiz, Suleymanov Ruslan, et al.
Geoderma Regional (2024) Vol. 38, pp. e00855-e00855
Closed Access | Times Cited: 1
Methods and Challenges in Digital Soil Mapping: Applied Modelling with R Examples
Elpídio Inácio Fernandes Filho, Cássio Marques Moquedace, Luís Flávio Pereira, et al.
Progress in soil science (2024), pp. 263-283
Closed Access | Times Cited: 1
Fine-resolution baseline maps of soil nutrients in farmland of Jiangxi Province using digital soil mapping and interpretable machine learning
Bifeng Hu, Yibo Geng, Kejian Shi, et al.
CATENA (2024) Vol. 249, pp. 108635-108635
Closed Access | Times Cited: 1
Insights into the prediction uncertainty of machine-learning-based digital soil mapping through a local attribution approach
Jérémy Rohmer, Stéphane Belbeze, Dominique Guyonnet
(2024)
Open Access
Comment on egusphere-2024-323

(2024)
Open Access
Comment on egusphere-2024-323
Jérémy Rohmer, Stéphane Belbeze, Dominique Guyonnet
(2024)
Open Access
Reply on RC1
Jérémy Rohmer
(2024)
Open Access
Determination of soil organic carbon by conventional and spectral methods, including assessment of the use of biostimulants, N-fertilisers, and economic benefits
Julija Rukaitė, Darius Juknevičius, Zita Kriaučiūnienė, et al.
Journal of Agriculture and Food Research (2024), pp. 101434-101434
Open Access
Insights into the prediction uncertainty of machine-learning-based digital soil mapping through a local attribution approach
Jérémy Rohmer, Stéphane Belbeze, Dominique Guyonnet
SOIL (2024) Vol. 10, Iss. 2, pp. 679-697
Open Access
Drivers of Soil Organic Carbon Spatial Distribution in the Southern Ural Mountains: A Machine Learning Approach
Azamat Suleymanov, Ruslan Suleymanov, Larisa Belan, et al.
Eurasian Soil Science (2024) Vol. 57, Iss. 11, pp. 1942-1949
Closed Access
Understanding the risks of peri-urbanization to food systems to help establish sustainable agriculture near cities
Xingjia Wang, Jiamin Ma, Dongyan Wang
Environmental Impact Assessment Review (2024) Vol. 112, pp. 107777-107777
Closed Access
Mapping of Cropland Humus Content of Bryansk Oblast Using Machine-Learning Methods
L. Yu. Konoplina, J. L. Meshalkina, В. П. Самсонова, et al.
Moscow University Soil Science Bulletin (2024) Vol. 79, Iss. 4, pp. 500-508
Closed Access
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