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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:

Using an ensemble model coupled with portable X-ray fluorescence and visible near-infrared spectroscopy to explore the viability of mapping and estimating arsenic in an agricultural soil
James Kobina Mensah Biney, Radim Vašát, Johanna Ruth Blöcher, et al.
The Science of The Total Environment (2021) Vol. 818, pp. 151805-151805
Closed Access | Times Cited: 22

Showing 22 citing articles:

Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives
Xiaotong Wu, Qixing Zhou, Mu Li, et al.
Journal of Hazardous Materials (2022) Vol. 438, pp. 129487-129487
Closed Access | Times Cited: 43
Inversion of heavy metal content in soil using hyperspectral characteristic bands-based machine learning method
Zhiyong Zou, Qianlong Wang, Qingsong Wu, et al.
Journal of Environmental Management (2024) Vol. 355, pp. 120503-120503
Closed Access | Times Cited: 13
Artificial intelligence in soil science
Alexandre M.J.‐C. Wadoux
European Journal of Soil Science (2025) Vol. 76, Iss. 2
Open Access | Times Cited: 1
Predicting key soil properties from Vis-NIR spectra by applying dual-wavelength indices transformations and stacking machine learning approaches
Hamed Tavakoli, José Correa, Marmar Sabetizade, et al.
Soil and Tillage Research (2023) Vol. 229, pp. 105684-105684
Closed Access | Times Cited: 23
Prediction of topsoil organic carbon content with Sentinel-2 imagery and spectroscopic measurements under different conditions using an ensemble model approach with multiple pre-treatment combinations
James Kobina Mensah Biney, Radim Vašát, Stephen M. Bell, et al.
Soil and Tillage Research (2022) Vol. 220, pp. 105379-105379
Closed Access | Times Cited: 24
A stacking ensemble model for predicting soil organic carbon content based on visible and near-infrared spectroscopy
Ke Tang, Xing Zhao, Zong Xu, et al.
Infrared Physics & Technology (2024) Vol. 140, pp. 105404-105404
Closed Access | Times Cited: 6
Prediction of the concentration of cadmium in agricultural soil in the Czech Republic using legacy data, preferential sampling, Sentinel-2, Landsat-8, and ensemble models
Prince Chapman Agyeman, Luboš Borůvka, Ndiye Michael Kebonye, et al.
Journal of Environmental Management (2023) Vol. 330, pp. 117194-117194
Closed Access | Times Cited: 12
Application of subspace ensemble radical basis function networks to quantitative analysis of near-infrared and mid-infrared spectroscopy
Hui Chen, Chao Tan, Zan Lin
Microchemical Journal (2025), pp. 113354-113354
Closed Access
Applications of machine learning in potentially toxic elemental contamination in soils: A review
Yan Li, Xiang Bao, Tianyang Wang, et al.
Ecotoxicology and Environmental Safety (2025) Vol. 295, pp. 118110-118110
Open Access
Using spectral indices and terrain attribute datasets and their combination in the prediction of cadmium content in agricultural soil
Prince Chapman Agyeman, Vahid Khosravi, Ndiye Michael Kebonye, et al.
Computers and Electronics in Agriculture (2022) Vol. 198, pp. 107077-107077
Closed Access | Times Cited: 16
Improving Soil Quality Index Prediction by Fusion of Vis-NIR and pXRF spectral data
Jianghui Song, Xiaoyan Shi, Haijiang Wang, et al.
Geoderma (2024) Vol. 447, pp. 116938-116938
Open Access | Times Cited: 3
Classification and geographic origin discrimination of Chinese prickly ash using X-ray fluorescence and machine learning
Meng Zhang, Qing Xiong, Cheng‐Hui Li, et al.
LWT (2023) Vol. 183, pp. 114947-114947
Open Access | Times Cited: 7
Information system for remediation and cleanup of contaminated soil with machine learning
Lyazat Naizabayeva, Ch.A. Nurzhanov, Maxatbek Satymbekov, et al.
Procedia Computer Science (2024) Vol. 231, pp. 145-150
Open Access | Times Cited: 2
Verifying the predictive performance for soil organic carbon when employing field Vis-NIR spectroscopy and satellite imagery obtained using two different sampling methods
James Kobina Mensah Biney
Computers and Electronics in Agriculture (2022) Vol. 194, pp. 106796-106796
Closed Access | Times Cited: 10
Multivariate analysis applied to X-ray fluorescence to assess soil contamination pathways: case studies of mass magnetic susceptibility in soils near abandoned coal and W/Sn mines
Jelena Milinović, Patrícia Santos, Helena Sant’Ovaia, et al.
Environmental Geochemistry and Health (2024) Vol. 46, Iss. 6
Open Access | Times Cited: 1
Developing Vis–NIR libraries to predict cation exchange capacity (CEC) and pH in Australian sugarcane soil
Xueyu Zhao, Jie Wang, Triven Koganti, et al.
Computers and Electronics in Agriculture (2024) Vol. 221, pp. 109004-109004
Open Access | Times Cited: 1
Prediction of soil heavy metal content around mine tailings using multiple methods combined with transformed hyperspectral reflectance data
Chenguang Xiang, Hubing Xiao, Fu-Shun He, et al.
Ore and Energy Resource Geology (2024), pp. 100072-100072
Closed Access | Times Cited: 1
Using Data Analysis Methods for Predicting the Concentration of Toxic Elements in Soil
Lyazat Naizabayeva, Gulnara Zakirova
(2023), pp. 573-578
Closed Access | Times Cited: 1
Prediction of lead in agricultural soils: An integrated approach using machine learning, terrain attributes and reflectance spectra
Prince Chapman Agyeman, Kingsley John, Ndiye Michael Kebonye, et al.
Pedosphere (2024)
Closed Access
Improved X-ray fluorescence spectroscopic monitoring of potentially toxic elements by multivariate analysis: A case study of soils near abandoned coal mines (NW Portugal)
Jelena Milinović, Carlos Vale, Aurora Futuro, et al.
Journal of environmental chemical engineering (2024), pp. 114437-114437
Open Access
From 2015 to 2023: How Machine Learning Aids Natural Product Analysis
Suwen Shi, Ziwei Huang, Xingxin Gu, et al.
Chemistry Africa (2024)
Open Access
Optimal zinc level and uncertainty quantification in agricultural soils via visible near-infrared reflectance and soil chemical properties
Prince Chapman Agyeman, Ndiye Michael Kebonye, Vahid Khosravi, et al.
Journal of Environmental Management (2022) Vol. 326, pp. 116701-116701
Closed Access | Times Cited: 2
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