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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:

Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review
Sushil Lamichhane, Lalit Kumar, Brian Wilson
Geoderma (2019) Vol. 352, pp. 395-413
Closed Access | Times Cited: 378

Showing 1-25 of 378 citing articles:

Machine learning for digital soil mapping: Applications, challenges and suggested solutions
Alexandre M.J.‐C. Wadoux, Budiman Minasny, Alex B. McBratney
Earth-Science Reviews (2020) Vol. 210, pp. 103359-103359
Open Access | Times Cited: 407
Digital mapping of GlobalSoilMap soil properties at a broad scale: A review
Songchao Chen, Dominique Arrouays, Vera Leatitia Mulder, et al.
Geoderma (2021) Vol. 409, pp. 115567-115567
Open Access | Times Cited: 333
Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran
Mostafa Emadi, Ruhollah Taghizadeh–Mehrjardi, Ali Cherati, et al.
Remote Sensing (2020) Vol. 12, Iss. 14, pp. 2234-2234
Open Access | Times Cited: 221
High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms
Tao Zhou, Yajun Geng, Jie Chen, et al.
The Science of The Total Environment (2020) Vol. 729, pp. 138244-138244
Closed Access | Times Cited: 199
Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates
Mojtaba Zeraatpisheh, Younes Garosi, Hamidreza Owliaie, et al.
CATENA (2021) Vol. 208, pp. 105723-105723
Closed Access | Times Cited: 150
Machine learning in space and time for modelling soil organic carbon change
G.B.M. Heuvelink, Marcos E. Angelini, Laura Poggio, et al.
European Journal of Soil Science (2020) Vol. 72, Iss. 4, pp. 1607-1623
Open Access | Times Cited: 141
Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery
A. Narmilan, Felipe González, Arachchige Surantha Ashan Salgadoe, et al.
Remote Sensing (2022) Vol. 14, Iss. 5, pp. 1140-1140
Open Access | Times Cited: 83
Soil inorganic carbon, the other and equally important soil carbon pool: Distribution, controlling factors, and the impact of climate change
Amin Sharififar, Budiman Minasny, Dominique Arrouays, et al.
Advances in agronomy (2023), pp. 165-231
Closed Access | Times Cited: 59
Soil organic matter content prediction based on two-branch convolutional neural network combining image and spectral features
Hao Li, Weiliang Ju, Yamei Song, et al.
Computers and Electronics in Agriculture (2024) Vol. 217, pp. 108561-108561
Closed Access | Times Cited: 20
The effect of covariates on Soil Organic Matter and pH variability: a digital soil mapping approach using random forest model
Yassine Bouslıhım, Kingsley John, Abdelhalim Miftah, et al.
Annals of GIS (2024) Vol. 30, Iss. 2, pp. 215-232
Open Access | Times Cited: 20
Impacts of straw return methods on crop yield, soil organic matter, and salinity in saline-alkali land in North China
Ying Song, Mingxiu Gao, Zhi Li
Field Crops Research (2025) Vol. 322, pp. 109752-109752
Closed Access | Times Cited: 3
Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images
Tao Zhou, Yajun Geng, Cheng Ji, et al.
The Science of The Total Environment (2020) Vol. 755, pp. 142661-142661
Closed Access | Times Cited: 138
Depth-to-bedrock map of China at a spatial resolution of 100 meters
Fapeng Yan, Wei Shangguan, Jing Zhang, et al.
Scientific Data (2020) Vol. 7, Iss. 1
Open Access | Times Cited: 135
Conventional and digital soil mapping in Iran: Past, present, and future
Mojtaba Zeraatpisheh, Azam Jafari, Mohsen Bagheri Bodaghabadi, et al.
CATENA (2019) Vol. 188, pp. 104424-104424
Closed Access | Times Cited: 120
Game theory interpretation of digital soil mapping convolutional neural networks
José Padarian, Alex B. McBratney, Budiman Minasny
SOIL (2020) Vol. 6, Iss. 2, pp. 389-397
Open Access | Times Cited: 114
Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions
Daniel Žížala, Robert Minařík, Tereza Zádorová
Remote Sensing (2019) Vol. 11, Iss. 24, pp. 2947-2947
Open Access | Times Cited: 103
Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images
Xianglin He, Lin Yang, Anqi Li, et al.
CATENA (2021) Vol. 205, pp. 105442-105442
Closed Access | Times Cited: 87
Mapping soil organic carbon content using multi-source remote sensing variables in the Heihe River Basin in China
Tao Zhou, Yajun Geng, Jie Chen, et al.
Ecological Indicators (2020) Vol. 114, pp. 106288-106288
Open Access | Times Cited: 85
Spatial prediction of soil aggregate stability and soil organic carbon in aggregate fractions using machine learning algorithms and environmental variables
Mojtaba Zeraatpisheh, Shamsollah Ayoubi, Zahra Mirbagheri, et al.
Geoderma Regional (2021) Vol. 27, pp. e00440-e00440
Closed Access | Times Cited: 81
Mapping soil organic carbon stocks and trends with satellite-driven high resolution maps over South Africa
Zander S. Venter, Heidi‐Jayne Hawkins, Michael D. Cramer, et al.
The Science of The Total Environment (2021) Vol. 771, pp. 145384-145384
Open Access | Times Cited: 80
A deep learning method to predict soil organic carbon content at a regional scale using satellite-based phenology variables
Lin Yang, Yanyan Cai, Lei Zhang, et al.
International Journal of Applied Earth Observation and Geoinformation (2021) Vol. 102, pp. 102428-102428
Open Access | Times Cited: 80
Evaluation and Prediction of Topsoil organic carbon using Machine learning and hybrid models at a Field-scale
Hamid Reza Matinfar, Ziba Maghsodi, Seyed Roohollah Mousavi, et al.
CATENA (2021) Vol. 202, pp. 105258-105258
Closed Access | Times Cited: 75
Quantile regression as a generic approach for estimating uncertainty of digital soil maps produced from machine-learning
Babak Kasraei, Brandon Heung, Daniel D. Saurette, et al.
Environmental Modelling & Software (2021) Vol. 144, pp. 105139-105139
Closed Access | Times Cited: 74
Digital mapping of soil salinization based on Sentinel-1 and Sentinel-2 data combined with machine learning algorithms
Guolin Ma, Jianli Ding, Lijng Han, et al.
Regional Sustainability (2021) Vol. 2, Iss. 2, pp. 177-188
Open Access | Times Cited: 67
Smart Sensing with Edge Computing in Precision Agriculture for Soil Assessment and Heavy Metal Monitoring: A Review
Mohammad Nishat Akhtar, Abdurrahman Javid Shaikh, Ambareen Khan, et al.
Agriculture (2021) Vol. 11, Iss. 6, pp. 475-475
Open Access | Times Cited: 66
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