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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:

A novel intelligence approach based active and ensemble learning for agricultural soil organic carbon prediction using multispectral and SAR data fusion
Thu Thủy Nguyễn, Tien Dat Pham, Chi Trung Nguyen, et al.
The Science of The Total Environment (2021) Vol. 804, pp. 150187-150187
Closed Access | Times Cited: 95

Showing 1-25 of 95 citing articles:

A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm
Thu Thủy Nguyễn, Huu Hao Ngo, Wenshan Guo, et al.
The Science of The Total Environment (2022) Vol. 833, pp. 155066-155066
Closed Access | Times Cited: 73
Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions
Arslan Yousaf, Vahid Kayvanfar, Annamaria Mazzoni, et al.
Frontiers in Sustainable Food Systems (2023) Vol. 6
Open Access | Times Cited: 41
Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus
Fuat Kaya, Ali Keshavarzi, Rosa Francaviglia, et al.
Agriculture (2022) Vol. 12, Iss. 7, pp. 1062-1062
Open Access | Times Cited: 44
Application of deep learning models to detect coastlines and shorelines
Kinh Bac Dang, Van Bao Dang, Ngô Văn Liêm, et al.
Journal of Environmental Management (2022) Vol. 320, pp. 115732-115732
Closed Access | Times Cited: 40
Advances in Earth observation and machine learning for quantifying blue carbon
Tien Dat Pham, Nam Thang Ha, Neil Saintilan, et al.
Earth-Science Reviews (2023) Vol. 243, pp. 104501-104501
Open Access | Times Cited: 36
Satellite-based soil organic carbon mapping on European soils using available datasets and support sampling
Onur Yüzügüllü, Noura Fajraoui, Axel Don, et al.
Science of Remote Sensing (2024) Vol. 9, pp. 100118-100118
Open Access | Times Cited: 11
A novel framework to assess apple leaf nitrogen content: Fusion of hyperspectral reflectance and phenology information through deep learning
Riqiang Chen, Wenping Liu, Hao Yang, et al.
Computers and Electronics in Agriculture (2024) Vol. 219, pp. 108816-108816
Closed Access | Times Cited: 10
Carbon Farming: Bridging Technology Development with Policy Goals
George Kyriakarakos, Theodoros Petropoulos, Vasso Marinoudi, et al.
Sustainability (2024) Vol. 16, Iss. 5, pp. 1903-1903
Open Access | Times Cited: 8
Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System
Venkata Kanaka Srivani Maddala, K. Jayarajan, M. Braveen, et al.
Journal of Food Quality (2022) Vol. 2022, pp. 1-10
Open Access | Times Cited: 30
Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms
Boqiang Xie, Jianli Ding, Xiangyu Ge, et al.
Sensors (2022) Vol. 22, Iss. 7, pp. 2685-2685
Open Access | Times Cited: 28
Remote estimates of soil organic carbon using multi-temporal synthetic images and the probability hybrid model
Wang Xiang, Liping Wang, Sijia Li, et al.
Geoderma (2022) Vol. 425, pp. 116066-116066
Closed Access | Times Cited: 28
Incorporating agricultural practices in digital mapping improves prediction of cropland soil organic carbon content: The case of the Tuojiang River Basin
Qi Wang, Julia Le Noë, Qiquan Li, et al.
Journal of Environmental Management (2023) Vol. 330, pp. 117203-117203
Closed Access | Times Cited: 20
Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets
Hayfa Zayani, Youssef Fouad, Didier Michot, et al.
Remote Sensing (2023) Vol. 15, Iss. 17, pp. 4264-4264
Open Access | Times Cited: 17
Deep learning models for monitoring landscape changes in a UNESCO Global Geopark
Thi Tram Pham, Kinh Bac Dang, Tuan Linh Giang, et al.
Journal of Environmental Management (2024) Vol. 354, pp. 120497-120497
Closed Access | Times Cited: 7
Machine learning-based global trends and the development prospects of wastewater treatment: A bibliometric analysis
Libo Xia, Xiaoxuan Hao, Yun Zhou
Journal of environmental chemical engineering (2024) Vol. 12, Iss. 3, pp. 112732-112732
Closed Access | Times Cited: 6
A review on digital mapping of soil carbon in cropland: progress, challenge, and prospect
Haili Huang, Lin Yang, Lei Zhang, et al.
Environmental Research Letters (2022) Vol. 17, Iss. 12, pp. 123004-123004
Open Access | Times Cited: 26
Cropland carbon stocks driven by soil characteristics, rainfall and elevation
Fangzheng Chen, Puyu Feng, Matthew Tom Harrison, et al.
The Science of The Total Environment (2022) Vol. 862, pp. 160602-160602
Closed Access | Times Cited: 25
Modelling and mapping soil nutrient depletion in humid highlands of East Africa using ensemble machine learning: A case study from Rwanda
Yves Uwiragiye, Mbezele Junior Yannick Ngaba, Mengzhen Zhao, et al.
CATENA (2022) Vol. 217, pp. 106499-106499
Closed Access | Times Cited: 23
A Framework for High-Resolution Mapping of Soil Organic Matter (SOM) by the Integration of Fourier Mid-Infrared Attenuation Total Reflectance Spectroscopy (FTIR-ATR), Sentinel-2 Images, and DEM Derivatives
Xuebin Xu, Changwen Du, Fei Ma, et al.
Remote Sensing (2023) Vol. 15, Iss. 4, pp. 1072-1072
Open Access | Times Cited: 15
Synergetic use of DEM derivatives, Sentinel-1 and Sentinel-2 data for mapping soil properties of a sloped cropland based on a two-step ensemble learning method
Zhenwang Li, Feng Liu, Xiuyuan Peng, et al.
The Science of The Total Environment (2023) Vol. 866, pp. 161421-161421
Closed Access | Times Cited: 14
Geospatial prediction of total soil carbon in European agricultural land based on deep learning
Dorijan Radočaj, Mateo Gašparović, Petra Radočaj, et al.
The Science of The Total Environment (2023) Vol. 912, pp. 169647-169647
Closed Access | Times Cited: 14
National-scale spatial prediction of soil organic carbon and total nitrogen using long-term optical and microwave satellite observations in Google Earth Engine
Tao Zhou, Wenhao Lv, Yajun Geng, et al.
Computers and Electronics in Agriculture (2023) Vol. 210, pp. 107928-107928
Closed Access | Times Cited: 13
Spatial prediction of PM2.5 concentration using hyper-parameter optimization XGBoost model in China
Yingqiang Song, Changjian Zhang, Xin Jin, et al.
Environmental Technology & Innovation (2023) Vol. 32, pp. 103272-103272
Open Access | Times Cited: 13
HPO-empowered machine learning with multiple environment variables enables spatial prediction of soil heavy metals in coastal delta farmland of China
Yingqiang Song, Dexi Zhan, Zhenxin He, et al.
Computers and Electronics in Agriculture (2023) Vol. 213, pp. 108254-108254
Closed Access | Times Cited: 13
Mapping Soil Organic Carbon Stock and Uncertainties in an Alpine Valley (Northern Italy) Using Machine Learning Models
Sara Agaba, Chiara Ferré, Marco Musetti, et al.
Land (2024) Vol. 13, Iss. 1, pp. 78-78
Open Access | Times Cited: 4
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