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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:

Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing
Vasit Sagan, Kyle T. Peterson, Maitiniyazi Maimaitijiang, et al.
Earth-Science Reviews (2020) Vol. 205, pp. 103187-103187
Open Access | Times Cited: 464

Showing 1-25 of 464 citing articles:

Machine Learning in Agriculture: A Comprehensive Updated Review
Lefteris Benos, Aristotelis C. Tagarakis, Georgios Dolias, et al.
Sensors (2021) Vol. 21, Iss. 11, pp. 3758-3758
Open Access | Times Cited: 526
A review of the application of machine learning in water quality evaluation
Mengyuan Zhu, Jiawei Wang, Yang Xiao, et al.
Eco-Environment & Health (2022) Vol. 1, Iss. 2, pp. 107-116
Open Access | Times Cited: 390
A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges
Haibo Yang, Jialin Kong, Huihui Hu, et al.
Remote Sensing (2022) Vol. 14, Iss. 8, pp. 1770-1770
Open Access | Times Cited: 215
Machine learning in natural and engineered water systems
Ruixing Huang, Chengxue Ma, Jun Ma, et al.
Water Research (2021) Vol. 205, pp. 117666-117666
Closed Access | Times Cited: 203
Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects
Jinyue Chen, Shuisen Chen, Rao Fu, et al.
Earth s Future (2022) Vol. 10, Iss. 2
Open Access | Times Cited: 133
Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques
Godson Ebenezer Adjovu, Haroon Stephen, David E. James, et al.
Remote Sensing (2023) Vol. 15, Iss. 14, pp. 3534-3534
Open Access | Times Cited: 113
Toward smarter management and recovery of municipal solid waste: A critical review on deep learning approaches
Kunsen Lin, Youcai Zhao, Jia‐Hong Kuo, et al.
Journal of Cleaner Production (2022) Vol. 346, pp. 130943-130943
Closed Access | Times Cited: 104
Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters
Godson Ebenezer Adjovu, Haroon Stephen, David E. James, et al.
Remote Sensing (2023) Vol. 15, Iss. 7, pp. 1938-1938
Open Access | Times Cited: 103
A Systematic Review on Advancements in Remote Sensing for Assessing and Monitoring Land Use and Land Cover Changes Impacts on Surface Water Resources in Semi-Arid Tropical Environments
Makgabo Johanna Mashala, Timothy Dube, Bester Tawona Mudereri, et al.
Remote Sensing (2023) Vol. 15, Iss. 16, pp. 3926-3926
Open Access | Times Cited: 80
Evaluation of River Water Quality Index Using Remote Sensing and Artificial Intelligence Models
Mohammad Najafzadeh, Sajad Basirian
Remote Sensing (2023) Vol. 15, Iss. 9, pp. 2359-2359
Open Access | Times Cited: 63
Enhancing flood susceptibility modeling using multi-temporal SAR images, CHIRPS data, and hybrid machine learning algorithms
Mostafa Riazi, Khabat Khosravi, Kaka Shahedi, et al.
The Science of The Total Environment (2023) Vol. 871, pp. 162066-162066
Closed Access | Times Cited: 51
Advances in machine learning and IoT for water quality monitoring: A comprehensive review
Ismail Essamlali, Hasna Nhaila, Mohamed El Khaïli
Heliyon (2024) Vol. 10, Iss. 6, pp. e27920-e27920
Open Access | Times Cited: 40
Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective
Ahmed E. Alprol, Abdallah Tageldein Mansour, E. M. Ibrahim, et al.
Water (2024) Vol. 16, Iss. 2, pp. 314-314
Open Access | Times Cited: 37
Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review
Yongjian Sun, Kefeng Deng, Kaijun Ren, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2024) Vol. 208, pp. 14-38
Closed Access | Times Cited: 36
Sensors for Emerging Water Contaminants: Overcoming Roadblocks to Innovation
Mohamed Ateia, Haoran Wei, Silvana Andreescu
Environmental Science & Technology (2024) Vol. 58, Iss. 6, pp. 2636-2651
Closed Access | Times Cited: 32
Utilizing Deep Learning and the Internet of Things to Monitor the Health of Aquatic Ecosystems to Conserve Biodiversity
Bobir Odilov, Askariy Madraimov, Otabek Y. Yusupov, et al.
Natural and Engineering Sciences (2024) Vol. 9, Iss. 1, pp. 72-83
Open Access | Times Cited: 29
Reliable water quality prediction and parametric analysis using explainable AI models
M. K. Nallakaruppan, E. Gangadevi, M. Lawanya Shri, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 28
A review on monitoring, forecasting, and early warning of harmful algal bloom
Zahir Muhammad, Yuping Su, Muhammad Imran Shahzad, et al.
Aquaculture (2024) Vol. 593, pp. 741351-741351
Closed Access | Times Cited: 28
UAV and satellite remote sensing for inland water quality assessments: a literature review
Eden T. Wasehun, Leila Hashemi-Beni, Courtney A. Di Vittorio
Environmental Monitoring and Assessment (2024) Vol. 196, Iss. 3
Closed Access | Times Cited: 27
Artificial intelligence and water quality: From drinking water to wastewater
Christian Hazael Pérez-Beltrán, Alicia Robles, N. Rodríguez, et al.
TrAC Trends in Analytical Chemistry (2024) Vol. 172, pp. 117597-117597
Closed Access | Times Cited: 19
Predicting Chlorophyll-a Concentrations in the World’s Largest Lakes Using Kolmogorov-Arnold Networks
Mohammad Javad Saravani, Roohollah Noori, Changhyun Jun, et al.
Environmental Science & Technology (2025)
Closed Access | Times Cited: 12
Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability
Danish Raza, Hong Shu, Muhsan Ehsan, et al.
Cogent Food & Agriculture (2025) Vol. 11, Iss. 1
Open Access | Times Cited: 4
Integrating Remote Sensing and Machine Learning for Dynamic Monitoring of Eutrophication in River Systems: A Case Study of Barato River, Japan
Dang Guansan, Ram Avtar, Gowhar Meraj, et al.
Water (2025) Vol. 17, Iss. 1, pp. 89-89
Open Access | Times Cited: 2
Monitoring phycocyanin in global inland waters by remote sensing: Progress and future developments
Chong Fang, Kaishan Song, Zhifeng Yan, et al.
Water Research (2025) Vol. 275, pp. 123176-123176
Closed Access | Times Cited: 2
Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China
Jingzhe Wang, Tiezhu Shi, Danlin Yu, et al.
Environmental Pollution (2020) Vol. 266, pp. 115412-115412
Closed Access | Times Cited: 98
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