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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:

Comparison of Machine and Deep Learning Methods to Estimate Shrub Willow Biomass from UAS Imagery
Haifa Tamiminia, Bahram Salehi, Masoud Mahdianpari, et al.
Canadian Journal of Remote Sensing (2021) Vol. 47, Iss. 2, pp. 209-227
Closed Access | Times Cited: 20

Showing 20 citing articles:

Spatiotemporal dynamics of grassland aboveground biomass and its driving factors in North China over the past 20 years
J. Ge, Mengjing Hou, Tiangang Liang, et al.
The Science of The Total Environment (2022) Vol. 826, pp. 154226-154226
Closed Access | Times Cited: 76
State-wide forest canopy height and aboveground biomass map for New York with 10 m resolution, integrating GEDI, Sentinel-1, and Sentinel-2 data
Haifa Tamiminia, Bahram Salehi, Masoud Mahdianpari, et al.
Ecological Informatics (2023) Vol. 79, pp. 102404-102404
Open Access | Times Cited: 29
Lane change strategy analysis and recognition for intelligent driving systems based on random forest
Qinyu Sun, Chang Wang, Rui Fu, et al.
Expert Systems with Applications (2021) Vol. 186, pp. 115781-115781
Closed Access | Times Cited: 36
A Drone-Powered Deep Learning Methodology for High Precision Remote Sensing in California’s Coastal Shrubs
Jon Detka, Hayley Coyle, Marcella Gomez, et al.
Drones (2023) Vol. 7, Iss. 7, pp. 421-421
Open Access | Times Cited: 16
Evaluation of LAI Estimation of Mangrove Communities Using DLR and ELR Algorithms With UAV, Hyperspectral, and SAR Images
Bolin Fu, Jun Sun, Yeqiao Wang, et al.
Frontiers in Marine Science (2022) Vol. 9
Open Access | Times Cited: 21
Above-Ground Biomass Prediction for Croplands at a Sub-Meter Resolution Using UAV–LiDAR and Machine Learning Methods
Jaime Caballer Revenga, Katerina Trepekli, Stefan Oehmcke, et al.
Remote Sensing (2022) Vol. 14, Iss. 16, pp. 3912-3912
Open Access | Times Cited: 21
Assessing the effect of ensemble learning algorithms and validation approach on estimating forest aboveground biomass: a case study of natural secondary forest in Northeast China
Hung-Il Jin, Yinghui Zhao, Unil Pak, et al.
Geo-spatial Information Science (2024), pp. 1-20
Open Access | Times Cited: 4
Dynamic of woody plant encroachment (WPE) in an anthropized wetland since the 1950s: origins and effects
Thomas Lafitte, Marc Robin, Françoise Debaine, et al.
Botany Letters (2025), pp. 1-18
Closed Access
Estimation of Amorphophallus Konjac Above-Ground Biomass by Integrating Spectral and Texture Information from Unmanned Aerial Vehicle-Based RGB Images
Ziyi Yang, Hai Qi, Kunrong Hu, et al.
Drones (2025) Vol. 9, Iss. 3, pp. 220-220
Open Access
How can integrated Space–Air–Ground observation contribute in aboveground biomass of shrub plants estimation in shrub-encroached Grasslands?
Bin Sun, Rong Rong, Hanwen Cui, et al.
International Journal of Applied Earth Observation and Geoinformation (2024) Vol. 130, pp. 103856-103856
Open Access | Times Cited: 2
Machine learning applications in forest and biomass supply chain management: a review
Jinghan Zhao, Jingxin Wang, Nathaniel Anderson
International Journal of Forest Engineering (2024) Vol. 35, Iss. 3, pp. 371-380
Closed Access | Times Cited: 2
Mapping Shrub Biomass at 10 m Resolution by Integrating Field Measurements, Unmanned Aerial Vehicles, and Multi-Source Satellite Observations
Wenchao Liu, Jie Wang, Yang Hu, et al.
Remote Sensing (2024) Vol. 16, Iss. 16, pp. 3095-3095
Open Access | Times Cited: 2
Hybrid deep learning models for mapping surface NO2 across China: One complicated model, many simple models, or many complicated models?
Xinyi Liu, Chunyuan Li, Dongren Liu, et al.
Atmospheric Research (2022) Vol. 278, pp. 106339-106339
Closed Access | Times Cited: 11
Improving the accuracy of models to map alpine grassland above‐ground biomass using Google earth engine
Yan Shi, Jay Gao, Gary Brierley, et al.
Grass and Forage Science (2023) Vol. 78, Iss. 2, pp. 237-253
Open Access | Times Cited: 5
High-Resolution Mapping of Shrub Biomass in Arid and Semi-Arid Environments by Integrating Remote Sensing Observations across Multiple Spatial Scales
Wenchao Liu, Jie Wang, Yang Hu, et al.
(2024)
Open Access | Times Cited: 1
High-Resolution Mapping of Shrub Biomass in Arid and Semi-Arid Environments by Integrating Remote Sensing Observations across Multiple Spatial Scales
Wenchao Liu, Jie Wang, Yang Hu, et al.
(2024)
Open Access
Hybrid CNN & Random Forest Model for Effective Clove Leaf Disease Dignosis
Deepak Banerjee, Neha Sharma, Deepak Upadhyay, et al.
(2024), pp. 1-6
Closed Access
China’s bioenergy potential will be stable and decoupling from economy and population at national level: A study based on machine regression prediction model
Yushu Chen, Zetao Huang, Chongjian Ma, et al.
Sustainable Energy Technologies and Assessments (2024) Vol. 69, pp. 103927-103927
Closed Access
A novel merging strategy model considering the remaining distance in the acceleration lane
Menglu Gu, Yanqi Su, Chang Wang, et al.
IET Intelligent Transport Systems (2023) Vol. 17, Iss. 9, pp. 1879-1890
Open Access
Hybrid Deep Learning Models for Mapping Surface No2 Across China: One Complicated Model, Many Simple Models, or Many Complicated Models?
Xinyi Liu, Chunyuan Li, Dongren Liu, et al.
SSRN Electronic Journal (2022)
Closed Access
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