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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 Review of Regional and Global Gridded Forest Biomass Datasets
Yuzhen Zhang, Shunlin Liang, Lu Yang
Remote Sensing (2019) Vol. 11, Iss. 23, pp. 2744-2744
Open Access | Times Cited: 60

Showing 1-25 of 60 citing articles:

A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps
Arnan Araza, Sytze de Bruin, Martin Herold, et al.
Remote Sensing of Environment (2022) Vol. 272, pp. 112917-112917
Open Access | Times Cited: 79
Review of Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation: Progress, Challenges, and Prospects
Lei Tian, Xiaocan Wu, Tao Yu, et al.
Forests (2023) Vol. 14, Iss. 6, pp. 1086-1086
Open Access | Times Cited: 75
Mapping high-resolution forest aboveground biomass of China using multisource remote sensing data
Qiuli Yang, Chunyue Niu, Xiaoqiang Liu, et al.
GIScience & Remote Sensing (2023) Vol. 60, Iss. 1
Open Access | Times Cited: 45
An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products
Yuzhen Zhang, Jun Ma, Shunlin Liang, et al.
Remote Sensing (2020) Vol. 12, Iss. 24, pp. 4015-4015
Open Access | Times Cited: 107
Estimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data
Yueting Wang, Xiaoli Zhang, Zhengqi Guo
Ecological Indicators (2021) Vol. 126, pp. 107645-107645
Open Access | Times Cited: 64
Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices
Haibo Yang, Fei Li, Wei Wang, et al.
Remote Sensing (2021) Vol. 13, Iss. 12, pp. 2339-2339
Open Access | Times Cited: 58
A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets
Yuzhen Zhang, Jun Ma, Shunlin Liang, et al.
GIScience & Remote Sensing (2022) Vol. 59, Iss. 1, pp. 234-249
Open Access | Times Cited: 54
A critical review of methods, principles and progress for estimating the gross primary productivity of terrestrial ecosystems
Zhangze Liao, Binghuang Zhou, Jingyu Zhu, et al.
Frontiers in Environmental Science (2023) Vol. 11
Open Access | Times Cited: 34
Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019
Zhongbing Chang, Lei Fan, Jean‐Pierre Wigneron, et al.
Journal of Remote Sensing (2023) Vol. 3
Open Access | Times Cited: 26
Forest aboveground biomass estimation by GEDI and multi-source EO data fusion over Indian forest
Jayantrao Mohite, Suryakant Sawant, Ankur Pandit, et al.
International Journal of Remote Sensing (2024) Vol. 45, Iss. 4, pp. 1304-1338
Closed Access | Times Cited: 10
Advances in Laser Scanning to Assess Carbon in Forests: From Ground-Based to Space-Based Sensors
Nicholas C. Coops, Liam Irwin, Harry Seely, et al.
Current Forestry Reports (2025) Vol. 11, Iss. 1
Closed Access | Times Cited: 1
Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map
Yuzhen Zhang, Shunlin Liang
Remote Sensing (2020) Vol. 12, Iss. 16, pp. 2559-2559
Open Access | Times Cited: 57
Prediction of Forest Aboveground Biomass Using Multitemporal Multispectral Remote Sensing Data
Parth Naik, Michele Dalponte, Lorenzo Bruzzone
Remote Sensing (2021) Vol. 13, Iss. 7, pp. 1282-1282
Open Access | Times Cited: 50
Advancements in high-resolution land surface satellite products: A comprehensive review of inversion algorithms, products and challenges
Shunlin Liang, Tao He, Jianxi Huang, et al.
Science of Remote Sensing (2024) Vol. 10, pp. 100152-100152
Open Access | Times Cited: 6
A lesson unlearned? Underestimating tree cover in drylands biases global restoration maps
Matthew E. Fagan
Global Change Biology (2020) Vol. 26, Iss. 9, pp. 4679-4690
Open Access | Times Cited: 47
A New Method for Generating a Global Forest Aboveground Biomass Map From Multiple High-Level Satellite Products and Ancillary Information
Lu Yang, Shunlin Liang, Yuzhen Zhang
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2020) Vol. 13, pp. 2587-2597
Open Access | Times Cited: 41
Carbon stocks and dynamics of different land uses on the Cerrado agricultural frontier
Emily Ane Dionizio, F. M. Pimenta, Lucas Barbosa Lima, et al.
PLoS ONE (2020) Vol. 15, Iss. 11, pp. e0241637-e0241637
Open Access | Times Cited: 41
Spatial Scale Effect and Correction of Forest Aboveground Biomass Estimation Using Remote Sensing
Ying Yu, Yan Pan, Xiguang Yang, et al.
Remote Sensing (2022) Vol. 14, Iss. 12, pp. 2828-2828
Open Access | Times Cited: 23
A Proposed Ensemble Feature Selection Method for Estimating Forest Aboveground Biomass from Multiple Satellite Data
Yuzhen Zhang, Jingjing Liu, Wenhao Li, et al.
Remote Sensing (2023) Vol. 15, Iss. 4, pp. 1096-1096
Open Access | Times Cited: 16
Floristic Composition, Structure, and Aboveground Biomass of the Moraceae Family in an Evergreen Andean Amazon Forest, Ecuador
Walter García-Cox, Rolando López-Tobar, Robinson J. Herrera-Feijoo, et al.
Forests (2023) Vol. 14, Iss. 7, pp. 1406-1406
Open Access | Times Cited: 14
A Survey of Computer Vision Techniques for Forest Characterization and Carbon Monitoring Tasks
Svetlana Illarionova, Dmitrii Shadrin, Polina Tregubova, et al.
Remote Sensing (2022) Vol. 14, Iss. 22, pp. 5861-5861
Open Access | Times Cited: 22
Using Airborne Lidar, Multispectral Imagery, and Field Inventory Data to Estimate Basal Area, Volume, and Aboveground Biomass in Heterogeneous Mixed Species Forests: A Case Study in Southern Alabama
Schyler Brown, Lana L. Narine, John R. Gilbert
Remote Sensing (2022) Vol. 14, Iss. 11, pp. 2708-2708
Open Access | Times Cited: 19
Assessing Above-Ground Biomass Dynamics and Carbon Sequestration Potential Using Machine Learning and Spaceborne LiDAR in Hilly Conifer Forests of Mansehra District, Pakistan
Muhammad Imran, Guanhua Zhou, Guifei Jing, et al.
Forests (2025) Vol. 16, Iss. 2, pp. 330-330
Open Access
Remote sensing estimation of aboveground biomass of different forest types in Xinjiang based on machine learning
Zhou Jia, Mei Zan, Lili Zhai, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Uncertainty quantification for forest attribute maps with conformal prediction and k-nearest neighbor method
Mikko Kuronen, Janne Räty, Petteri Packalén, et al.
Remote Sensing of Environment (2025) Vol. 325, pp. 114758-114758
Open Access
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