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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:

Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region
Yukun Gao, Dengsheng Lu, Guiying Li, et al.
Remote Sensing (2018) Vol. 10, Iss. 4, pp. 627-627
Open Access | Times Cited: 168

Showing 1-25 of 168 citing articles:

Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms
Zhenzhen Liu, Mingyang Li, Chao Li, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 296
Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system
Ning Lu, Jie Zhou, Zixu Han, et al.
Plant Methods (2019) Vol. 15, Iss. 1
Open Access | Times Cited: 184
Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing
Yichao Tian, Huang Hu, Guoqing Zhou, et al.
The Science of The Total Environment (2021) Vol. 781, pp. 146816-146816
Closed Access | Times Cited: 113
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: 78
Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region
Shoaib Ahmad Anees, Kaleem Mehmood, Waseem Razzaq Khan, et al.
Ecological Informatics (2024) Vol. 82, pp. 102732-102732
Open Access | Times Cited: 50
Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI
Chu Wang, Wangfei Zhang, Yongjie Ji, et al.
Forests (2024) Vol. 15, Iss. 1, pp. 215-215
Open Access | Times Cited: 20
Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms
Zhenzhen Liu, Chao Li, Mingyang Li, et al.
Forests (2019) Vol. 10, Iss. 12, pp. 1073-1073
Open Access | Times Cited: 136
Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery
Lin Chen, Chunying Ren, Bai Zhang, et al.
Forests (2018) Vol. 9, Iss. 10, pp. 582-582
Open Access | Times Cited: 129
Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam
Tien Dat Pham, Naoto Yokoya, Junshi Xia, et al.
Remote Sensing (2020) Vol. 12, Iss. 8, pp. 1334-1334
Open Access | Times Cited: 111
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: 109
Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data
Lin Chen, Yeqiao Wang, Chunying Ren, et al.
Remote Sensing (2019) Vol. 11, Iss. 4, pp. 414-414
Open Access | Times Cited: 99
Leveraging Google Earth Engine (GEE) and machine learning algorithms to incorporate in situ measurement from different times for rangelands monitoring
Bo Zhou, Gregory S. Okin, Junzhe Zhang
Remote Sensing of Environment (2019) Vol. 236, pp. 111521-111521
Closed Access | Times Cited: 91
Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data
Fardin Moradi, Ali Asghar Darvishsefat, Manizheh Rajab Pourrahmati, et al.
Forests (2022) Vol. 13, Iss. 1, pp. 104-104
Open Access | Times Cited: 51
Aboveground biomass and carbon stock estimation using UAV photogrammetry in Indonesian mangroves and other competing land uses
Mohammad Basyuni, Anindya Wirasatriya, Sigit Bayhu Iryanthony, et al.
Ecological Informatics (2023) Vol. 77, pp. 102227-102227
Closed Access | Times Cited: 33
Continuous mapping of aboveground biomass using Landsat time series
Paulo Arévalo, Alessandro Baccini, Curtis E. Woodcock, et al.
Remote Sensing of Environment (2023) Vol. 288, pp. 113483-113483
Open Access | Times Cited: 28
A brief overview and perspective of using airborne Lidar data for forest biomass estimation
Dengsheng Lu, Xiandie Jiang
International Journal of Image and Data Fusion (2024) Vol. 15, Iss. 1, pp. 1-24
Closed Access | Times Cited: 14
Remote Sensing Estimation of Forest Carbon Stock Based on Machine Learning Algorithms
Fengyun Cheng, Guanglong Ou, Meng Wang, et al.
Forests (2024) Vol. 15, Iss. 4, pp. 681-681
Open Access | Times Cited: 12
UAV-LiDAR Integration with Sentinel-2 Enhances Precision in AGB Estimation for Bamboo Forests
Lingjun Zhang, Yinyin Zhao, Chao Chen, et al.
Remote Sensing (2024) Vol. 16, Iss. 4, pp. 705-705
Open Access | Times Cited: 11
SCARF: A new algorithm for continuous prediction of biomass dynamics using machine learning and Landsat time series
Yingchun Fu, Runhao Li, Zhe Zhu, et al.
Remote Sensing of Environment (2024) Vol. 314, pp. 114348-114348
Open Access | Times Cited: 11
Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time – and how this affects applications
Göran Ståhl, Terje Gobakken, Svetlana Saarela, et al.
Forest Ecosystems (2024) Vol. 11, pp. 100164-100164
Open Access | Times Cited: 10
Estimating Forest Growing Stock Volume Using Feature Selection and Advanced Remote Sensing Algorithm
Yabing Zhao, Famiao Guo, Yin Wang, et al.
Remote Sensing Applications Society and Environment (2025), pp. 101458-101458
Closed Access | Times Cited: 1
Enhancing aboveground biomass estimation in Moso bamboo forests: the role of on-year and off-year phenomena in remote sensing
Nan Li, Mianda Hu, Jingyi Xie, et al.
Frontiers in Forests and Global Change (2025) Vol. 8
Open Access | Times Cited: 1
Exploring Bamboo Forest Aboveground Biomass Estimation Using Sentinel-2 Data
Yu‐Yun Chen, Longwei Li, Dengsheng Lu, et al.
Remote Sensing (2018) Vol. 11, Iss. 1, pp. 7-7
Open Access | Times Cited: 60
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
Estimating Forest Aboveground Biomass Using Gaofen-1 Images, Sentinel-1 Images, and Machine Learning Algorithms: A Case Study of the Dabie Mountain Region, China
Haoshuang Han, Rongrong Wan, Bing Li
Remote Sensing (2021) Vol. 14, Iss. 1, pp. 176-176
Open Access | Times Cited: 42
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