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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:

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

Showing 26-50 of 136 citing articles:

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
Progress and Limitations in Forest Carbon Stock Estimation Using Remote Sensing Technologies: A Comprehensive Review
Weifeng Xu, Yu-Hao Cheng, Mengyuan Luo, et al.
Forests (2025) Vol. 16, Iss. 3, pp. 449-449
Open Access | Times Cited: 1
Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm
Fugen Jiang, Feng Zhao, Kaisen Ma, et al.
Remote Sensing (2021) Vol. 13, Iss. 8, pp. 1535-1535
Open Access | Times Cited: 55
Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery
Aliny Aparecida dos Reis, J. P. S. Werner, Bruna C. Silva, et al.
Remote Sensing (2020) Vol. 12, Iss. 16, pp. 2534-2534
Open Access | Times Cited: 54
A stochastic conceptual-data-driven approach for improved hydrological simulations
John Quilty, Anna E. Sikorska‐Senoner, David Hah
Environmental Modelling & Software (2022) Vol. 149, pp. 105326-105326
Open Access | Times Cited: 37
Prediction of Urban Forest Aboveground Carbon Using Machine Learning Based on Landsat 8 and Sentinel-2: A Case Study of Shanghai, China
Huimian Li, Guilian Zhang, Qicheng Zhong, et al.
Remote Sensing (2023) Vol. 15, Iss. 1, pp. 284-284
Open Access | Times Cited: 22
Modeling forest above-ground biomass using freely available satellite and multisource datasets
Ai Hojo, Ram Avtar, Tatsuro Nakaji, et al.
Ecological Informatics (2023) Vol. 74, pp. 101973-101973
Closed Access | Times Cited: 20
Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data
Yam Bahadur KC, Qijing Liu, Pradip Saud, et al.
Land (2024) Vol. 13, Iss. 2, pp. 213-213
Open Access | Times Cited: 8
A novel explainable PSO-XGBoost model for regional flood frequency analysis at a national scale: Exploring spatial heterogeneity in flood drivers
Yousef Kanani‐Sadat, Abdolreza Safari, Mohsen Nasseri, et al.
Journal of Hydrology (2024) Vol. 638, pp. 131493-131493
Closed Access | Times Cited: 8
Mapping aboveground biomass of Moso bamboo (Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress using Sentinel-2 imagery
Lingyan Chen, Anqi He, Zhanghua Xu, et al.
Ecological Indicators (2024) Vol. 158, pp. 111564-111564
Open Access | Times Cited: 7
Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna
Yong Wu, Guanglong Ou, Teng-Fei Lu, et al.
Remote Sensing (2024) Vol. 16, Iss. 7, pp. 1276-1276
Open Access | Times Cited: 7
Machine learning feature importance selection for predicting aboveground biomass in African savannah with landsat 8 and ALOS PALSAR data
Saad Eddin Ibrahim, Heiko Balzter, Kevin Tansey
Machine Learning with Applications (2024) Vol. 16, pp. 100561-100561
Open Access | Times Cited: 7
Estimating Urban Vegetation Biomass from Sentinel-2A Image Data
Long Li, Xisheng Zhou, Longqian Chen, et al.
Forests (2020) Vol. 11, Iss. 2, pp. 125-125
Open Access | Times Cited: 50
Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method
Fugen Jiang, Mykola Kutia, Arbi J. Sarkissian, et al.
Sensors (2020) Vol. 20, Iss. 24, pp. 7248-7248
Open Access | Times Cited: 41
Saline soil moisture mapping using Sentinel-1A synthetic aperture radar data and machine learning algorithms in humid region of China's east coast
Jianjun Wang, Fei Wu, Jiali Shang, et al.
CATENA (2022) Vol. 213, pp. 106189-106189
Closed Access | Times Cited: 26
Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China
Tao Li, Mingyang Li, Fang Ren, et al.
Remote Sensing (2022) Vol. 14, Iss. 11, pp. 2541-2541
Open Access | Times Cited: 26
Improving Land Use/Cover Classification Accuracy from Random Forest Feature Importance Selection Based on Synergistic Use of Sentinel Data and Digital Elevation Model in Agriculturally Dominated Landscape
Saad Eddin Ibrahim
Agriculture (2022) Vol. 13, Iss. 1, pp. 98-98
Open Access | Times Cited: 24
A machine learning-based stochastic subspace approach for operational modal analysis of civil structures
Kang Zhou, Dongliang Xie, Kang Xu, et al.
Journal of Building Engineering (2023) Vol. 76, pp. 107187-107187
Closed Access | Times Cited: 14
An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass
Aliasghar Bazrafkan, Nadia Delavarpour, Peter G. Oduor, et al.
Remote Sensing (2023) Vol. 15, Iss. 14, pp. 3543-3543
Open Access | Times Cited: 14
Biomass Estimation and Saturation Value Determination Based on Multi-Source Remote Sensing Data
Rula Sa, Yonghui Nie, С. И. Чумаченко, et al.
Remote Sensing (2024) Vol. 16, Iss. 12, pp. 2250-2250
Open Access | Times Cited: 6
Estimation of Forest Above-Ground Biomass in the Study Area of Greater Khingan Ecological Station with Integration of Airborne LiDAR, Landsat 8 OLI, and Hyperspectral Remote Sensing Data
Lu Wang, Yilin Ju, Yongjie Ji, et al.
Forests (2024) Vol. 15, Iss. 11, pp. 1861-1861
Open Access | Times Cited: 6
Estimation aboveground biomass in subtropical bamboo forests based on an interpretable machine learning framework
Xuejian Li, Huaqiang Du, Fangjie Mao, et al.
Environmental Modelling & Software (2024) Vol. 178, pp. 106071-106071
Open Access | Times Cited: 5
Combining Kriging Interpolation to Improve the Accuracy of Forest Aboveground Biomass Estimation Using Remote Sensing Data
Zhenzhen Liu, Mingyang Li, Zhenzhen Liu, et al.
IEEE Access (2020) Vol. 8, pp. 128124-128139
Open Access | Times Cited: 36
Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods
Xinchuan Li, Juhua Luo, Xiuliang Jin, et al.
Remote Sensing (2020) Vol. 12, Iss. 21, pp. 3609-3609
Open Access | Times Cited: 34
Above-Ground Biomass Estimation for Coniferous Forests in Northern China Using Regression Kriging and Landsat 9 Images
Fugen Jiang, Hua Sun, Erxue Chen, et al.
Remote Sensing (2022) Vol. 14, Iss. 22, pp. 5734-5734
Open Access | Times Cited: 21

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