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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:

Aboveground biomass estimates over Brazilian savannas using hyperspectral metrics and machine learning models: experiences with Hyperion/EO-1
Aline Daniele Jacon, Lênio Soares Galvão, Ricardo Dalagnol, et al.
GIScience & Remote Sensing (2021) Vol. 58, Iss. 7, pp. 1112-1129
Open Access | Times Cited: 24

Showing 24 citing articles:

Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models
Mi Luo, Shoaib Ahmad Anees, Qiuyan Huang, et al.
Forests (2024) Vol. 15, Iss. 6, pp. 975-975
Open Access | Times Cited: 30
Cross-scale mapping of above-ground biomass and shrub dominance by integrating UAV and satellite data in temperate grassland
Ang Chen, Cong Xu, Min Zhang, et al.
Remote Sensing of Environment (2024) Vol. 304, pp. 114024-114024
Closed Access | Times Cited: 22
Development of forest aboveground biomass estimation, its problems and future solutions: A review
Taiyong Ma, Chao Zhang, Liping Ji, et al.
Ecological Indicators (2024) Vol. 159, pp. 111653-111653
Open Access | Times Cited: 17
Estimation of Individual Tree Biomass in Natural Secondary Forests Based on ALS Data and WorldView-3 Imagery
Yinghui Zhao, Ye Ma, Lindi J. Quackenbush, et al.
Remote Sensing (2022) Vol. 14, Iss. 2, pp. 271-271
Open Access | Times Cited: 27
Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities
Abdulhakim M. Abdi, Martin Brandt, Christin Abel, et al.
Journal of Remote Sensing (2022) Vol. 2022
Open Access | Times Cited: 26
Retrieval of grassland aboveground biomass across three ecoregions in China during the past two decades using satellite remote sensing technology and machine learning algorithms
Huoqi Wu, Shuai An, Bin Meng, et al.
International Journal of Applied Earth Observation and Geoinformation (2024) Vol. 130, pp. 103925-103925
Open Access | Times Cited: 5
Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud
Itiya Aneece, Prasad S. Thenkabail
Remote Sensing (2021) Vol. 13, Iss. 22, pp. 4704-4704
Open Access | Times Cited: 30
Discrimination of Degraded Pastures in the Brazilian Cerrado Using the PlanetScope SuperDove Satellite Constellation
Angela Gabrielly Pires Silva, Lênio Soares Galvão, Laerte Guimarães Ferreira, et al.
Remote Sensing (2024) Vol. 16, Iss. 13, pp. 2256-2256
Open Access | Times Cited: 4
Application of Machine Learning for Aboveground Biomass Modeling in Tropical and Temperate Forests from Airborne Hyperspectral Imagery
Patrick Osei Darko, Samy Metari, J. Pablo Arroyo‐Mora, et al.
Forests (2025) Vol. 16, Iss. 3, pp. 477-477
Open Access
Improved soil moisture estimation: Synergistic use of satellite observations and land surface models over CONUS based on machine learning
Jaese Lee, Soomin Park, Jungho Im, et al.
Journal of Hydrology (2022) Vol. 609, pp. 127749-127749
Closed Access | Times Cited: 13
Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests
Ye Ma, Lianjun Zhang, Jungho Im, et al.
Remote Sensing (2023) Vol. 15, Iss. 18, pp. 4364-4364
Open Access | Times Cited: 7
Assessment of Aboveground Biomass in a Tropical Dry Deciduous Forest Using PRISMA Data
Rajani Kant Verma, Laxmi Kant Sharma, Kariya Ishita Bhaveshkumar, et al.
Journal of the Indian Society of Remote Sensing (2024) Vol. 52, Iss. 4, pp. 747-756
Closed Access | Times Cited: 2
Improving the Accuracy of Vegetation Index Retrieval for Biomass by Combining Ground-UAV Hyperspectral Data–A New Method for Inner Mongolia Typical Grasslands
Ruochen Wang, Jianjun Dong, Lishan Jin, et al.
Phyton (2024) Vol. 93, Iss. 2, pp. 387-411
Open Access | Times Cited: 2
On the combined use of phenological metrics derived from different PlanetScope vegetation indices for classifying savannas in Brazil
Isadora Haddad, Lênio Soares Galvão, Fábio Marcelo Breunig, et al.
Remote Sensing Applications Society and Environment (2022) Vol. 26, pp. 100764-100764
Closed Access | Times Cited: 10
Estimation of aboveground carbon stock using Sentinel-2A data and Random Forest algorithm in scrub forests of the Salt Range, Pakistan
Sobia Bhatti, Sajid Rashid Ahmad, Muhammad Asif, et al.
Forestry An International Journal of Forest Research (2022) Vol. 96, Iss. 1, pp. 104-120
Closed Access | Times Cited: 9
Estimating Aboveground Biomass of Wetland Plant Communities from Hyperspectral Data Based on Fractional-Order Derivatives and Machine Learning
Huazhe Li, Xiying Tang, Lijuan Cui, et al.
Remote Sensing (2024) Vol. 16, Iss. 16, pp. 3011-3011
Open Access | Times Cited: 1
Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir Grassland
Qiuying Zhi, Xiaosheng Hu, Ping Wang, et al.
Remote Sensing (2024) Vol. 16, Iss. 19, pp. 3709-3709
Open Access | Times Cited: 1
On a Data-Driven Approach for Detecting Disturbance in the Brazilian Savannas Using Time Series of Vegetation Indices
Alana Almeida de Souza, Lênio Soares Galvão, Thales Sehn Körting, et al.
Remote Sensing (2021) Vol. 13, Iss. 24, pp. 4959-4959
Open Access | Times Cited: 9
Sensitivity of hyperspectral vegetation indices to rainfall seasonality in the Brazilian savannahs: an analysis using PRISMA data
Juliana de Abreu Araújo, Lênio Soares Galvão, Ricardo Dalagnol
Remote Sensing Letters (2023) Vol. 14, Iss. 3, pp. 277-287
Closed Access | Times Cited: 3
Evaluating changes with vegetation cover in PRISMA's spectral, spatial, and temporal attributes and their performance for classifying savannahs in Brazil
Juliana de Abreu Araújo, Lênio Soares Galvão, Ricardo Dalagnol
Remote Sensing Applications Society and Environment (2023) Vol. 32, pp. 101074-101074
Closed Access | Times Cited: 2
Comparison of the hybrid of radiative transfer model and machine learning methods in leaf area index of grassland mapping
Gexia Qin, Jing Wu, Chunbin Li, et al.
Theoretical and Applied Climatology (2023) Vol. 155, Iss. 4, pp. 2757-2773
Open Access | Times Cited: 2
A systematic review of remote sensing and machine learning approaches for accurate carbon storage estimation in natural forests
Collins Matiza, Onisimo Mutanga, Kabir Peerbhay, et al.
Southern Forests a Journal of Forest Science (2023) Vol. 85, Iss. 3-4, pp. 123-141
Closed Access | Times Cited: 1
Artificial Neural Network and Remote Sensing combined to predict the Aboveground Biomass in the Cerrado biome
Paula Oliveira, Eraldo Aparecido Trondoli Matricardi, Éder Pereira Miguel, et al.
Anais da Academia Brasileira de Ciências (2024) Vol. 96, Iss. 3
Open Access
Phenol identification and mapping of rangeland species at laboratory and landscape scales using PRISMA hyperspectral data
Leila Ghorbani, Reza Jafari, Pooran Golkar, et al.
International Journal of Remote Sensing (2024) Vol. 45, Iss. 18, pp. 6096-6122
Closed Access
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