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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:

Quantifying Forest Biomass Carbon Stocks From Space
Pedro Rodríguez‐Veiga, James Wheeler, Valentin Louis, et al.
Current Forestry Reports (2017) Vol. 3, Iss. 1, pp. 1-18
Open Access | Times Cited: 142

Showing 1-25 of 142 citing articles:

Tackling Climate Change with Machine Learning
David Rolnick, Priya L. Donti, Lynn H. Kaack, et al.
ACM Computing Surveys (2022) Vol. 55, Iss. 2, pp. 1-96
Open Access | Times Cited: 470
The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations
Maurizio Santoro, Oliver Cartus, Nuno Carvalhais, et al.
Earth system science data (2021) Vol. 13, Iss. 8, pp. 3927-3950
Open Access | Times Cited: 261
A high-resolution canopy height model of the Earth
Nico Lang, Walter Jetz, Konrad Schindler, et al.
Nature Ecology & Evolution (2023) Vol. 7, Iss. 11, pp. 1778-1789
Open Access | Times Cited: 232
Tackling Climate Change with Machine Learning
David Rolnick, Priya L. Donti, Lynn H. Kaack, et al.
arXiv (Cornell University) (2019)
Closed Access | Times Cited: 165
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
Informing Nature‐based Climate Solutions for the United States with the best‐available science
Kimberly A. Novick, Stefan Metzger, William R. L. Anderegg, et al.
Global Change Biology (2022) Vol. 28, Iss. 12, pp. 3778-3794
Open Access | Times Cited: 70
Assessing and improving the transferability of current global spatial prediction models
Marvin Ludwig, Álvaro Moreno‐Martínez, Norbert Hölzel, et al.
Global Ecology and Biogeography (2023) Vol. 32, Iss. 3, pp. 356-368
Open Access | Times Cited: 53
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: 36
Harmonizing remote sensing and ground data for forest aboveground biomass estimation
Ying Su, Zhifeng Wu, Xiaoman Zheng, et al.
Ecological Informatics (2025) Vol. 86, pp. 103002-103002
Open Access | Times Cited: 4
Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them
Maxime Réjou‐Méchain, Nicolas Barbier, Pierre Couteron, et al.
Surveys in Geophysics (2019) Vol. 40, Iss. 4, pp. 881-911
Closed Access | Times Cited: 100
Forest biomass retrieval approaches from earth observation in different biomes
Pedro Rodríguez‐Veiga, S. Quegan, João M. B. Carreiras, et al.
International Journal of Applied Earth Observation and Geoinformation (2019) Vol. 77, pp. 53-68
Open Access | Times Cited: 96
Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example
Lana L. Narine, Sorin Popescu, Lonesome Malambo
Remote Sensing (2020) Vol. 12, Iss. 11, pp. 1824-1824
Open Access | Times Cited: 90
Available and missing data to model impact of climate change on European forests
Paloma Ruiz‐Benito, Giorgio Vacchiano, Emily R. Lines, et al.
Ecological Modelling (2019) Vol. 416, pp. 108870-108870
Open Access | Times Cited: 81
Large Soil Carbon Storage in Terrestrial Ecosystems of Canada
Camile Sothe, Alemu Gonsamo, Joyce Arabian, et al.
Global Biogeochemical Cycles (2022) Vol. 36, Iss. 2
Open Access | Times Cited: 65
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: 63
Fusing GEDI with earth observation data for large area aboveground biomass mapping
Yuri Shendryk
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 115, pp. 103108-103108
Open Access | Times Cited: 61
Spatially Continuous Mapping of Forest Canopy Height in Canada by Combining GEDI and ICESat-2 with PALSAR and Sentinel
Camile Sothe, Alemu Gonsamo, Ricardo Barros Lourenço, et al.
Remote Sensing (2022) Vol. 14, Iss. 20, pp. 5158-5158
Open Access | Times Cited: 45
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: 27
Past decade above-ground biomass change comparisons from four multi-temporal global maps
Arnan Araza, Martin Herold, Sytze de Bruin, et al.
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 118, pp. 103274-103274
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
Remote Sensing Technologies for Unlocking New Groundwater Insights: A Comprehensive Review
Abba Ibrahim, Aimrun Wayayok, Helmi Zulhaidi Mohd Shafri, et al.
Journal of Hydrology X (2024) Vol. 23, pp. 100175-100175
Open Access | Times Cited: 10
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: 9
Evaluating machine learning approaches for aboveground biomass prediction in fragmented high-elevated forests using multi-sensor satellite data
Asim Qadeer, Muhammad Shakir, Li Wang, et al.
Remote Sensing Applications Society and Environment (2024) Vol. 36, pp. 101291-101291
Closed Access | Times Cited: 8
Improving aboveground biomass density mapping of arid and semi-arid vegetation by combining GEDI LiDAR, Sentinel-1/2 imagery and field data
Luis Ángel Hernández-Martínez, Juan Manuel Dupuy, Alfonso Medel‐Narváez, et al.
Science of Remote Sensing (2025) Vol. 11, pp. 100204-100204
Open Access | Times Cited: 1
Synergetic of PALSAR-2 and Sentinel-1A SAR Polarimetry for Retrieving Aboveground Biomass in Dipterocarp Forest of Malaysia
Hamdan Omar, Muhamad Afizzul Misman, Abd Rahman Kassim
Applied Sciences (2017) Vol. 7, Iss. 7, pp. 675-675
Open Access | Times Cited: 73
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