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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:

Magnitude, spatial distribution and uncertainty of forest biomass stocks in Mexico
Pedro Rodríguez‐Veiga, Sassan Saatchi, Kevin Tansey, et al.
Remote Sensing of Environment (2016) Vol. 183, pp. 265-281
Open Access | Times Cited: 106

Showing 1-25 of 106 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: 82
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
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: 144
Identifying effects of land use cover changes and climate change on terrestrial ecosystems and carbon stocks in Mexico
Alma Mendoza‐Ponce, Rogelio O. Corona‐Núñez, Florian Kraxner, et al.
Global Environmental Change (2018) Vol. 53, pp. 12-23
Open Access | Times Cited: 142
Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico
Mikhail Urbazaev, Christian Thiel, Felix Cremer, et al.
Carbon Balance and Management (2018) Vol. 13, Iss. 1
Open Access | Times Cited: 138
Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery
Yanan Liu, Weishu Gong, Yanqiu Xing, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2019) Vol. 151, pp. 277-289
Open Access | Times Cited: 132
Improved digital soil mapping with multitemporal remotely sensed satellite data fusion: A case study in Iran
Solmaz Fathololoumi, Ali Reza Vaezi, Seyed Kazem Alavipanah, et al.
The Science of The Total Environment (2020) Vol. 721, pp. 137703-137703
Closed Access | Times Cited: 128
Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China
Huabing Huang, Caixia Liu, Xiaoyi Wang, et al.
Remote Sensing of Environment (2018) Vol. 221, pp. 225-234
Closed Access | Times Cited: 111
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
Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery
Mariano Garcı́a, Sassan Saatchi, Susan L. Ustin, et al.
International Journal of Applied Earth Observation and Geoinformation (2017) Vol. 66, pp. 159-173
Open Access | Times Cited: 95
Under predicted climate change: Distribution and ecological niche modelling of six native tree species in Gilgit-Baltistan, Pakistan
Hammad Gilani, Muhammad Arif Goheer, Hammad Ahmad, et al.
Ecological Indicators (2020) Vol. 111, pp. 106049-106049
Closed Access | Times Cited: 85
Estimating aboveground biomass in subtropical forests of China by integrating multisource remote sensing and ground data
Rong Zhang, Xuhui Zhou, Zutao Ouyang, et al.
Remote Sensing of Environment (2019) Vol. 232, pp. 111341-111341
Closed Access | Times Cited: 79
Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests
Pablito Marcelo López-Serrano, José Luis Cárdenas Domínguez, José Javier Corral‐Rivas, et al.
Forests (2019) Vol. 11, Iss. 1, pp. 11-11
Open Access | Times Cited: 79
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: 2
Assessment of multi-wavelength SAR and multispectral instrument data for forest aboveground biomass mapping using random forest kriging
Lin Chen, Yeqiao Wang, Chunying Ren, et al.
Forest Ecology and Management (2019) Vol. 447, pp. 12-25
Closed Access | Times Cited: 73
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: 62
Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review
Marco Ottinger, Claudia Kuenzer
Remote Sensing (2020) Vol. 12, Iss. 14, pp. 2228-2228
Open Access | Times Cited: 57
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
Improving aboveground biomass maps of tropical dry forests by integrating LiDAR, ALOS PALSAR, climate and field data
José Luis Hernández‐Stefanoni, Miguel Santiago, Jean‐François Mas, et al.
Carbon Balance and Management (2020) Vol. 15, Iss. 1
Open Access | Times Cited: 55
Improved Object-Based Estimation of Forest Aboveground Biomass by Integrating LiDAR Data from GEDI and ICESat-2 with Multi-Sensor Images in a Heterogeneous Mountainous Region
Lin Chen, Chunying Ren, Guangdao Bao, et al.
Remote Sensing (2022) Vol. 14, Iss. 12, pp. 2743-2743
Open Access | Times Cited: 30
Potential aboveground biomass increase in Brazilian Atlantic Forest fragments with climate change
Igor José Malfetoni Ferreira, Wesley A. Campanharo, Marisa Gesteira Fonseca, et al.
Global Change Biology (2023) Vol. 29, Iss. 11, pp. 3098-3113
Closed Access | Times Cited: 21
High-resolution mapping of forest structure and carbon stock using multi-source remote sensing data in Japan
Hantao Li, Takuya Hiroshima, Xiaoxuan Li, et al.
Remote Sensing of Environment (2024) Vol. 312, pp. 114322-114322
Closed Access | Times Cited: 7
Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods
Zhu Jia, Zhihong Huang, Hua Sun, et al.
Remote Sensing (2017) Vol. 9, Iss. 3, pp. 241-241
Open Access | Times Cited: 51
Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach
Polyanna da Conceição Bispo, Pedro Rodríguez‐Veiga, Bárbara Zimbres, et al.
Remote Sensing (2020) Vol. 12, Iss. 17, pp. 2685-2685
Open Access | Times Cited: 46
Mapping the stock and spatial distribution of aboveground woody biomass in the native vegetation of the Brazilian Cerrado biome
Bárbara Zimbres, Pedro Rodríguez‐Veiga, Julia Z. Shimbo, et al.
Forest Ecology and Management (2021) Vol. 499, pp. 119615-119615
Open Access | Times Cited: 37
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