Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets - A case study
Gerald Forkuor, Jean-Bosco Benewinde Zoungrana, Kangbéni Dimobe, et al.
Remote Sensing of Environment (2019) Vol. 236, pp. 111496-111496
Closed Access | Times Cited: 137

Showing 1-25 of 137 citing articles:

Improving above ground biomass estimates of Southern Africa dryland forests by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery
Ruusa Magano David, Nick Rosser, Daniel N.M. Donoghue
Remote Sensing of Environment (2022) Vol. 282, pp. 113232-113232
Closed Access | Times Cited: 75
Estimating vertically growing crop above-ground biomass based on UAV remote sensing
Jibo Yue, Hao Yang, Guijun Yang, et al.
Computers and Electronics in Agriculture (2023) Vol. 205, pp. 107627-107627
Closed Access | Times Cited: 61
Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods
Dongbo Xie, Hongchao Huang, Linyan Feng, et al.
Remote Sensing (2023) Vol. 15, Iss. 13, pp. 3344-3344
Open Access | Times Cited: 50
Mapping tree species diversity in temperate montane forests using Sentinel-1 and Sentinel-2 imagery and topography data
Xiang Liu, Julian Frey, Catalina Munteanu, et al.
Remote Sensing of Environment (2023) Vol. 292, pp. 113576-113576
Closed Access | Times Cited: 43
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
Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery
Xiaoli Zhang, Hanwen Shen, Tian‐Bao Huang, et al.
Ecological Indicators (2024) Vol. 159, pp. 111752-111752
Open Access | Times Cited: 27
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
A comparative analysis of machine learning techniques for aboveground biomass estimation: A case study of the Western Ghats, India
Kurian Ayushi, Kanda Naveen Babu, Narayanan Ayyappan, et al.
Ecological Informatics (2024) Vol. 80, pp. 102479-102479
Open Access | Times Cited: 17
Towards delivering on the Sustainable Development Goals using Earth observations
Argyro Kavvada, Graciela Metternicht, Flora Kerblat, et al.
Remote Sensing of Environment (2020) Vol. 247, pp. 111930-111930
Closed Access | Times Cited: 95
Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring
Ann-Kathrin Holtgrave, Norbert Röder, Andrea Ackermann, et al.
Remote Sensing (2020) Vol. 12, Iss. 18, pp. 2919-2919
Open Access | Times Cited: 77
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: 65
Aboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network
Chen Chen, Yi Ma, Guangbo Ren, et al.
Remote Sensing of Environment (2022) Vol. 270, pp. 112885-112885
Closed Access | Times Cited: 55
Nationwide native forest structure maps for Argentina based on forest inventory data, SAR Sentinel-1 and vegetation metrics from Sentinel-2 imagery
Eduarda Martiniano de Oliveira Silveira, Volker C. Radeloff, Sebastián Martinuzzi, et al.
Remote Sensing of Environment (2022) Vol. 285, pp. 113391-113391
Open Access | Times Cited: 53
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
How can UAV bridge the gap between ground and satellite observations for quantifying the biomass of desert shrub community?
Peng Mao, Junjie Ding, Biqian Jiang, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2022) Vol. 192, pp. 361-376
Open Access | Times Cited: 47
Forest Height Mapping Using Feature Selection and Machine Learning by Integrating Multi-Source Satellite Data in Baoding City, North China
Nan Zhang, Mingjie Chen, Fan Yang, et al.
Remote Sensing (2022) Vol. 14, Iss. 18, pp. 4434-4434
Open Access | Times Cited: 40
Integrating Sentinel-1 and 2 with LiDAR data to estimate aboveground biomass of subtropical forests in northeast Guangdong, China
Linjing Zhang, Xiaoxue Zhang, Zhenfeng Shao, et al.
International Journal of Digital Earth (2023) Vol. 16, Iss. 1, pp. 158-182
Open Access | Times Cited: 31
New Methodology for Shoreline Extraction Using Optical and Radar (SAR) Satellite Imagery
Sara Zollini, Donatella Dominici, Maria Alicandro, et al.
Journal of Marine Science and Engineering (2023) Vol. 11, Iss. 3, pp. 627-627
Open Access | Times Cited: 27
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
OpenForest: a data catalog for machine learning in forest monitoring
Arthur Ouaknine, Teja Kattenborn, Étienne Laliberté, et al.
Environmental Data Science (2025) Vol. 4
Open Access | Times Cited: 1
A framework for montane forest canopy height estimation via integrating deep learning and multi-source remote sensing data
Hongbin Luo, Guanglong Ou, Cairong Yue, et al.
International Journal of Applied Earth Observation and Geoinformation (2025), pp. 104474-104474
Open Access | Times Cited: 1
A Sentinel-2 based multispectral convolutional neural network for detecting artisanal small-scale mining in Ghana: Applying deep learning to shallow mining
Jane Gallwey, Carlo Robiati, John Coggan, et al.
Remote Sensing of Environment (2020) Vol. 248, pp. 111970-111970
Open Access | Times Cited: 66
Approaches of Satellite Remote Sensing for the Assessment of Above-Ground Biomass across Tropical Forests: Pan-tropical to National Scales
Sawaid Abbas, Man Sing Wong, Jin Wu, et al.
Remote Sensing (2020) Vol. 12, Iss. 20, pp. 3351-3351
Open Access | Times Cited: 53
Machine Learning Techniques for Fine Dead Fuel Load Estimation Using Multi-Source Remote Sensing Data
Marina D’Este, Mario Elia, Vincenzo Giannico, et al.
Remote Sensing (2021) Vol. 13, Iss. 9, pp. 1658-1658
Open Access | Times Cited: 48
A novel approach for estimation of aboveground biomass of a carbon-rich mangrove site in India
Sujit Madhab Ghosh, Mukunda Dev Behera, Buddola Jagadish, et al.
Journal of Environmental Management (2021) Vol. 292, pp. 112816-112816
Closed Access | Times Cited: 43
Page 1 - Next Page

Scroll to top