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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:

Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation
Shezhou Luo, Cheng Wang, Xiaohuan Xi, et al.
Ecological Indicators (2016) Vol. 73, pp. 378-387
Closed Access | Times Cited: 122

Showing 1-25 of 122 citing articles:

Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine
Maitiniyazi Maimaitijiang, Abduwasit Ghulam, Paheding Sidike, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2017) Vol. 134, pp. 43-58
Open Access | Times Cited: 337
Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges
Tien Dat Pham, Naoto Yokoya, Dieu Tien Bui, et al.
Remote Sensing (2019) Vol. 11, Iss. 3, pp. 230-230
Open Access | Times Cited: 242
Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)
Sasan Vafaei, Javad Soosani, Kamran Adeli, et al.
Remote Sensing (2018) Vol. 10, Iss. 2, pp. 172-172
Open Access | Times Cited: 237
Vegetation Index Weighted Canopy Volume Model (CVMVI) for soybean biomass estimation from Unmanned Aerial System-based RGB imagery
Maitiniyazi Maimaitijiang, Vasit Sagan, Paheding Sidike, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2019) Vol. 151, pp. 27-41
Closed Access | Times Cited: 200
Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches
Nicolò Camarretta, Peter A. Harrison, Tanya G. Bailey, et al.
New Forests (2019) Vol. 51, Iss. 4, pp. 573-596
Closed Access | Times Cited: 164
Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery
Yaxiao Niu, Liyuan Zhang, Huihui Zhang, et al.
Remote Sensing (2019) Vol. 11, Iss. 11, pp. 1261-1261
Open Access | Times Cited: 149
A model suitable for estimating above-ground biomass of potatoes at different regional levels
Yang Liu, Yiguang Fan, Jibo Yue, et al.
Computers and Electronics in Agriculture (2024) Vol. 222, pp. 109081-109081
Closed Access | Times Cited: 34
Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms
Catherine Torres de Almeida, Lênio Soares Galvão, Luiz Eduardo de Oliveira Cruz e Aragão, et al.
Remote Sensing of Environment (2019) Vol. 232, pp. 111323-111323
Closed Access | Times Cited: 136
Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging
Yuri Shendryk, Jeremy Sofonia, Robert Garrard, et al.
International Journal of Applied Earth Observation and Geoinformation (2020) Vol. 92, pp. 102177-102177
Open Access | Times Cited: 118
Lidar Boosts 3D Ecological Observations and Modelings: A Review and Perspective
Qinghua Guo, Yanjun Su, Tianyu Hu, et al.
IEEE Geoscience and Remote Sensing Magazine (2020) Vol. 9, Iss. 1, pp. 232-257
Open Access | Times Cited: 113
Estimating the Biomass of Maize with Hyperspectral and LiDAR Data
Cheng Wang, Sheng Nie, Xiaohuan Xi, et al.
Remote Sensing (2016) Vol. 9, Iss. 1, pp. 11-11
Open Access | Times Cited: 104
Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example
Lana L. Narine, Sorin C. Popescu, Lonesome Malambo
Remote Sensing (2020) Vol. 12, Iss. 11, pp. 1824-1824
Open Access | Times Cited: 90
A brief overview and perspective of using airborne Lidar data for forest biomass estimation
Dengsheng Lu, Xiandie Jiang
International Journal of Image and Data Fusion (2024) Vol. 15, Iss. 1, pp. 1-24
Closed Access | Times Cited: 14
Estimation of above ground biomass in tropical heterogeneous forests in India using GEDI
Indu Indirabai, Mats Nilsson
Ecological Informatics (2024) Vol. 82, pp. 102712-102712
Open Access | Times Cited: 12
Enhancing snap bean yield prediction through synergistic integration of UAS-Based LiDAR and multispectral imagery
Fei Zhang, Amirhossein Hassanzadeh, Peter Letendre, et al.
Computers and Electronics in Agriculture (2025) Vol. 230, pp. 109923-109923
Open Access | Times Cited: 2
Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data
Tien Dat Pham, Kunihiko Yoshino, Nga Nhu Le, et al.
International Journal of Remote Sensing (2018) Vol. 39, Iss. 22, pp. 7761-7788
Closed Access | Times Cited: 83
Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks
Tien Dat Pham, Kunihiko Yoshino, Dieu Tien Bui
GIScience & Remote Sensing (2016) Vol. 54, Iss. 3, pp. 329-353
Closed Access | Times Cited: 73
Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China
Luodan Cao, Jianjun Pan, Ruijuan Li, et al.
Remote Sensing (2018) Vol. 10, Iss. 4, pp. 532-532
Open Access | Times Cited: 66
Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales
Wanxue Zhu, Zhigang Sun, Jinbang Peng, et al.
Remote Sensing (2019) Vol. 11, Iss. 22, pp. 2678-2678
Open Access | Times Cited: 66
Integration of GF2 Optical, GF3 SAR, and UAV Data for Estimating Aboveground Biomass of China’s Largest Artificially Planted Mangroves
Yuanhui Zhu, Kai Liu, Soe W. Myint, et al.
Remote Sensing (2020) Vol. 12, Iss. 12, pp. 2039-2039
Open Access | Times Cited: 62
Spatial patterns and driving factors of aboveground and belowground biomass over the eastern Eurasian steppe
Lei Ding, Zhenwang Li, Beibei Shen, et al.
The Science of The Total Environment (2021) Vol. 803, pp. 149700-149700
Open Access | Times Cited: 44
Estimating Forest Aboveground Biomass Using Gaofen-1 Images, Sentinel-1 Images, and Machine Learning Algorithms: A Case Study of the Dabie Mountain Region, China
Haoshuang Han, Rongrong Wan, Bing Li
Remote Sensing (2021) Vol. 14, Iss. 1, pp. 176-176
Open Access | Times Cited: 42
UAV-based indicators of crop growth are robust for distinct water and nutrient management but vary between crop development phases
Wanxue Zhu, Ehsan Eyshi Rezaei, Hamideh Nouri, et al.
Field Crops Research (2022) Vol. 284, pp. 108582-108582
Closed Access | Times Cited: 38
An overview of remote monitoring methods in biodiversity conservation
Rout George Kerry, Francis Jesmar P. Montalbo, Rajeswari Das, et al.
Environmental Science and Pollution Research (2022) Vol. 29, Iss. 53, pp. 80179-80221
Open Access | Times Cited: 38
Estimating the Aboveground Biomass of Various Forest Types with High Heterogeneity at the Provincial Scale Based on Multi-Source Data
Tian‐Bao Huang, Guanglong Ou, Yong Wu, et al.
Remote Sensing (2023) Vol. 15, Iss. 14, pp. 3550-3550
Open Access | Times Cited: 22
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