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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:

Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data
Marildo Guerini Filho, Tatiana Mora Kuplich, Fernando Luíz Ferreira de Quadros
International Journal of Remote Sensing (2019) Vol. 41, Iss. 8, pp. 2861-2876
Closed Access | Times Cited: 102

Showing 1-25 of 102 citing articles:

Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China
Jiaqiang Wang, Jie Peng, Hongyi Li, et al.
Remote Sensing (2021) Vol. 13, Iss. 2, pp. 305-305
Open Access | Times Cited: 93
Estimating Pasture Biomass Using Sentinel-2 Imagery and Machine Learning
Yun Chen, Juan Pablo Guerschman, Yuri Shendryk, et al.
Remote Sensing (2021) Vol. 13, Iss. 4, pp. 603-603
Open Access | Times Cited: 79
Using Hybrid Artificial Intelligence and Evolutionary Optimization Algorithms for Estimating Soybean Yield and Fresh Biomass Using Hyperspectral Vegetation Indices
Mohsen Yoosefzadeh-Najafabadi, Dan Tulpan, Milad Eskandari
Remote Sensing (2021) Vol. 13, Iss. 13, pp. 2555-2555
Open Access | Times Cited: 63
Predicting plant biomass and species richness in temperate grasslands across regions, time, and land management with remote sensing and deep learning
Javier Muro, Anja Linstädter, Paul Magdon, et al.
Remote Sensing of Environment (2022) Vol. 282, pp. 113262-113262
Closed Access | Times Cited: 44
A novel UAV-based approach for biomass prediction and grassland structure assessment in coastal meadows
Miguel Villoslada, Thaisa Bergamo, Raymond D. Ward, et al.
Ecological Indicators (2020) Vol. 122, pp. 107227-107227
Open Access | Times Cited: 68
Monitoring sustainable development by means of earth observation data and machine learning: a review
Bruno Ferreira, Muriel Iten, Rui Silva
Environmental Sciences Europe (2020) Vol. 32, Iss. 1
Open Access | Times Cited: 55
Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands
Ricardo Martínez Prentice, Miguel Villoslada, Raymond D. Ward, et al.
Remote Sensing (2021) Vol. 13, Iss. 18, pp. 3669-3669
Open Access | Times Cited: 50
A systematic review on the use of remote sensing technologies in quantifying grasslands ecosystem services
Anita Masenyama, Onisimo Mutanga, Timothy Dube, et al.
GIScience & Remote Sensing (2022) Vol. 59, Iss. 1, pp. 1000-1025
Open Access | Times Cited: 35
Spatiotemporal fusion of multi-source remote sensing data for estimating aboveground biomass of grassland
Zhou Yajun, Tingxi Liu, Okke Batelaan, et al.
Ecological Indicators (2023) Vol. 146, pp. 109892-109892
Open Access | Times Cited: 19
Coupling of machine learning and remote sensing for soil salinity mapping in coastal area of Bangladesh
Showmitra Kumar Sarkar, Rhyme Rubayet Rudra, Abid Reza Sohan, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 18
Improvement of pasture biomass modelling using high-resolution satellite imagery and machine learning
Michael Gbenga Ogungbuyi, Juan Pablo Guerschman, Andrew Fischer, et al.
Journal of Environmental Management (2024) Vol. 356, pp. 120564-120564
Open Access | Times Cited: 9
Comparing the Utility of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) on Sentinel-2 MSI to Estimate Dry Season Aboveground Grass Biomass
Mohamed Ismail Vawda, Romano Lottering, Onisimo Mutanga, et al.
Sustainability (2024) Vol. 16, Iss. 3, pp. 1051-1051
Open Access | Times Cited: 8
A machine learning scheme for estimating fine-resolution grassland aboveground biomass over China with Sentinel-1/2 satellite images
Huaqiang Li, Fei Li, Jingfeng Xiao, et al.
Remote Sensing of Environment (2024) Vol. 311, pp. 114317-114317
Closed Access | Times Cited: 8
Tools for Predicting Forage Growth in Rangelands and Economic Analyses—A Systematic Review
Srinivasagan N. Subhashree, C. Igathinathane, Adnan Akyüz, et al.
Agriculture (2023) Vol. 13, Iss. 2, pp. 455-455
Open Access | Times Cited: 16
Forest Canopy Fuel Loads Mapping Using Unmanned Aerial Vehicle High-Resolution Red, Green, Blue and Multispectral Imagery
Álvaro Agustín Chávez-Durán, Mariano Garcı́a, Miguel Olvera‐Vargas, et al.
Forests (2024) Vol. 15, Iss. 2, pp. 225-225
Open Access | Times Cited: 5
Using Sentinel-2 and canopy height models to derive a landscape-level biomass map covering multiple vegetation types
Fabian Ewald Fassnacht, Javiera Poblete-Olivares, Lucas Rivero, et al.
International Journal of Applied Earth Observation and Geoinformation (2020) Vol. 94, pp. 102236-102236
Open Access | Times Cited: 36
Prediction of aboveground biomass and dry‐matter content in Brachiaria pastures by combining meteorological data and satellite imagery
Igor L. Bretas, Domingos Sárvio Magalhães Valente, Fabyano Fonseca e Silva, et al.
Grass and Forage Science (2021) Vol. 76, Iss. 3, pp. 340-352
Open Access | Times Cited: 32
Comparing vegetation indices from Sentinel-2 and Landsat 8 under different vegetation gradients based on a controlled grazing experiment
Qi Qin, Dawei Xu, Lulu Hou, et al.
Ecological Indicators (2021) Vol. 133, pp. 108363-108363
Open Access | Times Cited: 32
Deep Learning-Based Estimation of Crop Biophysical Parameters Using Multi-Source and Multi-Temporal Remote Sensing Observations
Hazhir Bahrami, Saeid Homayouni, Abdolreza Safari, et al.
Agronomy (2021) Vol. 11, Iss. 7, pp. 1363-1363
Open Access | Times Cited: 31
Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments
Jun Li, Zhenjie Liu, Xinya Lei, et al.
Proceedings of the IEEE (2021) Vol. 109, Iss. 8, pp. 1350-1363
Open Access | Times Cited: 28
Above‐ground biomass retrieval with multi‐source data: Prediction and applicability analysis in Eastern Mongolia
Shuxin Ji, Batnyambuu Dashpurev, Thanh Noi Phan, et al.
Land Degradation and Development (2024) Vol. 35, Iss. 9, pp. 2982-2992
Open Access | Times Cited: 4
Estimating the aboveground biomass of the Hulunbuir Grassland and exploring its spatial and temporal variations over the past ten years
Chang Chang, Yu Chang, Zaiping Xiong, et al.
Ecological Indicators (2024) Vol. 161, pp. 112010-112010
Open Access | Times Cited: 4
A Meta-Analysis of Remote Sensing Technologies and Methodologies for Crop Characterization
Hazhir Bahrami, Heather McNairn, Masoud Mahdianpari, et al.
Remote Sensing (2022) Vol. 14, Iss. 22, pp. 5633-5633
Open Access | Times Cited: 17
Precision livestock farming applied to grazingland monitoring and management—A review
Igor L. Bretas, José Carlos Batista Dubeux, Priscila J. R. Cruz, et al.
Agronomy Journal (2023) Vol. 116, Iss. 3, pp. 1164-1186
Closed Access | Times Cited: 11
Estimation of Productivity and Above-Ground Biomass for Corn (Zea mays) via Vegetation Indices in Madeira Island
Fabrício Lopes de Macedo, Humberto Nóbrega, José G. R. de Freitas, et al.
Agriculture (2023) Vol. 13, Iss. 6, pp. 1115-1115
Open Access | Times Cited: 10
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