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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:

The potential of UAV-borne spectral and textural information for predicting aboveground biomass and N fixation in legume-grass mixtures
Esther Grüner, M. Wachendorf, Thomas Astor
PLoS ONE (2020) Vol. 15, Iss. 6, pp. e0234703-e0234703
Open Access | Times Cited: 61

Showing 1-25 of 61 citing articles:

A Review of Estimation Methods for Aboveground Biomass in Grasslands Using UAV
Clara Oliva Gonçalves Bazzo, Bahareh Kamali, Christoph Hütt, et al.
Remote Sensing (2023) Vol. 15, Iss. 3, pp. 639-639
Open Access | Times Cited: 47
Monitoring Yield and Quality of Forages and Grassland in the View of Precision Agriculture Applications—A Review
Abid Ali, Hans‐Peter Kaul
Remote Sensing (2025) Vol. 17, Iss. 2, pp. 279-279
Open Access | Times Cited: 3
Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning
Prakriti Sharma, Larry Leigh, Jiyul Chang, et al.
Sensors (2022) Vol. 22, Iss. 2, pp. 601-601
Open Access | Times Cited: 59
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
Nitrogen variability assessment of pasture fields under an integrated crop-livestock system using UAV, PlanetScope, and Sentinel-2 data
Francisco R. da S. Pereira, Joaquim Pedro de Lima, Rodrigo Greggio de Freitas, et al.
Computers and Electronics in Agriculture (2022) Vol. 193, pp. 106645-106645
Closed Access | Times Cited: 41
Ensemble and single algorithm models to handle multicollinearity of UAV vegetation indices for predicting rice biomass
Radhwane Derraz, Farrah Melissa Muharam, Khairudin Nurulhuda, et al.
Computers and Electronics in Agriculture (2023) Vol. 205, pp. 107621-107621
Closed Access | Times Cited: 24
Garlic yield monitoring using vegetation indices and texture features derived from UAV multispectral imagery
Andrea Marcone, Giorgio Impollonia, Michele Croci, et al.
Smart Agricultural Technology (2024) Vol. 8, pp. 100513-100513
Open Access | Times Cited: 9
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
Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning
Sebastián Varela, Taylor Pederson, Carl J. Bernacchi, et al.
Remote Sensing (2021) Vol. 13, Iss. 9, pp. 1763-1763
Open Access | Times Cited: 46
Novel intelligent grazing strategy based on remote sensing, herd perception and UAVs monitoring
Tao Chen, Han Zheng, Jian Chen, et al.
Computers and Electronics in Agriculture (2024) Vol. 219, pp. 108807-108807
Closed Access | Times Cited: 8
Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems
Andrew M. Cunliffe, Karen Anderson, Fabio Boschetti, et al.
Remote Sensing in Ecology and Conservation (2021) Vol. 8, Iss. 1, pp. 57-71
Open Access | Times Cited: 35
Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning
Joanna Pranga, Irene Borra‐Serrano, Jonas Aper, et al.
Remote Sensing (2021) Vol. 13, Iss. 17, pp. 3459-3459
Open Access | Times Cited: 35
Fine-Scale Mapping of Soil Organic Matter in Agricultural Soils Using UAVs and Machine Learning
Jannis Heil, Christoph Jörges, Britta Stumpe
Remote Sensing (2022) Vol. 14, Iss. 14, pp. 3349-3349
Open Access | Times Cited: 27
An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass
Aliasghar Bazrafkan, Nadia Delavarpour, Peter G. Oduor, et al.
Remote Sensing (2023) Vol. 15, Iss. 14, pp. 3543-3543
Open Access | Times Cited: 14
Prediction of Biomass and N Fixation of Legume–Grass Mixtures Using Sensor Fusion
Esther Grüner, Thomas Astor, M. Wachendorf
Frontiers in Plant Science (2021) Vol. 11
Open Access | Times Cited: 29
Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing
Matthias Wengert, Hans‐Peter Piepho, Thomas Astor, et al.
Remote Sensing (2021) Vol. 13, Iss. 14, pp. 2751-2751
Open Access | Times Cited: 28
Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams
Yahui Guo, Shouzhi Chen, Yongshuo H. Fu, et al.
Remote Sensing (2022) Vol. 14, Iss. 2, pp. 244-244
Open Access | Times Cited: 21
Utilizing Spectral, Structural and Textural Features for Estimating Oat Above-Ground Biomass Using UAV-Based Multispectral Data and Machine Learning
Rakshya Dhakal, Maitiniyazi Maimaitijiang, Jiyul Chang, et al.
Sensors (2023) Vol. 23, Iss. 24, pp. 9708-9708
Open Access | Times Cited: 12
Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data – a comparison of sensors, algorithms, and predictor sets
Anne Schucknecht, Bumsuk Seo, Alexander Krämer, et al.
Biogeosciences (2022) Vol. 19, Iss. 10, pp. 2699-2727
Open Access | Times Cited: 17
Herbage Mass, N Concentration, and N Uptake of Temperate Grasslands Can Adequately Be Estimated from UAV-Based Image Data Using Machine Learning
Ulrike Lussem, Andreas Bolten, Ireneusz Kleppert, et al.
Remote Sensing (2022) Vol. 14, Iss. 13, pp. 3066-3066
Open Access | Times Cited: 17
A method based on a one-dimensional convolutional neural network for UV-vis spectrometric quantification of nitrate and COD in water under random turbidity disturbance scenario
Meng Xia, Ruifang Yang, Gaofang Yin, et al.
RSC Advances (2022) Vol. 13, Iss. 1, pp. 516-526
Open Access | Times Cited: 17
Prediction of pasture yield using machine learning-based optical sensing: a systematic review
Christoph Stumpe, Joerg Leukel, Tobias Zimpel
Precision Agriculture (2023) Vol. 25, Iss. 1, pp. 430-459
Open Access | Times Cited: 10
Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of Grapevines
Ronald P. Dillner, Maria A. Wimmer, Matthias Porten, et al.
Sensors (2025) Vol. 25, Iss. 2, pp. 431-431
Open Access
Are We Up to the Best Practises in Forage and Grassland Precision Harvest? A Review
Roberta Martelli, Abid Ali, Valda Rondelli, et al.
Grass and Forage Science (2025)
Open Access
Forecasting yield and market classes of Vidalia sweet onions: A UAV-based multispectral and texture data-driven approach
Marcelo Rodrigues Barbosa Júnior, Lucas de Azevedo Sales, Regimar Garcia dos Santos, et al.
Smart Agricultural Technology (2025), pp. 100808-100808
Open Access
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