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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:

High resolution wheat yield mapping using Sentinel-2
Merryn Hunt, George Alan Blackburn, Luis Carrasco, et al.
Remote Sensing of Environment (2019) Vol. 233, pp. 111410-111410
Open Access | Times Cited: 210

Showing 1-25 of 210 citing articles:

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications
Joel Segarra, Ma. Luisa Buchaillot, J. L. Araus, et al.
Agronomy (2020) Vol. 10, Iss. 5, pp. 641-641
Open Access | Times Cited: 355
A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices
Ana Paula Marques Ramos, Lucas Prado Osco, Danielle Elis Garcia Furuya, et al.
Computers and Electronics in Agriculture (2020) Vol. 178, pp. 105791-105791
Closed Access | Times Cited: 217
Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling
Yan Zhao, Andries Potgieter, Miao Zhang, et al.
Remote Sensing (2020) Vol. 12, Iss. 6, pp. 1024-1024
Open Access | Times Cited: 143
Mapping temperate forest tree species using dense Sentinel-2 time series
Jan Hemmerling, Dirk Pflugmacher, Patrick Hostert
Remote Sensing of Environment (2021) Vol. 267, pp. 112743-112743
Closed Access | Times Cited: 141
Integrating satellite-derived climatic and vegetation indices to predict smallholder maize yield using deep learning
Liangliang Zhang, Zhao Zhang, Yuchuan Luo, et al.
Agricultural and Forest Meteorology (2021) Vol. 311, pp. 108666-108666
Closed Access | Times Cited: 107
Field-level crop yield estimation with PRISMA and Sentinel-2
Michael Marshall, Mariana Belgiu, Mirco Boschetti, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2022) Vol. 187, pp. 191-210
Open Access | Times Cited: 92
A county-level soybean yield prediction framework coupled with XGBoost and multidimensional feature engineering
Yuanchao Li, Hongwei Zeng, Miao Zhang, et al.
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 118, pp. 103269-103269
Open Access | Times Cited: 71
Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques
Ahmed Kayad, Marco Sozzi, Simone Gatto, et al.
Remote Sensing (2019) Vol. 11, Iss. 23, pp. 2873-2873
Open Access | Times Cited: 139
Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches
Liangliang Zhang, Zhao Zhang, Yuchuan Luo, et al.
Remote Sensing (2019) Vol. 12, Iss. 1, pp. 21-21
Open Access | Times Cited: 117
A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements
Lucas Prado Osco, Ana Paula Marques Ramos, Mayara Maezano Faita Pinheiro, et al.
Remote Sensing (2020) Vol. 12, Iss. 6, pp. 906-906
Open Access | Times Cited: 115
Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques
Lucas Prado Osco, José Marcato, Ana Paula Marques Ramos, et al.
Remote Sensing (2020) Vol. 12, Iss. 19, pp. 3237-3237
Open Access | Times Cited: 110
A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery
Lucas Prado Osco, Mauro dos Santos de Arruda, Diogo Nunes Gonçalves, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2021) Vol. 174, pp. 1-17
Open Access | Times Cited: 103
Developing a machine learning based cotton yield estimation framework using multi-temporal UAS data
Akash Ashapure, Jinha Jung, Anjin Chang, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2020) Vol. 169, pp. 180-194
Closed Access | Times Cited: 102
A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt
Jillian M. Deines, R. N. Patel, Sang-Zi Liang, et al.
Remote Sensing of Environment (2020) Vol. 253, pp. 112174-112174
Open Access | Times Cited: 96
Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers
Luka Rumora, Mario Miler, Damir Medak
ISPRS International Journal of Geo-Information (2020) Vol. 9, Iss. 4, pp. 277-277
Open Access | Times Cited: 81
Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal
Rubén Fernández-Beltran, Tina Baidar, Jian Kang, et al.
Remote Sensing (2021) Vol. 13, Iss. 7, pp. 1391-1391
Open Access | Times Cited: 73
Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China
Luojia Hu, Nan Xu, Jian Liang, et al.
Remote Sensing (2020) Vol. 12, Iss. 19, pp. 3120-3120
Open Access | Times Cited: 71
Modelling wheat yield with antecedent information, satellite and climate data using machine learning methods in Mexico
Diego Gómez, Pablo Salvador, J. Sanz, et al.
Agricultural and Forest Meteorology (2021) Vol. 300, pp. 108317-108317
Open Access | Times Cited: 70
Crop Type Mapping from Optical and Radar Time Series Using Attention-Based Deep Learning
Stella Ofori-Ampofo, Charlotte Pelletier, Stefan Lang
Remote Sensing (2021) Vol. 13, Iss. 22, pp. 4668-4668
Open Access | Times Cited: 65
Crop phenotyping in a context of global change: What to measure and how to do it
J. L. Araus, Shawn C. Kefauver, Omar Vergara‐Díaz, et al.
Journal of Integrative Plant Biology (2021) Vol. 64, Iss. 2, pp. 592-618
Open Access | Times Cited: 61
Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
Tatiana Fernanda Canata, Marcelo Chan Fu Wei, Leonardo Felipe Maldaner, et al.
Remote Sensing (2021) Vol. 13, Iss. 2, pp. 232-232
Open Access | Times Cited: 57
Subfield maize yield prediction improves when in-season crop water deficit is included in remote sensing imagery-based models
Guanyuan Shuai, Bruno Basso
Remote Sensing of Environment (2022) Vol. 272, pp. 112938-112938
Open Access | Times Cited: 53
Role of existing and emerging technologies in advancing climate-smart agriculture through modeling: A review
Debaditya Gupta, Nihal Gujre, Siddhartha Singha, et al.
Ecological Informatics (2022) Vol. 71, pp. 101805-101805
Closed Access | Times Cited: 51
Farming and Earth Observation: Sentinel-2 data to estimate within-field wheat grain yield
Joel Segarra, J. L. Araus, Shawn C. Kefauver
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 107, pp. 102697-102697
Open Access | Times Cited: 49
Pixel-based yield mapping and prediction from Sentinel-2 using spectral indices and neural networks
Gregor Perich, Mehmet Özgür Türkoglu, Lukas Valentin Graf, et al.
Field Crops Research (2023) Vol. 292, pp. 108824-108824
Open Access | Times Cited: 35
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