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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:

Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world
Pierre Defourny, Sophie Bontemps, Nicolas Bellemans, et al.
Remote Sensing of Environment (2018) Vol. 221, pp. 551-568
Open Access | Times Cited: 351

Showing 1-25 of 351 citing articles:

Remote sensing for agricultural applications: A meta-review
Marie Weiss, Frédéric Jacob, Grégory Duveiller
Remote Sensing of Environment (2019) Vol. 236, pp. 111402-111402
Open Access | Times Cited: 1289
Assimilation of remote sensing into crop growth models: Current status and perspectives
Jianxi Huang, José Gómez‐Dans, Hai Huang, et al.
Agricultural and Forest Meteorology (2019) Vol. 276-277, pp. 107609-107609
Open Access | Times Cited: 418
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
The 10-m crop type maps in Northeast China during 2017–2019
Nanshan You, Jinwei Dong, Jianxi Huang, et al.
Scientific Data (2021) Vol. 8, Iss. 1
Open Access | Times Cited: 268
Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review
Michel Eustáquio Dantas Chaves, Michelle Cristina Araújo Picoli, Ieda Del’Arco Sanches
Remote Sensing (2020) Vol. 12, Iss. 18, pp. 3062-3062
Open Access | Times Cited: 259
Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany
Lukas Blickensdörfer, Marcel Schwieder, Dirk Pflugmacher, et al.
Remote Sensing of Environment (2021) Vol. 269, pp. 112831-112831
Open Access | Times Cited: 246
Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine
Nanshan You, Jinwei Dong
ISPRS Journal of Photogrammetry and Remote Sensing (2020) Vol. 161, pp. 109-123
Closed Access | Times Cited: 237
Deep learning on edge: Extracting field boundaries from satellite images with a convolutional neural network
François Waldner, Foivos I. Diakogiannis
Remote Sensing of Environment (2020) Vol. 245, pp. 111741-111741
Open Access | Times Cited: 228
From parcel to continental scale – A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations
Raphaël d’Andrimont, Astrid Verhegghen, Guido Lemoine, et al.
Remote Sensing of Environment (2021) Vol. 266, pp. 112708-112708
Open Access | Times Cited: 186
Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images
Jie Wang, Xiangming Xiao, Luo Liu, et al.
Remote Sensing of Environment (2020) Vol. 247, pp. 111951-111951
Open Access | Times Cited: 170
Monitoring cropland abandonment with Landsat time series
He Yin, Amintas Brandão, Johanna Buchner, et al.
Remote Sensing of Environment (2020) Vol. 246, pp. 111873-111873
Open Access | Times Cited: 156
Challenges and opportunities in remote sensing-based crop monitoring: a review
Bingfang Wu, Miao Zhang, Hongwei Zeng, et al.
National Science Review (2022) Vol. 10, Iss. 4
Open Access | Times Cited: 117
An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping
Xiao‐Peng Song, Wenli Huang, Matthew C. Hansen, et al.
Science of Remote Sensing (2021) Vol. 3, pp. 100018-100018
Open Access | Times Cited: 113
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
10 m crop type mapping using Sentinel-2 reflectance and 30 m cropland data layer product
Khuong H. Tran, Hankui K. Zhang, John McMaine, et al.
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 107, pp. 102692-102692
Open Access | Times Cited: 74
Rapid early-season maize mapping without crop labels
Nanshan You, Jinwei Dong, Jing Li, et al.
Remote Sensing of Environment (2023) Vol. 290, pp. 113496-113496
Closed Access | Times Cited: 64
Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation
Haijun Li, Xiao‐Peng Song, Matthew C. Hansen, et al.
Remote Sensing of Environment (2023) Vol. 294, pp. 113623-113623
Closed Access | Times Cited: 57
Crop Monitoring in Smallholder Farms Using Unmanned Aerial Vehicles to Facilitate Precision Agriculture Practices: A Scoping Review and Bibliometric Analysis
Shaeden Gokool, Maqsooda Mahomed, Richard Kunz, et al.
Sustainability (2023) Vol. 15, Iss. 4, pp. 3557-3557
Open Access | Times Cited: 50
Remote Sensing for Agriculture in the Era of Industry 5.0—A Survey
Nancy Victor, Praveen Kumar Reddy Maddikunta, Delphin Raj Kesari Mary, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2024) Vol. 17, pp. 5920-5945
Open Access | Times Cited: 25
CROPGRIDS: a global geo-referenced dataset of 173 crops
Fiona H. M. Tang, Thu Ha Nguyen, Giulia Conchedda, et al.
Scientific Data (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 22
GreenGuard CNN-Enhanced Paddy Leaf Detection for Crop Health Monitoring
S.M. Mustafa Nawaz, K. Maharajan, Nimisha Jose, et al.
International Journal of Computational and Experimental Science and Engineering (2025) Vol. 11, Iss. 1
Open Access | Times Cited: 3
Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images
Tao Zhou, Yajun Geng, Cheng Ji, et al.
The Science of The Total Environment (2020) Vol. 755, pp. 142661-142661
Closed Access | Times Cited: 138
Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series
Raphaël d’Andrimont, Matthieu Taymans, Guido Lemoine, et al.
Remote Sensing of Environment (2020) Vol. 239, pp. 111660-111660
Open Access | Times Cited: 135
No pixel left behind: Toward integrating Earth Observations for agriculture into the United Nations Sustainable Development Goals framework
A. K. Whitcraft, Inbal Becker‐Reshef, Christopher O. Justice, et al.
Remote Sensing of Environment (2019) Vol. 235, pp. 111470-111470
Open Access | Times Cited: 129
Satellite- and drone-based remote sensing of crops and soils for smart farming – a review
Yoshio Inoue
Soil Science & Plant Nutrition (2020) Vol. 66, Iss. 6, pp. 798-810
Open Access | Times Cited: 113
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