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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:

Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France
Emile Ndikumana, Dinh Ho Tong Minh, Nicolas Baghdadi, et al.
Remote Sensing (2018) Vol. 10, Iss. 8, pp. 1217-1217
Open Access | Times Cited: 242

Showing 1-25 of 242 citing articles:

Deep learning in environmental remote sensing: Achievements and challenges
Qiangqiang Yuan, Huanfeng Shen, Tongwen Li, et al.
Remote Sensing of Environment (2020) Vol. 241, pp. 111716-111716
Closed Access | Times Cited: 1218
Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture
Bing Lu, Phuong D. Dao, Jiangui Liu, et al.
Remote Sensing (2020) Vol. 12, Iss. 16, pp. 2659-2659
Open Access | Times Cited: 748
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
Charlotte Pelletier, Geoffrey I. Webb, François Petitjean
Remote Sensing (2019) Vol. 11, Iss. 5, pp. 523-523
Open Access | Times Cited: 465
Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments
Muhammad Hammad Saleem, Johan Potgieter, Khalid Mahmood Arif
Precision Agriculture (2021) Vol. 22, Iss. 6, pp. 2053-2091
Closed Access | Times Cited: 268
Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture
Dino Ienco, Roberto Interdonato, Raffaele Gaetano, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2019) Vol. 158, pp. 11-22
Open Access | Times Cited: 257
Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives
Xiao Xiang Zhu, Sina Montazeri, Mohsin Ali, et al.
IEEE Geoscience and Remote Sensing Magazine (2021) Vol. 9, Iss. 4, pp. 143-172
Open Access | Times Cited: 229
Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium
Kristof Van Tricht, Anne Gobin, Sven Gilliams, et al.
Remote Sensing (2018) Vol. 10, Iss. 10, pp. 1642-1642
Open Access | Times Cited: 221
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
Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine
Jarrett Adrian, Vasit Sagan, Maitiniyazi Maimaitijiang
ISPRS Journal of Photogrammetry and Remote Sensing (2021) Vol. 175, pp. 215-235
Open Access | Times Cited: 176
A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery
Lucas Prado Osco, Mauro dos Santos de Arruda, José Marcato, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2019) Vol. 160, pp. 97-106
Closed Access | Times Cited: 161
Crop Monitoring Using Sentinel-1 Data: A Case Study from The Netherlands
Saeed Khabbazan, Paul Vermunt, Susan Steele‐Dunne, et al.
Remote Sensing (2019) Vol. 11, Iss. 16, pp. 1887-1887
Open Access | Times Cited: 159
Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France
Hassan Bazzi, Nicolas Baghdadi, Mohammad El Hajj, et al.
Remote Sensing (2019) Vol. 11, Iss. 7, pp. 887-887
Open Access | Times Cited: 158
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
Roberto Interdonato, Dino Ienco, Raffaele Gaetano, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2019) Vol. 149, pp. 91-104
Open Access | Times Cited: 149
Towards interpreting multi-temporal deep learning models in crop mapping
Jinfan Xu, Jie Yang, Xingguo Xiong, et al.
Remote Sensing of Environment (2021) Vol. 264, pp. 112599-112599
Closed Access | Times Cited: 129
Boost Precision Agriculture with Unmanned Aerial Vehicle Remote Sensing and Edge Intelligence: A Survey
Jia Liu, Jianjian Xiang, Yongjun Jin, et al.
Remote Sensing (2021) Vol. 13, Iss. 21, pp. 4387-4387
Open Access | Times Cited: 113
A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning
Jianbin Xiong, Dezheng Yu, Shuangyin Liu, et al.
Electronics (2021) Vol. 10, Iss. 1, pp. 81-81
Open Access | Times Cited: 111
An ARIMA-LSTM model for predicting volatile agricultural price series with random forest technique
Soumik Ray, Achal Lama, Pradeep Mishra, et al.
Applied Soft Computing (2023) Vol. 149, pp. 110939-110939
Closed Access | Times Cited: 60
Multi-Temporal SAR Data Large-Scale Crop Mapping Based on U-Net Model
Sisi Wei, Hong Zhang, Chao Wang, et al.
Remote Sensing (2019) Vol. 11, Iss. 1, pp. 68-68
Open Access | Times Cited: 141
Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China
Hongwei Zhao, Zhongxin Chen, Hao Jiang, et al.
Remote Sensing (2019) Vol. 11, Iss. 22, pp. 2673-2673
Open Access | Times Cited: 115
Satellite Image Time Series Classification With Pixel-Set Encoders and Temporal Self-Attention
Vivien Sainte Fare Garnot, Loïc Landrieu, Sébastien Giordano, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Open Access | Times Cited: 105
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
Mapping Paddy Rice with Satellite Remote Sensing: A Review
Rongkun Zhao, Yuechen Li, Mingguo Ma
Sustainability (2021) Vol. 13, Iss. 2, pp. 503-503
Open Access | Times Cited: 95
Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
Hassan Bazzi, Nicolas Baghdadi, Dino Ienco, et al.
Remote Sensing (2019) Vol. 11, Iss. 15, pp. 1836-1836
Open Access | Times Cited: 84
Deep machine learning with Sentinel satellite data to map paddy rice production stages across West Java, Indonesia
Kelly R. Thorp, D. Drajat
Remote Sensing of Environment (2021) Vol. 265, pp. 112679-112679
Open Access | Times Cited: 84
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