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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:

Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes
Julien Denize, Laurence Hubert‐Moy, Julie Betbeder, et al.
Remote Sensing (2018) Vol. 11, Iss. 1, pp. 37-37
Open Access | Times Cited: 84

Showing 1-25 of 84 citing articles:

Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition
Thanh Noi Phan, Verena Kuch, Lukas Lehnert
Remote Sensing (2020) Vol. 12, Iss. 15, pp. 2411-2411
Open Access | Times Cited: 448
Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data
Dipankar Mandal, Vineet Kumar, Debanshu Ratha, et al.
Remote Sensing of Environment (2020) Vol. 247, pp. 111954-111954
Closed Access | Times Cited: 302
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
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
Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods
Vahid Nasiri, Azade Deljouei, Fardin Moradi, et al.
Remote Sensing (2022) Vol. 14, Iss. 9, pp. 1977-1977
Open Access | Times Cited: 147
Crop monitoring by multimodal remote sensing: A review
Priyabrata Karmakar, Shyh Wei Teng, Manzur Murshed, et al.
Remote Sensing Applications Society and Environment (2023) Vol. 33, pp. 101093-101093
Open Access | Times Cited: 52
Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region
Chuanliang Sun, Yan Shan Bian, Tao Zhou, et al.
Sensors (2019) Vol. 19, Iss. 10, pp. 2401-2401
Open Access | Times Cited: 139
High-resolution satellite imagery applications in crop phenotyping: An overview
Chongyuan Zhang, Afef Marzougui, Sindhuja Sankaran
Computers and Electronics in Agriculture (2020) Vol. 175, pp. 105584-105584
Open Access | Times Cited: 117
Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies
Aiym Orynbaikyzy, Ursula Geßner, Benjamin Mack, et al.
Remote Sensing (2020) Vol. 12, Iss. 17, pp. 2779-2779
Open Access | Times Cited: 99
Deeply synergistic optical and SAR time series for crop dynamic monitoring
Wenzhi Zhao, Yang Qu, Jiage Chen, et al.
Remote Sensing of Environment (2020) Vol. 247, pp. 111952-111952
Closed Access | Times Cited: 90
Estimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data
Yueting Wang, Xiaoli Zhang, Zhengqi Guo
Ecological Indicators (2021) Vol. 126, pp. 107645-107645
Open Access | Times Cited: 65
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
The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine
Jinxi Yao, Ji Wu, Chengzhi Xiao, et al.
Remote Sensing (2022) Vol. 14, Iss. 12, pp. 2758-2758
Open Access | Times Cited: 50
Accelerating Crop Yield: Multisensor Data Fusion and Machine Learning for Agriculture Text Classification
A. Reyana, Sandeep Kautish, P. M. Sharan Karthik, et al.
IEEE Access (2023) Vol. 11, pp. 20795-20805
Open Access | Times Cited: 35
Improving Crop Classification Accuracy with Integrated Sentinel-1 and Sentinel-2 Data: a Case Study of Barley and Wheat
Gaylan Rasul Faqe Ibrahim, Azad Rasul, Haidi Abdullah
Journal of Geovisualization and Spatial Analysis (2023) Vol. 7, Iss. 2
Closed Access | Times Cited: 26
Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications
Francisco Mena, Diego Arenas, Marlon Nuske, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2024) Vol. 17, pp. 4797-4818
Open Access | Times Cited: 11
A random forest-based framework for crop mapping using temporal, spectral, textural and polarimetric observations
Iman Khosravi, Seyed Kazem Alavipanah
International Journal of Remote Sensing (2019) Vol. 40, Iss. 18, pp. 7221-7251
Closed Access | Times Cited: 56
Evaluation and comparison of the earth observing sensors in land cover/land use studies using machine learning algorithms
Pankaj Prasad, Victor J. Loveson, Priyankar Chandra, et al.
Ecological Informatics (2021) Vol. 68, pp. 101522-101522
Open Access | Times Cited: 54
National Crop Mapping Using Sentinel-1 Time Series: A Knowledge-Based Descriptive Algorithm
Carole Planque, Richard Lucas, Suvarna Punalekar, et al.
Remote Sensing (2021) Vol. 13, Iss. 5, pp. 846-846
Open Access | Times Cited: 42
Using Remote Sensing Vegetation Indices for the Discrimination and Monitoring of Agricultural Crops: A Critical Review
Roxana Vidican, Anamaria Mălinaş, Ovidiu RANTA, et al.
Agronomy (2023) Vol. 13, Iss. 12, pp. 3040-3040
Open Access | Times Cited: 21
Effect of the Synergetic Use of Sentinel-1, Sentinel-2, LiDAR and Derived Data in Land Cover Classification of a Semiarid Mediterranean Area Using Machine Learning Algorithms
Carmen Valdivieso-Ros, Francisco Alonso‐Sarría, Francisco Gomáriz-Castillo
Remote Sensing (2023) Vol. 15, Iss. 2, pp. 312-312
Open Access | Times Cited: 17
Input imagery, classifiers, and cloud computing: Insights from multi-temporal LULC mapping in the Cambodian Mekong Delta
Christina Orieschnig, Gilles Belaud, Jean‐Philippe Venot, et al.
European Journal of Remote Sensing (2021) Vol. 54, Iss. 1, pp. 398-416
Open Access | Times Cited: 36
Object-Based Multi-Temporal and Multi-Source Land Cover Mapping Leveraging Hierarchical Class Relationships
Yawogan Jean Eudes Gbodjo, Dino Ienco, Louise Leroux, et al.
Remote Sensing (2020) Vol. 12, Iss. 17, pp. 2814-2814
Open Access | Times Cited: 35
Integration of Sentinel optical and radar data for mapping smallholder coffee production systems in Vietnam
Gina Maskell, Abel Chemura, Huong Thi Thanh Nguyen, et al.
Remote Sensing of Environment (2021) Vol. 266, pp. 112709-112709
Open Access | Times Cited: 32
Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier
Binita Shrestha, Haroon Stephen, Sajjad Ahmad
Remote Sensing (2021) Vol. 13, Iss. 15, pp. 3040-3040
Open Access | Times Cited: 29
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