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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:

Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud
Adam Oliphant, Prasad S. Thenkabail, Pardhasaradhi Teluguntla, et al.
International Journal of Applied Earth Observation and Geoinformation (2019) Vol. 81, pp. 110-124
Open Access | Times Cited: 189

Showing 1-25 of 189 citing articles:

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: 225
Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects
Liangyun Liu, Xiao Zhang, Yuan Gao, et al.
Journal of Remote Sensing (2021) Vol. 2021
Open Access | Times Cited: 173
Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin
Michael Gomez Selvaraj, Alejandro Perdomo Vergara, Frank Montenegro, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2020) Vol. 169, pp. 110-124
Open Access | Times Cited: 165
Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine
Aparna Phalke, Mutlu Özdoğan, Prasad S. Thenkabail, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2020) Vol. 167, pp. 104-122
Open Access | Times Cited: 152
Quantifying Nutrient Budgets for Sustainable Nutrient Management
Xin Zhang, Eric A. Davidson, Tan Zou, et al.
Global Biogeochemical Cycles (2020) Vol. 34, Iss. 3
Open Access | Times Cited: 146
Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review
Liping Yang, Joshua Driscol, Sarigai Sarigai, et al.
Remote Sensing (2022) Vol. 14, Iss. 14, pp. 3253-3253
Open Access | Times Cited: 146
Impacts of droughts and floods on croplands and crop production in Southeast Asia – An application of Google Earth Engine
Manjunatha Venkatappa, Nophea Sasaki, Han Phoumin, et al.
The Science of The Total Environment (2021) Vol. 795, pp. 148829-148829
Closed Access | Times Cited: 113
Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors
Hossein Shafizadeh‐Moghadam, Morteza Khazaei, Seyed Kazem Alavipanah, et al.
GIScience & Remote Sensing (2021) Vol. 58, Iss. 6, pp. 914-928
Open Access | Times Cited: 103
Future Scenarios of Land Use/Land Cover (LULC) Based on a CA-Markov Simulation Model: Case of a Mediterranean Watershed in Morocco
Mohamed Beroho, Hamza Briak, El Khalil Cherif, et al.
Remote Sensing (2023) Vol. 15, Iss. 4, pp. 1162-1162
Open Access | Times Cited: 64
Long-Term Changes of Open-Surface Water Bodies in the Yangtze River Basin Based on the Google Earth Engine Cloud Platform
Yue Deng, Weiguo Jiang, Zhenghong Tang, et al.
Remote Sensing (2019) Vol. 11, Iss. 19, pp. 2213-2213
Open Access | Times Cited: 139
Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud
Murali Krishna Gumma, Prasad S. Thenkabail, Pardhasaradhi Teluguntla, et al.
GIScience & Remote Sensing (2019) Vol. 57, Iss. 3, pp. 302-322
Open Access | Times Cited: 132
Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges
Xiaochuang Yao, Guoqing Li, Junshi Xia, et al.
Remote Sensing (2019) Vol. 12, Iss. 1, pp. 62-62
Open Access | Times Cited: 97
Large-scale rice mapping under different years based on time-series Sentinel-1 images using deep semantic segmentation model
Pengliang Wei, Dengfeng Chai, Tao Lin, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2021) Vol. 174, pp. 198-214
Closed Access | Times Cited: 93
A new framework to map fine resolution cropping intensity across the globe: Algorithm, validation, and implication
Chong Liu, Qi Zhang, Shiqi Tao, et al.
Remote Sensing of Environment (2020) Vol. 251, pp. 112095-112095
Open Access | Times Cited: 83
Mapping crop types in complex farming areas using SAR imagery with dynamic time warping
Getachew Workineh Gella, W. Bijker, Mariana Belgiu
ISPRS Journal of Photogrammetry and Remote Sensing (2021) Vol. 175, pp. 171-183
Open Access | Times Cited: 80
Exploring the potential of land surface phenology and seasonal cloud free composites of one year of Sentinel-2 imagery for tree species mapping in a mountainous region
Andreas Kollert, Magnus Bremer, Markus Löw, et al.
International Journal of Applied Earth Observation and Geoinformation (2020) Vol. 94, pp. 102208-102208
Open Access | Times Cited: 79
Oil palm mapping over Peninsular Malaysia using Google Earth Engine and machine learning algorithms
Nur Shafira Nisa Shaharum, Helmi Zulhaidi Mohd Shafri, Wan Azlina Wan Ab Karim Ghani, et al.
Remote Sensing Applications Society and Environment (2020) Vol. 17, pp. 100287-100287
Open Access | Times Cited: 73
Mapping the vegetation distribution and dynamics of a wetland using adaptive-stacking and Google Earth Engine based on multi-source remote sensing data
Xiangren Long, Xinyu Li, Hui Lin, et al.
International Journal of Applied Earth Observation and Geoinformation (2021) Vol. 102, pp. 102453-102453
Open Access | Times Cited: 72
Random Forest Classification of Land Use, Land-Use Change and Forestry (LULUCF) Using Sentinel-2 Data—A Case Study of Czechia
Jan Svoboda, Přemysl Štych, Josef Laštovička, et al.
Remote Sensing (2022) Vol. 14, Iss. 5, pp. 1189-1189
Open Access | Times Cited: 53
Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea
Yong Piao, Dong Kun Lee, Sang-Jin Park, et al.
Geomatics Natural Hazards and Risk (2022) Vol. 13, Iss. 1, pp. 432-450
Open Access | Times Cited: 47
Mapping corn dynamics using limited but representative samples with adaptive strategies
Yanan Wen, Xuecao Li, Haowei Mu, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2022) Vol. 190, pp. 252-266
Open Access | Times Cited: 44
Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security
Murali Krishna Gumma, Prasad S. Thenkabail, Pranay Panjala, et al.
GIScience & Remote Sensing (2022) Vol. 59, Iss. 1, pp. 1048-1077
Open Access | Times Cited: 43
Mapping cropland abandonment and distinguishing from intentional afforestation with Landsat time series
Changqiao Hong, Alexander V. Prishchepov, Xiaobin Jin, et al.
International Journal of Applied Earth Observation and Geoinformation (2024) Vol. 127, pp. 103693-103693
Open Access | Times Cited: 11
Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images
G. Wang, Di Meng, Riqiang Chen, et al.
Remote Sensing (2024) Vol. 16, Iss. 2, pp. 277-277
Open Access | Times Cited: 9
Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data
Leandro Parente, Evandro Carrijo Taquary, Ana Silva, et al.
Remote Sensing (2019) Vol. 11, Iss. 23, pp. 2881-2881
Open Access | Times Cited: 63
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