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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 irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine
Yanhua Xie, Tyler J. Lark, J. F. Brown, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2019) Vol. 155, pp. 136-149
Open Access | Times Cited: 137

Showing 1-25 of 137 citing articles:

Google Earth Engine for geo-big data applications: A meta-analysis and systematic review
Haifa Tamiminia, Bahram Salehi, Masoud Mahdianpari, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2020) Vol. 164, pp. 152-170
Closed Access | Times Cited: 965
Cropland expansion in the United States produces marginal yields at high costs to wildlife
Tyler J. Lark, S. Spawn, Matthew Bougie, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 246
Towards a multiscale crop modelling framework for climate change adaptation assessment
Bin Peng, Kaiyu Guan, Jinyun Tang, et al.
Nature Plants (2020) Vol. 6, Iss. 4, pp. 338-348
Closed Access | Times Cited: 235
Mapping three decades of annual irrigation across the US High Plains Aquifer using Landsat and Google Earth Engine
Jillian M. Deines, A. D. Kendall, Morgan A. Crowley, et al.
Remote Sensing of Environment (2019) Vol. 233, pp. 111400-111400
Open Access | Times Cited: 175
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: 155
Mapping 20 years of irrigated croplands in China using MODIS and statistics and existing irrigation products
Chao Zhang, Jinwei Dong, Quansheng Ge
Scientific Data (2022) Vol. 9, Iss. 1
Open Access | Times Cited: 74
A 30 m annual cropland dataset of China from 1986 to 2021
Ying Tu, Shengbiao Wu, Бин Чэн, et al.
Earth system science data (2024) Vol. 16, Iss. 5, pp. 2297-2316
Open Access | Times Cited: 23
Global Changes in Urban Vegetation Cover
Daniel R. Richards, Richard N. Belcher
Remote Sensing (2019) Vol. 12, Iss. 1, pp. 23-23
Open Access | Times Cited: 121
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: 104
Quantifying irrigation cooling benefits to maize yield in the US Midwest
Yan Li, Kaiyu Guan, Bin Peng, et al.
Global Change Biology (2020) Vol. 26, Iss. 5, pp. 3065-3078
Closed Access | Times Cited: 97
Mapping annual irrigation from Landsat imagery and environmental variables across the conterminous United States
Yanhua Xie, Tyler J. Lark
Remote Sensing of Environment (2021) Vol. 260, pp. 112445-112445
Open Access | Times Cited: 89
Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada
Meisam Amani, Mohammad Kakooei, Armin Moghimi, et al.
Remote Sensing (2020) Vol. 12, Iss. 21, pp. 3561-3561
Open Access | Times Cited: 86
GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery
Miao Zhang, Bingfang Wu, Hongwei Zeng, et al.
Earth system science data (2021) Vol. 13, Iss. 10, pp. 4799-4817
Open Access | Times Cited: 72
Challenges and opportunities in precision irrigation decision-support systems for center pivots
Jingwen Zhang, Kaiyu Guan, Bin Peng, et al.
Environmental Research Letters (2021) Vol. 16, Iss. 5, pp. 053003-053003
Open Access | Times Cited: 65
Convolutional neural network based tea leaf disease prediction system on smart phone using paas cloud
Madhusudan G. Lanjewar, Kamini G. Panchbhai
Neural Computing and Applications (2022) Vol. 35, Iss. 3, pp. 2755-2771
Closed Access | Times Cited: 50
IrriMap_CN: Annual irrigation maps across China in 2000–2019 based on satellite observations, environmental variables, and machine learning
Chao Zhang, Jinwei Dong, Quansheng Ge
Remote Sensing of Environment (2022) Vol. 280, pp. 113184-113184
Closed Access | Times Cited: 45
Mapping global maximum irrigation extent at 30m resolution using the irrigation performances under drought stress
Bingfang Wu, Fuyou Tian, Mohsen Nabil, et al.
Global Environmental Change (2023) Vol. 79, pp. 102652-102652
Closed Access | Times Cited: 37
Crop mapping using supervised machine learning and deep learning: a systematic literature review
Mouad Alami Machichi, Loubna El Mansouri, Yasmina Imani, et al.
International Journal of Remote Sensing (2023) Vol. 44, Iss. 8, pp. 2717-2753
Open Access | Times Cited: 26
Assessment of Machine Learning Algorithms for Land Cover Classification in a Complex Mountainous Landscape
Gomal Amin, Iqra Imtiaz, Ehsan Haroon, et al.
Journal of Geovisualization and Spatial Analysis (2024) Vol. 8, Iss. 2
Open Access | Times Cited: 10
Synthesizing regional irrigation data using machine learning – Towards global upscaling via metamodeling
Søren Julsgaard Kragh, Raphael Schneider, Rasmus Fensholt, et al.
Agricultural Water Management (2025) Vol. 311, pp. 109404-109404
Open Access | Times Cited: 1
Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data
Licheng Zhao, Yun Shi, Bin Liu, et al.
Remote Sensing (2019) Vol. 11, Iss. 24, pp. 3012-3012
Open Access | Times Cited: 69
Continues monitoring of subsidence water in mining area from the eastern plain in China from 1986 to 2018 using Landsat imagery and Google Earth Engine
Tingting He, Wu Xiao, Zhao Yanling, et al.
Journal of Cleaner Production (2020) Vol. 279, pp. 123610-123610
Closed Access | Times Cited: 59
IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S.
David Ketchum, Kelsey Jencso, Marco Maneta, et al.
Remote Sensing (2020) Vol. 12, Iss. 14, pp. 2328-2328
Open Access | Times Cited: 51
Systematic method for mapping fine-resolution water cover types in China based on time series Sentinel-1 and 2 images
Yang Li, Zhenguo Niu
International Journal of Applied Earth Observation and Geoinformation (2021) Vol. 106, pp. 102656-102656
Open Access | Times Cited: 48
Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017
Yanhua Xie, Holly Gibbs, Tyler J. Lark
Earth system science data (2021) Vol. 13, Iss. 12, pp. 5689-5710
Open Access | Times Cited: 47
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