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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 ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing
Corinne Carter, Shunlin Liang
International Journal of Applied Earth Observation and Geoinformation (2019) Vol. 78, pp. 86-92
Open Access | Times Cited: 82

Showing 1-25 of 82 citing articles:

The Global Land Surface Satellite (GLASS) Product Suite
Shunlin Liang, Jie Cheng, Kun Jia, et al.
Bulletin of the American Meteorological Society (2020) Vol. 102, Iss. 2, pp. E323-E337
Open Access | Times Cited: 462
Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms
Zander S. Venter, TC Chakraborty, Xuhui Lee
Science Advances (2021) Vol. 7, Iss. 22
Open Access | Times Cited: 211
A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting
Karim Ibrahim, Yuk Feng Huang, Ali Najah Ahmed, et al.
Alexandria Engineering Journal (2021) Vol. 61, Iss. 1, pp. 279-303
Open Access | Times Cited: 190
A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data
Shima Amani, Hossein Shafizadeh‐Moghadam
Agricultural Water Management (2023) Vol. 284, pp. 108324-108324
Open Access | Times Cited: 58
Optical remote sensing of crop biophysical and biochemical parameters: An overview of advances in sensor technologies and machine learning algorithms for precision agriculture
Mahlatse Kganyago, Clement Adjorlolo, Paidamwoyo Mhangara, et al.
Computers and Electronics in Agriculture (2024) Vol. 218, pp. 108730-108730
Open Access | Times Cited: 31
Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review
Min Yan Chia, Yuk Feng Huang, Chai Hoon Koo, et al.
Agronomy (2020) Vol. 10, Iss. 1, pp. 101-101
Open Access | Times Cited: 90
A review of drought monitoring with big data: Issues, methods, challenges and research directions
Hanen Balti, Ali Ben Abbes, Nédra Mellouli, et al.
Ecological Informatics (2020) Vol. 60, pp. 101136-101136
Open Access | Times Cited: 85
On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient
Yun Bai, Sha Zhang, Nishan Bhattarai, et al.
Agricultural and Forest Meteorology (2021) Vol. 298-299, pp. 108308-108308
Closed Access | Times Cited: 83
Improving terrestrial evapotranspiration estimation across China during 2000–2018 with machine learning methods
Lichang Yin, Fulu Tao, Yi Chen, et al.
Journal of Hydrology (2021) Vol. 600, pp. 126538-126538
Open Access | Times Cited: 80
Modeling monthly reference evapotranspiration process in Turkey: application of machine learning methods
Savaş Bayram, Hatice Çıtakoğlu
Environmental Monitoring and Assessment (2022) Vol. 195, Iss. 1
Closed Access | Times Cited: 60
Multi-scale evaluation of global evapotranspiration products derived from remote sensing images: Accuracy and uncertainty
Wenbin Zhu, Shengrong Tian, Jiaxing Wei, et al.
Journal of Hydrology (2022) Vol. 611, pp. 127982-127982
Closed Access | Times Cited: 48
Prediction of streamflow based on the long-term response of streamflow to climatic factors in the source region of the Yellow River
Ruirui Xu, Dexun Qiu, Peng Gao, et al.
Journal of Hydrology Regional Studies (2024) Vol. 52, pp. 101681-101681
Open Access | Times Cited: 12
Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration
Shima Amani, Hossein Shafizadeh‐Moghadam, Saeid Morid
Water Resources Management (2024) Vol. 38, Iss. 6, pp. 1921-1942
Open Access | Times Cited: 8
Optimizing actual evapotranspiration simulation to identify evapotranspiration partitioning variations: A fusion of physical processes and machine learning techniques
Xiaoman Jiang, Yuntao Wang, A Yinglan, et al.
Agricultural Water Management (2024) Vol. 295, pp. 108755-108755
Open Access | Times Cited: 8
Estimating latent heat flux of subtropical forests using machine learning algorithms
Harekrushna Sahu, Pramit Kumar Deb Burman, Palingamoorthy Gnanamoorthy, et al.
Meteorological Applications (2025) Vol. 32, Iss. 1
Open Access | Times Cited: 1
Spatio-temporal patterns of evapotranspiration based on upscaling eddy covariance measurements in the dryland of the North China Plain
Beijing Fang, Huimin Lei, Yucui Zhang, et al.
Agricultural and Forest Meteorology (2019) Vol. 281, pp. 107844-107844
Closed Access | Times Cited: 70
Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data
Roberto Filgueiras, Thomé Simpliciano Almeida, Everardo Chartuni Mantovani, et al.
Agricultural Water Management (2020) Vol. 241, pp. 106346-106346
Open Access | Times Cited: 56
Monitoring sustainable development by means of earth observation data and machine learning: a review
Bruno Ferreira, Muriel Iten, Rui Silva
Environmental Sciences Europe (2020) Vol. 32, Iss. 1
Open Access | Times Cited: 55
Applications of Gaussian process regression for predicting blue water footprint: Case study in Ad Daqahliyah, Egypt
Ahmed Elbeltagi, Nasrin Azad, Arfan Arshad, et al.
Agricultural Water Management (2021) Vol. 255, pp. 107052-107052
Closed Access | Times Cited: 54
Estimating actual evapotranspiration at field-to-continent scales by calibrating the CMRSET algorithm with MODIS, VIIRS, Landsat and Sentinel-2 data
Juan Pablo Guerschman, Tim R. McVicar, Jamie Vleeshower, et al.
Journal of Hydrology (2021) Vol. 605, pp. 127318-127318
Closed Access | Times Cited: 44
Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning
Reda ElGhawi, Basil Kraft, Christian Reimers, et al.
Environmental Research Letters (2023) Vol. 18, Iss. 3, pp. 034039-034039
Open Access | Times Cited: 20
Estimation of Coastal Wetland Vegetation Aboveground Biomass by Integrating UAV and Satellite Remote Sensing Data
Xiaomeng Niu, Binjie Chen, Weiwei Sun, et al.
Remote Sensing (2024) Vol. 16, Iss. 15, pp. 2760-2760
Open Access | Times Cited: 6
Evapotranspiration Acquired with Remote Sensing Thermal-Based Algorithms: A State-of-the-Art Review
Vicente García‐Santos, Juan Manuel Sánchez, Joan Cuxart
Remote Sensing (2022) Vol. 14, Iss. 14, pp. 3440-3440
Open Access | Times Cited: 27
The accuracy of multisource evapotranspiration products and their applicability in streamflow simulation over a large catchment of Southern China
Jie Ding, Qian Zhu
Journal of Hydrology Regional Studies (2022) Vol. 41, pp. 101092-101092
Open Access | Times Cited: 24
Quantifying climate and anthropogenic impacts on runoff using the SWAT model, a Budyko-based approach and empirical methods
Ruirui Xu, Dexun Qiu, Changxue Wu, et al.
Hydrological Sciences Journal (2023) Vol. 68, Iss. 10, pp. 1358-1371
Closed Access | Times Cited: 16
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