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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:

Machine learning models to predict daily actual evapotranspiration of citrus orchards under regulated deficit irrigation
Antonino Pagano, Federico Amato, Matteo Ippolito, et al.
Ecological Informatics (2023) Vol. 76, pp. 102133-102133
Open Access | Times Cited: 23

Showing 23 citing articles:

A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain
Hanmi Zhou, Linshuang Ma, Xiaoli Niu, et al.
Agricultural Water Management (2024) Vol. 296, pp. 108807-108807
Open Access | Times Cited: 9
Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models
Khabat Khosravi, Aitazaz A. Farooque, Seyed Amir Naghibi, et al.
Ecological Informatics (2024) Vol. 85, pp. 102933-102933
Open Access | Times Cited: 9
Machine Learning-Based Estimation of Daily Cropland Evapotranspiration in Diverse Climate Zones
Changmin Du, Shouzheng Jiang, Chuqiang Chen, et al.
Remote Sensing (2024) Vol. 16, Iss. 5, pp. 730-730
Open Access | Times Cited: 5
Advanced long-term actual evapotranspiration estimation in humid climates for 1958–2021 based on machine learning models enhanced by the RReliefF algorithm
Ahmed Elbeltagi, Salim Heddam, Okan Mert Katipoğlu, et al.
Journal of Hydrology Regional Studies (2024) Vol. 56, pp. 102043-102043
Open Access | Times Cited: 5
A practical data-driven approach for precise stem water potential monitoring in pistachio and almond orchards using supervised machine learning algorithms
Mehrad Mortazavi, Stefano Carpin, Arash Toudeshki, et al.
Computers and Electronics in Agriculture (2025) Vol. 231, pp. 110004-110004
Open Access
Integrating machine learning and empirical evapotranspiration modeling with DSSAT: Implications for agricultural water management
Niguss Solomon Hailegnaw, Haimanote K. Bayabil, Mulatu Liyew Berihun, et al.
The Science of The Total Environment (2023) Vol. 912, pp. 169403-169403
Closed Access | Times Cited: 11
Generalization Ability of Bagging and Boosting Type Deep Learning Models in Evapotranspiration Estimation
Manoranjan Kumar, Yash Agrawal, Sirisha Adamala, et al.
(2024)
Open Access | Times Cited: 3
1D convolutional neural networks-based soil fertility classification and fertilizer prescription
M. Sujatha, Jaidhar C.D., Mallikarjuna Lingappa
Ecological Informatics (2023) Vol. 78, pp. 102295-102295
Closed Access | Times Cited: 7
Advancing SDGs: Predicting Future Shifts in Saudi Arabia’s Terrestrial Water Storage Using Multi-Step-Ahead Machine Learning Based on GRACE Data
Mohamed A. Yassin, Sani I. Abba, Arya Pradipta, et al.
Water (2024) Vol. 16, Iss. 2, pp. 246-246
Open Access | Times Cited: 2
Assessing the performance of the Gaussian Process Regression algorithm to fill gaps in the time-series of daily actual evapotranspiration of different crops in temperate and continental zones using ground and remotely sensed data
Dario De, Matteo Ippolito, Marcella Cannarozzo, et al.
Agricultural Water Management (2023) Vol. 290, pp. 108596-108596
Open Access | Times Cited: 5
Quantifying effects of climate change and farmers' information demand on wheat yield in India: a deep learning approach with regional clustering
Samarth Godara, Pratap S. Birthal, G. Avinash, et al.
Frontiers in Sustainable Food Systems (2024) Vol. 8
Open Access | Times Cited: 1
How to Measure Evapotranspiration in Landscape-Ecological Studies? Overview of Concepts and Methods
Tereza Pohanková, Pavel Vyvlečka, Vilém Pechanec
Journal of Landscape Ecology (2024) Vol. 17, Iss. 3, pp. 38-59
Open Access | Times Cited: 1
Predicting Tree Water Status in Pistachio and Almond Orchards Using Supervised Machine Learning
Mehrad Mortazavi, Reza Ehsani, Stefano Carpin, et al.
(2023)
Closed Access | Times Cited: 2
Energy balance determination of crop evapotranspiration using a wireless sensor network
José A. Jiménez-Berni, Arantxa Cabello-Leblic, Alicia Lopez-Guerrero, et al.
Frontiers in Agronomy (2023) Vol. 5
Open Access | Times Cited: 2
Artificial Neural Networks Estimate Evapotranspiration For Miscanthus × Giganteus As Effectively As Empirical Model But With Fewer Inputs
Guler Aslan Sungur, Caitlin E. Moore, Carl J. Bernacchi, et al.
Research Square (Research Square) (2024)
Open Access
Development of Neural Networks and Performance Appraisal of Supervised Learning Models for Predicting Organic Carbon in Soils Under Different Cropping Systems
Gagandeep Kaur, Sandeep Sharma, Pritpal Singh
Journal of soil science and plant nutrition (2024)
Closed Access
Comparison of principal component analysis algorithms for imputation in agrometeorological data in high dimension and reduced sample size
Valter Cesar de Souza, Sérgio Augusto Rodrigues, Luís Roberto Almeida Gabriel Filho
PLoS ONE (2024) Vol. 19, Iss. 12, pp. e0315574-e0315574
Open Access
Generalization Ability of Bagging and Boosting Type Deep Learning Models in Evapotranspiration Estimation
Manoranjan Kumar, Yash Agrawal, Sirisha Adamala, et al.
Water (2024) Vol. 16, Iss. 16, pp. 2233-2233
Open Access
Applicability analysis of different evapotranspiration models for maize farmland in the lower Yellow River Plain based on eddy covariance measurements
Xiaojuan Ren, Guodong Li, Shengyan Ding, et al.
Ecohydrology & Hydrobiology (2024)
Closed Access
Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields
Larona Keabetswe, Yiyin He, Chao Li, et al.
Agricultural Water Management (2024) Vol. 306, pp. 109191-109191
Open Access
Artificial Neural Networks Estimate Evapotranspiration for Miscanthus × Giganteus as Effectively as Empirical Model But with Fewer Inputs
Guler Aslan Sungur, Caitlin E. Moore, Carl J. Bernacchi, et al.
(2023)
Open Access | Times Cited: 1
Assessing the Performance of the Gaussian Process Regression Algorithm to Fill Gaps in the Time-Series of Daily Actual Evapotranspiration of Different Crops in Temperate E Continental Zones Using Ground and Remotely Sensed Data
Dario De, Matteo Ippolito, Marcella Cannarozzo, et al.
(2023)
Closed Access
Internet of Things and Artificial Intelligence for Sustainable Agriculture: A Use Case in Citrus Orchards
Antonino Pagano, Federico Amato, Matteo Ippolito, et al.
(2023), pp. 01-06
Closed Access
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