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:

DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection
Santiago Belda, Luca Pipia, Pablo Morcillo-Pallarés, et al.
Environmental Modelling & Software (2020) Vol. 127, pp. 104666-104666
Open Access | Times Cited: 80

Showing 1-25 of 80 citing articles:

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity
Lammert Kooistra, Katja Berger, Benjamin Brede, et al.
Biogeosciences (2024) Vol. 21, Iss. 2, pp. 473-511
Open Access | Times Cited: 22

Reduced growth of Qinghai spruce due to snow cover loss in high Asian elevations since the late 20th century
Jiachang Wei, Wenhui Tang, Feng Chen, et al.
Journal of Forestry Research (2025) Vol. 36, Iss. 1
Closed Access | Times Cited: 2

Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine
Luca Pipia, Eatidal Amin, Santiago Belda, et al.
Remote Sensing (2021) Vol. 13, Iss. 3, pp. 403-403
Open Access | Times Cited: 63

Recurrent-based regression of Sentinel time series for continuous vegetation monitoring
Anatol Garioud, Silvia Valero, Sébastien Giordano, et al.
Remote Sensing of Environment (2021) Vol. 263, pp. 112419-112419
Open Access | Times Cited: 59

A novel data-driven machine learning techniques to predict compressive strength of fly ash and recycled coarse aggregates based self-compacting concrete
Surbhi Gupta Aggarwal, Rajwinder Singh, Ayush Rathore, et al.
Materials Today Communications (2024) Vol. 39, pp. 109294-109294
Closed Access | Times Cited: 10

HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network
Van Tam Nguyen, Vinh Ngoc Tran, Hoang Tran, et al.
Ecological Informatics (2025) Vol. 85, pp. 102994-102994
Open Access | Times Cited: 1

From Roots to Leaves: Tree Growth Phenology in Forest Ecosystems
Roberto Silvestro, Annie Deslauriers, Peter Prislan, et al.
Current Forestry Reports (2025) Vol. 11, Iss. 1
Closed Access | Times Cited: 1

Enhancing regional-scale simulation accuracy of paddy runoff by coupling water balance models with remote sensing
Housheng Wang, Rui Ren, Xiang Gao, et al.
Journal of Hydrology (2025), pp. 132798-132798
Closed Access | Times Cited: 1

Unraveling Meteorological Drivers of Leaf Phenology in the Western Ghats, India
Karun Jose, Nasla Najeeb, Aritra Bandopadhyay, et al.
Trees Forests and People (2025), pp. 100861-100861
Open Access | Times Cited: 1

Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression
Matías Salinero-Delgado, José Estévez, Luca Pipia, et al.
Remote Sensing (2021) Vol. 14, Iss. 1, pp. 146-146
Open Access | Times Cited: 45

A scalable software package for time series reconstruction of remote sensing datasets on the Google Earth Engine platform
Jie Zhou, Massimo Menenti, Jia Li, et al.
International Journal of Digital Earth (2023) Vol. 16, Iss. 1, pp. 988-1007
Open Access | Times Cited: 23

Characterisation of Banana Plant Growth Using High-Spatiotemporal-Resolution Multispectral UAV Imagery
Aaron Aeberli, Stuart Phinn, Kasper Johansen, et al.
Remote Sensing (2023) Vol. 15, Iss. 3, pp. 679-679
Open Access | Times Cited: 21

Principles for satellite monitoring of vegetation carbon uptake
I. Colin Prentice, Manuela Balzarolo, Keith J. Bloomfield, et al.
Nature Reviews Earth & Environment (2024) Vol. 5, Iss. 11, pp. 818-832
Closed Access | Times Cited: 8

Evaluating the impacts of models, data density and irregularity on reconstructing and forecasting dense Landsat time series
Junxue Zhang, Rong Shang, Chadwick D. Rittenhouse, et al.
Science of Remote Sensing (2021) Vol. 4, pp. 100023-100023
Open Access | Times Cited: 40

Greening of Svalbard
Stein Rune Karlsen, Arve Elvebakk, Laura Stendardi, et al.
The Science of The Total Environment (2024) Vol. 945, pp. 174130-174130
Open Access | Times Cited: 5

Machine learning-based analysis of nutrient and water uptake in hydroponically grown soybeans
Sambandh Bhusan Dhal, Shikhadri Mahanta, Janie Moore, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 5

Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring
Santiago Belda, Luca Pipia, Pablo Morcillo-Pallarés, et al.
Agronomy (2020) Vol. 10, Iss. 5, pp. 618-618
Open Access | Times Cited: 34

Methods for interpolating missing data in aerobiological databases
Antonio Picornell, José Oteros, Rocío Ruiz-Mata, et al.
Environmental Research (2021) Vol. 200, pp. 111391-111391
Open Access | Times Cited: 28

Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI
Eatidal Amin, Santiago Belda, Luca Pipia, et al.
Remote Sensing (2022) Vol. 14, Iss. 8, pp. 1812-1812
Open Access | Times Cited: 21

Investigating mangrove canopy phenology in coastal areas of China using time series Sentinel-1/2 images
Jingjing Cao, Xin Xu, Li Zhuo, et al.
Ecological Indicators (2023) Vol. 154, pp. 110815-110815
Open Access | Times Cited: 13

Grassland mowing event detection using combined optical, SAR, and weather time series
Ann-Kathrin Holtgrave, Felix Lobert, Stefan Erasmi, et al.
Remote Sensing of Environment (2023) Vol. 295, pp. 113680-113680
Open Access | Times Cited: 12

Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery
Katja Berger, Tobias Hank, Andrej Halabuk, et al.
Remote Sensing (2021) Vol. 13, Iss. 22, pp. 4711-4711
Open Access | Times Cited: 26

Difference in seasonal peak timing of soybean far-red SIF and GPP explained by canopy structure and chlorophyll content
Genghong Wu, Chongya Jiang, Hyungsuk Kimm, et al.
Remote Sensing of Environment (2022) Vol. 279, pp. 113104-113104
Open Access | Times Cited: 19

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