Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

The Performance of Random Forest Classification Based on Phenological Metrics Derived from Sentinel-2 and Landsat 8 to Map Crop Cover in an Irrigated Semi-arid Region
Abdelaziz Htitiou, Abdelghani Boudhar, Youssef Lebrini, et al.
Remote Sensing in Earth Systems Sciences (2019) Vol. 2, Iss. 4, pp. 208-224
Closed Access | Times Cited: 72

Showing 1-25 of 72 citing articles:

Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review
Michel Eustáquio Dantas Chaves, Michelle Cristina Araújo Picoli, Ieda Del’Arco Sanches
Remote Sensing (2020) Vol. 12, Iss. 18, pp. 3062-3062
Open Access | Times Cited: 258
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: 80
Sentinel-1 and 2 Time-Series for Vegetation Mapping Using Random Forest Classification: A Case Study of Northern Croatia
Dino Dobrinić, Mateo Gašparović, Damir Medak
Remote Sensing (2021) Vol. 13, Iss. 12, pp. 2321-2321
Open Access | Times Cited: 64
Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data
Amal Chakhar, David Hernández‐López, Rocío Ballesteros, et al.
Remote Sensing (2021) Vol. 13, Iss. 2, pp. 243-243
Open Access | Times Cited: 58
Monitoring and Mapping Vegetation Cover Changes in Arid and Semi-Arid Areas Using Remote Sensing Technology: A Review
Raid Almalki, Mehdi Khaki, Patricia Saco, et al.
Remote Sensing (2022) Vol. 14, Iss. 20, pp. 5143-5143
Open Access | Times Cited: 58
Comparing Pan-sharpened Landsat-9 and Sentinel-2 for Land-Use Classification Using Machine Learning Classifiers
Yassine Bouslıhım, Mohamed Hakim Kharrou, Abdelhalim Miftah, et al.
Journal of Geovisualization and Spatial Analysis (2022) Vol. 6, Iss. 2
Closed Access | Times Cited: 39
Integrating random forest and crop modeling improves the crop yield prediction of winter wheat and oil seed rape
Maninder Singh Dhillon, Thorsten Dahms, Carina Kübert, et al.
Frontiers in Remote Sensing (2023) Vol. 3
Open Access | Times Cited: 30
Assessment of Soil Suitability Using Machine Learning in Arid and Semi-Arid Regions
Maryem Ismaili, Samira Krimissa, Mustapha Namous, et al.
Agronomy (2023) Vol. 13, Iss. 1, pp. 165-165
Open Access | Times Cited: 23
Analysis of Four Decades of Land Use and Land Cover Change in Semiarid Tunisia Using Google Earth Engine
Nesrine Kadri, Sihem Jebari, Xavier Augusseau, et al.
Remote Sensing (2023) Vol. 15, Iss. 13, pp. 3257-3257
Open Access | Times Cited: 22
Assessing the Accuracy of Multiple Classification Algorithms for Crop Classification Using Landsat-8 and Sentinel-2 Data
Amal Chakhar, Damián Ortega-Terol, David Hernández‐López, et al.
Remote Sensing (2020) Vol. 12, Iss. 11, pp. 1735-1735
Open Access | Times Cited: 60
Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data
Yupeng Kang, Qingyan Meng, Miao Liu, et al.
Sensors (2021) Vol. 21, Iss. 13, pp. 4328-4328
Open Access | Times Cited: 41
Multi-temporal phenological indices derived from time series Sentinel-1 images to country-wide crop classification
Edyta Woźniak, Marcin Rybicki, W. Kofman, et al.
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 107, pp. 102683-102683
Open Access | Times Cited: 34
Application of Convolutional Neural Networks With Object-Based Image Analysis for Land Cover and Land Use Mapping in Coastal Areas: A Case Study in Ain Témouchent, Algeria
Narimane Zaabar, Simona Niculescu, Mustapha Kamel Mihoubi
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2022) Vol. 15, pp. 5177-5189
Open Access | Times Cited: 32
Remote Sensing Phenology of the Brazilian Caatinga and Its Environmental Drivers
Rodolpho Luiz Barros de Medeiros, João Andrade, Desirée M. Ramos, et al.
Remote Sensing (2022) Vol. 14, Iss. 11, pp. 2637-2637
Open Access | Times Cited: 30
Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria
Maninder Singh Dhillon, Thorsten Dahms, Carina Kübert, et al.
Remote Sensing (2022) Vol. 14, Iss. 3, pp. 677-677
Open Access | Times Cited: 28
Bridging the gap of perception is the only way to align soil protection actions
Adil Salhi, Tarik Benabdelouahab, Javier Martín Vide, et al.
The Science of The Total Environment (2020) Vol. 718, pp. 137421-137421
Closed Access | Times Cited: 46
National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine
Abdelaziz Htitiou, Abdelghani Boudhar, Abdelghani Chehbouni, et al.
Remote Sensing (2021) Vol. 13, Iss. 21, pp. 4378-4378
Open Access | Times Cited: 37
Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop Yield Predictions: A Case Study Using STARFM NDVI in Bavaria, Germany
Maninder Singh Dhillon, Carina Kübert, Thorsten Dahms, et al.
Remote Sensing (2023) Vol. 15, Iss. 7, pp. 1830-1830
Open Access | Times Cited: 15
Mapping of irrigated vineyard areas through the use of machine learning techniques and remote sensing
Esther López-Pérez, Carles Sanchís Ibor, M.A. Jiménez-Bello, et al.
Agricultural Water Management (2024) Vol. 302, pp. 108988-108988
Open Access | Times Cited: 5
A comparative analysis of different phenological information retrieved from Sentinel-2 time series images to improve crop classification: a machine learning approach
Abdelaziz Htitiou, Abdelghani Boudhar, Youssef Lebrini, et al.
Geocarto International (2020) Vol. 37, Iss. 5, pp. 1426-1449
Closed Access | Times Cited: 39
An improved random forest algorithm and its application to wind pressure prediction
Lang Li, Tiancai Liang, Shan Ai, et al.
International Journal of Intelligent Systems (2021) Vol. 36, Iss. 8, pp. 4016-4032
Open Access | Times Cited: 28
Classification of Vegetation Types in the Mountainous Terrain Using Random Forest Machine Learning Technique
Raj Singh, Arun Pratap Mishra, Manoj Kumar, et al.
Springer climate (2023), pp. 615-628
Closed Access | Times Cited: 12
Accuracy assessment and bias correction of remote sensing–based rainfall products over semiarid watersheds
Hamza Ouatiki, Abdelghani Boudhar, Abdelghani Chehbouni
Theoretical and Applied Climatology (2023) Vol. 154, Iss. 3-4, pp. 763-780
Closed Access | Times Cited: 12
An accurate snow cover product for the Moroccan Atlas Mountains: Optimization of the MODIS NDSI index threshold and development of snow fraction estimation models
Mostafa Bousbaa, Abdelghani Boudhar, Christophe Kinnard, et al.
International Journal of Applied Earth Observation and Geoinformation (2024) Vol. 129, pp. 103851-103851
Open Access | Times Cited: 4
Mapping drought severity impact on arboriculture systems over Tadla and lower Tassaout plains in Morocco using Sentinel-2 data and machine learning approaches
Sabir Oussaoui, Abdelghani Boudhar, Abdessamad Hadri, et al.
Geocarto International (2025) Vol. 40, Iss. 1
Open Access
Page 1 - Next Page

Scroll to top