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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:

Identification of Burned Areas and Severity Using SAR Sentinel-1
Rosa Lasaponara, Biagio Tucci
IEEE Geoscience and Remote Sensing Letters (2019) Vol. 16, Iss. 6, pp. 917-921
Open Access | Times Cited: 44

Showing 1-25 of 44 citing articles:

Satellite Remote Sensing Contributions to Wildland Fire Science and Management
Emilio Chuvieco, Inmaculada Aguado, Javier Salas, et al.
Current Forestry Reports (2020) Vol. 6, Iss. 2, pp. 81-96
Closed Access | Times Cited: 162
Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa
Mihai A. Tanase, Miguel A. Belenguer-Plomer, Ekhi Roteta, et al.
Remote Sensing (2020) Vol. 12, Iss. 2, pp. 334-334
Open Access | Times Cited: 58
A workflow based on Sentinel-1 SAR data and open-source algorithms for unsupervised burned area detection in Mediterranean ecosystems
Giandomenico De Luca, João M. N. Silva, Giuseppe Modica
GIScience & Remote Sensing (2021) Vol. 58, Iss. 4, pp. 516-541
Open Access | Times Cited: 47
Advancements in high-resolution land surface satellite products: A comprehensive review of inversion algorithms, products and challenges
Shunlin Liang, Tao He, Jianxi Huang, et al.
Science of Remote Sensing (2024) Vol. 10, pp. 100152-100152
Open Access | Times Cited: 7
Monthly Burned-Area Mapping using Multi-Sensor Integration of Sentinel-1 and Sentinel-2 and machine learning: Case Study of 2019's fire events in South Sumatra Province, Indonesia
Sanjiwana Arjasakusuma, Sandiaga Swahyu Kusuma, Yenni Vetrita, et al.
Remote Sensing Applications Society and Environment (2022) Vol. 27, pp. 100790-100790
Closed Access | Times Cited: 23
Improved burned area mapping using monotemporal Landsat-9 imagery and convolutional shift-transformer
Seyd Teymoor Seydi, Mojtaba Sadegh
Measurement (2023) Vol. 216, pp. 112961-112961
Closed Access | Times Cited: 14
Regional-scale burned area mapping in Mediterranean regions based on the multitemporal composite integration of Sentinel-1 and Sentinel-2 data
Giandomenico De Luca, João M. N. Silva, Giuseppe Modica
GIScience & Remote Sensing (2022) Vol. 59, Iss. 1, pp. 1678-1705
Open Access | Times Cited: 20
Evaluating the potential of burn severity mapping and transferability of Copernicus EMS data using Sentinel-2 imagery and machine learning approaches
Kyungil Lee, Byeongcheol Kim, Seonyoung Park
GIScience & Remote Sensing (2023) Vol. 60, Iss. 1
Open Access | Times Cited: 13
Application of Remote Sensing Technology in Wildfire Research: Bibliometric Perspective
Xiaolian Li, Jie Li, Milad Haghani
Fire Technology (2024) Vol. 60, Iss. 1, pp. 579-616
Closed Access | Times Cited: 4
A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data
Dodi Sudiana, Anugrah Indah Lestari, Indra Riyanto, et al.
Remote Sensing (2023) Vol. 15, Iss. 3, pp. 728-728
Open Access | Times Cited: 11
An Unsupervised Saliency-Guided Deep Convolutional Neural Network for Accurate Burn Mapping from Sentinel-1 SAR Data
Ali Radman, Reza Shah-Hosseini, Saeid Homayouni
Remote Sensing (2023) Vol. 15, Iss. 5, pp. 1184-1184
Open Access | Times Cited: 11
Potential of Sentinel-1 C-band SAR data in tandem with Sentinel-2 optical data towards in-season mapping of paddy residue burnt area
Mamta Kumari, Abhishek Chakraborty, Rajendra Adhikari, et al.
Remote Sensing Applications Society and Environment (2025), pp. 101514-101514
Closed Access
Design and evaluation of a cloud-oriented procedure based on SAR and Multispectral data to detect burnt areas
Cristina Vittucci, Flavio Cordari, Leila Guerriero, et al.
Earth Science Informatics (2025) Vol. 18, Iss. 3
Open Access
On the Mapping of Burned Areas and Burn Severity Using Self Organizing Map and Sentinel-2 Data
Rosa Lasaponara, Antonio Proto, Angelo Aromando, et al.
IEEE Geoscience and Remote Sensing Letters (2019) Vol. 17, Iss. 5, pp. 854-858
Open Access | Times Cited: 26
CaBuAr: California burned areas dataset for delineation [Software and Data Sets]
Daniele Rege Cambrin, Luca Colomba, Paolo Garza
IEEE Geoscience and Remote Sensing Magazine (2023) Vol. 11, Iss. 3, pp. 106-113
Open Access | Times Cited: 8
Detecting fire disturbances in forests by using GNSS reflectometry and machine learning: A case study in Angola
E. Santi, Maria Paola Clarizia, Davide Comite, et al.
Remote Sensing of Environment (2022) Vol. 270, pp. 112878-112878
Closed Access | Times Cited: 12
Burned area detection using Sentinel-1 SAR data: A case study of Kangaroo Island, South Australia
Maryamsadat Hosseini, Samsung Lim
Applied Geography (2022) Vol. 151, pp. 102854-102854
Closed Access | Times Cited: 11
Near Real-Time Burned Area Progression Mapping with Multi-Spectral Data Using Ensemble Learning
Xikun Hu, Hao Wen, Puzhao Zhang, et al.
IEEE Geoscience and Remote Sensing Letters (2024) Vol. 21, pp. 1-5
Closed Access | Times Cited: 2
A Dataset for Burned Area Delineation and Severity Estimation from Satellite Imagery
Luca Colomba, Alessandro Farasin, Simone Monaco, et al.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management (2022), pp. 3893-3897
Open Access | Times Cited: 9
Imaging Burned Areas and Fire Severity in Mediterranean Fragmented Ecosystems Using Sentinel-1 and Sentinel-2: The Case Study of Tortoli–Ogliastra Fire (Sardinia)
Rosa Lasaponara, Carmen Fattore, Giuseppe Modica
IEEE Geoscience and Remote Sensing Letters (2023) Vol. 20, pp. 1-5
Open Access | Times Cited: 4
Wildfire Impact Analysis and Spread Dynamics Estimation on Satellite Images Using Deep Learning
R. Shanmuga Priya, K. Vani
Journal of the Indian Society of Remote Sensing (2024) Vol. 52, Iss. 6, pp. 1385-1403
Closed Access | Times Cited: 1
Tracking Burned Area Progression in an Unsupervised Manner Using Sentinel-1 SAR Data in Google Earth Engine
Daniel Paluba, Lorenzo Giuliano Papale, Josef Laštovička, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2024) Vol. 17, pp. 15612-15634
Open Access | Times Cited: 1
Evaluation of Combining Optical and SAR Imagery for Burned Area Mapping using Machine Learning
Anugrah Indah Lestari, Mia Rizkinia, Dodi Sudiana
2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) (2021), pp. 0052-0059
Closed Access | Times Cited: 8
Multitemporal dual-pol Sentinel-1 data to support monitoring of forest post-fire dynamics
Samuele De Petris, Evelyn Joan Momo, Filippo Sarvia, et al.
Geocarto International (2022) Vol. 37, Iss. 27, pp. 15463-15484
Closed Access | Times Cited: 6
Suitability of band angle indices for burned area mapping in the Maule Region (Chile)
Patricia Oliva, Roxana Mansilla, Ekhi Roteta, et al.
Frontiers in Forests and Global Change (2023) Vol. 5
Open Access | Times Cited: 3
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