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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:

Country-wide high-resolution vegetation height mapping with Sentinel-2
Nico Lang, Konrad Schindler, Jan Dirk Wegner
Remote Sensing of Environment (2019) Vol. 233, pp. 111347-111347
Open Access | Times Cited: 169

Showing 1-25 of 169 citing articles:

Mapping global forest canopy height through integration of GEDI and Landsat data
Peter Potapov, Xinyuan Li, Andrés Hernández-Serna, et al.
Remote Sensing of Environment (2020) Vol. 253, pp. 112165-112165
Open Access | Times Cited: 927
Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission
Laura Duncanson, James R. Kellner, John Armston, et al.
Remote Sensing of Environment (2022) Vol. 270, pp. 112845-112845
Open Access | Times Cited: 259
A high-resolution canopy height model of the Earth
Nico Lang, Walter Jetz, Konrad Schindler, et al.
Nature Ecology & Evolution (2023) Vol. 7, Iss. 11, pp. 1778-1789
Open Access | Times Cited: 254
High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data
Wang Li, Zheng Niu, Rong Shang, et al.
International Journal of Applied Earth Observation and Geoinformation (2020) Vol. 92, pp. 102163-102163
Open Access | Times Cited: 227
Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
Nico Lang, Nikolai Kalischek, John Armston, et al.
Remote Sensing of Environment (2021) Vol. 268, pp. 112760-112760
Open Access | Times Cited: 183
Mapping temperate forest tree species using dense Sentinel-2 time series
Jan Hemmerling, Dirk Pflugmacher, Patrick Hostert
Remote Sensing of Environment (2021) Vol. 267, pp. 112743-112743
Closed Access | Times Cited: 141
Deep Learning and Earth Observation to Support the Sustainable Development Goals: Current approaches, open challenges, and future opportunities
Claudio Persello, Jan Dirk Wegner, Ronny Hänsch, et al.
IEEE Geoscience and Remote Sensing Magazine (2022) Vol. 10, Iss. 2, pp. 172-200
Open Access | Times Cited: 89
Measuring the 3-30-300 rule to help cities meet nature access thresholds
Matthew H. E. M. Browning, Dexter H. Locke, Cecil C. Konijnendijk, et al.
The Science of The Total Environment (2023) Vol. 907, pp. 167739-167739
Open Access | Times Cited: 50
LiDAR GEDI derived tree canopy height heterogeneity reveals patterns of biodiversity in forest ecosystems
Michele Torresani, Duccio Rocchini, Alessandro Alberti, et al.
Ecological Informatics (2023) Vol. 76, pp. 102082-102082
Open Access | Times Cited: 46
High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach
Martin A. Schwartz, Philippe Ciais, Catherine Ottlé, et al.
International Journal of Applied Earth Observation and Geoinformation (2024) Vol. 128, pp. 103711-103711
Open Access | Times Cited: 27
Sub-meter tree height mapping of California using aerial images and LiDAR-informed U-Net model
Fabien Wagner, Sophia Roberts, Alison L. Ritz, et al.
Remote Sensing of Environment (2024) Vol. 305, pp. 114099-114099
Open Access | Times Cited: 22
Improving National Forest Mapping in Romania Using Machine Learning and Sentinel-2 Multispectral Imagery
Mohamed Islam Keskes, Aya Hamed Mohamed, Stelian Alexandru Borz, et al.
Remote Sensing (2025) Vol. 17, Iss. 4, pp. 715-715
Open Access | Times Cited: 2
Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data
Bahareh Kalantar, Naonori Ueda, Mohammed Oludare Idrees, et al.
Remote Sensing (2020) Vol. 12, Iss. 22, pp. 3682-3682
Open Access | Times Cited: 139
Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation
Sean P. Healey, Zhiqiang Yang, Noel Gorelick, et al.
Remote Sensing (2020) Vol. 12, Iss. 17, pp. 2840-2840
Open Access | Times Cited: 75
A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks
Chunping Qiu, Michael Schmitt, Christian Geiß, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2020) Vol. 163, pp. 152-170
Open Access | Times Cited: 74
Maize and soybean heights estimation from unmanned aerial vehicle (UAV) LiDAR data
Shezhou Luo, Weiwei Liu, Yaqian Zhang, et al.
Computers and Electronics in Agriculture (2021) Vol. 182, pp. 106005-106005
Closed Access | Times Cited: 71
Semantic segmentation of cracks: Data challenges and architecture
Fabio Panella, Aldo Lipani, J. Boehm
Automation in Construction (2022) Vol. 135, pp. 104110-104110
Open Access | Times Cited: 64
Factors affecting relative height and ground elevation estimations of GEDI among forest types across the conterminous USA
Cangjiao Wang, Andrew J. Elmore, Izaya Numata, et al.
GIScience & Remote Sensing (2022) Vol. 59, Iss. 1, pp. 975-999
Open Access | Times Cited: 61
GEDI Elevation Accuracy Assessment: A Case Study of Southwest Spain
Elia Quirós, María Eugenia Polo García, Laura Fragoso‐Campón
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2021) Vol. 14, pp. 5285-5299
Open Access | Times Cited: 57
Nationwide native forest structure maps for Argentina based on forest inventory data, SAR Sentinel-1 and vegetation metrics from Sentinel-2 imagery
Eduarda Martiniano de Oliveira Silveira, Volker C. Radeloff, Sebastián Martinuzzi, et al.
Remote Sensing of Environment (2022) Vol. 285, pp. 113391-113391
Open Access | Times Cited: 53
Forest Canopy Height Mapping by Synergizing ICESat-2, Sentinel-1, Sentinel-2 and Topographic Information Based on Machine Learning Methods
Zhilong Xi, Huadong Xu, Yanqiu Xing, et al.
Remote Sensing (2022) Vol. 14, Iss. 2, pp. 364-364
Open Access | Times Cited: 45
Cocoa plantations are associated with deforestation in Côte d’Ivoire and Ghana
Nikolai Kalischek, Nico Lang, Cécile Renier, et al.
Nature Food (2023) Vol. 4, Iss. 5, pp. 384-393
Open Access | Times Cited: 42
Retrieval of cloud properties from thermal infrared radiometry using convolutional neural network
Quan Wang, Chen Zhou, Xiaoyong Zhuge, et al.
Remote Sensing of Environment (2022) Vol. 278, pp. 113079-113079
Open Access | Times Cited: 41
Forest Height Mapping Using Feature Selection and Machine Learning by Integrating Multi-Source Satellite Data in Baoding City, North China
Nan Zhang, Mingjie Chen, Fan Yang, et al.
Remote Sensing (2022) Vol. 14, Iss. 18, pp. 4434-4434
Open Access | Times Cited: 41
Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale
Sizhuo Li, Martin Brandt, Rasmus Fensholt, et al.
PNAS Nexus (2023) Vol. 2, Iss. 4
Open Access | Times Cited: 41
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