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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:

Combining transfer learning and hyperspectral reflectance analysis to assess leaf nitrogen concentration across different plant species datasets
Liang Wan, Weijun Zhou, Yong He, et al.
Remote Sensing of Environment (2021) Vol. 269, pp. 112826-112826
Closed Access | Times Cited: 77

Showing 1-25 of 77 citing articles:

Transfer learning in environmental remote sensing
Yuchi Ma, Shuo Chen, Stefano Ermon, et al.
Remote Sensing of Environment (2023) Vol. 301, pp. 113924-113924
Closed Access | Times Cited: 117
Field-level crop yield estimation with PRISMA and Sentinel-2
Michael Marshall, Mariana Belgiu, Mirco Boschetti, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2022) Vol. 187, pp. 191-210
Open Access | Times Cited: 87
Improving retrieval of leaf chlorophyll content from Sentinel-2 and Landsat-7/8 imagery by correcting for canopy structural effects
Liang Wan, Youngryel Ryu, Benjamin Dechant, et al.
Remote Sensing of Environment (2024) Vol. 304, pp. 114048-114048
Closed Access | Times Cited: 19
Hyperspectral-to-image transform and CNN transfer learning enhancing soybean LCC estimation
Jibo Yue, Hao Yang, Haikuan Feng, et al.
Computers and Electronics in Agriculture (2023) Vol. 211, pp. 108011-108011
Closed Access | Times Cited: 29
A spectral decomposition method for estimating the leaf nitrogen status of maize by UAV-based hyperspectral imaging
Meiyan Shu, Zhu Jinyu, Yang XiaoHong, et al.
Computers and Electronics in Agriculture (2023) Vol. 212, pp. 108100-108100
Closed Access | Times Cited: 24
Generality of leaf spectroscopic models for predicting key foliar functional traits across continents: A comparison between physically- and empirically-based approaches
Zhihui Wang, Jean‐Baptiste Féret, Nanfeng Liu, et al.
Remote Sensing of Environment (2023) Vol. 293, pp. 113614-113614
Open Access | Times Cited: 23
UAV-borne hyperspectral estimation of nitrogen content in tobacco leaves based on ensemble learning methods
Mingzheng Zhang, Tianen Chen, Xiaohe Gu, et al.
Computers and Electronics in Agriculture (2023) Vol. 211, pp. 108008-108008
Closed Access | Times Cited: 23
A novel framework to assess apple leaf nitrogen content: Fusion of hyperspectral reflectance and phenology information through deep learning
Riqiang Chen, Wenping Liu, Hao Yang, et al.
Computers and Electronics in Agriculture (2024) Vol. 219, pp. 108816-108816
Closed Access | Times Cited: 10
Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset
Fujiang Ji, Fa Li, Dalei Hao, et al.
New Phytologist (2024) Vol. 243, Iss. 1, pp. 111-131
Open Access | Times Cited: 10
Combining spectrum, thermal, and texture features using machine learning algorithms for wheat nitrogen nutrient index estimation and model transferability analysis
Shaohua Zhang, Jianzhao Duan, Xinghui Qi, et al.
Computers and Electronics in Agriculture (2024) Vol. 222, pp. 109022-109022
Closed Access | Times Cited: 10
A refined deep-learning-based algorithm for harmful-algal-bloom remote-sensing recognition using Noctiluca scintillans algal bloom as an example
Rongjie Liu, Binge Cui, Wenwen Dong, et al.
Journal of Hazardous Materials (2024) Vol. 467, pp. 133721-133721
Closed Access | Times Cited: 9
Calibration transfer of cross soluble solids content of different kiwifruit cultivars based on Two-stage TrAdaBoost.R2
Jiaxin Wang, Ye Yang, Sirui Li, et al.
Postharvest Biology and Technology (2024) Vol. 210, pp. 112783-112783
Closed Access | Times Cited: 8
Machine learning assisted remote forestry health assessment: a comprehensive state of the art review
Juan Sebastián Estrada, Andrés Fuentes, Pedro Reszka, et al.
Frontiers in Plant Science (2023) Vol. 14
Open Access | Times Cited: 16
Combing transfer learning with the OPtical TRApezoid Model (OPTRAM) to diagnosis small-scale field soil moisture from hyperspectral data
Ruiqi Du, Youzhen Xiang, Fucang Zhang, et al.
Agricultural Water Management (2024) Vol. 298, pp. 108856-108856
Open Access | Times Cited: 7
Tea yield estimation using UAV images and deep learning
Shumao Wang, Cui-Ping Yu, Ma JunHui, et al.
Industrial Crops and Products (2024) Vol. 212, pp. 118358-118358
Closed Access | Times Cited: 6
Correcting confounding canopy structure, biochemistry and soil background effects improves leaf area index estimates across diverse ecosystems from Sentinel-2 imagery
Liang Wan, Youngryel Ryu, Benjamin Dechant, et al.
Remote Sensing of Environment (2024) Vol. 309, pp. 114224-114224
Open Access | Times Cited: 6
Estimating leaf nitrogen content by coupling a nitrogen allocation model with canopy reflectance
Dong Li, Jing M. Chen, Yan Yan, et al.
Remote Sensing of Environment (2022) Vol. 283, pp. 113314-113314
Closed Access | Times Cited: 26
Maize Canopy and Leaf Chlorophyll Content Assessment from Leaf Spectral Reflectance: Estimation and Uncertainty Analysis across Growth Stages and Vertical Distribution
Hongye Yang, Bo Ming, Chenwei Nie, et al.
Remote Sensing (2022) Vol. 14, Iss. 9, pp. 2115-2115
Open Access | Times Cited: 23
Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods
Mingzheng Zhang, Tianen Chen, Xiaohe Gu, et al.
Frontiers in Plant Science (2023) Vol. 14
Open Access | Times Cited: 15
Incremental learning for crop growth parameters estimation and nitrogen diagnosis from hyperspectral data
Ruiqi Du, Junying Chen, Youzhen Xiang, et al.
Computers and Electronics in Agriculture (2023) Vol. 215, pp. 108356-108356
Closed Access | Times Cited: 15
Prior knowledge and active learning enable hybrid method for estimating leaf chlorophyll content from multi-scale canopy reflectance
Liang Wan, Yufei Liu, Yong He, et al.
Computers and Electronics in Agriculture (2023) Vol. 214, pp. 108308-108308
Closed Access | Times Cited: 13
Monitoring of Nitrogen Concentration in Soybean Leaves at Multiple Spatial Vertical Scales Based on Spectral Parameters
Tao Sun, Zhijun Li, Zhangkai Wang, et al.
Plants (2024) Vol. 13, Iss. 1, pp. 140-140
Open Access | Times Cited: 5
Image‐based crop disease detection using machine learning
Aria Dolatabadian, Ting Xiang Neik, Monica F. Danilevicz, et al.
Plant Pathology (2024)
Open Access | Times Cited: 5
Machine Learning and Deep Learning in Remote Sensing Data Analysis
Hankui K. Zhang, Shi Qiu, Ji Won Suh, et al.
Elsevier eBooks (2024)
Closed Access | Times Cited: 4
Large-scale mapping of plastic-mulched land from Sentinel-2 using an index-feature-spatial-attention fused deep learning model
Lizhen Lu, Yunci Xu, Xinyu Huang, et al.
Science of Remote Sensing (2025), pp. 100188-100188
Open Access
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