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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:

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy
Jibo Yue, Haikuan Feng, Guijun Yang, et al.
Remote Sensing (2018) Vol. 10, Iss. 1, pp. 66-66
Open Access | Times Cited: 167

Showing 1-25 of 167 citing articles:

Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture
Bing Lu, Phuong D. Dao, Jiangui Liu, et al.
Remote Sensing (2020) Vol. 12, Iss. 16, pp. 2659-2659
Open Access | Times Cited: 724
Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data
Liang Han, Guijun Yang, Huayang Dai, et al.
Plant Methods (2019) Vol. 15, Iss. 1
Open Access | Times Cited: 357
Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices
Jibo Yue, Guijun Yang, Qingjiu Tian, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2019) Vol. 150, pp. 226-244
Closed Access | Times Cited: 326
Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation
Songyang Li, Fei Yuan, Syed Tahir Ata-Ul-Karim, et al.
Remote Sensing (2019) Vol. 11, Iss. 15, pp. 1763-1763
Open Access | Times Cited: 181
Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system
Ning Lu, Jie Zhou, Zixu Han, et al.
Plant Methods (2019) Vol. 15, Iss. 1
Open Access | Times Cited: 178
A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone
Niko Viljanen, Eija Honkavaara, Roope Näsi, et al.
Agriculture (2018) Vol. 8, Iss. 5, pp. 70-70
Open Access | Times Cited: 171
Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data
Huilin Tao, Haikuan Feng, Liangji Xu, et al.
Sensors (2020) Vol. 20, Iss. 5, pp. 1296-1296
Open Access | Times Cited: 138
Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region
Xiangyu Ge, Jianli Ding, Xiuliang Jin, et al.
Remote Sensing (2021) Vol. 13, Iss. 8, pp. 1562-1562
Open Access | Times Cited: 103
Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height
Yang Liu, Haikuan Feng, Jibo Yue, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 70
Estimating potato above-ground biomass by using integrated unmanned aerial system-based optical, structural, and textural canopy measurements
Yang Liu, Haikuan Feng, Jibo Yue, et al.
Computers and Electronics in Agriculture (2023) Vol. 213, pp. 108229-108229
Closed Access | Times Cited: 60
Improved potato AGB estimates based on UAV RGB and hyperspectral images
Yang Liu, Haikuan Feng, Jibo Yue, et al.
Computers and Electronics in Agriculture (2023) Vol. 214, pp. 108260-108260
Closed Access | Times Cited: 60
Estimating vertically growing crop above-ground biomass based on UAV remote sensing
Jibo Yue, Hao Yang, Guijun Yang, et al.
Computers and Electronics in Agriculture (2023) Vol. 205, pp. 107627-107627
Closed Access | Times Cited: 58
Estimating potato above-ground biomass based on vegetation indices and texture features constructed from sensitive bands of UAV hyperspectral imagery
Yang Liu, Yiguang Fan, Haikuan Feng, et al.
Computers and Electronics in Agriculture (2024) Vol. 220, pp. 108918-108918
Closed Access | Times Cited: 51
Monitoring of soil heavy metals based on hyperspectral remote sensing: A review
Yulong Wang, Bin Zou, Liyuan Chai, et al.
Earth-Science Reviews (2024) Vol. 254, pp. 104814-104814
Closed Access | Times Cited: 16
A Comparison of Crop Parameters Estimation Using Images from UAV-Mounted Snapshot Hyperspectral Sensor and High-Definition Digital Camera
Jibo Yue, Haikuan Feng, Xiuliang Jin, et al.
Remote Sensing (2018) Vol. 10, Iss. 7, pp. 1138-1138
Open Access | Times Cited: 156
Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features
Roope Näsi, Niko Viljanen, Jere Kaivosoja, et al.
Remote Sensing (2018) Vol. 10, Iss. 7, pp. 1082-1082
Open Access | Times Cited: 146
Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques
Ahmed Kayad, Marco Sozzi, Simone Gatto, et al.
Remote Sensing (2019) Vol. 11, Iss. 23, pp. 2873-2873
Open Access | Times Cited: 136
Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images
Huilin Tao, Haikuan Feng, Liangji Xu, et al.
Sensors (2020) Vol. 20, Iss. 4, pp. 1231-1231
Open Access | Times Cited: 102
Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data
Lin Chen, Yeqiao Wang, Chunying Ren, et al.
Remote Sensing (2019) Vol. 11, Iss. 4, pp. 414-414
Open Access | Times Cited: 99
UAS-Based Plant Phenotyping for Research and Breeding Applications
Wei Guo, Matthew E. Carroll, Arti Singh, et al.
Plant Phenomics (2021) Vol. 2021
Open Access | Times Cited: 95
Energy Consumption Prediction Using Machine Learning; A Review
Amir Mosavi, Abdullah Bahmani
(2019)
Open Access | Times Cited: 80
Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.)
Yishan Ji, Zhen Chen, Qian Cheng, et al.
Plant Methods (2022) Vol. 18, Iss. 1
Open Access | Times Cited: 66
A Comparative Estimation of Maize Leaf Water Content Using Machine Learning Techniques and Unmanned Aerial Vehicle (UAV)-Based Proximal and Remotely Sensed Data
Helen S. Ndlovu, John Odindi, Mbulisi Sibanda, et al.
Remote Sensing (2021) Vol. 13, Iss. 20, pp. 4091-4091
Open Access | Times Cited: 61
Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices
Haibo Yang, Fei Li, Wei Wang, et al.
Remote Sensing (2021) Vol. 13, Iss. 12, pp. 2339-2339
Open Access | Times Cited: 58
Estimation of Above-Ground Biomass of Winter Wheat Based on Consumer-Grade Multi-Spectral UAV
Falv Wang, Mao Yang, Longfei Ma, et al.
Remote Sensing (2022) Vol. 14, Iss. 5, pp. 1251-1251
Open Access | Times Cited: 57
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