Research Publications

Alireza Sanaeifar, Ce Yang, Miguel de la Guardia, Wenkai Zhang, Xiaoli Li, Yong He, Proximal hyperspectral sensing of abiotic stresses in plants, Science of The Total Environment 861, 160652 (2023).

S. Md. Mansoor Roomi, B. Sathya Bama, V. Puvi Lakshmi, M. Vaishnavi, Hyperspectral dataset of pure and pesticide-coated apples for measuring the level of fertilizers used, Data in Brief 49, 109321 (2023).

Juncheng Ma, Binhui Liu, Lin Ji, Zhicheng Zhu, Yongfeng Wu, Weihua Jiao, Field-scale yield prediction of winter wheat under different irrigation regimes based on dynamic fusion of multimodal UAV imagery, International Journal of Applied Earth Observation and Geoinformation 118, 103292 (2023).

Irene Teixido-Orries, Francisco Molino, Ferran Gatius, Vicente Sanchis, Sonia Marín, Near-infrared hyperspectral imaging as a novel approach for T-2 and HT-2 toxins estimation in oat samples, Food Control 153, 109952 (2023).

Sai Xu, Huazhong Lu, Changxiang Fan, Guangjun Qiu, Christopher Ference, Xin Liang, Jian Peng, Visible and near-infrared hyperspectral imaging as an intelligent tool for parasite detection in sashimi, LWT 181, 114747 (2023).

Wijayanti Nurul Khotimah, Mohammed Bennamoun, Farid Boussaid, Lian Xu, David Edwards, Ferdous Sohel, MCE-ST: Classifying crop stress using hyperspectral data with a multiscale conformer encoder and spectral-based tokens, International Journal of Applied Earth Observation and Geoinformation 118, 103286 (2023).

Chiranjibi Poudyal, Hardev Sandhu, Yiannis Ampatzidis, Dennis Calvin Odero, Orlando Coto Arbelo, Ronald H. Cherry, Lucas Fideles Costa, Prediction of morpho-physiological traits in sugarcane using aerial imagery and machine learning, Smart Agricultural Technology 3, 100104 (2023).

Jean-Frédéric Guay, William Champagne-Cauchon, Valérie Fournier, and Conrad Cloutier, Wild host fruit–niche diversity of Drosophila suzukii in lowbush blueberry agroecosystems in Saguenay-Lac-Saint-Jean, Québec, Canada, The Canadian Entomologist 155(e2), 1-20 (2023).

Abdulridha J, Ampatzidis Y, Qureshi J, and Roberts P, Identification and Classification of Downy Mildew Severity Stages in Watermelon Utilizing Aerial and Ground Remote Sensing and Machine Learning , Frontiers in Plant Science , 20 May (2022).

Zongpeng Li , Zhen Chen , Qian Cheng , Fuyi Duan , Ruixiu Sui, Xiuqiao Huang, and Honggang Xu , UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat , Agronomy 12(1), 202 (2022).

Shubhangi Srivastava and Hari Niwas Mishra, Detection of insect damaged rice grains using visible and near infrared hyperspectral imaging technique, Chemometrics and Intelligent Laboratory Systems 221, 104489 (2022).

Lucas Costa, Jordan McBreen, Yiannis Ampatzidis, Jia Guo, Mostafa Reisi Gahrooei, Md Ali Babar , Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress environments for the purpose of stable yielding genotypes, Precision Agriculture 23, 622-642 (2022).

Shengnan Wang, Yong Tan, Chunyu Liu, Shaozhong Song, Zheng Li, Variety Classification and Identification of Soybean Seeds Based on Hyperspectral Imaging Method, Journal of Sensor Technology and Application 10(2), 177-186 (2022).

Wenyang Jia, Saskia van Ruth, Nigel Scollan, and Anastasios Koidis, Hyperspectral Imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends, Current Research in Food Science 5, 1017-1027 (2022).

Hongyu Liu, Fuheng Qu, Yong Yang, Wanting Li, and Zhonglin Hao, Soybean Variety Identification Based on Improved ResNet18 Hyperspectral Image, Journal of Physics: Conference Series 2284, 012017 (2022).

Xiang Yun, Chen Qijun, Su Zhongjing, Zhang Lu, Chen Zuohui, Zhou Guozhi, Yao Zhuping, Xuan Qi, and Cheng Yuan, Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation, Frontiers in Plant Science 13, 860656 (2022).

Hu Y, Wang Z, Li X, Li L, Wang X, Wei Y., Nondestructive Classification of Maize Moldy Seeds by Hyperspectral Imaging and Optimal Machine Learning Algorithms, Sensors 22(16), 6064 (2022).

Li, Zongpeng and Chen, Zhen and Cheng, Qian and Duan, Fuyi and Sui, Ruixiu and Huang, Xiuqiao and Xu, Honggang, UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat, Agronomy 12(1), 202 (2022).

Iost Filho, Fernando Henrique and de Bastos Pazini, Juliano and de Medeiros, André Dantas and Rosalen, David Luciano and Yamamoto, Pedro Takao, Assessment of Injury by Four Major Pests in Soybean Plants Using Hyperspectral Proximal Imaging, Agronomy 12(7), 1516 (2022).

Mónica Pineda and Matilde Barón , Health Status of Oilseed Rape Plants Grown under Potential Future Climatic Conditions Assessed by Invasive and Non-Invasive Techniques, Agronomy 12(8), 1845 (2022).

Myongkyoon Yang, Physiological Disorder Diagnosis of Plant Leaves Based on Full-Spectrum Hyperspectral Images with Convolutional Neural Network, Horticulturae 8(9), 854 (2022).

Eshkabilov, Sulaymon, John Stenger, Elizabeth N. Knutson, Erdem Küçüktopcu, Halis Simsek, and Chiwon W. Lee, Hyperspectral Image Data and Waveband Indexing Methods to Estimate Nutrient Concentration on Lettuce (Lactuca sativa L.) Cultivars, Sensors 22(21), 8158 (2022).

Sherry B. Hildreth, Evan S. Littleton, Leor C. Clark, Gabrielle C. Puller, Shihoko Kojima and Brenda S. J. Winkel, Mutations that alter Arabidopsis flavonoid metabolism affect the circadian clock, The Plant Journal , 15718 (2022).

Ali Missaoui, Sergio Bernardes, Holly Wright, Back in the numbers game: High throughput phenotyping of biomass yield in perennial forage crops with multiple harvests, North American Plant Phenotyping Network November 1, Preprint (2022).

Luís Guilherme Teixeira Crusiol, Liang Sun, Zheng Sun, Ruiqing Chen, Yongfeng Wu, Juncheng Ma, and Chenxi Song, In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data, Sustainablitity 14, 9039 (2022).

I. Tahmasbian, N.K. Morgan, S.H. Bai , M.W. Dunlop, and A.F. Moss, Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat, Remote Sens. 13, 1128 (2021).

W. Yang, T. Nigon, Z. Hao, G.D. Paiao, F.G. Fernandez, D. Mulla, and C. Yang, Estimation of corn yield based on hyperspectral imagery and convolutional neural network, Comp. Elec. in Ag. 184, 106092 (2021).

Z. Yang, J. Tian, K. Feng, X. Gong, and J. Liu, Application of a hyperspectral imaging system to quantify leaf-scale chlorophyll, nitrogen and chlorophyll fluorescence parameters in grapevine, Plant Phys. and Biochem. 166, 723 (2021).

Y. Zhang, J. Hui, Q. Qin, Y. Sun, T. Zhang, H. Sun, and M. Li, Transfer-learning-based approach for leaf chlorophyll content estimation of winter wheat from hyperspectral data, Remote Sens. Env. 267, 112724 (2021).

S. Yang, L. Hu, H. Wu, H. Ren, H. Qiao, P. Li, and W. Fan, Integration of Crop Growth Model and Random Forest for Winter Wheat Yield Estimation From UAV Hyperspectral Imagery, IEEE J. Selected Topics Appl. Earth Obs. Remote Sens. 14, 6253 (2021).

T.J. Nigon, et al., Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery, Remote Sens. 12, 12081234 (2020).

K. Zhu, et al., Remotely sensed canopy resistance model for analyzing the stomatal behavior of environmentally-stressed winter wheat, ISPRS J. Phot. Remote Sens. 168, 197 (2020).

Y. Wei, X. Li, X. Pan, and L. Li, Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms, Sensors 20, 6980 (2020).

M.A. Faqeerzada, M. Perez, S. Lohumi, H. Lee, G. Kim, C. Wakholi, R. Joshi, and B.K. Cho, Online Application of a Hyperspectral Imaging System for the Sorting of Adulterated Almonds, Applied Sciences 10, 6569 (2020).

María Belén Bainotti, Aplicación de técnicas de imágenes hiperespectrales en el infrarrojo cercano para la determinación de contaminación fungica y deoxinivalenol en trigo, Máster en Gestión e Innovación en la Industria Alimentaria , Master Thesis (2020).

Jaafar Abdulridha, Yiannis Ampatzidis, Jawwad Qureshi, and Pamela Roberts, Laboratory and UAV-Based Identification and Classification of Tomato Yellow Leaf Curl, Bacterial Spot, and Target Spot Diseases in Tomato Utilizing Hyperspectral Imaging and Machine Learning, Remote Sensing 12(17), 2732 (2020).

A. Moghimi, C. Yang, J.A. Anderson, Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat, eprint arXiv:1906.09666 , arXiv: 09666 (2019).

S. Yang, L. Hu, H. Wu, W. Fan, and H. Ren, Estimation Model of Winter Wheat Yield Based on Uav Hyperspectral Data, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium , 7212 (2019).

J. Zhang, Y. Huang, K.N. Reddy, and B. Wang, Assessing crop damage from dicamba on non-dicamba-tolerant soybean by hyperspectral imaging through machine learning, Pest Manag. Sci. 75, 3260 (2019).

X. Ji'An, C. HongXin, Y. YuWang, Z. WeiXin, W. Qian, X. Lei, G. DaoKuo, Z. WenYu, K. YaQi, and H. Bo, Detection of waterlogging stress based on hyperspectral images of oilseed rape leaves (Brassica napus L.), Computers and Electronics in Agriculture 159, 59 (2019).

Jaafar Naser Abdulridha, Yiannis Ampatzidis, Sri Charan Kakarla, and Pamela D. Roberts , Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques, Precision Agriculture 21, 955 - 978 (2019).

L. Ribeiro, A. Klock, J. Filho, M. Tramontin, A. Trapp, A. Mithofer, and C. Nansen, Hyperspectral imaging to characterize plant-plant communication in response to insect herbivory, Plant Methods 14:54, (2018).

P.W. Nugent, J.A. Shaw, P. Jha, B. Scherrer, A. Donelick, and V. Kumar, Discrimination of herbicide-resistant kochia with hyperspectral imaging, J. of Appl. Remote Sens. 12(1), 016037 (2018).

Y. Huang, M.A. Lee, V .K. Nandula, and K.N. Reddy, Hyperspectral Imaging for Differentiating Glyphosate-Resistant and Glyphosate-Susceptible Italian Ryegrass, Am. J. Plant Sci. 9, 1467 (2018).

M. Kanning, I. Kühling, D. Trautz, and T. Jarmer, High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction, Remote Sensing 10, 2000 (2018).

C. Nansen and M. R. Strand, Proximal Remote Sensing to Non-destructively Detect and Diagnose Physiological Responses by Host Insect Larvae to Parasitism, Front. Physiol. 9, 01716 (2018).

A. Moghimi, C. Yang, M.E. Miller, S.F. Kianian, and P.M. Marchetto, A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging, Front. Plant Sci. 24, 01182 (2018).

A. Moghimi, C. Yang, and P.M. Marchetto, Ensemble Feature Selection for Plant Phenotyping: A Journey From Hyperspectral to Multispectral Imaging, IEEE Access 6, 2872801 (2018).

S. Gutiérrez, J. Fernández-Novales, M.P. Diago and J. Tardaguila, On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties, Front. Plant Sci. 9, 01102 (2018).

S. Gutierrez, J. Tardaguila, J. Fernandez-Novales and M.P. Diago, On-the-go hyperspectral imaging for the in-field estimation of grape berry soluble solids and anthocyanin concentration, Aust. J. Grape Wine Res. 18, 173-182 (2018).

N. Vasquez a, C. Magan, J. Oblitas, T. Chuquizuta, H. Avila-George, and W. Castro, Comparison between artificial neural network and partial least squares regression models for hardness modeling during the ripening process of Swiss-type cheese using spectral profiles, J. Food E.ng. 219, 8-15 (2018).

W. Castro, J.M. Prieto, R. Guerra, T. Chuquizuta, W.T. Medina, B. Acevedo-Juarez, and H. Avila-George, Feasibility of using spectral profiles for modeling water activity in five varieties of white quinoa grains, J. Food Eng. 238, 95-102 (2018).

M. Morin, R. Lawrence, K. Repasky, T. Sterling, C. McCann, and S. Powell, Agreement analysis and spatial sensitivity of multispectral and hyperspectral sensors in detecting vegetation stress at , J. of Appl. Remote Sens. 11(4) , 046025 (2017).

C. McCann, K.S. Repasky, R. Lawrence, and S. Powell, Multi-temporal mesoscale hyperspectral data of mixed agricultural and grassland regions for anomaly detection, ISPRS Journal of Photogrammetry and Remote Sensing 131, 121 (2017).

Li X, Xu H, Feng L, Fu X, Zhang Y, Nansen C, Using proximal remote sensing in non-invasive phenotyping of invertebrates, PLoS One 12(5), e0176392 (2017).

M. Matzrafi, I. Herrmann, C. Nansen, T. Kliper, Y. Zait, T. Ignat, D. Siso, B. Rubin, A. Karnieli, and H. Eizenberg, Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri, Frontiers in Plant Science 8, 474 (2017).

V. Aredo, L. Velasquez, and R. Siche, Prediction of beef marbling using Hyperspectral Imagin (HSI) and Partial Least Squares Regression (PLSR), Scientia Agropecuaria 8, 169-174 (2017).

C. Nansen, K. Singh, A. Mian, B.J. Allison, C.W. Simmons, Using hyperspectral imaging to characterize consistency of coffee brands and their respective roasting classes, J. Food Eng. 190, 34-39 (2016).

C Nansen, G. Zhao, N. Dakin, C. Zhao, and S.R. Turner, Using hyperspectral imaging to determine germination of native Australian plant seeds, Journal of Photochemistry & Photobiology B 145, 19 (2015).

M.A. Lee, Y. Huang, V.K. Nandula, and K.N. Reddy, Differentiating glyphosate-resistant and glyphosate-sensitive Italian ryegrass using hyperspectral imagery, Proc. SPIE 9108, (2014).

K.N. Reddy, Y. Huang, M.A., Lee, V.K. Nandula, R.S. Fletcher, S.J. Thomson and F. Zhao, Glyphosate-resistant and glyphosate-susceptible Palmer amaranth (Amaranthus palmeri S. Wats.): hyperspectral reflectance, Pest Management Science , (2014).

C. Nansen, X. Zhang, N. Aryamanesh, and G. Yan, Use of variogram analysis to classify field peas with and without internal defects caused by weevil infestation, J. Food Eng. 123, 17 (2014).

P. Wilcox, T.M. Horton, E.Youn, M.K. Jeong, D. Tate, T. Herman, and C. Nansen, Evolutionary refinement approaches for band selection of hyperspectral images with applications to automatic monitoring , Intelligent Data Analysis 18, 25 (2014).

C.I. Zhao, B. Qi, and C. Nansen, Use of local fuzzy variance to extract the scattered regions of spatial stray light influence in hyperspectral images, Optik 124, 6696 (2013).

C. Nansen, A.J. Sidumo, X. Martini, K. Stefanova, and J.D. Roberts, Reflectance-based assessment of spider mite "bio-response" to maize leaves and plant potassium content in different irri, Computers and Electronics in Agriculture 97, 21 (2013).

C. Nansen, Use of Variogram Parameters in Analysis of Hyperspectral Imaging Data Acquired from Dual-Stressed Crop Leaves, Remote Sensing 4, 180 (2012).

C. Nansen, S. Prager, B. Qi, X. Martini, M. Lewis, and K. Vaugn, Using Hyperspectral Imaging in ZC Research, Proc. 11th Annual SCRI Zebra Chip Reporting Session , 70 (2011).

C. Nansen, T. Herrman, and R. Swanson, Machine Vision Detection of Bonemeal in Animal Feed Samples, Applied Spectroscopy 64, 637 (2010).

C. Nansen, A.J. Sidumo, and S. Capareda, Vairogram analysis of hyperspectral data to characterize the impact of biotic and abiotic stress of maize plans and to e, Applied Spectroscopy 64, 6 (2010).

S. Jay, R. Lawrence, K. Repasky, and C. Keith, Invasive species mapping using low cost hyperspectral imagery, ASPRS Annual Conference, Baltimore MD , (2009).

C. Nansen, T. Macedo, R. Swanson, and D.K. Weaver, Use of spatial structure analysis of hyperspectral data cubes for detection of insect-induced stress in wheat plants, International Journal of Remote Sensing 30, 2447 (2009).

C. Nansen, M. Kolomeits, and X. Gao, Considerations regarding the use of hyperspectral imaging data in classifications of food products, exemplified by analy, J. of Ag. and Food Chem. 14, 2933 (2008).

Ting Zhang, Bo Tian, Yujue Wang, Dongyan Liu, Yunxuan Zhou, Daphne van der Wal, Mapping depth-integrated microphytobenthic biomass on an estuarine tidal flat using Sentinel satellite data, International Journal of Applied Earth Observation and Geoinformation 122, 103417 (2023).

Sureka Thiruchittampalam, Simit Raval, Nancy Glenn, and Furqan Le-Hussain, Indirect remote sensing techniques for long term monitoring of CO2 leakage in geological carbon sequestration: A review, Journal of Natural Gas Science and Engineering 100, 104488 (2022).

Zhou, Quan, Linfeng Yu, Xudong Zhang, Yujie Liu, Zhongyi Zhan, Lili Ren, and Youqing Luo, Fusion of UAV Hyperspectral Imaging and LiDAR for the Early Detection of EAB Stress in Ash and a New EAB Detection Index—NDVI(776,678), Remote Sensing 14 no. 10 , 2428 (2022).

Mohamed Karim El Oufir, Karem Chokmani , Anas El Alem, and Monique Bernier, Using Ensemble-Based Systems with Near-Infrared Hyperspectral Data to Estimate Seasonal Snowpack Density, Remote Sensing 14 no. 5, 1089 (2022).

R Ganesh Babu and C Chellaswamy, Different stages of disease detection in squash plant based on machine learning, Journal of Biosciences 47, 9 (2022).

Christopher Donahue, S. McKenzie Skiles, and Kevin Hammonds, Mapping liquid water content in snow at the millimeter scale: an intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements, The Cryosphere 16, 43-59 (2022).

Quanzhou Yu, Robert A. Mickler, Tianquan Liang, Yujie Liu, Jie Jiang, Kaishan Song, and Shaoqiang Wang, Hyperspectral differences between sunlit and shaded leaves in a Manchurian ash canopy in Northeast China, Remote Sensing Letters 13:8, 800-811 (2022).

Bowen Niu, Quanlong Feng, Boan Chen, Cong Ou, Yiming Liu, Jianyu Yanga, HSI-TransUNet: A transformer based semantic segmentation model for crop mapping from UAV hyperspectral imagery, Computers and Electronics in Agriculture 201, 107297 (2022).

Costa, L., McBreen, J., Ampatzidis, Y. et al., Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress environments for the purpose of stable yielding genotypes, Precision Agriculture 23, 622-642 (2022).

Zhou Quan, Linfeng Yu, Xudong Zhang, Yujie Liu, Zhongyi Zhan, Lili Ren, and Youqing Luo, Fusion of UAV Hyperspectral Imaging and LiDAR for the Early Detection of EAB Stress in Ash and a New EAB Detection Index—NDVI(776,678), Remote Sensing 14(10), 2428 (2022).

J. Abdulridha, O. Batuman, and Y. Ampatzidis, UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning, Remote Sensing 11, 1373 (2019).

C. Zhihu, T. Qiangqian, Z. Zeying, and Y. Yanbin, Research on hyperspectral remote sensing image acquisition and processing technology based on uav, Journal of Guizhou Normal University (Natural Sciences) 37, 52-57 (2019).

B. Scherrer, J. Sheppard, P. Jha, J.A. Shaw, Hyperspectral imaging and neural networks to classify herbicide-resistant weeds, J. of Appl. Remote Sens. 13(4), 044516 (2019).

R. Hruska, J. Mitchell, M. Anderson and N.F. Glenn, Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle, Remote Sensing 4, 2736 (2012).

M.C.L. Patterson and A. Brescia, Operation of small sensor payloads on tactical sized unmanned air vehicles, Aeronautical Journal 114, 427 (2010).

R.C. Swanson, M.R. Kehoe, C.W. Smith, T.S. Moon, R. Bousquet, S.W. Brown, K.R. Lykke, P. Maciejewski, and K. Barnard, Compact Anamorphic Imaging Spectrometer, 2007 Meeting of the Military Sensing Symposia (MSS) Speciality Group On Camouflage, Concealment & Deception 1, (2007).

Jonathan Teague, David A. Megson-Smith, Michael J. Allen, John C.C. Day, and Thomas B. Scott , A Review of Current and New Optical Techniques for Coral Monitoring, Oceans 3(1), 30-45 (2022).

Francisco Rodríguez Lorenzo, Miguel Placer Lorenzo, Luz Herrero Castilla, Juan Antonio Álvarez Rodríguez, Sandra Iglesias, Santiago Gómez, Juan Manuel Fernández Montenegro, Estel Rueda, Rubén Diez-Montero, Joan Garcia and Eva Gonzalez-Flo, Monitoring PHB production in Synechocystis sp. with hyperspectral images, Sater Science and Technology 86 No1, 211 (2022).

Christopher Donahue and Kevin Hammonds , Laboratory Observations of Preferential Flow Paths in Snow Using Upward-Looking Polarimetric Radar and Hyperspectral Imaging, Remote Sensing 14(10), 2297 (2022).

Joseph Michael Odhiambo, Dr. Mgala Mvurya, Dr. Anthony Luvanda, Dr. Fullgence Mwakondo, Deep Learning Algorithm for Identifying Microplastics in Open Sewer Systems: A Systematic Review, The International Journal of Engineering and Science 11-5, 11-18 (2022).

Liang Xinlian, Kukko Antero, Balenovic Ivan, Saarinen Ninni, Junttila Samuli, Kankare Ville, Holopainen Markus, Mokroš Martin, Surový Peter, Kaartinen Luka, Honkavaara Eija, Näsi Jingbin, Hollaus Markus, Tian Jiaojiao, Yu Xiaowei, Pan Jie, Cai Shangshu, Virtanen Juho-Pekk, Wang Yunsheng, Hyyppä Juha, Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions, IEEE Geoscience and Remote Sensing Magazine 10-3, 32-71 (2022).

Hao Zhong, Wenshu Lin, Haoran Liu, Nan Ma, Kangkang Liu, Rongzhen Cao, Tiantian Wang, and Zhengzhao Ren, Identification of tree species based on the fusion of UAV hyperspectral image and LiDAR data in a coniferous and broad-leaved mixed forest in Northeast China, Frontiers in Plant Science 13, 1-17 (2022).

Yu R, Huo L, Huang H, Yuan Y, Gao B, Liu Y, Yu L, Li H, Yang L, Ren L, Luo Y, Early detection of pine wilt disease tree candidates using time-series of spectral signatures, Frontiers in Plant Science 13, 1000093 (2022).

R. Yu, Y. Luo, Q. Zhou, X. Zhang, D. Wu, and L. Ren, A machine learning algorithm to detect pine wilt disease using UAV-based hyperspectral imagery and LiDAR data at the tree level , Int. J. Appl. Ear. Obs. Geo. 101, 102363 (2021).

H. Qin, W. Zhou, Y. Yao, and W. Wang, Estimating Aboveground Carbon Stock at the Scale of Individual Trees in Subtropical Forests Using UAV LiDAR and Hyperspectral Data, Rem. Sens. 13, 4969 (2021).

Chunmao Zhu, Yugo Kanaya, Masashi Tsuchiya, Ryota Nakajima, Hidetaka Nomaki, Tomo Kitahashi, Katsunori Fujikura, Optimization of a hyperspectral imaging system for rapid detection of microplastics down to 100 µm, MethodsX 8, 101175 (2021).

Run Yu, Lili Ren, and Youqing Luo, Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery, Forest Ecosystems 8, 44 (2021).

Run Yu, Youqing Luo, Haonan Li, Liyuan Yang, Huaguo Huang, Linfeng Yu, and Lili Ren, Three-Dimensional Convolutional Neural Network Model for Early Detection of PineWilt Disease Using UAV-Based Hyperspectral Images, Remote Sensing 13(20), 4065 (2021).

W. Pi, Y. Bi, J. Du, H. Yang, X Zhang, and Y. Kang, Classification of Grassland Desertification in China Based on Vis-NIR UAV Hyperspectral Remote Sensing, Spectroscopy Online 35, 39-50 , (2020).

C. Donahue, S.M. Skiles, and K. Hammonds, In situ effective snow grain size mapping using a compact hyperspectral imager, Journal of Glaciology 68, 1-9 (2020).

J. Lekki, S. Ruberg, C. Binding, R. Anderson, and A. VanderWoude, Airborne hyperspectral and satellite imaging of harmful algal blooms in the Great Lakes Region: Successes in sensing algal blooms, J. Great Lakes Research 45, 405 (2019).

Ricardo Flores & Hector Loro, Analysis of contamination of soils by petroleum hydrocarbons using hyperspectral images in the NIR, XVIII Meeting of physics , (2019).

N. Scott and I. Moore, Nonnegative matrix factorization based feature selection analysis for hyperspectral imagery of sediment-laden riverine flow, Proc. SPIE 10631, 2301273 (2018).

A. Chennu, P. Farber, N. Volkenborn, M.A.A. Al-Najjar, F. Janssen, D. deBeer, and L. Polerecky, Hyperspectral imaging of the microscale distribution and dynamics of microphytobenthos in intertidal sediments, Limnol. Oceanogr.: Methods 11, 511 (2013).

L.H. Spangler et al, A shallow subsurface controlled release facility in Bozeman, Montana, USA, for testing near surface CO2 detection techni, Env. Earth Sci. 60, 227 (2010).

John Adler, A. Behar, & N. Jacobson, Airborne Hyperspectral Imaging of Supraglacial Lakes in Greenland's Ablation Zone, AGU Fall Meeting Abstracts , (2010).

CJ Keith, KS Repasky, RL Lawrence, SC Jay, JL Carlsten, Monitoring effects of a controlled subsurface carbon dioxide release on vegetation using a hyperspectral imager, Int. J. of Greenhouse Gas Control 3, 626 (2009).