Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture

Weiss, 2020, Remote sensing for agricultural applications: A meta-review, Remote Sens. Environ., 236, 111402, 10.1016/j.rse.2019.111402

Liu, 2005, Variability of seasonal CASI image data products and potential application for management zone delineation for precision agriculture, Can. J. Remote Sens., 31, 400, 10.5589/m05-023

Jensen, J.R. (2006). Remote Sensing of the Environment: An Earth Resource Perspective, Prentice Hall.

Sahoo, 2015, Hyperspectral remote sensing of agriculture, Curr. Sci., 108, 848

Alonso, 1991, Comparing two methodologies for crop area estimation in Spain using Landsat TM images and ground-gathered data, Remote Sens. Environ., 35, 29, 10.1016/0034-4257(91)90063-C

McNairn, 2009, Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories, ISPRS J. Photogramm., 64, 434, 10.1016/j.isprsjprs.2008.07.006

Shoshany, 2013, Monitoring of agricultural soil degradation by remote-sensing methods: A review, Int. J. Remote Sens., 34, 6152, 10.1080/01431161.2013.793872

Hunt, 2018, What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?, Int. J. Remote Sens., 39, 5345, 10.1080/01431161.2017.1410300

Thenkabail, 2003, Biophysical and yield information for precision farming from near-real-time and historical Landsat TM images, Int. J. Remote Sens., 24, 2879, 10.1080/01431160710155974

Shang, 2015, Mapping spatial variability of crop growth conditions using RapidEye data in Northern Ontario, Canada, Remote Sens. Environ., 168, 113, 10.1016/j.rse.2015.06.024

Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J. (2017). Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sens., 9.

Lucieer, 2014, HyperUAS-imaging spectroscopy from a multirotor unmanned aircraft system, J. Field Robot., 31, 571, 10.1002/rob.21508

Hernandez, 2015, Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping, Remote Sens., 7, 13586, 10.3390/rs71013586

Lee, 2004, Hyperspectral versus multispectral data for estimating leaf area index in four different biomes, Remote Sens. Environ., 91, 508, 10.1016/j.rse.2004.04.010

Mariotto, 2013, Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission, Remote Sens. Environ., 139, 291, 10.1016/j.rse.2013.08.002

Marshall, 2015, Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation, ISPRS J. Photogramm., 108, 205, 10.1016/j.isprsjprs.2015.08.001

Sun, J., Yang, J., Shi, S., Chen, B., Du, L., Gong, W., and Song, S. (2017). Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance. Remote Sens., 9.

Darvishzadeh, 2012, Inversion of a radiative transfer model for estimation of rice canopy chlorophyll content using a lookup-table approach, IEEE J.-STARS, 5, 1222

Hruska, 2012, Radiometric and geometric analysis of hyperspectral imagery acquired from an unmanned aerial vehicle, Remote Sens., 4, 2736, 10.3390/rs4092736

Transon, J., d’Andrimont, R., Maugnard, A., and Defourny, P. (2018). Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context. Remote Sens., 10.

Lodhi, 2019, Hyperspectral Imaging System: Development Aspects and Recent Trends, Sens. Imaging, 20, 1, 10.1007/s11220-019-0257-8

Hatfield, 2010, Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices, Remote Sens., 2, 562, 10.3390/rs2020562

Zhang, 2013, Fusion of remotely sensed data from airborne and ground-based sensors to enhance detection of cotton plants, Comput. Electron. Agric., 93, 55, 10.1016/j.compag.2013.02.001

Mahajan, 2017, Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing, Precis. Agric., 18, 736, 10.1007/s11119-016-9485-2

Driggers, 2016, A compact combined hyperspectral and polarimetric imager, Proceedings of the Society of Photo-Optical Instrumentation Engineers, Volume 6395, 44

Suarez, 2013, Spatial resolution effects on chlorophyll fluorescence retrieval in a heterogeneous canopy using hyperspectral imagery and radiative transfer simulation, IEEE Geosci. Remote Soc., 10, 937, 10.1109/LGRS.2013.2252877

Lu, 2019, Comparing the Performance of Multispectral and Hyperspectral Images for Estimating Vegetation Properties, IEEE J. STARS, 12, 1784

(2020, August 03). ISS Utilization: MUSES-DESIS (Multi-User System for Earth Sensing) with DESIS instrument. Available online: https://directory.eoportal.org/web/eoportal/satellite-missions/content/-/article/iss-muses.

(2020, August 03). PRISMA (Hyperspectral Precursor and Application Mission). Available online: https://directory.eoportal.org/web/eoportal/satellite-missions/p/prisma-hyperspectral#launch.

(2019, November 10). Satellite Missions Database. Available online: https://directory.eoportal.org/web/eoportal/satellite-missions.

(2020, August 03). EnMAP (Environmental Monitoring and Analysis Program). Available online: https://directory.eoportal.org/web/eoportal/satellite-missions/e/enmap.

Mitchell, J.J., Glenn, N.F., Anderson, M.O., Hruska, R.C., Halford, A., Baun, C., and Nydegger, N. (2012, January 4–7). Unmanned Aerial Vehicle (UAV) hyperspectral remote sensing for dryland vegetation monitoring. Proceedings of the 2012 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Shanghai, China.

Catalina, 2013, Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV), Agric. Forest Meteorol., 171, 281

Copenhaver, 2008, Use of spectral vegetation indices derived from airborne hyperspectral imagery for detection of European corn borer infestation in Iowa corn plots, J. Econ. Entomol., 101, 1614, 10.1093/jee/101.5.1614

Ryu, 2011, Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing, Field Crops Res., 122, 214, 10.1016/j.fcr.2011.03.013

Lu, B., and He, Y. (2019). Evaluating Empirical Regression, Machine Learning, and Radiative Transfer Modelling for Estimating Vegetation Chlorophyll Content Using Bi-Seasonal Hyperspectral Images. Remote Sens., 11.

Yu, 2017, Radiative transfer models (RTMs) for field phenotyping inversion of rice based on UAV hyperspectral remote sensing, Int. J. Agric. Biol. Eng., 10, 150

Teke, M., Deveci, H.S., Haliloglu, O., Gurbuz, S.Z., and Sakarya, U. (2013, January 12–14). A short survey of hyperspectral remote sensing applications in agriculture. Proceedings of the 2013 6th International Conference on Recent Advances in Space Technologies (RAST), Istanbul, Turkey.

Dale, 2013, Hyperspectral Imaging Applications in Agriculture and Agro-Food Product Quality and Safety Control: A Review, Appl. Spectrosc. Rev., 48, 142, 10.1080/05704928.2012.705800

(2020, August 03). Tiangong/Shenzhou: China’s Human Spaceflight Program/Tianzhou Cargo Spaceship. Available online: https://directory.eoportal.org/web/eoportal/satellite-missions/t/tiangong.

Apan, 2004, Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery, Int. J. Remote Sens., 25, 489, 10.1080/01431160310001618031

Dutta, 2006, Disease detection in mustard crop using eo-1 hyperion satellite data, J. Indian Soc. Remote, 34, 325, 10.1007/BF02990661

Moharana, 2016, Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery, ISPRS J. Photogramm., 122, 17, 10.1016/j.isprsjprs.2016.09.002

Thenkabail, 2013, Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion/EO-1 Data, IEEE J. STARS, 6, 427

Wu, 2010, An evaluation of EO-1 hyperspectral Hyperion data for chlorophyll content and leaf area index estimation, Int. J. Remote Sens., 31, 1079, 10.1080/01431160903252335

Bannari, 2015, Spatial Variability Mapping of Crop Residue Using Hyperion (EO-1) Hyperspectral Data, Remote Sens., 7, 8107, 10.3390/rs70608107

Galloza, M.S., and Crawford, M. (2011, January 24–29). Exploiting multisensor spectral data to improve crop residue cover estimates for management of agricultural water quality. Proceedings of the IEEE Geoscience and Remote Sensing Society Symposium, Vancouver, BC, Canada.

2016, A comparative study of target detection algorithms in hyperspectral imagery applied to agricultural crops in Colombia, Revista Tecnura, 20, 86, 10.14483/udistrital.jour.tecnura.2016.3.a06

Gomez, 2008, Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study, Geoderma, 146, 403, 10.1016/j.geoderma.2008.06.011

Zhang, 2013, Estimation of agricultural soil properties with imaging and laboratory spectroscopy, J. Appl. Remote Sens., 7, 73587, 10.1117/1.JRS.7.073587

Bostan, S., Ortak, M.A., Tuna, C., Akoguz, A., Sertel, E., and Ustundag, B.B. (2016, January 18–20). Comparison of classification accuracy of co-located hyperspectral & multispectral images for agricultural purposes. Proceedings of the 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Tianjin, China.

Lodhi, 2018, Hyperspectral Imaging for Earth Observation: Platforms and Instruments, J. Indian Inst. Sci., 98, 429, 10.1007/s41745-018-0070-8

Aasen, 2018, Multi-temporal high-resolution imaging spectroscopy with hyperspectral 2D imagers - From theory to application, Remote Sens. Environ., 205, 374, 10.1016/j.rse.2017.10.043

Jia, X., Li, S., Ke, S., and Hu, B. (2019, January 28–30). Overview of spaceborne hyperspectral imagers and the research progress in bathymetric maps. Proceedings of the Second Target Recognition and Artificial Intelligence Summit Forum. International Society for Optics and Photonics, Shenyang, China.

(2020, May 08). Headwall Hyperspectral Sensors. Available online: https://www.headwallphotonics.com/hyperspectral-sensors.

Pullanagari, R.R., Kereszturi, G., and Yule, I. (2018). Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression. Remote Sens., 10.

Verger, 2011, Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS/PROBA observations, Remote Sens. Environ., 115, 415, 10.1016/j.rse.2010.09.012

Antony, 2011, Discrimination of wheat crop stage using CHRIS/PROBA multi-angle narrowband data, Remote Sens. Lett., 2, 71, 10.1080/01431161.2010.493184

Casa, 2013, A comparison of sensor resolution and calibration strategies for soil texture estimation from hyperspectral remote sensing, Geoderma, 197, 17, 10.1016/j.geoderma.2012.12.016

Qian, 2015, Hyperspectral Imager Onboard Indian Mini Satellite-1, Optical Payloads for Space Missions, Volume 6, 141

(2020, March 31). IMS-1 (Indian Microsatellite-1). Available online: https://directory.eoportal.org/web/eoportal/satellite-missions/i/ims-1.

Raval, 2014, Hyperspectral Imaging: A Paradigm in Remote Sensing, CSI Commun., 7, 7

Khobragade, A.N., and Raghuwanshi, M.M. (2015). Contextual Soft Classification Approaches for Crops Identification Using Multi-sensory Remote Sensing Data: Machine Learning Perspective for Satellite Images. Artificial Intelligence Perspectives and Applications, Springer.

(2020, April 01). Hyperspectral Imager for the Coastal Ocean. Available online: http://hico.coas.oregonstate.edu/.

Krutz, D., Müller, R., Knodt, U., Günther, B., Walter, I., Sebastian, I., Säuberlich, T., Reulke, R., Carmona, E., and Eckardt, A. (2019). The Instrument Design of the DLR Earth SensingImaging Spectrometer (DESIS). Sensors, 19.

(2020, April 01). ISS Utilization: HISUI (Hyperspectral Imager Suite). Available online: https://eoportal.org/web/eoportal/satellite-missions/content/-/article/iss-utilization-hisui-hyperspectral-imager-suite-#launch.

Pignatti, S., Palombo, A., Pascucci, S., Romano, F., Santini, F., Simoniello, T., Umberto, A., Vincenzo, C., Acito, N., and Diani, M. (2013, January 21–26). The PRISMA hyperspectral mission: Science activities and opportunities for agriculture and land monitoring. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS, Melbourne, VIC, Australia.

(2019, December 01). EnMap Hyperspectral Imager. Available online: http://www.enmap.org/index.html.

Qian, S.E. (2015). SHALOM—A Commercial Hyperspectral Space Mission. Optical Payloads for Space Missions, John Wiley & Sons, Ltd.

Thenkabail, P.S., Lyon, J.G., and Huete, A. (2018). The Use of Hyperspectral Earth Observation Data for Land Use/Cover Classification: Present Status, Challenges, and Future Outlook. Hyperspectral Remote Sensing of Vegetation, CRC Press. [2nd ed.].

(2020, August 01). HyspIRI Mission Study, Available online: https://hyspiri.jpl.nasa.gov/.

Malec, 2015, Capability of Spaceborne Hyperspectral EnMAP Mission for Mapping Fractional Cover for Soil Erosion Modeling, Remote Sens., 7, 11776, 10.3390/rs70911776

Siegmann, 2015, The Potential of Pan-Sharpened EnMAP Data for the Assessment of Wheat LAI, Remote Sens., 7, 12737, 10.3390/rs71012737

Locherer, 2015, Retrieval of Seasonal Leaf Area Index from Simulated EnMAP Data through Optimized LUT-Based Inversion of the PROSAIL Model, Remote Sens., 7, 10321, 10.3390/rs70810321

Bachmann, 2015, Estimating the Influence of Spectral and Radiometric Calibration Uncertainties on EnMAP Data Products—Examples for Ground Reflectance Retrieval and Vegetation Indices, Remote Sens., 7, 10689, 10.3390/rs70810689

Castaldi, 2016, Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon, Remote Sens. Environ., 179, 54, 10.1016/j.rse.2016.03.025

Castaldi, 2015, Reducing the Influence of Soil Moisture on the Estimation of Clay from Hyperspectral Data: A Case Study Using Simulated PRISMA Data, Remote Sens., 7, 15561, 10.3390/rs71115561

Ghasrodashti, E., Karami, A., Heylen, R., and Scheunders, P. (2017). Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation. Remote Sens., 9.

Yang, J., Li, Y., Chan, J., and Shen, Q. (2017). Image Fusion for Spatial Enhancement of Hyperspectral Image via Pixel Group Based Non-Local Sparse Representation. Remote Sens., 9.

Zhao, 2014, Hyperspectral Imagery Super-Resolution by Spatial-Spectral Joint Nonlocal Similarity, IEEE J. STARS, 7, 2671

Loncan, 2015, Hyperspectral pansharpening: A review, IEEE Geosci. Remote Sens. Mag., 3, 27, 10.1109/MGRS.2015.2440094

Asner, 2003, Imaging spectroscopy for desertification studies: Comparing aviris and eo-1 hyperion in argentina drylands, IEEE Trans. Geosci. Remote, 41, 1283, 10.1109/TGRS.2003.812903

Weng, 2010, A Spectral Index for Estimating Soil Salinity in the Yellow River Delta Region of China Using EO-1 Hyperion Data, Pedosphere, 20, 378, 10.1016/S1002-0160(10)60027-6

Mulla, 2013, Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps, Biosyst. Eng., 114, 358, 10.1016/j.biosystemseng.2012.08.009

Jacquemoud, 1995, Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors, Remote Sens. Environ., 52, 163, 10.1016/0034-4257(95)00018-V

Gat, N., Erives, H., Fitzgerald, G.J., Kaffka, S.R., and Maas, S.J. (2000). Estimating sugar beet yield using AVIRIS-derived indices. Summaries of the 9th JPL Airborne Earth Science Workshop. Unpaginated CD, Jet Propulsion Laboratory.

Estep, 2004, Crop stress detection using AVIRIS hyperspectral imagery and artificial neural networks, Int. J. Remote Sens., 25, 4999, 10.1080/01431160412331291242

Cheng, 2008, Water content estimation from hyperspectral images and MODIS indexes in Southeastern Arizona, Remote Sens. Environ., 112, 363, 10.1016/j.rse.2007.01.023

Ustin, 1998, Remote Sensing of Soil Properties in the Santa Monica Mountains I. Spectral Analysis, Remote Sens. Environ., 65, 170, 10.1016/S0034-4257(98)00024-8

Gat, N., Erives, H., Maas, S.J., and Fitzgerald, G.J. (1999). Application of low altitude AVIRIS imagery of agricultural fields in the San Joaquin Valley, CA, to precision farming. The 8th JPL Airborne Earth Science Workshop, Academia. Available online: https://www.researchgate.net/publication/2434575_Application_Of_Low_Altitude_Aviris_Imagery_Of_Agricultural_Fields_In_The_San_Joaquin_Valley_Ca_To_Precision_Farming.

Nigam, 2019, Crop type discrimination and health assessment using hyperspectral imaging, Curr. Sci., 116, 1108, 10.18520/cs/v116/i7/1108-1123

Shivers, 2019, Using paired thermal and hyperspectral aerial imagery to quantify land surface temperature variability and assess crop stress within California orchards, Remote Sens. Environ., 222, 215, 10.1016/j.rse.2018.12.030

Ran, 2015, Hyperspectral image classification for mapping agricultural tillage practices, J. Appl. Remote Sens., 9, 97298, 10.1117/1.JRS.9.097298

Shivers, S.W., Roberts, D.A., McFadden, J.P., and Tague, C. (2018). Using Imaging Spectrometry to Study Changes in Crop Area in California’s Central Valley during Drought. Remote Sens., 10.

Haboudane, 2002, Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture, Remote Sens. Environ., 81, 416, 10.1016/S0034-4257(02)00018-4

Liu, 2008, Crop fraction estimation from casi hyperspectral data using linear spectral unmixing and vegetation indices, Can. J. Remote Sens., 34, S124, 10.5589/m07-062

Goel, 2003, Hyperspectral image classification to detect weed infestations and nitrogen status in corn, Trans. ASAE, 46, 539

Richter, K., Hank, T., and Mauser, W. (2010, January 22). Preparatory analyses and development of algorithms for agricultural applications in the context of the EnMAP hyperspectral mission. Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XII. International Society for Optics and Photonics, Toulouse, France.

Jarmer, 2013, Spectroscopy and hyperspectral imagery for monitoring summer barley, Int. J. Remote Sens., 34, 6067, 10.1080/01431161.2013.793871

Thomas, 2013, Retrieving the Bioenergy Potential from Maize Crops Using Hyperspectral Remote Sensing, Remote Sens., 5, 254, 10.3390/rs5010254

Mewes, 2011, Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection, Precis. Agric., 12, 795, 10.1007/s11119-011-9222-9

Hbirkou, 2012, Airborne hyperspectral imaging of spatial soil organic carbon heterogeneity at the field-scale, Geoderma, 175–176, 21, 10.1016/j.geoderma.2012.01.017

Cilia, 2014, Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery, Remote Sens., 6, 6549, 10.3390/rs6076549

Ambrus, 2015, Estimating biomass of winter wheat using narrowband vegetation indices for precision agriculture, J. Cent. Eur. Green Innov., 3, 13

Oppelt, 2004, Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data, Int. J. Remote Sens., 25, 145, 10.1080/0143116031000115300

Bannari, 2006, Estimating and mapping crop residues cover on agricultural lands using hyperspectral and IKONOS data, Remote Sens. Environ., 104, 447, 10.1016/j.rse.2006.05.018

Tychon, 2011, Soil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy, Int. J. Appl. Earth Obs., 13, 81

Finn, 2011, Remote Sensing of Soil Moisture Using Airborne Hyperspectral Data, Gisci. Remote Sens., 48, 522, 10.2747/1548-1603.48.4.522

Xie, 2014, Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat, IEEE J. STARS, 7, 3586

Castaldi, F., Chabrillat, S., Jones, A., Vreys, K., Bomans, B., and van Wesemael, B. (2018). Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database. Remote Sens., 10.

Luo, S., Wang, C., Xi, X., Zeng, H., Li, D., Xia, S., and Wang, P. (2016). Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification. Remote Sens., 8.

Mart, 2006, Atmospheric correction algorithm applied to CASI multi-height hyperspectral imagery, Parameters, 1, 4

(2020, August 01). AVIRIS Data—New Data Acquisitions, Available online: https://aviris.jpl.nasa.gov/data/newdata.html.

Lu, 2017, Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland, ISPRS J. Photogramm., 128, 73, 10.1016/j.isprsjprs.2017.03.011

Stafford, J.V. (2019). UAV-based hyperspectral imaging for weed discrimination in maize. Precision Agriculture ‘19, Wageningen Academic Publishers.

Dao, 2019, Maximizing the quantitative utility of airborne hyperspectral imagery for studying plant physiology: An optimal sensor exposure setting procedure and empirical line method for atmospheric correction, Int. J. Appl. Earth Obs., 77, 140

Capolupo, 2015, Estimating plant traits of grasslands from UAV-acquired hyperspectral images: A comparison of statistical approaches, ISPRS Int. J. Geo Inf., 4, 2792, 10.3390/ijgi4042792

Lu, 2018, Optimal spatial resolution of Unmanned Aerial Vehicle (UAV)-acquired imagery for species classification in a heterogeneous grassland ecosystem, Gisci. Remote Sens., 55, 205, 10.1080/15481603.2017.1408930

Bohnenkamp, D., Behmann, J., and Mahlein, A. (2019). In-Field Detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale. Remote Sens., 11.

Habib, A., Han, Y., Xiong, W., He, F., Zhang, Z., and Crawford, M. (2016). Automated Ortho-Rectification of UAV-Based Hyperspectral Data over an Agricultural Field Using Frame RGB Imagery. Remote Sens., 8.

Honkavaara, 2013, Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture, Remote Sens., 5, 5006, 10.3390/rs5105006

Saari, H., Pellikka, I., Pesonen, L., Tuominen, S., Heikkila, J., Holmlund, C., Makynen, J., Ojala, K., and Antila, T. (2011, January 6). Unmanned Aerial Vehicle (UAV) operated spectral camera system for forest and agriculture applications. Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII. International Society for Optics and Photonics, Prague, Czech Republic.

Honkavaara, 2012, Hyperspectral reflectance signatures and point clouds for precision agriculture by light weight UAV imaging system, ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., 7, 353, 10.5194/isprsannals-I-7-353-2012

Yue, J., Yang, G., Li, C., Li, Z., Wang, Y., Feng, H., and Xu, B. (2017). Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models. Remote Sens., 9.

Pölönen, I., Saari, H., Kaivosoja, J., Honkavaara, E., and Pesonen, L. (2013, January 16). Hyperspectral imaging based biomass and nitrogen content estimations from light-weight UAV. Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XV. International Society for Optics and Photonics, Dresden, Germany.

Kaivosoja, J., Pesonen, L., Kleemola, J., Pölönen, I., Salo, H., Honkavaara, E., Saari, H., Mäkynen, J., and Rajala, A. (2013, January 24–26). A case study of a precision fertilizer application task generation for wheat based on classified hyperspectral data from UAV combined with farm history data. Proceedings of the SPIE Remote Sensing, Dresden, Germany.

Akhtman, 2017, Application of hyperspectural images and ground data for precision farming, Geogr. Environ. Sustain., 10, 117, 10.24057/2071-9388-2017-10-4-117-128

Izzo, R.R., Lakso, A.N., Marcellus, E.D., Bauch, T.D., Raqueno, N.G., and van Aardt, J. (2019). An initial analysis of real-time sUAS-based detection of grapevine water status in the Finger Lakes Wine Country of Upstate New York. Proceedings of the Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, International Society for Optics and Photonics.

Scherrer, 2019, Hyperspectral imaging and neural networks to classify herbicide-resistant weeds, J. Appl. Remote Sens., 13, 044516, 10.1117/1.JRS.13.044516

Yue, J., Feng, H., Jin, X., Yuan, H., Li, Z., Zhou, C., Yang, G., and Tian, Q. (2018). A Comparison of Crop Parameters Estimation Using Images from UAV-Mounted Snapshot Hyperspectral Sensor and High-Definition Digital Camera. Remote Sens., 10.

Dalponte, 2013, Tree Species Classification in Boreal Forests with Hyperspectral Data, IEEE Trans. Geosci. Remote, 51, 2632, 10.1109/TGRS.2012.2216272

Aasen, 2014, Introduction and preliminary results of a calibration for full-frame hyperspectral cameras to monitor agricultural crops with UAVs, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., XL-7, 1, 10.5194/isprsarchives-XL-7-1-2014

Zhu, W., Sun, Z., Huang, Y., Lai, J., Li, J., Zhang, J., Yang, B., Li, B., Li, S., and Zhu, K. (2019). Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs. Remote Sens., 11.

Zhao, 2020, A robust spectral-spatial approach to identifying heterogeneous crops using remote sensing imagery with high spectral and spatial resolutions, Remote Sens. Environ., 239, 111605, 10.1016/j.rse.2019.111605

Berni, 2012, Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera, Remote Sens. Environ., 117, 322, 10.1016/j.rse.2011.10.007

Lu, 2018, Mapping vegetation biophysical and biochemical properties using unmanned aerial vehicles-acquired imagery, Int. J. Remote Sens., 39, 5265, 10.1080/01431161.2017.1363441

Malmir, 2019, Prediction of soil macro- and micro-elements in sieved and ground air-dried soils using laboratory-based hyperspectral imaging technique, Geoderma, 340, 70, 10.1016/j.geoderma.2018.12.049

Mertens, 2020, In-field detection of Altemaria solani in potato crops using hyperspectral imaging, Comput. Electron. Agric., 168, 105106, 10.1016/j.compag.2019.105106

Eddy, 2008, Hybrid segmentation - Artificial Neural Network classification of high resolution hyperspectral imagery for Site-Specific Herbicide Management in agriculture, Photogramm. Eng. Remote Sens., 74, 1249, 10.14358/PERS.74.10.1249

Feng, 2017, Accurate Digitization of the Chlorophyll Distribution of Individual Rice Leaves Using Hyperspectral Imaging and an Integrated Image Analysis Pipeline, Front. Plant Sci., 8, 1238, 10.3389/fpls.2017.01238

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