Advances in hyperspectral sensing in agriculture: a review

Marcio Regys Rabelo de Oliveira, Sharon Gomes Ribeiro, Jean-Francois Mas, Adunias dos Santos Teixeira


In view of the exponential growth in the volume of data that is considered in intelligent decision-making, hyperspectral remote sensing (HRS) has, without doubt, brought greater dominance over agricultural crops as it goes beyond the paradigm of little information being available about the targets. In this review of the state of the art of HRS, complementary views on the use of sensors and analytical techniques in agriculture over the last decade are grouped together. State-of-the-art technologies, and research trends associated with each level of data collection are cited. There is still a long way to go in the agricultural sciences; however, specialists in precision agriculture are devotees of the valuable insights offered with the increased availability of hyperspectral data. In this respect, this review is organised as follows: Section 1 helps the reader to contextualise and conceptualise the basics of remote sensing; the second section discusses the types of sensors and their resolutions; section 3 presents four subsections that show recent applications of these technologies according to their level of acquisition; finally, the fourth section offers the reader a discussion on the positive trends achieved in managing vegetation, soils and waterbodies over the last ten years, as well as the needs and challenges of the next decade.


Sensors; Monitoring; Reflectance

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ABDULRIDHA J. et al. Detecting powdery mildew disease in squash at different stages using UAV-based hyperspectral imaging and artificial intelligence. Biosystems Engineering, 197:135-48, Sep 2020.

ABDULRIDHA, J. et al. A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses. Computers and Electronics in Agriculture, v. 156, p. 549-557, 2019.

AINIWAER M. et al. Regional scale soil moisture content estimation based on multi-source remote sensing parameters. International Journal of Remote Sensing, v. 41, n. 9, p. 46-67, May 2020.

ALMEIDA, E. L. et al. Sensoriamento remoto hiperespectral aerotransportado aplicado na determinação da textura de um Cambissolo da Chapada do Apodi-CE, Revista Ciência Agronômica, 2020.

AMARAL, C. H. D. et al. Characterization of indicator tree species in neotropical environments and implications for geological mapping. Remote Sensing of Environment, p.385-400, 2018.

AMARAL, C. H. et al. Mapping invasive species and spectral mixture relationships with neotropical woody formations in southeastern Brazil. ISPRS Journal of Photogrammetry and Remote Sensing, v. 8, p. 80-93, 2015.

ANEECE I.; THENKABAIL P. Accuracies achieved in classifying five leading world crop types and their growth stages using optimal earth observing-1 hyperion hyperspectral narrowbands on google earth engine. Remote Sensing, Dec 2018.

ANGEL Y. et al. Automated Georectification and Mosaicking of UAV-Based Hyperspectral Imagery from Push-Broom Sensors. Remote Sensing, v. 2, p. 34, 2020.

ARDILA C. E., RAMIREZ L.A., ORTIZ F.A. Spectral analysis for the early detection of anthracnose in fruits of Sugar Mango (Mangifera indica). Computers and Electronics in Agriculture, v. 173, p. 345-357, Jun 2020.

ASADZADEH, S.; SOUZA FILHO, C. R., Spectral remote sensing for onshore seepage characterization: A critical overview. Earth-Science Reviews, v. 168, p. 48-72, 2017.

BANDYOPADHYAY D. et al. Red edge index as an indicator of vegetation growth and vigor using hyperspectral remote sensing data. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences. v. 87, p. 879-888, Dec 2017.

CALOU, V. B. C. et al. The use of UAVs in monitoring yellow sigatoka in banana. Biosystems Engineering, v. 193, p. 115-125, 2020.

CASTALDI, F. et al. Reducing the influence of soil moisture on the estimation of clay from hyperspectral data: A case study using simulated PRISMA data. Remote Sensing, v. 7, p. 15561-15582, 2015.

CHAVES, M.E.D. et al. Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review. Remote Sensing, v. 12, p. 3062, 2020.

CHAWADE A. et al. High-throughput field-phenotyping tools for plant breeding and precision agriculture. Agronomy, v. 9, p. 258, May 2019.

CHEN Y. et al. Algorithm development for land surface temperature retrieval: Application to Chinese Gaofen-5 data. Remote Sensing, v. 9, p. 161, Feb 2017.

CHIEN H.Y. et al. Fast honey classification using infrared spectrum and machine learning. Mathematical biosciences and engineering: MBE, v. 16, p. 6874-6891, Jul 2019.

COLLIN A.M. et al. The superspectral/hyperspatial worldview-3 as the link between spaceborne hyperspectral and airborne hyperspatial sensors: the case study of the complex tropical coast. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, Jun 2019.

DAHM, K.D.; DAHM, D.J. Separating the effects of scatter and absorption using the representative layer. Journal of Near Infrared Spectroscopy, v. 21, p. 351-357, 2013.

DALE, L.M. et al. Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: A review. Applied Spectroscopy Reviews, v. 48, p. 142-159, 2013.

DONG, T. et al. Estimating crop biomass using leaf area index derived from Landsat 8 and Sentinel-2 data. ISPRS Journal of Photogrammetry and Remote Sensing, v. 168, p. 236-250, 2020.

FAJARDO, J. U. et al. Early detection of black Sigatoka in banana leaves using hyperspectral images. Applications in plant sciences, v. 8, p. 83-113, 2020.

FAN Y. et al. Fast detection of striped stem-borer (Chilo suppressalis Walker) infested rice seedling based on visible/near-infrared hyperspectral imaging system. Sensors, v. 17, p. 2470, Nov 2017.

FUE, K.G. et al. An Extensive Review of Mobile Agricultural Robotics for Field Operations: Focus on Cotton Harvesting. AgriEngineering, v. 2, p. 150-174, 2020.

GE, X. et al. Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring. PeerJ, v. 7, p. 6926, 2019.

GUO L. et al. Prediction of soil organic carbon stock by laboratory spectral data and airborne hyperspectral images. Geoderma. v. 337, p. 32-41, 2019.

GÜRTLER S. et al. Determination of changes in leaf and canopy spectra of plants grown in soils contaminated with petroleum hydrocarbons. ISPRS Journal of Photogrammetry and Remote Sensing, v. 146. p. 272-288, Dec 2018.

JENSEN, J. R., Sensoriamento Remoto do Ambiente: Uma Perspectiva em Recursos Terrestres,São José dos Campos-SP, Parêntese, 582p., 2011.

KANG K.K. et al. Operating Procedures and Calibration of a Hyperspectral Sensor Onboard a Remotely Piloted Aircraft System For Water and Agriculture Monitoring. In:IGARSS 2019-2019, IEEE International Geoscience and Remote Sensing Symposium, p. 9200-9203, Jul 2019.

KARAS B.Y., GRISHKANICH A.S. Portable Raman dual-laser spectrometer for oil and gas. InEnvironmental Effects on Light Propagation and Adaptive Systems III, International Society for Optics and Photonics, v. 11532, p. 1153-1206, Sep 2020.

KELLER S. et al. Hyperspectral data and machine learning for estimating CDOM, chlorophyll a, diatoms, green algae and turbidity. International journal of environmental research and public health, v. 15, p. 1881, Sep 2018.

KELLER, S. et al. Hyperspectral data and machine learning for estimating CDOM, chlorophyll a, diatoms, green algae and turbidity. International journal of environmental research and public health, v. 15, p. 1881, 2018.

KHOBRAGADE, A.N.; RAGHUWANSHI, M.M. 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: Cham, Switzerland, p. 333-346, 2015.

KIMBAHUNE S. et al. Hyperspectral sensing based analysis for determining milk adulteration. InHyperspectral Imaging Sensors: Innovative Applications and Sensor Standards, International Society for Optics and Photonics, v. 9860, p. 986, May 2016.

KINGRA, P. K. et al. Application of remote sensing and GIS in agriculture and natural resource management under changing climatic conditions. Agric Res J, v. 53, p. 295-302, 2016.

KOBAYASHI T. et al. Assessment of rice panicle blast disease using airborne hyperspectral imagery. The Open Agriculture Journal. v. 10, Jun 2016.

LEE C.M. et al. An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities. Remote Sensing of Environment. Sep 15;167:6-19, 2015.

LIM J., KIM K.M., JIN R. Tree species classification using hyperion and sentinel-2 data with machine learning in south Korea and China. ISPRS International Journal of Geo-Information, v. 8, p. 150, Mar 2019.

LU B. et al. Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture. Remote Sensing, v. 12, p. 26-59, Jan 2020.

LUO H., CHAI X., CHEN C. Based on multi-scale hyperspectral near ground remote to sensing the quality of Southern Xinjiang jujube. InFifth Symposium on Novel Optoelectronic Detection Technology and Application. International Society for Optics and Photonics, v. 12, p. 110-232, 2019.

MAHLEIN A.K. et al. Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed!. Plant biology, v. 50, p. 156-162, Aug 2019.

MALEC, S. et al. Capability of spaceborne hyperspectral EnMAP mission for mapping fractional cover for soil erosion modeling. Remote Sensing, v. 7, p. 11776-11800, 2015.

MATSUSHITA, B. et al. Monitoring Water Quality with Remote Sensing Image Data. Remote Sensing for Sustainability; CRC Press, p. 163-189. 2016.

MOHARANA, S.; DUTTA, S., Estimation of water stress variability for a rice agriculture system from space-borne hyperion imagery. Agricultural Water Management, v. 213, p. 260-269, 2019.

MOREIRA, L.C. J. et al. Potential of multispectral and hyperspectral data to detect saline-exposed soils in Brazil. GIScience & Remote Sensing, v. 52, p. 416-436, 2015.

NIGAM, R. et al. Wheat blast detection and assessment combining ground-based hyperspectral and satellite based multispectral data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 42, 2019.

NOVO, E. M. L. M. Sensoriamento remoto:princípios e aplicações. 4ª ed. São Paulo. Blucher, 2010.

OLIVEIRA R. A. et al. Real-time and post-processed georeferencing for hyperpspectral drone remote sensing. The international archives of the photogrammetry, remote sensing and spatial information sciences, 2018.

OLIVEIRA, M. R .R. et al. Reflectance spectrometry applied to the analysis of nitrogen and potassium deficiency in cotton. Revista Ciência Agronômica, v. 51, p. 1-10, 2020.

PANTERAS G.; CERVONE G. Enhancing the temporal resolution of satellite-based flood extent generation using crowdsourced data for disaster monitoring. International Journal of Remote Sensing. v. 39, p. 1459-1474, 2018.

PEÓN J. et al. Evaluation of the spectral characteristics of five hyperspectral and multispectral sensors for soil organic carbon estimation in burned areas. International Journal of Wildland Fire. v. 30, p. 230-239, 2017.

PHINZI K., NGETAR N.S. Mapping soil erosion in a quaternary catchment in Eastern Cape using geographic information system and remote sensing. South African Journal of Geomatics. v. 8, p. 11-29, 2017.

POLDER G, et al. Potato virus Y detection in seed potatoes using deep learning on hyperspectral images. Frontiers in plant science, v. 10, p. 209, Mar 2019.

POURREZA A. et al. Identification of citrus Huanglongbing disease at the pre-symptomatic stage using polarized imaging technique. IFAC-Papers, v. 49, p. 110-115, 2016.

ROCHA NETO, O.C.D. et al. Hyperspectral remote sensing for detecting soil salinization using prospectir-vs aerial imagery and sensor simulation. Remote Sensing, v. 9, p. 42, 2017.

SANCHES, I. D.; SOUZA FILHO, C. R.; KOKALY, R. F. Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680 nm absorption feature with continuum removal. ISPRS Journal of Photogrammetry and Remote Sensing, v. 97, p. 111-122, 2014.

SANKARAN S. et al. Mid-infrared spectroscopy for detection of Huanglongbing (greening) in citrus leaves. Talanta, v. 83, p. 574-581, 2010.

SHANMUGAPRIYA, P. et al. Applications of remote sensing in agriculture-A Review. Int. J. Current Microbiol. Appl. Sci, v. 8, p. 2270-2283, 2019.

SILVA JUNIOR, C.A. et al. Soybean varieties discrimination using non-imaging hyperspectral sensor. Infrared Physics & Technology, v. 89, p. 338-350, 2018.

STAENZ K, MUELLER A, HEIDEN U. Overview of terrestrial imaging spectroscopy missions. IEEE International Geoscience and Remote Sensing -IGARSS, v. 21, p. 3502-3505, 2013.

SWAMY S., ASUTKAR S.M., ASUTKAR G.M.. Remote sensing HSI classification and estimation of MIMETITE mineral spectral signatures from ISRO, India. International Conference on Trends in Electronics and Informatics (ICEI), 2017.

TANG B.H; LI Z.L. Estimation of land surface temperature from Chinese Gaofen-5 satellite data. InIGARSS 2018-2018. IEEE International Geoscience and Remote Sensing, v. 22, p. 2559-2562, Jul 2018.

TAO, H. et al. Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data. Sensors, v. 20, p. 1296, 2020.

TEKE, M. et al. A short survey of hyperspectral remote sensing applications in agriculture. In 2013 6th International Conference on Recent Advances in Space Technologies, p. 171-176, June 2013.

TESFAMICHAEL S.G. et al. Field spectroradiometer and simulated multispectral bands for discriminating invasive species from morphologically similar cohabitant plants. GIScience & Remote Sensing. v. 55, p. 417-436, 2018.

VAN DE VIJVER, R., et al. In-field detection of Alternaria solani in potato crops using hyperspectral imaging. Computers and Electronics in Agriculture, v. 168, p. 105-106, 2020.

VANEGAS F. et al. A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data. Sensors, v. 18, p. 260, 2018.

WALLACE, L. et al. Non destructive estimation of above ground surface and near surface biomass using 3D terrestrial remote sensing techniques. Methods in Ecology and Evolution, v. 8, p. 1607-1616, 2017.

WANG, N.; MASRY, G. Bruise detection of apples using hyperspectral imaging. Agricultural Enhineering, p. 295-320, 2010.

WANG, Q. et al. The potential of forest biomass inversion based on vegetation indices using multi-angle CHRIS/PROBA data. Remote Sensing, v. 8, p. 891, 2016.

WEI L. et al. Spatial–spectral fusion based on conditional random fields for the fine classification of crops in UAV-borne hyperspectral remote sensing imagery. Remote Sensing. v. 11, p. 780, Jun 2019.

WEI, J. et al. Estimating 1-km-resolution PM25 concentrations across China using the space-time random forest approach. Remote Sensing of Environment, v. 231, p. 111-221, 2019.

WEI, L. et al. Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing. Sensors, v. 20, p. 40-56, 2020.

WEN P.F. et al. Estimating leaf nitrogen concentration considering unsynchronized maize growth stages with canopy hyperspectral technique. Ecological Indicators. v. 107, p. 575-590, Dec 2019.

YAN, X. et al. Monitoring wetland changes both outside and inside reclamation areas for coastal management of the Northern Liaodong Bay, China. Wetlands, v. 37, p. 885-897, 2017.

YENDREK C.R. et al. High-throughput phenotyping of maize leaf physiological and biochemical traits using hyperspectral reflectance. Plant physiology. v. 173, p. 614-626, 2017.

ZARCO-TEJADA P. J., GUILLÉN-CLIMENT M. L., R. HERNÁNDEZ-CLMENTE E.T. Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV), Agricultural Forest Meteorology, v. 171, p. 281-294, 2013.

ZHANG C. et al. Assessment of the application of copper stress vegetation index on Hyperion image in Dexing Copper Mine, China. Journal of Applied Remote Sensing, v. 13, p. 501-511, 2019.

ZHANG L. et al. Mapping maize water stress based on UAV multispectral remote sensing. Remote Sensing. v. 11, p. 605, 2019.

ZHOU, X. et al. Assessment of Leaf Chlorophyll Content Models for Winter Wheat Using Landsat-8 Multispectral Remote Sensing Data. Remote Sensing, v. 12, p. 25-74. 2020.

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