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Open Access

Airborne remote sensing systems for precision agriculture applications

U. S. Department of Agriculture, Agricultural Research Service, Southern Plans Agricultural Research Center, College Station, Texas 77845, USA
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Abstract

Remote sensing has been used as an important data acquisition tool for precision agriculture for decades. Based on their height above the earth, remote sensing platforms mainly include satellites, manned aircraft, unmanned aircraft systems (UAS) and ground-based vehicles. A vast majority of sensors carried on these platforms are imaging sensors, though other sensors such as lidars can be mounted. In recent years, advances in satellite imaging sensors have greatly narrowed the gaps in spatial, spectral and temporal resolutions with aircraft-based sensors. More recently, the availability of UAS as a low-cost remote sensing platform has significantly filled the gap between manned aircraft and ground-based platforms. Nevertheless, manned aircraft remain to be a major remote sensing platform and offer some advantages over satellites or UAS. Compared with UAS, manned aircraft have flexible flight height, fast speed, large payload capacity, long flight time, few flight restrictions and great weather tolerance. The first section of the article provided an overview of the types of remote sensors and the three major remote sensing platforms (i.e., satellites, manned aircraft and UAS). The next two sections focused on manned aircraft-based airborne imaging systems that have been used for precision agriculture, including those consisting of consumer-grade cameras mounted on agricultural aircraft. Numerous custom-made and commercial airborne imaging systems were reviewed, including multispectral, hyperspectral and thermal cameras. Five application examples were provided in the fourth section to illustrate how different types of remote sensing imagery have been used for crop growth assessment and crop pest management for practical precision agriculture applications. Finally, some challenges and future efforts on the use of different platforms and imaging systems for precision agriculture were briefly discussed.

References

[1]

Campbell J B. Introduction to Remote Sensing (3rd ed)[M]. New York: The Guilford Press, 2002.

[2]

Chen X, Ma J, Qiao H, et al. Detecting infestation of take-all disease in wheat using Landsat Thematic Mapper imagery[J]. International Journal of Remote Sensing, 2007, 28(22): 5183-5189.

[3]
Satellite Imaging Corporation[EB/OL]. [2020-1-11]. https://www.satimagingcorp.com/satellite-sensors/.
[4]
Planet Labs Inc[EB/OL]. [2020-1-11]. https://www.planet.com/products/planet-imagery/.
[5]

Yang C. High resolution satellite imaging sensors for precision agriculture[J]. Frontiers of Agricultural Science and Engineering, 2018, 5(4): 393-405.

[6]
Wikipedia[EB/OL]. [2020-1-11]. https://en.wikipedia.org/wiki/Light-sport_aircraft.
[7]
Federal Aviation Administration. Light sport aircraft[EB/OL]. [2019-9-6]. https://www.faa.gov/aircraft/gen_av/light_sport/.
[8]

Zhao B, Zhang J, Yang C, et al. Rapeseed seedling stand counting and seeding performance evaluation at two early growth stages based on unmanned aerial vehicle imagery[J]. Frontiers in Plant Science, 2018, 9: 1362.

[9]

Holman F H, Riche A B, Michalski A, et al. High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing[J]. Remote Sensing, 2016, 8(12): 1031.

[10]

Varela S, Assefa Y, Prasad P V V, et al. Spatio-temporal evaluation of plant height in corn via unmanned aerial systems[J]. Journal of Applied Remote Sensing, 2017, 11(3): 36013.

[11]

Roth L H, Aasen A, Walter F, et al. Extracting leaf area index using viewing geometry effects - A new perspective on high-resolution unmanned aerial system photography[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 141: 161-175.

[12]

Bendig J, Yu K, Aasen H, et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 39: 79-87.

[13]

Yue J, Yang G, Li C, et al. Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models[J]. Remote Sensing, 2017, 9(7): no. 708.

[14]

Kefauver S C, Vicente R, Vergara-Díaz O, et al. Comparative UAV and field phenotyping to assess yield and nitrogen use efficiency in hybrid and conventional barley[J]. Frontiers in Plant Science, 2017, 8: no. 1733.

[15]

Gong Y, Duan B, Fang S, et al. Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis[J]. Plant Methods, 2018, 14: 70.

[16]

Hassan M A, Yang M, Rasheed A, et al. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multispectral UAV platform[J]. Plant Science, 2019, 282: 95-103.

[17]

Zhang L, Zhang H, Niu Y, et al. Mapping maize water stress based on UAV multispectral remote sensing[J]. Remote Sensing, 2019, 11: no. 605.

[18]

Torres-Sánchez J, López-Granados F, De Castro A I, et al. Configuration and specifications of an unmanned aerial vehicle (UAV) for early site-specific weed management[J]. PLoS ONE, 2013, 8(3): no. e58210.

[19]

Jiménez-Brenes F M, López-Granados F, Torres-Sánchez J, et al. Automatic UAV-based detection of Cynodon dactylon for site-specific vineyard management[J]. PLoS ONE, 2019, 14(6): no. e0218132.

[20]

Cao F, Liu F, Guo H, et al. Fast detection of Sclerotinia sclerotiorum on oilseed rape leaves using low-altitude remote sensing technology[J]. Sensors, 2018, 18(12): no. 4464.

[21]

Heim R H J, Wright I J, Scarth P, et al. Multispectral, aerial disease detection for myrtle rust (Austropuccinia psidii) on a lemon myrtle plantation[J]. Drones, 2019, 3: 25.

[22]

Caturegli L, Corniglia M, Gaetani M, et al. Unmanned aerial vehicle to estimate nitrogen status of turfgrasses[J]. PLoS ONE, 2016, 11(6): no. e0158268.

[23]

Vanegas F, Bratanov D, Powell K, et al. A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data[J]. Sensors, 2018, 18: no. 260.

[24]
Federal Aviation Administration. Certificated remote pilots including commercial operates[EB/OL]. [2019-9-4]. https://www.faa.gov/uas/commercial_operators/.
[25]

Mausel P W, Everitt J H, Escobar D E, et al. Airborne videography: current status and future perspectives[J]. Phtotogrammetric Engineering & Remote Sensing, 1992, 58(8): 1189-1195.

[26]

King D J. Airborne multispectral digital camera and video sensors: a critical review of systems designs and applications[J]. Canadian Journal of Remote Sensing, 1995, 21(3): 245-273.

[27]

Moran M S, Inoue Y, Barnes E M. Opportunities and limitations for image-based remote sensing in precision crop management[J]. Remote Sensing of Environment, 1997, 61(3): 319-346.

[28]

Moran M S, Fitzgerald G, Rango A, et al. Sensor development and radiometric correction for agricultural applications[J]. Photogrammetric Engineering & Remote Sensing, 2003, 69(6): 705-718.

[29]

PinterJr P J, Hatfield J L, Schepers J S, et al. Remote sensing for crop management[J]. Photogrammetric Engineering & Remote Sensing, 2003, 69(6): 647-664.

[30]

Yang C, Odvody G N, Thomasson, J A, et al. Change detection of cotton root rot infection over 10-year intervals using airborne multispectral imagery[J]. Computers and Electronics in Agriculture, 2016, 123: 154-162.

[31]

Everitt J H, Escobar D E, Cavazos I, et al. A three-camera multispectral digital video imaging system[J]. Remote Sensing of Environment, 1995, 54(3): 333-337.

[32]

Escobar D E, Everitt J H, Noriega J R, et al. A twelve-band airborne digital video imaging system (ADVIS)[J]. Remote Sensing of Environment, 1998, 66(2): 122-128.

[33]

Gorsevski P V, Gessler P E. The design and the development of a hyperspectral and multispectral airborne mapping system[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2009, 64(2): 184-192.

[34]

Yang C. A high resolution airborne four-camera imaging system for agricultural applications remote sensing[J]. Computers and Electronics in Agriculture, 2012, 88: 13-24.

[35]
Airborne Data Systems, Inc[EB/OL]. [2020-1-11]. http://www.airbornedatasystems.com/.
[36]
OptechTeledyne[EB/OL]. [2020-1-11]. http://www.teledyneoptech.com/en/products/airborne-survey/.
[38]
ITRES Research Limited[EB/OL]. [2020-1-11]. https://www.itres.com/.
[39]
Bayer B E. Color Imaging Array: US Patent 3971065[P]. 1976.
[40]
Hirakawa K, Wolfe P J. Spatio-spectral sampling and color filter array design[C]// In Single-Sensor Imaging: Methods and Applications for Digital Cameras, Lukac, R (ed), CRC Press, Boca Raton, Florida, USA, 2008.
[41]

Song X, Yang C, Wu M, et al. Evaluation of Sentinel-2A imagery for mapping cotton root rot[J]. Remote Sensing, 2017, 9(9): 906.

[42]
Airborne Visible InfraRed Imaging Spectrometer (AVIRIS)[EB/OL]. [2020-1-11]. https://aviris.jpl.nasa.gov/.
[43]

Yang C, Hoffmann W C. Converting aerial imagery to application maps[J]. Agricultural Aviation, 2016, 43(4): 72-74.

[44]

Westbrook J K, Ritchie S E, Yang C, et al. Airborne multispectral identification of individual cotton plants using consumer-grade cameras[J]. Remote Sensing Applications: Society and Environment, 2016, 4: 37-43.

[45]

Schiefer S, Hostert P, Damm A. Correcting brightness gradients in hyperspectral data from urban areas[J]. Remote Sensing of Environment, 2006, 101(1): 25-37.

[46]

Dehaan R L, Taylor G R. Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization[J]. Remote Sensing of Environment, 2002, 80(3): 406-417.

[47]

Galvao L S, Ponzoni F J, Epiphanio J C N, et al. Sun and view angle effects on NDVI determination of land cover types in the Brazilian Amazon region with hyperspectral data[J]. International Journal of Remote Sensing, 2004, 25(10): 1861-1879.

[48]
Cocks T, Jenssen R, Stewart A, et al. The HyMAP airborne hyperspectral sensor: The system, calibration and performance[C]// 1st EARSeL Workshop on Imaging Spectroscopy, Univ Zurich, Remote Sensing Lab, Zurich, Switzerlands, 1998.
[49]

Yang C, Hoffmann W C. Low-cost single-camera imaging system for aerial applicators[J]. Journal of Applied Remote Sensing, 2015, 9: no. 096064.

[50]

Green R O, Eastwood M L, Sarture C M, et al. Imaging Spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS)[J]. Remote Sensing of Environment, 1998, 65(3): 227-248.

[51]
Spectral Imaging Ltd[EB/OL]. [2020-1-11]. https://www.specim.fi/afx/#1576656091402-52f5761e-8ca1.
[52]
PhotonicsHeadwall, Inc[EB/OL]. [2020-1-11]. https://www.headwallphotonics.com/fov-calculator.
[53]

Varvel G E, Schlemmer M R, Schepers J S. Relationship between spectral data from an aerial image and soil organic matter and phosphorus levels[J]. Precision Agriculture, 1999, 1(3): 291-300.

[54]

Yang C, Everitt J H. Relationships between yield monitor data and airborne multidate multispectral digital imagery for grain sorghum[J]. Precision Agriculture, 2002, 3(4): 373-388.

[55]

Inman D, Khosla R, Reich R, et al. Normalized difference vegetation index and soil color-based management zones in irrigated maize[J]. Agronomy Journal, 2008, 100(1): 60-66.

[56]

Sui R, Hartley B E, Gibson J M, et al. Yield estimate of biomass sorghum with aerial imagery[J]. Journal of Applied Remote Sensing, 2011, 5(1): no. 053523.

[57]

Cohen Y, Alchanatis V, Saranga Y, et al. Mapping water status based on aerial thermal imagery: comparison of methodologies for upscaling from a single leaf to commercial fields[J]. Precision Agriculture, 2017, 18(5): 801-822.

[58]

Yang C, Martin D E. Integration of aerial imaging and variable rate technology for site-specific aerial herbicide application[J]. Transactions of the ASABE, 2017, 60(3): 635-644.

[59]

Backoulou G F, Elliott N C, Giles K L, et al. Processed multispectral imagery differentiates wheat crop stress caused by greenbug from other causes[J]. Computers and Electronics in Agriculture, 2015, 115: 34-39.

[60]

Yang C, Martin D E. Integration of aerial imaging and variable rate technology for site-specific aerial herbicide application[J]. Transactions of the ASABE, 2017, 60(3): 635-644.

[61]

Mulla D J. Twenty five years of remote sensing in precision agriculture-Key advances and remaining knowledge gaps[J]. Biosystems Engineering, 2013, 114 (4): 358-371.

[62]

Goel P K, Prasher S O, Landry J A, et al. Estimation of crop biophysical parameters through airborne and field hyperspectral remote sensing[J]. Transactions of the ASAE, 2003, 46(4): 1235-1246.

[63]

Zarco-Tejada, P J, Ustin, S L, et al. Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery[J]. Agronomy Journal, 2005, 97(3): 641-653.

[64]

Yang C, Everitt J H, Bradford J M. Airborne hyperspectral imagery and linear spectral unmixing for mapping variation in crop yield[J]. Precision Agriculture, 2007, 8(6): 279-296.

[65]

Fitzgerald G J, Maas S J, Detar W R. Spidermite detection in cotton using hyperspectral imagery and spectral mixture analysis[J]. Precision Agriculture, 2004, 5(3): 275-289.

[66]

Huang W, David W L, Niu Z, et al. Identification of yellow rust in wheat using in situ spectral reflectance measurements and airborne hyperspectral imaging[J]. Precision Agriculture, 2007, 8(4-5): 187-197.

[67]

Li H, Lee W S, Wang K, et al. Extended spectral angle mapping (ESAM) for citrus greening disease detection using airborne hyperspectral imaging[J]. Precision Agriculture, 2014, 15(2): 162-183.

[68]

MacDonald S L, Staid M, Staid M, et al. Remote hyperspectral imaging of grapevine leafroll-associated virus 3 in cabernet sauvignon vineyards[J]. Computers and Electronics in Agriculture, 2016, 130: 109-117.

[69]

Yang C, Anderson G L. Airborne videography to identify spatial plant growth variability for grain sorghum[J]. Precision Agriculture, 1999, 1(1): 67-79.

[70]

Yang C, Anderson G L. Mapping grain sorghum yield variability using airborne digital videography[J]. Precision Agriculture, 2000, 2(1): 7-23.

[71]

Yang C, Everitt J H, Bradford J M. Airborne hyperspectral imagery and yield monitor data for estimating grain sorghum yield variability[J]. Transactions of the ASAE, 2004, 47(3): 915-924.

[72]

Yang C, Everitt J H, Davis M R, et al. A CCD camera-based hyperspectral imaging system for stationary and airborne applications[J]. Geocarto International, 2003, 18(2): 71-80.

[73]

Smith L A, Thomson S J. GPS position latency determination and ground speed calibration for the Satloc Airstar M3[J]. Applied Engineering in Agriculture, 2005, 21(5): 769-776.

[74]

Thomson S J, Smith L A, Hanks J E. Evaluation of application accuracy and performance of a hydraulically operated variable-rate aerial application system[J]. Transactions of the ASABE, 2009, 52(3): 715-722.

Smart Agriculture
Pages 1-22
Cite this article:
Yang C. Airborne remote sensing systems for precision agriculture applications. Smart Agriculture, 2020, 2(1): 1-22. https://doi.org/10.12133/j.smartag.2020.2.1.201909-SA004

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Received: 24 September 2019
Revised: 07 December 2019
Published: 30 March 2020
© The Author(s) 2020.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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