Multi-temporal monitoring of wheat growth by using images from satellite and unmanned aerial vehicle

International Journal of Agricultural and Biological Engineering

Volumn 10, Issue 5, 2017, Pages 1-13

  Du, Meng Meng ^a   Noguchi, Noboru ^a   Itoh, Atsushi ^b   Shibuya, Yukinori ^b  

^a HOKKAIDO UNIVERSITY   (Japan)

^b NATIONAL AGRICULTURAL RESEARCH CENTER FOR HOKKAIDO REGION   (Japan)

Author keywords

Satellite remote sensing; UAV remote sensing; Wheat growth monitoring; Wheat lodging; Wheat protein content

Indexed keywords

EID: 85030834006     PISSN: 19346344     EISSN: 19346352     Source Type: Journal    
DOI: 10.25165/j.ijabe.20171005.3180     Document Type: Article

References (24)

1. Zadoks J C, Chang T T, Konzak C F. A decimal code for the growth stages of cereals. Weed Res., 1974: 14: 415-421.
2. Poole N. Cereal growth stages, Grains research & development corporation, Lincoln, New Zealand, FAR, 2005.
3. Weisz R. Small grain production guide revised March 2013, http://www.smallgrains.ncsu.edu/production-guide.html. Accessed on [2016-12-01].
4. Rouse J W, Haas R H, Schell J A, Deering D W. Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS Symposium, NASA SP-351 I, 1973; pp.309-317.
5. Benedetti R, Rossini P. On the use of NDVI profiles as a tool for agricultural statistics: The case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sensing of Environment, 1993; 45(3): 311-326.
6. Mulla D J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 2013; 114: 358-371.
7. Satellite image corporation, RapidEye Satellite Sensor. http://www.satimagingcorp.com/satellite-sensors/other-satellite-sensors/rapideye/. Accessed on [2016-12-05]
8. Chander G, Haque M O, Sampath A, Brunn A, Trosset G, Hoffmann D, et al. Radiometric and geometric assessment of data from the RapidEye constellation of satellites, International Journal of Remote Sensing, 2013; 34(16): 5905-5925.
9. Colewell R N. Determining the prevalence of certain cereal crop diseases by means of aerial photography. Hilgardia, 1956; 26: 223-286.
10. Trout T J, Johnson L F, Gartung J. Remote sensing of canopy cover in horticultural crops. Hort. Science, 2008; 43(2): 333-337.
11. Eisenbeiss H. A mini unmanned aerial vehicle (UAV): System overview and image acquisition. Image Acquisiton International Workshop on Processing and Visualization using High-Resolution Imagery, Pitsanulok, Thailand, 2004.
12. Du M M, Noguchi N. Monitoring of wheat growth status and mapping of wheat yield’s within-field spatial variations using color images acquired from UAV-camera system. Remote Sensing, 2017; 9(3): 289.
13. Campbell J B, Wynne R H. Introduction to remote sensing. 5th edition. The Guilford Press, New York, USA, 2011; pp.72-102.
14. Wang X Q, Wang M M, Wang S Q, Wu Y D. Extraction of vegetation information from visible unmanned aerial vehicle images. Transactions of the CSAE, 2015; 31(5): 152-159.
15. Hunt E R, Hively J W D, Fujikawa S J, Linden D S, Daughtry C S T, McCarty G W. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sens, 2010; 2: 290-305.
16. Torres-Sánchez J, López-Granados F, de Castro A I, Peña-Barragán J M. Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PLoS One, 2013; 8(3): e58210.
17. Gamon J A, Surfus J S. Assessing leaf pigment content and activity with a reflectometer. New Phytologist, 1999; 143(1): 105-117.
18. Woebbecke D M, Meyer G E, Von Bargen K, Mortensen D A. Color indices for weed identification under various soil, residue and lighting conditions. Transactions of the ASAE 1995; 38(1): 259-269.
19. Rasmussen J, Ntakos G, Nielsen J, Svensgaard J, Poulsen R N, Christensen S. Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? European Journal of Agronomy, 2016; 74: 75-92.
20. http://www.data.jma.go.jp/obd/stats/etrn/view/annually_a.php?prec_no=20&block_no=0115&year=2015&month=&day= &view=p1. Accessed on [2017-05-20]
21. MAFF. http://www.maff.go.jp/. Accessed on [2016-11-10]
22. Vermote E F, Tanre D, Deuze J L, Herman M, Morcrette J J. Second simulation of the satellite signal in the solar spectrum, 6S: An overview. IEEE T. Geosci. Remote, 1997; 35: 675-686.
23. Berk A, Bernstein L, Robertson D. MODTRAN: a moderate resolution model for LOWTRAN7, Tech. Rep. GL-TR-89-0122, Air Force Geophysics Lab, Hanscom AFB, Massachusetts, USA, 1989.
24. de Carvalho Júnior O A, Guimarães R F, Silva N C, Gillespie A R, Gomes R A T, Silva C R, et al. Radiometric normalization of temporal images combining automatic detection of pseudo-invariant features from the distance and similarity spectral measures, density scatterplot analysis, and robust regression. Remote Sens., 2013; 5: 2763-2794.