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

Du Mengmeng, Noguchi Noboru, Itoh Atsushi, Shibuya Yukinori

Abstract


Recently near-ground remote sensing using unmanned aerial vehicles (UAV) witnessed wide applications in obtaining field information. In this research, four Rapideye satellite images and eight RGB images acquired from UAV were used from early June to the end of July, 2015 covering two experimental winter wheat fields, in order to monitor wheat canopy growth status and analyze the correlation among satellite images based normalized difference vegetation index (NDVI) with UAV’s RGB images based visible-band difference vegetation index (VDVI) and ground variables of the sampled grain protein contents. Firstly, through image interpretation of UAV’s multi-temporal RGB images with fine spatial resolution, the wheat canopy color changes could be intuitively and clearly monitored. Subsequently, by monitoring the changes of satellite images based NDVI as well as VDVI values and UAV’s RGB images based VDVI values, the conclusions were made that these three vegetation indices demonstrated the same and synchronized trend of increasing at the early stage of wheat growth season, reaching up to peak values at the same timing, and starting to decrease since then. The results of the correlation analysis between NDVI of satellite images and sampled grain protein contents show that NDVI has good predicative capability for mapping grain protein content before ripening growth stage around June7, 2015, while the reliability of using satellite image based NDVI to predict grain protein contents becomes worse as ripening stage approaches. The regression analysis between UAV’s RGB image based VDVI and satellite image based VDVI as well as NDVI showed good coefficients of determination. It is concluded that it is feasible and practical to temporally complement satellite remote sensing by using UAV’s RGB images based vegetation indices to monitor wheat growth status and to map within-field spatial variations of grain protein contents for small scale farmlands.
Keywords: satellite remote sensing, UAV remote sensing, wheat growth monitoring, wheat lodging; wheat protein content, multi-temporal images, NDVI
DOI: 10.25165/j.ijabe.20171005.3180

Citation: Du M M, Noguchi N, Itoh A, Shibuya Y. Multi-temporal monitoring of wheat growth by using images from satellite and unmanned aerial vehicle. Int J Agric & Biol Eng, 2017; 10(5): 1–13.

Keywords


satellite remote sensing, UAV remote sensing, wheat growth monitoring, wheat lodging; wheat protein content, multi-temporal images, NDVI

References


Zadoks J C, Chang T T, Konzak C F. A decimal code for the growth stages of cereals. Weed Res., 1974: 14: 415–21.

Poole N. Cereal growth stages, Grains research & development corporation, Lincoln, New Zealand, FAR, 2005.

Weisz R. Small grain production guide revised March 2013, http://www.smallgrains.ncsu.edu/production-guide.html. Accessed on [2016-12-01].

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.

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.

Mulla D J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 2013; 114: 358–371.

Satellite image corporation, RapidEye Satellite Sensor. http://www.satimagingcorp.com/satellite-sensors/other-satellite-sensors/rapideye/. Accessed on [2016-12-05]

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.

Colewell R N. Determining the prevalence of certain cereal crop diseases by means of aerial photography. Hilgardia, 1956; 26: 223–286.

Trout T J, Johnson L F, Gartung J. Remote sensing of canopy cover in horticultural crops. Hort. Science, 2008; 43(2): 333–337.

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.

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.

Campbell J B, Wynne R H. Introduction to remote sensing. 5th edition. The Guilford Press, New York, USA, 2011; pp.72–102.

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.

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.

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.

Gamon J A, Surfus J S. Assessing leaf pigment content and activity with a reflectometer. New Phytologist, 1999; 143(1): 105–117.

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.

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.

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]

MAFF. http://www.maff.go.jp/. Accessed on [2016-11-10]

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.

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.

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.


Full Text: PDF

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.