Hostname: page-component-848d4c4894-8kt4b Total loading time: 0 Render date: 2024-06-26T08:14:02.976Z Has data issue: false hasContentIssue false
  • English
  • Français

Identification of High-Variation Fields based on Open Satellite Imagery

Published online by Cambridge University Press:  01 June 2017

J. H. Jeppesen ,
R. H. Jacobsen ,
R. N. Jørgensen ,
A. Halberg  and
T. S. Toftegaard
J. H. Jeppesen*
Affiliation:
Department of Engineering, Aarhus University, Finlandsgade 22, 8000 Aarhus C, Denmark
R. H. Jacobsen
Affiliation:
Department of Engineering, Aarhus University, Finlandsgade 22, 8000 Aarhus C, Denmark
R. N. Jørgensen
Affiliation:
Department of Engineering, Aarhus University, Finlandsgade 22, 8000 Aarhus C, Denmark
A. Halberg
Affiliation:
Airinov Denmark, Vejstrupgaards Alle 5882 Vejstrup, Denmark
T. S. Toftegaard
Affiliation:
Department of Engineering, Aarhus University, Finlandsgade 22, 8000 Aarhus C, Denmark
*
†E-mail: jhj@eng.au.dk
Get access

Abstract

This paper proposes a simple method for categorizing fields on a regional level, with respect to intra-field variations. It aims to identify fields where the potential benefits of applying precision agricultural practices are highest from an economic and environmental perspective. The categorization is based on vegetation indices derived from Sentinel-2 satellite imagery. A case study on 7678 winter wheat fields is presented, which employs open data and open source software to analyze the satellite imagery. Furthermore, the method can be automated to deliver categorizations at every update of satellite imagery, hence coupling the geospatial data analysis to direct improvements for the farmers, contractors, and consultants.

Keywords

Remote sensingvegetation indexzonal statisticsopen datadecision support
Type
Satellite Applications
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 388 - 393
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barnes, E, Clarke, T, Richards, S, Colaizzi, PD, Haberland, J, Kostrzewski, M, et al. 2000. Coincident Detection of Crop Water Stress, Nitrogen Status and Canopy Density Using Ground-Based Multispectral Data. Proceedings of the Fifth International Conference on Precision Agriculture.Google Scholar
Clevers, JGPW, de Jong, SM, Epema, GF, van der Meer, F, Bakker, WH, Skidmore, AK and Addink, EA 2001. MERIS and the red-edge position. International Journal of Applied Earth Observation and Geoinformation 3 (4), 313–320.CrossRefGoogle Scholar
Delin, S, Gruvaeus, I, Wetterlind, J, Stenberg, M, Frostgård, G, Börling, K, et al. 2015. Fertilisation for optimised yield can minimise nitrate leaching in grain production. Proceedings of the International Fertiliser Society 774.Google Scholar
Drusch, M, Del Bello, U, Carlier, S, Colin, O, Fernandez, V, Gascon, F, et al. 2012. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment 120, 25–36.CrossRefGoogle Scholar
Gitelson, AA, Kaufman, YJ and Merzlyak, MN 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58 (3), 289–298.CrossRefGoogle Scholar
Haghighattalab, A, González Pérez, L, Mondal, S, Singh, D, Schinstock, D, Rutkoski, J et al. 2016. Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods 12 (1), 35.CrossRefGoogle ScholarPubMed
Herrmann, I, Pimstein, A, Karnieli, A, Cohen, Y, Alchanatis, V and Bonfil, DJ 2011. LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Remote Sensing of Environment 115 (8), 2141–2151.CrossRefGoogle Scholar
Jeppesen, JH, Jacobsen, RH, Jørgensen, RN and Toftegaard, TS 2016. Towards Data-Driven Precision Agriculture using Open Data and Open Source Software. CIGR-AgEng conference. http://conferences.au.dk/cigr-2016/full-papers/ (retrieved 07/12/16).Google Scholar
Kindred, DR, Milne, AE, Webster, R, Marchant, BP and Sylvester-Bradley, R 2014. Exploring the spatial variation in the fertilizer-nitrogen requirement of wheat within fields. The Journal of Agricultural Science 153 (01), 25–41.CrossRefGoogle Scholar
Mamo, M, Malzer, GL, Mulla, DJ, Huggins, DR and Strock, J 2003. Spatial and Temporal Variation in Economically Optimum Nitrogen Rate for Corn. Agronomy Journal 95 (4), 958–964.CrossRefGoogle Scholar
Nilsson, C 2010. Möjligheter att minska kväveutlakningen genom att anpassa kvävegödslingen till variationer inom stråsädesfält. http://stud.epsilon.slu.se/1531/1/nilsson_c_100701.pdf (retrieved 05/12/16).Google Scholar
Raper, TB and Varco, JJ 2015. Canopy-scale wavelength and vegetative index sensitivities to cotton growth parameters and nitrogen status. Precision Agriculture 16, 62–76.CrossRefGoogle Scholar
Rouse, JW, Hass, RH, Schell, JA and Deering, DW 1973. Monitoring vegetation systems in the great plains with ERTS. Third Earth Resources Technology Satellite (ERTS) Symposium 1, 309–317.Google Scholar
Roy, DP, Wulder, MA, Loveland, TR, Woodcock, CE, Allen, RG, Anderson, MC, et al. 2014. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment 145, 154–172.CrossRefGoogle Scholar
Seelan, SK, Laguette, S, Casady, GM and Seielstad, GA 2003. Remote sensing applications for precision agriculture: A learning community approach. Remote Sensing of Environment 88 (1-2), 157–169.CrossRefGoogle Scholar
Skjødt, P, Hansen, PM and Nyholm, RN 2003. Sensor Based Nitrogen Fertilization Increasing Grain Protein Yield in Winter Wheat. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.193.135&rep=rep1&type=pdf (retrieved 01/12/16).Google Scholar
Wulder, MA, Masek, JG, Cohen, WB, Loveland, TR and Woodcock, CE 2012. Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sensing of Environment 122, 2–10.CrossRefGoogle Scholar