Plant Soil Environ., 2015, 61(9):410-416 | DOI: 10.17221/412/2015-PSE

Winter oilseed rape and winter wheat growth prediction using remote sensing methodsOriginal Paper

J.A. Domínguez1, J. Kumhálová1, P. Novák2
1 Department of Applied Geoinformatics and Spatial Planning, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic
2 Department of Agricultural Machines, Faculty of Engineering, Czech University of Life Sciences Prague, Prague, Czech Republic

Remote sensing is often used for yield prediction as well as for crop monitoring. This paper describes how Landsat satellite data can be used to derive a growth model calculated from normalised difference vegetation index that can predict winter wheat (Triticum aestivum) and winter oilseed rape (Brassica napus) phenological state using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie scale. Time series of Landsat images were chosen from the years 2004, 2008 and 2012, when winter oilseed rape was grown, and 2005, 2009, 2011 and 2013, when winter wheat was grown in the same experimental field. The images were selected from the whole growing season of both crops. An advantage of this method is the easy availability of the remote sensing and its easy application for deriving a prediction model from vegetation indices. Our results showed that Landsat images, after correct pre-processing, can be used for winter wheat and winter oilseed rape growth model prediction.

Keywords: plant growth modelling; phenological phases; spectral index; atmospheric correction; satellite images

Published: September 30, 2015  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Domínguez JA, Kumhálová J, Novák P. Winter oilseed rape and winter wheat growth prediction using remote sensing methods. Plant Soil Environ.. 2015;61(9):410-416. doi: 10.17221/412/2015-PSE.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Bartoszek K. (2014): Usefulness of MODIS data for assessment of the growth and development of winter oilseed rape. ®emdirbystė (Agriculture), 101: 445-452. Go to original source...
    2. Bégué A., Todoroff P., Pater J. (2008): Multi-time scale analysis of sugarcane within-field variability: Improved crop diagnosis using satellite time series? Precision Agriculture, 9: 161-171. Go to original source...
    3. Behrens T., Kraft M., Wiesler F. (2004): Influence of measuring angle, nitrogen fertilization, and variety on spectral reflectance of winter oilseed rape canopies. Journal of Plant Nutrition and Soil Science, 167: 99-105. Go to original source...
    4. Bernstein L.S., Jin X.M., Gregor B., Adler-Golden S.M. (2012): Quick atmospheric correction code: Algorithm description and recent upgrades. SPIE Optical Engineering, 51: doi:10.1117/1.OE.51.11.111719. Go to original source...
    5. Bleiholder H.T., Boom V.D., Langelüddecke P., Stauss R. (1989): Einheitliche Codierung der phänologischen Stadien bei Kultur und Schadpflanzen. Gesunde Pflanzen, 41: 381-384.
    6. Birth G.S., McVey G.R. (1968): Measuring the colour of growing turf with a reflectance spectrophotometer. Agronomy Journal, 60: 640-643. Go to original source...
    7. Chao Rodríguez Y., el Anjoumi A., Domínguez Gómez J.A., Rodríguez Pérez D., Rico E. (2014): Using Landsat image time series to study a small water body in Northern Spain. Environmental Monitoring and Assessment, 186: 3511-3522. Go to original source... Go to PubMed...
    8. Chuvieco E. (1990): Fundamentals of remote sensing. Madrid, Ediciones Rial S.A. (In Spanish)
    9. Domínguez J.A., Marcos C., Chao Y., Delgado G., Rodríguez D. (2011): Freshwater study using remote sensing. Madrid, UNED. (In Spanish)
    10. Doraiswamy P.C., Hatfield J.L., Jackson T.J., Akhmedov B., Prueger J., Stern A. (2004): Crop condition and yield simulations using Landsat and MODIS. Remote Sensing of Environment, 92: 548-559. Go to original source...
    11. Famìra O., Mayerová M., Bure¹ová I., Kouøimská L., Prá¹ilová M. (2015): Influence of selected factors on the content and properties of starch in the grain of non-food wheat. Plant, Soil and Environment, 61: 241-246. Go to original source...
    12. Franch B., Vermote E.F., Becker-Reshef I., Claverie M., Huang J., Zhang J., Justice C., Sobrino J.A. (2015): Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR growing degree day information. Remote Sensing of Environment, 161: 131-148. Go to original source...
    13. Haboudane D., Miller J.R., Pattey E., Zarco-Tejada P.J., Strachan I.B. (2004): Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90: 337-352. Go to original source...
    14. Hunkár M., Vincze E., Szenyán I., Dunkel Z. (2012): Application of phenological observations in agrometeorological models and climate change research. Quarterly Journal of the Hungarian Meteorological Service, 116: 195-209.
    15. Hunt Jr.E.R., Doraiswamy P.C., McMurtrey J.E., Daughtry C.S.T., Perry E.M., Akhmedov B. (2013): A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation and Geoinformation, 21: 103-112. Go to original source...
    16. Jongschaap R.E.E., Schouten L.S.M. (2005): Predicting wheat production at regional scale by integration of remote sensing data with a simulation model. Agronomy for Sustainable Development, 25: 481-489. Go to original source...
    17. Jamali S., Jönsson P., Eklundh L., Ardö J., Seaquist J. (2015): Detecting changes in vegetation trends using time series segmentation. Remote Sensing of Environment, 156: 182-195. Go to original source...
    18. Julien Y., Sobrino J.A., Jiménez-Muñoz J.-C. (2011): Land use classification from multitemporal Landsat imagery using the yearly land cover dynamics (YLCD) method. International Journal of Applied Earth Observation and Geoinformation, 13: 711-720. Go to original source...
    19. Krèek V., Baranyk P., Pulkrábek J., Urban J., ©keøíková M., Brant V., Zábranský P. (2014): Influence of crop management on winter oilseed rape yield formation - Evaluation of first year of experiment. In: Proceedings of the Conference Mendelnet 2014, Brno, 57-63.
    20. Kumhálová J., Moudrý V. (2014): Topographical characteristics for precision agriculture in conditions of the Czech Republic. Applied Geography, 50: 90-98. Go to original source...
    21. Kumhálová J., Zemek F., Novák P., Brovkina O., Mayerová M. (2014): Use of Landsat images for yield evaluation within a small plot. Plant, Soil and Environment, 60: 501-506. Go to original source...
    22. Lancashire P.D., Bleiholder H., van den Boom T., Langelüddecke P., Stauss R., Weber E., Witzenberger A. (1991): A uniform decimal code for growth stages of crops and weeds. Annals of Applied Biology, 119: 561-601. Go to original source...
    23. Laurila H., Karjalainen M., Kleemola J., Hyyppä J. (2010): Cereal yield modeling in Finland using optical and radar remote sensing. Remote Sensing, 2: 2185-2239. Go to original source...
    24. Li P., Jiang L., Feng Z. (2014): Cross-comparison of vegetation indices derived from Landsat-7 enhanced thematic mapper plus (ETM+) and Landsat-8 operational land imager (OLI) sensors. Remote Sensing, 6: 310-329. Go to original source...
    25. Li X.M., He Z.H., Xiao Y.G., Xia X.C., Trethowan R., Wang H.J., Chen X.M. (2015): QTL mapping for leaf senescence-related traits in common wheat under limited and full irrigation. Euphytica, 203: 569-582. Go to original source...
    26. McDonald A.J., Gemmell F.M., Lewis P.E. (1998): Investigation of the utility of spectral vegetation indices for determining information on coniferous forests. Remote Sensing of Environment, 66: 250-272. Go to original source...
    27. Michener W.K., Houhoulis P.F. (1997): Detection of vegetation changes associated with extensive flooding in a forested ecosystem. Photogrammetric Engineering and Remote Sensing, 63: 1363-1374.
    28. Mistele B., Schmidhalter B. (2008): Spectral measurements of the total aerial N and biomass dry weight in maize using a quadrilateral-view optic. Field Crops Research, 106: 94-103. Go to original source...
    29. Myneni R.B., Asrar G. (1994): Atmospheric effects and spectral vegetation indices. Remote Sensing of Environment, 47: 390-402. Go to original source...
    30. Pan Z., Huang J., Wang F. (2013): Multi range spectral feature fitting for hyperspectral imagery in extracting oilseed rape planting area. International Journal of Applied Earth Observation and Geoinformation, 25: 21-29. Go to original source...
    31. Peltonen-Sainio P., Jauhiainen L., Trnka M., Olesen J.E., Calanca P., Eckersten H., Eitzinger J., Gobin A., Kersebaum K.C., Kozyra J., Kumar S., Marta A.D., Micale F., Schaap B., Seguin B., Skjelvag A.O., Orlandini S. (2010): Coincidence of variation in yield and climate in Europe. Agriculture, Ecosystems and Environment, 139: 483-489. Go to original source...
    32. Rouse J.W., Haas R.H., Schel J.A., Deering D.W. (1974): Monitoring vegetation systems in the Great Plains with ERTS. In: Proceedings of the Third ERTS-1 Symposium, NASA Goddard, NASA SP-351, 309-317.
    33. Song C., Woodcock C.E., Seto K.C., Lenney M.P., Macomber S.A. (2001): Classification and change detection using Landsat TM data: When and how to correct atmospheric effects? Remote Sensing of Environment, 75: 230-244. Go to original source...
    34. Tornos L., Huesca M., Dominguez J.A., Moyano M.C., Cicuendez V., Recuero L., Palacios-Orueta A. (2014): Assessment of MODIS spectral indices for determining rice paddy agricultural practices and hydroperiod. ISPRS Journal of Photogrammetry and Remote Sensing, 101: 110-124. Go to original source...
    35. Zhu L., Xu J.F., Huang J.F., Wang F.M., Liu Z.Y., Wang Y. (2008): Study on hyperspectral estimation model of crop vegetation cover percentage. Spectroscopy and Spectral Analysis, 28: 1827-1831. (In Chinese) Go to PubMed...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content