Different Remote Sensing Data in Relative Biomass Determination and in Precision Fertilization Task Generation for Cereal Crops

  • Jere KaivosojaEmail author
  • Roope Näsi
  • Teemu Hakala
  • Niko Viljanen
  • Eija Honkavaara
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 953)


Recently, the area of passive remote sensing in agricultural fields has been developing fast. The prices of RPAS (remotely piloted aircraft system) equipment has gone down, new suitable sensors are coming into markets while simultaneously new and free relevant satellite data has become available. One of the most used applications for these methodologies is to calculate the relative biomass as a basis for additional nitrogen fertilization. In this work, we study the difference of biomass estimations based on Sentinel-2 imagery, tractor implemented commercial measurement system, a low-cost RPAS equipment with commercial software and a hyperspectral imaging system implemented in a professional RPAS system in the fertilization planning. There was a 23% spatial variation in our malt barley yield. Different relative biomass estimations produced similar and sufficient results and the observation time or the used methodology was not very critical. Also none of the methodologies were remarkably better. When we generated the nitrogen fertilization application tasks, different reasonable parameters conducted very different application tasks. This means that in our case, the relative biomass does not provide sufficient information for nitrogen shortage variation. Knowledge of the local conditions is essential.


Sentinel-2 RPAS UAV Variable Rate Application (VRA) 



We acknowledge ESA (ESRIN/Contract No. 4000117401/16/I-NB) and Business Finland (1617/31/2016) for funding the project.


  1. 1.
    Raun, W., et al.: Optical sensor based algorithm for crop nitrogen fertilization. Commun. Soil Sci. Plant Anal. 36, 2759–2781 (2005). Scholar
  2. 2.
    Lukina, E., et al.: Nitrogen fertilization optimization algorithm based on in-season estimates of yield and plant nitrogen uptake. J. Plant Nutr. 24, 885–898 (2001). Scholar
  3. 3.
    Söderström, M., Stadig, H., Martinsson, J., Piikki, K., Stenberg, M.: CropSAT – a public satellite-based decision support system for variable-rate nitrogen fertilization in Scandinavia. In: 13th International Conference on Precision Agriculture (ICPA)At, St Louis, MI, USA (2016).
  4. 4.
    Pena-Yewtukhiw, E., Grove, J., Schwab, G.: Fertilizer nitrogen rate prescription, interpretational algorithms, and individual sensor performance in an array. Agron. J. 107, 1691–1700 (2015). Scholar
  5. 5.
    Křížová, K., Kumhálová, J.: Comparison of selected remote sensing sensors for crop yield variability estimation. Agron. Res. 15(4) (2017).
  6. 6.
    Rasmussen, J., Ntakos, G., Nielson, 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? Eur. J. Agron. 74, 75–92 (2016)CrossRefGoogle Scholar
  7. 7.
    Dong, T., Meng, J., Shang, J., Liu, J., Wu, B.: Evaluation of chlorophyll-related vegetation indices using simulated Sentinel-2 data for estimation of crop fraction of absorbed photosynthetically active radiation. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 8(8), 4049–4059 (2015)CrossRefGoogle Scholar
  8. 8.
    Hunt, E., et al.: Monitoring nitrogen status of potatoes using small unmanned aerial vehicles. Precis. Agric., 1–20 (2017). Scholar
  9. 9.
    Bareth, G., et al.: Low-weight and UAV-based hyperspectral full-frame cameras for monitoring crops: spectral comparison with portable spectroradiometer measurements. Photogramm. - Fernerkund. - Geoinformation PFG 2015(1), 69–79 (2015). Scholar
  10. 10.
    Raun, W., Solie, J., Stone, M.: Independence of yield potential and crop nitrogen response. Precis. Agric. 12(4), 508–518 (2011). Scholar
  11. 11.
    Honkavaara, E., et al.: Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture. Remote. Sens. 5(10), 5006–5039 (2013)CrossRefGoogle Scholar
  12. 12.
    Pölönen, I., Saari, H., Kaivosoja, J., Honkavaara, E., Pesonen, L.: Hyperspectral imaging based biomass and nitrogen content estimations from light-weight UAV. In: Proceedings of SPIE 2013, vol. 8887, p. 88870J (2013)Google Scholar
  13. 13.
    Kaivosoja, J., et al.: A case study of a precision fertilizer application task generation for wheat based on classified hyperspectral data from UAV combined with farm history data. In: Proceedings of SPIE 2013, vol. 8887, p. 88870H (2013)Google Scholar
  14. 14.
    Varco, J.: Sensor Based Fertilizer Nitrogen Management. Crop Management Seminar, Memphis, TN, USA, 9–11 November 2010Google Scholar
  15. 15.
    Nissen, K.: Yara N-Sensor – sensible sensing, testing and certification of agricultural machinery, Riga, Latvia, 16–18 October 2012. Bjugstad, N., Nilsson, E., Birzietis, G. (eds.) NJF Report 8 6: 69-70 (2012)Google Scholar
  16. 16.
    Bendig, J., Bolten, A., Bareth, G.: UAV-based imaging for multi-temporal, very high resolution crop surface models to monitor crop growth variability. Photogramm. - Fernerkund. - Geoinformation 2013(6), 551–562 (2013)CrossRefGoogle Scholar
  17. 17.
    Li, W., Niu, Z., Chen, H., Li, D., Wu, M., Zhao, W.: Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecol. Indic. 67, 637–648 (2016). Scholar
  18. 18.
    Näsi, R., Viljanen, N., Kaivosoja, J., Alhonoja, K., Markelin, L., Honkavaara, E.: Estimating biomass and nitrogen amount of barley and grass using UAV and aircraft based spectral and photogrammetric 3D features. Remote. Sens. 10(7), 1082 (2018). Scholar
  19. 19.
    Shanahan, J., Kitchen, N., Raun, W., Schepers, J.: Responsive in-season nitrogen management for cereals. Comput. Electron. Agric. 61, 51–62 (2008)CrossRefGoogle Scholar
  20. 20.
    Van Evert, F., et al.: Using crop reflectance to determine side dress N rate in potato saves N and maintains yield. Eur. J. Agron. 43, 58–67 (2012)CrossRefGoogle Scholar
  21. 21.
    Rouse, J., Hass, R., Deering, D., Sehell, J.: Monitoring the vernal advancement and retrogradation (Green wave effect) of natural vegetation. Texas A&M university. Type I progress report-number 7 (1974)Google Scholar
  22. 22.
    Hardisky, M., Klemas, V., Smart, R.: The influence of soil salinity, growth form, and leaf moisture on-the spectral radiance of partina alterniflora canopies. Photogramm. Eng. Remote Sens. 49, 77–83 (1983)Google Scholar
  23. 23.
    Huete, A.: A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25(3), 259–309 (1988). Scholar
  24. 24.
    Gitelson, A., Kaufman, Y., Stark, R., Rundquist, D.: Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 80, 76–87 (2002)CrossRefGoogle Scholar
  25. 25.
    Microimages TNTGIS, Surface modeling tutorial. Accessed 21 Nov 2016 (2013)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Natural Resources Institute Finland (LUKE)TampereFinland
  2. 2.Finnish Geospatial Research InstituteMasalaFinland

Personalised recommendations