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Earth Observation in Agriculture

  • Silke MigdallEmail author
  • Lena Brüggemann
  • Heike Bach
Conference paper

Abstract

Different types of satellite information have found their way into agriculture in the past few decades. Starting from “precision farming”, using GNSS signals for guidance and auto-steering, farming practices are now shifting towards “smart farming”, using Earth Observation data for information-guided agriculture. Most currently available satellite-based services on the market focus on the analysis of optical remotely sensed data, even though radar data and thermal data also find application. An overview is given on the different types of data and information that can be derived from them, as well as the combination with advanced crop growth modelling. The example of nutrient management is used to showcase how satellite images can support agricultural management through the whole growing season.

Keywords

Agriculture Precision farming Smart farming Radiative transfer modelling Crop growth modelling Fertilization 

References

  1. 1.
    Airbus (2018) FarmStar webpage. http://www.intelligence-airbusds.com/en/7650-farmstar. Accessed 02 Feb 2018
  2. 2.
    Bach H, Mauser W (2018) Sustainable agriculture and smart farming. In: Mathieu P-P, Aubrecht C (eds) Earth observation open science and innovation, ISSI scientific report series, vol 15. Springer Open, Cham, pp 261–269CrossRefGoogle Scholar
  3. 3.
    Bach H, Mauser W, Klepper G (2016) Earth observation for food security and sustainable agriculture. In: ESA Living Planet Symposium 2016, Prague (Czech Republic), CD PublicationGoogle Scholar
  4. 4.
    Bach H, Friese M, Spannraft K, Migdall S, Dotzler S, Hank T, Frank T, Mauser W (2012) Integrative use of multitemporal RapideEye and TerraSAR-X data for agricultural monitoring. In: IGARSS2012 Munich, IEEE 2012 International Geoscience and Remote Sensing Symposium ProceedingsGoogle Scholar
  5. 5.
    Barrett B, Nitze I, Green S, Cawkwell F (2014) Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches. Remote Sens Environ 152:109–124. ISSN 0034-4257.  https://doi.org/10.1016/j.rse.2014.05.018CrossRefGoogle Scholar
  6. 6.
    Bellvert J, Zarco-Tejada PJ, Girona J et al (2014) Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precis Agric 15:361.  https://doi.org/10.1007/s11119-013-9334-5CrossRefGoogle Scholar
  7. 7.
    Brueggemann L, Ruf T, Bach H, Migdall S, Appel F, Hank T, Mauser W, Eiblmeier P (2016) Determination of winter wheat phenology in Bavaria - a contribution to regional crop health monitoring from space. In: ESA Special Publication 740—Living Planet Symposium 2016, Prague (Czech Republic), Proceeding, published.Google Scholar
  8. 8.
    Corwin DL, Lesch SM (2005) Apparent soil electrical conductivity measurements in agriculture. Comput Electron Agric 46(1–3):11–43. ISSN 0168-1699.  https://doi.org/10.1016/j.compag.2004.10.005CrossRefGoogle Scholar
  9. 9.
    Hank T, Bach H, Mauser W (2015) Using a remote sensing-supported hydro-agroecological model for field-scale simulation of heterogeneous crop growth and yield: application for wheat in Central Europe. Remote Sens 7(4):3934–3965CrossRefGoogle Scholar
  10. 10.
    International DLG Crop Production Center (2018) Hofbodenkarte webpage. http://www.dlg-ipz.de/standort/hofbodenkarte. Accessed 22 May 2018
  11. 11.
    Kenduiywo BK, Bargiel D, Soergel U (2017) Higher order dynamic conditional random fields ensemble for crop type classification in radar images. IEEE Trans Geosci Remote Sens 55(8):4638–4654CrossRefGoogle Scholar
  12. 12.
    Mauser W, Bach H (2009) PROMET—large scale distributed hydrological modelling to study the impact of climate change on the water flows of mountain watersheds. J Hydrol 376:362–377CrossRefGoogle Scholar
  13. 13.
    Migdall S, Bach H, Bobert J, Wehrhan M, Mauser W (2009) Inversion of a canopy reflectance model using hyperspectral imagery for monitoring wheat growth and estimating yield. Precis Agric.  https://doi.org/10.1007/s11119-009-9104-6CrossRefGoogle Scholar
  14. 14.
    TalkingFields (2018) TalkingFields webpage. http://www.talkingfields.de/. Accessed 02 Feb 2018
  15. 15.
    Verhoef W, Bach H (2007) Coupled soil-leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data. Remote Sens Environ 109(2007):166–182CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.VISTA Remote Sensing in Geosciences GmbHMunichGermany

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