Vegetation Screening Effect in Remote SensingMonitoring

  • Costas A. Varotsos
  • Vladimir F. Krapivin


Microwave radiometric tools of remote sensing are used to diagnose soil-plant formations, snow and ice covers, ocean surfaces, atmospheres and various natural-technogenic objects. In this light, it is necessary to solve the inverse radiometric tasks to estimate the parameters of the object to be diagnosed. In this case, knowledge of the attenuation characteristics of the electromagnetic waves undergoing propagation in the vegetation cover is important for solving the inverse tasks. It is also important for reliable radio communication. The attenuation of the electromagnetic waves of microwave-scale in the vegetation layer is a key factor in the study of radiation and the scattering of radio waves in the vegetation layers. In addition, data on the depending attenuation parameters on the frequency, the vegetation biomass and water content, as well as the structural characteristics of vegetation cover provide the basis for reconstructing the environment where radio waves are propagated.


  1. Achard F, Hansen MC (eds) (2012) Global forest monitoring from Earth observation. CRC Press, LondonGoogle Scholar
  2. Aires F, Prigent C, Rossow WB (2005) Sensitivity of satellite microwave and infrared observations to soil moisture at a global scale: 2. Global statistical relationships. J Geophys Res 110(D11103):1–14Google Scholar
  3. Cernicharo J, Verger A, Camacho F (2013) Empirical and physical estimation of canopy water content from CHRIS/PROBA data. Remote Sens 5:5265–5284CrossRefGoogle Scholar
  4. Chiu T, Sarabandi K (2000) Electromagnetic scattering from short branching vegetation. IEEE Trans Geosci Remote Sens 38(2):911–925CrossRefGoogle Scholar
  5. Chukhlantsev AA (2006) Microwave radiometry of vegetation canopies. Springer, BerlinGoogle Scholar
  6. Chukhlantsev AA, Shutko AM, Golovachev SP (2003) Attenuation of electromagnetic waves by vegetation canopies in the 100–1000 MHz frequency band (ISTC/IRE Technical report #2059-1)Google Scholar
  7. Disney M, Lewis P, Saich P (2006) 3D modeling of forest canopy structure for remote sensing simulations in the optical and microwave domains. Remote Sens Environ 100:114–132CrossRefGoogle Scholar
  8. Duke S (2013) Seasons of the boreal forest biome. Rourke Educational Media, Vero BeachGoogle Scholar
  9. Ferrazzoli P (1996) Passive microwave remote sensing of forests: a model investigation. IEEE Trans Geosci Remote Sens 34(2):433–443CrossRefGoogle Scholar
  10. Ferrazzoli P, Paloscia S, Pampaloni P, Schiavon G, Solimini D, Coppo P (1992) Sensitivity of microwave measurements to vegetation biomass and soil moisture content: a case study. IEEE Trans Geosci Remote Sens 30(4):750–756CrossRefGoogle Scholar
  11. Gamon JA, Field CB, Goulden ML, Griffin KL, Hartley AE, Joel G, Peńuelas J, Valentini R (1995) Relationships between NDVI, canopy structure, and photosynthesis in tree Californian vegetation types. Ecol Appl 5(1):28–41CrossRefGoogle Scholar
  12. Ghoraishi M, Takada J-I, Imai T (2013) Chapter 6: Radio wave propagation through vegetation. In: Zheng Y (ed) Wave propagation: theories and applications. InTechGoogle Scholar
  13. Guha A, Jacobs JM, Jackson TJ, Cosh MN, Hsu E-C, Judge J (2003) Soil moisture mapping using ESTAR under dry conditions from the southern Greet Plains experiment (SGP99). IEEE Transaction on Geoscience and Remote Sensing 41(10):2392–2397CrossRefGoogle Scholar
  14. Hansen MC, DeFries RS, Townshend JRG, Sohlberg R, Dimiceli C, Carroll M (2002) Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data. Remote Sens Environ 83(1–2):303–319CrossRefGoogle Scholar
  15. Ishimaru A (2017) Electromagnetic wave propagation, radiation, and scattering: from fundamentals to applications. Wiley, Washington, DCCrossRefGoogle Scholar
  16. Johannesson P (2001) Wave propagation through vegetation at 3.1 GHz and 5.8 GHz. Lund Institute of Technology, LundGoogle Scholar
  17. Karam MA, Fung AK, Lang RH, Chauhan NS (1992) A microwave scattering model for layered vegetation. IEEE Trans Geosci Remote Sens 30(4):767–784CrossRefGoogle Scholar
  18. Keane RE, Reinhardt ED, Scott J, Gray K, Reardon J (2005) Estimating forest canopy bulk density using six indirect methods. Can J For Res 35:724–739CrossRefGoogle Scholar
  19. Kimmins JP (2004) Forest ecology: a foundation for sustainable forest management and environmental ethics in forestry. Prentice Hall, Upper Saddle RiverGoogle Scholar
  20. Kondratyev KYA, Krapivin VF, Phillips GW (2003a) Arctic Basin pollution dynamics. In: Bobylev LP, Kondratyev KY, Johannessen OM (eds) Arctic environment variability in the context of global change. Springer/Praxis, Chichester, pp 309–362Google Scholar
  21. Kondratyev KYA, Krapivin VF, Varotsos CA (2003b) Global carbon cycle and climate change. Springer/PRAXIS, ChichesterGoogle Scholar
  22. Krapivin VF, Shutko AM, Chukhlantsev AA, Golovachev SP, Phillips GW (2006) GIMS-based method vegetation microwave monitoring. Environ Model Softw 21:330–345CrossRefGoogle Scholar
  23. Krapivin VF, Varotsos CA, Soldatov VY (2015) New Ecoinformatics tools in environmental science: applications and decision-making. Springer, London, U.K., 903 ppCrossRefGoogle Scholar
  24. Krapivin VF, Varotsos CA, Marechek SV (2018) The dependence of the soil microwave attenuation on frequency and water content in different types of vegetation: an empirical model. Water Air Soil Pollut 229(110):1–10Google Scholar
  25. Kruopis N, Praks J, Arslan AN, Alasalmi H, Koskinen JT, Hallikainen MT (1999) Passive microwave measurements of snow-covered forest areas in EMAC’95. IEEE Trans Geosci Remote Sens 37:2699–2705CrossRefGoogle Scholar
  26. Lang MW, Purkis S, Klemas VV, Tiner RW (2015) Chapter 25: Promising developments and future challenges for remote sensing of wetlands. In: Tiner RW, Lang MW, Klemas VV (eds) Remote sensing of wetlands: applications and advances. CRC Press, Boca Raton, pp 533–544CrossRefGoogle Scholar
  27. Lewis P (2007) Canopy modeling as a tool in remote sensing research. In: Vos J, Marcelis LFM, de Visser PHB, Struik PC, Evers JB (eds) Functional structural plant modeling in crop production. Springer, Dordrecht, pp 219–229CrossRefGoogle Scholar
  28. Liang S (2004) Quantitative remote sensing of land surfaces. Wileys, HobokenGoogle Scholar
  29. Liang P, Moghaddam M, Pierce LE, Lucas RM (2005) Radar backscattering model for multilayer mixed-species forests. IEEE Trans Geosci Remote Sens 43(11):2612–2626CrossRefGoogle Scholar
  30. Meng YS, Lee YH (2010) Investigations of foliage effect on modern wireless communication systems: a review. Prog Electromagn Res 105:313–332CrossRefGoogle Scholar
  31. Mironov VL, Yakubov VP, Telpukhovskiy ED, Novil SN, Chukhlantsev AA (2005) Spectral study of microwave attenuation in a larch forest stand for oblique wave incidence. In: Proceedings of the Geoscience and Remote Sensing Symposium, 29–29 July 2005, Seoul, South Korea, pp 3204–3207Google Scholar
  32. Pampaloni P, Paloscia S (1986) Microwave emission and plant water content: a comparison between field measurements and theory. IEEE Trans Geosci Remote Sens 24:900–905CrossRefGoogle Scholar
  33. Pretzsch H (2014) Canopy space filling and tree canopy morphology in mixed-species stands compared with monocultures. For Ecol Manag 327:251–264CrossRefGoogle Scholar
  34. Ranson KJ, Rock BN, Salas WA, Smith K, Williams DL (1992) Analysis of the dielectric properties of trunl wood in dominant conifer species from New England and Siberia. In: Proceedings of the international symposium on Geoscience and Remote Sensing, 26–29 May 1992, Houston, TX, USA, pp 1283–1285Google Scholar
  35. Rogers NC, Seville A, Richter J, Ndzi D, Savage N, Caldeirinha RFS, Shukla AK, Al-Nuaimi MO, Craig K, Vilar E, Austin J (2002) A generic model of 1–60 GHz radio propagation through vegetation, Final Report. QinetiQ for the UK Radiocommunications Agency, Malvern Technology Centre, MalvernGoogle Scholar
  36. Saleh K, Porté A, Guyon D, Ferrazzoli P, Wigneron J-P (2005) A forest geometric description of a maritime pine forest suitable for discrete microwave models. IEEE Trans Geosci Remote Sens 43(9):2024–2035CrossRefGoogle Scholar
  37. Savage N, Ndzi D, Seville A, Vilar E, Austin J (2003) Radio wave propagation through vegetation: factors influencing signal attenuation. Radio Sci 38(5):1088CrossRefGoogle Scholar
  38. Scaggs AK (ed) (2007) New research on forest ecology. Nova Science Publisher, New YorkGoogle Scholar
  39. Schmugge TJ, Jackson TJ (1992) A dielectric model of the vegetation effects on the microwave emission from soils. IEEE Trans Geosci Remote Sens 30(4):757–760CrossRefGoogle Scholar
  40. Shabanov NV, Huang D, Yang W, Tan B, Yu K, Myneni RB, Ahl DE, Gower ST, Huete AR, Aragão LEOC, Shimabukuro YE (2005) Analysis and optimization of the MODIS leaf area index algorithm retrievals over broadleaf forests. IEEE Trans Geosci Remote Sens 43(8):1855–1865CrossRefGoogle Scholar
  41. Shugart HH, Leemans R, Bonan GB (1992) A systems analysis of the global boreal forest. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  42. Sims DA, Gamon JA (2003) Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chrolophyll absorption features. Remote Sens Environ 84(4):526–537CrossRefGoogle Scholar
  43. Smith WK, Hinckley TM, Roy J (eds) (1994) Ecophsiology of coniferous forests. Academic, New YorkGoogle Scholar
  44. Van de Griend AA, Wigneron JP (2004) The b-factor as a function of frequency and canopy type at H-polarization. IEEE Trans Geosci Remote Sens 42:786–794CrossRefGoogle Scholar
  45. Varotsos CA, Nitu C, Krapivin VF (2018) Global ecoinformatics: theory and applications. Matrix ROM, BucharestGoogle Scholar
  46. Xu D, Guo X (2014) Compare NDVI extracted from Landsat 8 imagery with that from Landsat 7 imagery. Am J Remote Sens 2(2):10–14CrossRefGoogle Scholar
  47. Yang H, Yang X, Heskel M, Sun A, Tang J (2017) Seasonal variations of leaf and canopy properties tracked by ground-based NDVI imagery in a temperate forest. Sci Rep 7:1267–1276CrossRefGoogle Scholar
  48. Zhan X, Sohlberg RA, Townshend JRG, DiMiceli C, Carroll ML, Eastman JC, Hansen MC, DeFries RS (2002) Detection of land cover changes using MODIS 250 m data. Remote Sens Environ 83(1–2):336–350CrossRefGoogle Scholar
  49. Zhu Z, Guo W (2017) Frequency, moisture content, and temperature dependent dielectric properties of potato starch related to drying with radio-frequency/microwave energy. Sci Rep 7.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Costas A. Varotsos
    • 1
  • Vladimir F. Krapivin
    • 2
  1. 1.National and Kapodistrian University of Athens (NKUA)AthensGreece
  2. 2.Institute of Radio-Engineering and ElectronicsFryazinoRussia

Personalised recommendations