Advertisement

Microwave Remote Sensing

  • Joseph L. AwangeEmail author
  • John B. Kyalo Kiema
Chapter
Part of the Environmental Science and Engineering book series (ESE)

Abstract

Persistent cloud cover, especially within the tropics, offers limited clear views of the Earth’s surface from space. This presents a major impediment to the application of optical remote sensing discussed in Chap. 8 in providing global remote sensing coverage. Moreover, other than thermal sensors, most other optical imaging technologies best operate during day time when there is sufficient sunlight. The microwave region of the EM spectrum represents a principal atmospheric window that can be employed to overcome the above limitations in optical remote sensing. For instance, in view of their much longer wavelengths and contrary to optical sensors, microwaves can easily penetrate through vegetation canopies and even dry soils. In addition, microwave systems offer the user more choice and control over the properties of the incident microwave energy to be applied. Furthermore, they can be operated round the clock even under rainy or poor visibility conditions.

Keywords

Global Navigation Satellite System Global Navigation Satellite System Synthetic Aperture Radar Synthetic Aperture Radar Image Synthetic Aperture Radar Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. CCRS (2012) Canada Centre for Remote Sensing tutorial. http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/. Accessed 24 July 2012
  2. Ferretti A, Monti-Guarnieri A, Prati C, Rocca F, Massonnet D (2007) InSAR principles: guidelines for SAR interferometry processing and interpretation. ESA Publications, IthacaGoogle Scholar
  3. Hanssen RF (2001) Radar interferometry: data interpretation and error analysis. Remote sensing and digital image processing book series, vol 2. Kluwer Academic, DordrechtGoogle Scholar
  4. Henderson FM, Lewis AJ (eds) (1998) Principles and applications of imaging radar. Manual of remote sensing, vol 2, 3rd edn. Wiley, New YorkGoogle Scholar
  5. Kummerow C, Barnes W, Kozu T, Shiue J, Simpson J (1998) The tropical rainfall measuring mission (TRMM) sensor package. J Atmos Oceanic Technol 15(3):809–817CrossRefGoogle Scholar
  6. Lee J-S, Jurkevich L, Dewaele P, Wambacqb P, Oosterlinck A (1994) Speckle filtering of synthetic aperture radar images: a review. Remote Sens Rev 8(4):313–340. doi: 10.1080/02757259409532206 CrossRefGoogle Scholar
  7. Lee J-S, Grunes MR, de Grandi G (1999) Polarimetric SAR speckle filtering and its implication for classification. IEEE Trans Geosci Remote Sens 37(5):2363–2373CrossRefGoogle Scholar
  8. Lee J-S, Miller AR, Hoppel KW (1994) Statistics of phase difference and product magnitude of multi-look processed Gaussian signals. Waves in random media, pp 307–319. doi: 10.1088/0959-7174/4/3/006
  9. Massonnet D, Souyris J-C (2008) Imaging with synthetic aperture radar, EPFL. Taylor and Francis, Boca RatonCrossRefGoogle Scholar
  10. Mora O, Arbiol R, Pala V, Adell A, Torre M (2006) Generation of accurate DEMs using DInSAR methodology (TopoDInSAR). IEEE Geosci Remote Sens Lett. doi. 10.1109/LGRS.2006.879563
  11. Mott H (2007) Remote sensing with polarimetric radar. IEEE Press Wiley, HobokenGoogle Scholar
  12. Mueller PW, Hoffer RN (1989) Low-pass spatial filtering of satellite radar data. Photogram Eng Remote Sens (ISSN 0099-1112) 55:887–895Google Scholar
  13. Murai S (2004) Remote sensing and GIS courses —distance education. Japan International Cooperation Agency (JICA)-NetGoogle Scholar
  14. Richards JA (2009) Remote sensing with imaging radar. Springer, BerlinCrossRefGoogle Scholar
  15. Rio JNR (2000) Spatial filtering of radar data (RADARSAT) for wetlands (brackish marshes) classification. Remote Sens Environ 73(2):143–151CrossRefGoogle Scholar
  16. Shi Z, Fung KB (1994) A comparison of digital speckle filters, Geoscience and remote sensing symposium, 1994. IGARSS ’94. Surf Atmos Remote Sens: Technol Data Anal Interpret 4:2129–2133Google Scholar
  17. Ulaby FT, Elachi C (eds) (1990) Radar polarimetry for geoscience applications. Artech House, NorwoodGoogle Scholar
  18. Ulaby FT, Moore RK, Fung AK (1981a) Microwave remote sensing: active and passive. radar remote sensing and surface scattering and emission theory, vol 2. Addison-Wesley, ReadingGoogle Scholar
  19. Ulaby FT, Moore RK, Fung AK (1981b) Microwave remote sensing: active and passive. Microwave remote sensing and fundamentals. Vol 1, Addison-Wesley, ReadingGoogle Scholar
  20. Ulaby FT, Moore RK, Fung AK (1986) Microwave remote sensing: active and passive, Volume scattering and emission theory, advanced systems and applications, vol 3. Addison-Wesley, ReadingGoogle Scholar
  21. Wang BC (2008) Digital signal processing techniques and applications in radar image processing. Wiley, HobokenCrossRefGoogle Scholar
  22. Wolff C (2012) Radar tutorial. http://www.radartutorial.eu/20.airborne/ab07.en.html. Accessed 26 Oct 2012
  23. Woodhouse IH (2006) Introduction to microwave remote sensing. Taylor and Francis, Boca RatonGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Spatial SciencesCurtin University of TechnologyPerthAustralia
  2. 2.Karlsruhe Institute of TechnologyKarlsruheGermany
  3. 3.Kyoto UniversityKyotoJapan
  4. 4.School of EnvironmentMaseno UniversityKisumuKenya
  5. 5.Geospatial and Space TechnologyUniversity of NairobiNairobiKenya

Personalised recommendations